Tag Archives: Cloudflare Workers

New tools for production safety — Gradual deployments, Source maps, Rate Limiting, and new SDKs

Post Syndicated from Tanushree Sharma original https://blog.cloudflare.com/workers-production-safety


2024’s Developer Week is all about production readiness. On Monday. April 1, we announced that D1, Queues, Hyperdrive, and Workers Analytics Engine are ready for production scale and generally available. On Tuesday, April 2, we announced the same about our inference platform, Workers AI. And we’re not nearly done yet.

However, production readiness isn’t just about the scale and reliability of the services you build with. You also need tools to make changes safely and reliably. You depend not just on what Cloudflare provides, but on being able to precisely control and tailor how Cloudflare behaves to the needs of your application.

Today we are announcing five updates that put more power in your hands – Gradual Deployments, source mapped stack traces in Tail Workers, a new Rate Limiting API, brand-new API SDKs, and updates to Durable Objects – each built with mission-critical production services in mind. We build our own products using Workers, including Access, R2, KV, Waiting Room, Vectorize, Queues, Stream, and more. We rely on each of these new features ourselves to ensure that we are production ready – and now we’re excited to bring them to everyone.

Gradually deploy changes to Workers and Durable Objects

Deploying a Worker is nearly instantaneous – a few seconds and your change is live everywhere.

When you reach production scale, each change you make carries greater risk, both in terms of volume and expectations. You need to meet your 99.99% availability SLA, or have an ambitious P90 latency SLO. A bad deployment that’s live for 100% of traffic for 45 seconds could mean millions of failed requests. A subtle code change could cause a thundering herd of retries to an overwhelmed backend, if rolled out all at once. These are the kinds of risks we consider and mitigate ourselves for our own services built on Workers.

The way to mitigate these risks is to deploy changes gradually – commonly called rolling deployments:

  1. The current version of your application runs in production.
  2. You deploy the new version of your application to production, but only route a small percentage of traffic to this new version, and wait for it to “soak” in production, monitoring for regressions and bugs. If something bad happens, you’ve caught it early at a small percentage (e.g. 1%) of traffic and can revert quickly.
  3. You gradually increment the percentage of traffic until the new version receives 100%, at which point it is fully rolled out.

Today we’re opening up a first-class way to deploy code changes gradually to Workers and Durable Objects via the Cloudflare API, the Wrangler CLI, or the Workers dashboard. Gradual Deployments is entering open beta – you can use Gradual Deployments with any Cloudflare account that is on the Workers Free plan, and very soon you’ll be able to start using Gradual Deployments with Cloudflare accounts on the Workers Paid and Enterprise plans. You’ll see a banner on the Workers dashboard once your account has access.

When you have two versions of your Worker or Durable Object running concurrently in production, you almost certainly want to be able to filter your metrics, exceptions, and logs by version. This can help you spot production issues early, when the new version is only rolled out to a small percentage of traffic, or compare performance metrics when splitting traffic 50/50. We’ve also added observability at a version level across our platform:

  • You can filter analytics in the Workers dashboard and via the GraphQL Analytics API by version.
  • Workers Trace Events and Tail Worker events include the version ID of your Worker, along with optional version message and version tag fields.
  • When using wrangler tail to view live logs, you can view logs for a specific version.
  • You can access version ID, message, and tag from within your Worker’s code, by configuring the Version Metadata binding.

You may also want to make sure that each client or user only sees a consistent version of your Worker. We’ve added Version Affinity so that requests associated with a particular identifier (such as user, session, or any unique ID) are always handled by a consistent version of your Worker. Session Affinity, when used with Ruleset Engine, gives you full control over both the mechanism and identifier used to ensure “stickiness”.

Gradual Deployments is entering open beta. As we move towards GA, we’re working to support:

  • Version Overrides. Invoke a specific version of your Worker in order to test before it serves any production traffic. This will allow you to create Blue-Green Deployments.
  • Cloudflare Pages. Let the CI/CD system in Pages automatically progress the deployments on your behalf.
  • Automatic rollbacks. Roll back deployments automatically when the error rate spikes for a new version of your Worker.

We’re looking forward to hearing your feedback! Let us know what you think through this feedback form or reach out in our Developer Discord in the #workers-gradual-deployments-beta channel.

Source mapped stack traces in Tail Workers

Production readiness means tracking errors and exceptions, and trying to drive them down to zero. When an error occurs, the first thing you typically want to look at is the error’s stack trace – the specific functions that were called, in what order, from which line and file, and with what arguments.

Most JavaScript code – not just on Workers, but across platforms – is first bundled, often transpiled, and then minified before being deployed to production. This is done behind the scenes to create smaller bundles to optimize performance and convert from Typescript to JavaScript if needed.

If you’ve ever seen an exception return a stack trace like: /src/index.js:1:342,it means the error occurred on the 342nd character of your function’s minified code. This is clearly not very helpful for debugging.

Source maps solve this – they map compiled and minified code back to the original code that you wrote. Source maps are combined with the stack trace returned by the JavaScript runtime in order to present you with a human-readable stack trace. For example, the following stack trace shows that the Worker received an unexpected null value on line 30 of the down.ts file. This is a useful starting point for debugging, and you can move down the stack trace to understand the functions that were called that were set that resulted in the null value.

Unexpected input value: null
  at parseBytes (src/down.ts:30:8)
  at down_default (src/down.ts:10:19)
  at Object.fetch (src/index.ts:11:12)

Here’s how it works:

  1. When you set upload_source_maps = true in your wrangler.toml, Wrangler will automatically generate and upload any source map files when you run wrangler deploy or wrangler versions upload.
  2. When your Worker throws an uncaught exception, we fetch the source map and use it to map the stack trace of the exception back to lines of your Worker’s original source code.
  3. You can then view this deobfuscated stack trace in real-time logs or in Tail Workers.

Starting today, in open beta, you can upload source maps to Cloudflare when you deploy your Worker – get started by reading the docs. And starting on April 15th , the Workers runtime will start using source maps to deobfuscate stack traces. We’ll post a notification in the Cloudflare dashboard and post on our Cloudflare Developers X account when source mapped stack traces are available.

New Rate Limiting API in Workers

An API is only production ready if it has a sensible rate limit. And as you grow, so does the complexity and diversity of limits that you need to enforce in order to balance the needs of specific customers, protect the health of your service, or enforce and adjust limits in specific scenarios. Cloudflare’s own API has this challenge – each of our dozens of products, each with many API endpoints, may need to enforce different rate limits.

You’ve been able to configure Rate Limiting rules on Cloudflare since 2017. But until today, the only way to control this was in the Cloudflare dashboard or via the Cloudflare API. It hasn’t been possible to define behavior at runtime, or write code in a Worker that interacts directly with rate limits – you could only control whether a request is rate limited or not before it hits your Worker.

Today we’re introducing a new API, in open beta, that gives you direct access to rate limits from your Worker. It’s lightning fast, backed by memcached, and dead simple to add to your Worker. For example, the following configuration defines a rate limit of 100 requests within a 60-second period:

[[unsafe.bindings]]
name = "RATE_LIMITER"
type = "ratelimit"
namespace_id = "1001" # An identifier unique to your Cloudflare account

# Limit: the number of tokens allowed within a given period, in a single Cloudflare location
# Period: the duration of the period, in seconds. Must be either 60 or 10
simple = { limit = 100, period = 60 } 

Then, in your Worker, you can call the limit method on the RATE_LIMITER binding, providing a key of your choosing. Given the configuration above, this code will return a HTTP 429 response status code once more than 100 requests to a specific path are made within a 60-second period:

export default {
  async fetch(request, env) {
    const { pathname } = new URL(request.url)

    const { success } = await env.RATE_LIMITER.limit({ key: pathname })
    if (!success) {
      return new Response(`429 Failure – rate limit exceeded for ${pathname}`, { status: 429 })
    }

    return new Response(`Success!`)
  }
}

Now that Workers can connect directly to a data store like memcached, what else could we provide? Counters? Locks? An in-memory cache? Rate limiting is the first of many primitives that we’re exploring providing in Workers that address questions we’ve gotten for years about where a temporary shared state that spans many Worker isolates should live. If you rely on putting state in the global scope of your Worker today, we’re working on better primitives that are purpose-built for specific use cases.

The Rate Limiting API in Workers is in open beta, and you can get started by reading the docs.

New auto-generated SDKs for Cloudflare’s API

Production readiness means going from making changes by clicking buttons in a dashboard to making changes programmatically, using an infrastructure-as-code approach like Terraform or Pulumi, or by making API requests directly, either on your own or via an SDK.

The Cloudflare API is massive, and constantly adding new capabilities – on average we update our API schemas between 20 and 30 times per day. But to date, our API SDKs have been built and maintained manually, so we had a burning need to automate this.

We’ve done that, and today we’re announcing new client SDKs for the Cloudflare API in three languages – Typescript, Python and Go – with more languages on the way.

Each SDK is generated automatically using Stainless API, based on the OpenAPI schemas that define the structure and capabilities of each of our API endpoints. This means that when we add any new functionality to the Cloudflare API, across any Cloudflare product, these API SDKs are automatically regenerated, and new versions are published, ensuring that they are correct and up-to-date.

You can install the SDKs by running one of the following commands:

// Typescript
npm install cloudflare

// Python
pip install --pre cloudflare

// Go
go get -u github.com/cloudflare/cloudflare-go/v2

If you use Terraform or Pulumi, under the hood, Cloudflare’s Terraform Provider currently uses the existing, non-automated Go SDK. When you run terraform apply, the Cloudflare Terraform Provider determines which API requests to make in what order, and executes these using the Go SDK.

The new, auto-generated Go SDK clears a path towards more comprehensive Terraform support for all Cloudflare products, providing a base set of tools that can be relied upon to be both correct and up-to-date with the latest API changes. We’re building towards a future where any time a product team at Cloudflare builds a new feature that is exposed via the Cloudflare API, it is automatically supported by the SDKs. Expect more updates on this throughout 2024.

Durable Object namespace analytics and WebSocket Hibernation GA

Many of our own products, including Waiting Room, R2, and Queues, as well as platforms like PartyKit, are built using Durable Objects. Deployed globally, including newly added support for Oceania, you can think of Durable Objects like singleton Workers that can provide a single point of coordination and persist state. They’re perfect for applications that need real-time user coordination, like interactive chat or collaborative editing. Take Atlassian’s word for it:

One of our new capabilities is Confluence whiteboards, which provides a freeform way to capture unstructured work like brainstorming and early planning before teams document it more formally. The team considered many options for real-time collaboration and ultimately decided to use Cloudflare’s Durable Objects. Durable Objects have proven to be a fantastic fit for this problem space, with a unique combination of functionalities that has allowed us to greatly simplify our infrastructure and easily scale to a large number of users. – Atlassian

We haven’t previously exposed associated analytical trends in the dashboard, making it hard to understand the usage patterns and error rates within a Durable Objects namespace unless you used the GraphQL Analytics API directly. The Durable Objects dashboard has now been revamped, letting you drill down into metrics, and go as deep as you need.

From day one, Durable Objects have supported WebSockets, allowing many clients to directly connect to a Durable Object to send and receive messages.

However, sometimes client applications open a WebSocket connection and then eventually stop doing…anything. Think about that tab you’ve had sitting open in your browser for the last 5 hours, but haven’t touched. If it uses WebSockets to send and receive messages, it effectively has a long-lived TCP connection that isn’t being used for anything. If this connection is to a Durable Object, the Durable Object must stay running, waiting for something to happen, consuming memory, and costing you money.

We first introduced WebSocket Hibernation to solve this problem, and today we’re announcing that this feature is out of beta and is Generally Available. With WebSocket Hibernation, you set an automatic response to be used while hibernating and serialize state such that it survives hibernation. This gives Cloudflare the inputs we need in order to maintain open WebSocket connections from clients while “hibernating” the Durable Object such that it is not actively running, and you are not billed for idle time. The result is that your state is always available in-memory when you actually need it, but isn’t unnecessarily kept around when it’s not. As long as your Durable Object is hibernating, even if there are active clients still connected over a WebSocket, you won’t be billed for duration.

In addition, we’ve heard developer feedback on the costs of incoming WebSocket messages to Durable Objects, which favor smaller, more frequent messages for real-time communication. Starting today incoming WebSocket messages will be billed at the equivalent of 1/20th of a request (as opposed to 1 message being the equivalent of 1 request as it has been up until now). Following a pricing example:

WebSocket Connection Requests Incoming WebSocket Messages Billed Requests Request Billing
Before 10K 432M 432,010,000 $64.65
After 10K 432M 21,610,000 $3.09

Production ready, without production complexity

Becoming production ready on the last generation of cloud platforms meant slowing down how fast you shipped. It meant stitching together many disconnected tools or standing up whole teams to work on internal platforms. You had to retrofit your own productivity layers onto platforms that put up roadblocks.

The Cloudflare Developer Platform is grown up and production ready, and committed to being an integrated platform where products intuitively work together and where there aren’t 10 ways to do the same thing, with no need for a compatibility matrix to help understand what works together. Each of these updates shows this in action, integrating new functionality across products and parts of Cloudflare’s platform.

To that end, we want to hear from you about not only what you want to see next, but where you think we could be even simpler, or where you think our products could work better together. Tell us where you think we could do more – the Cloudflare Developers Discord is always open.

Announcing Pages support for monorepos, wrangler.toml, database integrations and more!

Post Syndicated from Nevi Shah original https://blog.cloudflare.com/pages-workers-integrations-monorepos-nextjs-wrangler


Pages launched in 2021 with the goal of empowering developers to go seamlessly from idea to production. With built-in CI/CD, Preview Deployments, integration with GitHub and GitLab, and support for all the most popular JavaScript frameworks, Pages lets you build and deploy both static and full-stack apps globally to our network in seconds.

Pages has superpowers like these that Workers does not have, and vice versa. Today you have to choose upfront whether to build a Worker or a Pages project, even though the two products largely overlap. That’s why during 2023’s Developer Week, we started bringing both products together to give developers the benefit of the best of both worlds. And it’s why we announced that like Workers, Pages projects can now directly access bindings to Cloudflare services — using workerd under-the-hood — even when using the local development server provided by a full-stack framework like Astro, Next.js, Nuxt, Qwik, Remix, SolidStart, or SvelteKit. Today, we’re thrilled to be launching some new improvements to Pages that bring functionality previously restricted to Workers. Welcome to the stage: monorepos, wrangler.toml, new additions to Next.js support, and database integrations!

Pages now supports monorepos

Many development teams use monorepos – repositories that contain multiple apps, with each residing in its own subdirectory. This approach is extremely helpful when these apps share code.

Previously, the Pages CI/CD set-up limited users to one repo per project. To use a monorepo with Pages, you had to directly upload it on your own, using the Wrangler CLI. If you did this, you couldn’t use Pages’ integration with GitHub or Gitlab, or have Pages CI/CD handle builds and deployments. With Pages support for monorepos, development teams can trigger builds to their various projects with each push.

Manage builds and move fast
You can now include and exclude specific paths to watch for in each of your projects to avoid unnecessary builds from commits to your repo.

Let’s say a monorepo contains 4 subdirectories – a marketing app, an ecommerce app, a design library, and a package. The marketing app depends on the design library, while the ecommerce app depends on the design library and the package.

Updates to the design library should rebuild and redeploy both applications, but an update to the marketing app shouldn’t rebuild and deploy the ecommerce app. However, by default, any push you make to my-monorepo triggers a build for both projects regardless of which apps were changed. Using the include/exclude build controls, you can specify paths to build and ignore for your project to help you track dependencies and build more efficiently.

Bring your own tools
Already using tools like Turborepo, NX, and Lerna? No problem! You can also bring your favorite monorepo management tooling to Pages to help manage your dependencies quickly and efficiently.

Whatever your tooling and however you’re set up, check out our documentation to get started with your monorepo right out of the box.

Configure Pages projects with wrangler.toml

Today, we’re excited to announce that you can now configure Pages projects using wrangler.toml — the same configuration file format that is already used for configuring Workers.

Previously, Pages projects had to be configured exclusively in the dashboard. This forced you to context switch from your development environment any time you made a configuration change, like adding an environment variable or binding. It also separated configuration from code, making it harder to know things like what bindings are being used in your project. If you were developing as a team, all the users on your team had to have access to your account to make changes – even if they had access to make changes to the source code via your repo.

With wrangler.toml, you can:

  • Store your configuration file in source control. Keep your configuration in your repo alongside the rest of your code.
  • Edit your configuration via your code editor. Remove the need to switch back and forth between interfaces.
  • Write configuration that is shared across environments. Define bindings and environment variables for local, preview, and production in one file.
  • Ensure better access control. By using a configuration file in your repo, you can control who has access to make changes without giving access to your Cloudflare dashboard.

Migrate existing projects
If you have an existing Pages project, we’ve added a new Wrangler CLI command that downloads your existing configuration and provides you with a valid wrangler.toml file.

$ npx wrangler@latest pages download config <PROJECT_NAME>

Run this command, add the wrangler.toml file that it generates to your project’s root directory, and then when you deploy, your project will be configured based on this configuration file.

If you are already using wrangler.toml to define your local development configuration, you can continue doing so. By default, your existing wrangler.toml file will continue to only apply to local development. When you run `wrangler pages deploy`, Wrangler will show you the additional fields that you must add in order for your configuration to apply to production and preview environments. Add these fields to your wrangler.toml, and then when you deploy your changes, the configuration you’ve defined in wrangler.toml will be used by your Pages project.

Refer to the documentation for more information on exactly what’s supported and how to leverage wrangler.toml in your development workflows.

Integrate Pages projects with your favorite database

You can already connect to D1, Cloudflare’s serverless SQL database, directly from Pages projects. And you can connect directly to your existing PostgreSQL database using Hyperdrive. Today, we’re making it even easier for you to connect 3rd party databases to Pages with just a couple of clicks. Pages now integrates directly with Neon, PlanetScale, Supabase, Turso, Upstash, and Xata!

Simply navigate to your Pages project’s settings, select your database provider, and we’ll add environment variables with credentials needed to connect as well a secret with the API key from the provider for you automatically.

Not ready to ship to production yet? You can deploy your changes to Pages’ preview environment alongside your staging database and test your deployment with its unique preview URL.

What’s coming up for integrations?
We’re just getting started with database integrations, with many more providers to come. In the future, we’re also looking to expand our integrations platform to include seamless set up when building other components of your app – think authentication and observability!

Want to bring your favorite tools to Cloudflare but don’t see the integration option? Want to build out your own integration?

Not only are we looking for user input on new integrations to add, but we’re also opening up the integrations platform to builders who want to submit their own products! We’ll be releasing step-by-step documentation and tooling to easily build and publish your own integration. If you’re interested in submitting your own integration, please fill out our integration intake form and we’ll be in touch!

Improved Next.js Support for Pages

With 30 minor and patch releases since the 1.0 launch of next-on-pages during Dev Week 2023, our Next.js integration has been continuously maturing and keeping up with the evolution of Next.js. In addition to performance improvements, and compatibility and bug fixes, we released three significant improvements.

First, the ESLint plugin eslint-plugin-next-on-pages is a great way to catch and fix compatibility issues as you are writing your code before you build and deploy applications. The plugin contains several rules for the most common coding mistakes we see developers make, with more being added as we identify problematic scenarios.

Another noteworthy change is the addition of getRequestContext() APIs, which provides you with access to Cloudflare-specific resources and metadata about the request currently being processed by your application, allowing for example you to take client’s location or browser preferences into account when generating a response.

Last but not least, as we just shared in a dedicated post, we have completely overhauled the local development workflow for Next.js as well as other full-stack frameworks. Thanks to the new setupDevPlatform() API, you can now use the default development server `next dev`, with support for instant edit & refresh experience, while also using D1, R2, KV and other resources provided by the Cloudflare development platform. Want to take it for a quick spin? Use C3 to scaffold a new Next.js application with just one command.

To learn more about our Next.js integration, check out our Next.js framework guide.

What’s next for the convergence of Workers and Pages?

While today’s launch represents just a few of the many upcoming additions to converge Pages and Workers, we also wanted to share a few milestones that are on the horizon, planned later in 2024

Pages features coming soon to Workers

  • Workers CI/CD. Later this year, we plan to bring the CI/CD system from Cloudflare Pages to Cloudflare Workers. Connect your repositories to Cloudflare and trigger builds for your Workers with every commit.
  • Serve static assets from Workers. You will be able to deploy and serve static assets as part of Workers – just like you can with Pages today – and build Workers using full-stack frameworks! This will also extend to Workers for Platforms, allowing you to build platforms that let your customers deploy complete, full-stack applications that serve both dynamic and static assets.
  • Workers preview URLs. Preview versions of your Workers with every change and share a unique URL with your team for testing.

Workers features coming soon to Pages

  • Add Tail Workers to Pages projects. Get observability into your Pages Functions by capturing console.log() messages, unhandled exceptions, and request metadata, and then forward the information to external destinations.
  • Workers Trace Events Logpush. Push your Pages Functions logs to supported destinations like R2, Datadog, or any HTTP destination for long term storage, auditing, and compliance.
  • Gradual Deployments. Gradually deploy new versions of your Pages Function to reduce risk when making changes to critical applications.

You might also notice that the Pages and Workers interfaces in the Cloudflare Dash will begin to look more similar through the rest of this year. These changes aren’t just superficial, or us porting over functionality from one product to another. Under-the-hood, we are unifying the way that Workers and Pages projects are composed and then deployed to our network, ensuring that as we add new products and features, they can work with both Pages and Workers on day one.

In the meantime, bring your monorepo, a wrangler.toml, and your favorite databases to Pages and let’s rock! Be sure to show off what you’ve built in the Cloudflare Developer Discord or by giving us a shout at @CloudflareDev.

Data Anywhere with Pipelines, Event Notifications, and Workflows

Post Syndicated from Matt Silverlock original https://blog.cloudflare.com/data-anywhere-events-pipelines-durable-execution-workflows


Data is fundamental to any real-world application: the database storing your user data and inventory, the analytics tracking sales events and/or error rates, the object storage with your web assets and/or the Parquet files driving your data science team, and the vector database enabling semantic search or AI-powered recommendations for your users.

When we first announced Workers back in 2017, and then Workers KV, Cloudflare R2, and D1, it was obvious that the next big challenge to solve for developers would be in making it easier to ingest, store, and query the data needed to build scalable, full-stack applications.

To that end, as part of our quest to make building stateful, distributed-by-default applications even easier, we’re launching our new Event Notifications service; a preview of our upcoming streaming ingestion product, Pipelines; and a sneak peek into our take on durable execution, Workflows.

Event-based architectures

When you’re writing data — whether that’s new data, changing existing data, or deleting old data — you often want to trigger other, asynchronous work to run in response. That could be processing user-driven uploads, updating search indexes as the underlying data changes, or removing associated rows in your SQL database when content is removed.

In order to make these event-driven workflows far easier to build across Cloudflare, we’re launching the first step towards a wider Event Notifications platform across Cloudflare, starting with notifications support in R2.

You can read more in the deep-dive on Event Notifications for R2, but in a nutshell: you can configure changes to content in any R2 bucket to write directly to a Queue, allowing you to reliably consume those events in a Worker or to pull from compute in a legacy cloud.

Event Notifications for R2 are just the beginning, though. There are many kinds of events you might want to trigger as a developer — these are just some of the event types we’re planning to support:

  • Changes (writes) to key-value pairs in your Workers KV namespaces.
  • Updates to your D1 databases, including changed rows or triggers.
  • Deployments to your Cloudflare Workers

Consuming event notifications from a single Worker is just one approach, though. As you start to consume events, you may want to trigger multi-step workflows that execute reliably, resume from errors or exceptions, and ensure that previous steps aren’t duplicated or repeated unnecessarily. An event notification framework turns out to be just the thing needed to drive a workflow engine that executes durably

Making it even easier to ingest data

When we launched Cloudflare R2, our object storage service, we knew that supporting the de facto-standard S3 API was critical in order to allow developers to bring the tooling and services they already had over to R2. But the S3 API is designed to be simple: at its core, it provides APIs for upload, download, multipart and metadata operations, and many tools don’t support the S3 API.

What if you want to batch clickstream data from your web services so that it’s efficient (and cost-effective) to query by your analytics team? Or partition data by customer ID, merchant ID, or locale within a structured data format like JSON?

Well, we want to help solve this problem too, and so we’re announcing Pipelines, an upcoming streaming ingestion service designed to ingest data at scale, aggregate it, and write it directly to R2, without you having to manage infrastructure, partitions, runners, or worry about durability.

With Pipelines, creating a globally scalable ingestion endpoint that can ingest tens-of-thousands of events per second doesn’t require any code:

$ wrangler pipelines create clickstream-ingest-prod --batch-size="1MB" --batch-timeout-secs=120 --batch-on-json-key=".merchantId" --destination-bucket="prod-cs-data"

✅ Successfully created new pipeline "clickstream-ingest-prod"
📥 Created endpoints:
➡ HTTPS: https://d458dbe698b8eef41837f941d73bc5b3.pipelines.cloudflarestorage.com/clickstream-ingest-prod
➡ WebSocket: wss://d458dbe698b8eef41837f941d73bc5b3.pipelines.cloudflarestorage.com:8443/clickstream-ingest-prod
➡ Kafka: d458dbe698b8eef41837f941d73bc5b3.pipelines.cloudflarestorage.com:9092 (topic: clickstream-ingest-prod)

As you can see here, we’re already thinking about how to make Pipelines protocol-agnostic: write from a HTTP client, stream events over a WebSocket, and/or redirect your existing Kafka producer (and stop having to manage and scale Kafka) directly to Pipelines.

But that’s just the beginning of our vision here. Scalable ingestion and simple batching is one thing, but what about if you have more complex needs? Well, we have a massively scalable compute platform (Cloudflare Workers) that can help address this too.

The code below is just an initial exploration for how we’re thinking about an API for running transforms over streaming data. If you’re aware of projects like Apache Beam or Flink, this programming model might even look familiar:

export default {    
   // Pipeline handler is invoked when batch criteria are met
   async pipeline(stream: StreamPipeline, env: Env, ctx: ExecutionContext): Promise<StreamingPipeline> {
      // ...
      return stream
         // Type: transform(label: string, transformFunc: TransformFunction): Promise<StreamPipeline>
         // Each transform has a label that is used in metrics to provide
    // per-transform observability and debugging
         .transform("human readable label", (events: Array<StreamEvent>) => {
            return events.map((e) => ...)
         })
         .transform("another transform", (events: Array<StreamEvent>) => {
            return events.map((e) => ...)
         })
         .writeToR2({
            format: "json",
            bucket: "MY_BUCKET_NAME",
            prefix: somePrefix,
            batchSize: "10MB"
         })
   }
}

Specifically:

  • The Worker describes a pipeline of transformations (mapping, reducing, filtering) that operates over each subset of events (records)
  • You can call out to other services — including D1 or KV — in order to synchronously or asynchronously hydrate data or lookup values during your stream processing
  • We take care of scaling horizontally based on records-per-second and/or any concurrency settings you configure based on processing latency requirements.

We’ll be bringing Pipelines into open beta later in 2024, and it will initially launch with support for HTTP ingestion and R2 as a destination (sink), but we’re already thinking bigger.

We’ll be sharing more as Pipelines gets closer to release. In the meantime, you can register your interest and share your use-case, and we’ll reach out when Pipelines reaches open beta.

Durable Execution

If the term “Durable Execution” is new to you, don’t worry: the term comes from the desire to run applications that can resume execution from where they left off, even if the underlying host or compute fails (where the “durable” part comes from).

As we’ve continued to build out our data and AI platforms, we’ve been acutely aware that developers need ways to create reliable, repeatable workflows that operate over that data, turn unstructured data into structured data, trigger on fresh data (or periodically), and automatically retry, restart, and export metrics for each step along the way. The industry calls this Durable Execution: we’re just calling it Workflows.

What makes Workflows different from other takes on Durable Execution is that we provide the underlying compute as part of the platform. You don’t have to bring-your-own compute, or worry about scaling it or provisioning it in the right locations. Workflows runs on top of Cloudflare Workers – you write the workflow, and we take care of the rest.

Here’s an early example of writing a Workflow that generates text embeddings using Workers AI and stores them (ready to query) in Vectorize as new content is written to (or updated within) R2.

  • Each Workflow run is triggered by an Event Notification consumed from a Queue, but could also be triggered by a HTTP request, another Worker, or even scheduled on a timer.
  • Individual steps within the Workflow allow us to define individually retriable units of work: in this case, we’re reading the new objects from R2, creating text embeddings using Workers AI, and then inserting.
  • State is durably persisted between steps: each step can emit state, and Workflows will automatically persist that so that any underlying failures, uncaught exceptions or network retries can resume execution from the last successful step.
  • Every call to step() automatically emits metrics associated with the unique Workflow run, making it easier to debug within each step and/or break down your application into its smallest units of execution, without having to worry about observability.

Step-by-step, it looks like this:

Transforming this series of steps into real code, here’s what this would look like with Workflows:

import { Ai } from "@cloudflare/ai";
import { Workflow } from "cloudflare:workers";

export interface Env {
  R2: R2Bucket;
  AI: any;
  VECTOR_INDEX: VectorizeIndex;
}

export default class extends Workflow {
  async run(event: Event) {
    const ai = new Ai(this.env.AI);

    // List of keys to fetch from our incoming event notification
    const keysToFetch = event.messages.map((val) => {
      return val.object.key;
    });

    // The return value of each step is stored (the "durable" part
    // of "durable execution")
    // This ensures that state can be persisted between steps, reducing
    // the need to recompute results ($$, time) should subsequent
    // steps fail.
    const inputs = await this.ctx.run(
      // Each step has a user-defined label
      // Metrics are emitted as each step runs (to success or failure)
// with this label attached and available within per-Workflow
// analytics in near-real-time.
"read objects from R2", async () => {
      const objects = [];

      for (const key of keysToFetch) {
        const object = await this.env.R2.get(key);
        objects.push(await object.text());
      }

      return objects;
    });


    // Persist the output of this step.
    const embeddings = await this.ctx.run(
      "generate embeddings",
      async () => {
        const { data } = await ai.run("@cf/baai/bge-small-en-v1.5", {
          text: inputs,
        });

        if (data.length) {
          return data;
        } else {
          // Uncaught exceptions trigger an automatic retry of the step
          // Retries and timeouts have sane defaults and can be overridden
    // per step
          throw new Error("Failed to generate embeddings");
        }
      },
      {
        retries: {
          limit: 5,
          delayMs: 1000,
          backoff: "exponential",
        },
      }
    );

    await this.ctx.run("insert vectors", async () => {
      const vectors = [];

      keysToFetch.forEach((key, index) => {
        vectors.push({
          id: crypto.randomUUID(),
          // Our embeddings from the previous step
          values: embeddings[index].values, 
          // The path to each R2 object to map back to during
 	    // vector search
          metadata: { r2Path: key },
        });
      });

      return this.env.VECTOR_INDEX.upsert();
    });
  }
}

This is just one example of what a Workflow can do. The ability to durably execute an application, modeled as a series of steps, applies to a wide number of domains. You can apply this model of execution to a number of use-cases, including:

  • Deploying software: each step can define a build step and subsequent health check, gating further progress until your deployment meets your criteria for “healthy”.
  • Post-processing user data: triggering a workflow based on user uploads (e.g. to Cloudflare R2) that then subsequently parses that data asynchronously, redacts PII or sensitive data, writes the sanitized output, and triggers a notification via email, webhook, or mobile push.
  • Payment and batch workflows: aggregating raw customer usage data on a periodic schedule by querying your data warehouse (or Workers Analytics Engine), triggering usage or spend alerts, and/or generating PDF invoices.

Each of these use cases model tasks that you want to run to completion, minimize redundant retries by persisting intermediate state, and (importantly) easily observe success and failure.

We’ll be sharing more about Workflows during the second quarter of 2024 as we work towards an open (public!) beta. This includes how we’re thinking about idempotency and interactions with our storage, per-instance observability and metrics, local development, and templates to bootstrap common workflows.

Putting it together

We’ve often thought of Cloudflare’s own network as one massively scalable parallel data processing cluster: data centers in 310+ cities, with the ability to run compute close to users and/or close to data, keep it within the bounds of regulatory or compliance requirements, and most importantly, use our massive scale to enable our customers to scale as well.

Recapping, a fully-fledged data platform needs to enable three things:

  1. Ingesting data: getting data into the platform (in the right format, from the right sources)
  2. Storing data: securely, reliably, and durably.
  3. Querying data: understanding and extracting insights from the data, and/or transforming it for use by other tools.

When we launched R2 we tackled the second part, but knew that we’d need to follow up with the first and third parts in order to make it easier for developers to get data in and make use of it.

If we look at how we can build a system that helps us solve each of these three parts together with Pipelines, Event Notifications, R2, and Workflows, we end up with an architecture that resembles this:

Specifically, we have Pipelines (1) scaling out to ingest data, batch it, filter it, and then durably store it in R2 (2) in a format that’s ready and optimized for querying. Workflows, ClickHouse, Databricks, or the query engine of your choice can then query (3) that data as soon as it’s ready — with “ready” being automatically triggered by an Event Notification as soon as the data is ingested and written to R2.

There’s no need to poll, no need to batch after the fact, no need to have your query engine slow down on data that wasn’t pre-aggregated or filtered, and no need to manage and scale infrastructure in order to keep up with load or data jurisdiction requirements. Create a Pipeline, write your data directly to R2, and query directly from it.

If you’re also looking at this and wondering about the costs of moving this data around, then we’re holding to one important principle: zero egress fees across all of our data products. Just as we set the stage for this with our R2 object storage, we intend to apply this to every data product we’re building, Pipelines included.

Start Building

We’ve shared a lot of what we’re building so that developers have an opportunity to provide feedback (including via our Developer Discord), share use-cases, and think about how to build their next application on Cloudflare.

How Picsart leverages Cloudflare’s Developer Platform to build globally performant services

Post Syndicated from Mark Dembo original https://blog.cloudflare.com/picsart-move-to-workers-huge-performance-gains


Delivering great user experiences with a global user base can be challenging. While serving requests quickly when you start out in a local market is straightforward, doing so for a global audience is much more difficult. Why? Even under optimal conditions, you cannot be faster than the speed of light, which brings single data center solutions to their performance limits.

In this post, we will cover how Picsart improved the performance of one of its most critical services by moving from a centralized architecture to a globally distributed service built on Cloudflare. Our serverless compute platform, Workers, distributed throughout 310+ cities around the world, and our globally distributed Workers KV storage allowed them to improve their performance significantly and drive real business impact.

Success driven by data-driven insights

Picsart is one of the world’s largest digital creation platforms and a long-standing Cloudflare partner. At its core, an advanced tech stack powers its comprehensive features, including AI-driven photo and video editing tools and community-driven content sharing. With its infrastructure spanning across multiple cloud environments and on-prem deployments, Picsart is engineered to handle billions of daily requests from its huge mobile and web user base and API integrations. For over a decade, Cloudflare has been integral to Picsart, providing support for performant content delivery and securing its digital ecosystem.  

Similar to many other tech giants, Picsart approaches product development in a data-driven way. At the core of the innovation is Picsart’s remote configuration and experimentation platform, which enables product managers, UX researchers, and others to segment their user base into different test groups. These test groups might get to see slightly different implementations of features or designs of the Picsart app. Users might also get early access to experimental features or see different in-app promotions. In combination with constant monitoring of relevant KPIs, this allows for informed product decisions based on user preference and business impact.

On each app start, the client device sends a request to the remote configuration service for the latest setup tailored to the user’s session. The assignment of experiments relies on factors like the operating system and previous sessions, making each request unique and uncachable. Picsart’s app showcases extensive remote configuration capabilities, enabling adjustments to nearly every element. This results in a response containing a 1.5 MB configuration file for mobile clients. While the long-term solution is to reduce the file size, which has grown over time as more teams adopted the powerful service, this is not possible in the near or mid-term as it requires a major rewrite of all clients.

This setup request is blocking in the “hot path” during app start, as the results of this request will decide how the app itself looks and behaves. Hence, performance is critical. To ensure users are not waiting for too long, Picsart apps will wait for 1500ms on mobile for the request to complete – if it does not, the user will not be assigned a test group and the app will fallback to default settings.

The clock is ticking

While a 1500ms round trip time seems like a sufficiently large time budget, the data suggested otherwise. Before the improvements were implemented, a staggering 50% of devices could not complete the requests in time. How come? In these 1.5 seconds the following steps need to complete:

  1. The request must travel from the users’ devices to the centralized backend servers
  2. The server processes the request based on dozens of user attributes provided in the request and thousands of defined remote configuration variations, running experiments, and segments metadata. Using all the info, the server selects the right variation of each remote setting entry and builds the response payload.
  3. The response must travel from the centralized backend servers to the user devices.

Looking at the data, it was clear to the Picsart team that their backend service was already well-optimized, taking only 30 milliseconds, a tiny fraction of the available time budget, to process each of the billions of monthly requests. The bulk of the request time came from network latency. Especially with mobile devices, last mile performance can be very volatile, eating away a significant amount of the available time budget. Not only that, but the data was clear: users closer to the origin server had a much higher chance of making the round trip in time versus users out of region. It quickly became obvious that Picsart, fueled by its global success, had outgrown a single-region setup.

To the drawing board

A solution that comes to mind would be to replicate the existing cloud infrastructure in multiple regions and use global load balancing to minimize the distance a request needs to travel. However, this introduces significant overhead and cost. On the infrastructure side, it is not only the additional compute instances and database clusters that incur cost, but also cross-region data transfer to keep data in sync. Moreover, technical teams would need to operate and monitor infrastructure in multiple regions, which can add a lot to the complexity and cognitive load, leading to decreased development velocity and productivity loss.

Picsart instead looked to Cloudflare – we already had a long-lasting relationship for Application Performance and Security, and they aimed to use our Developer Platform to tackle the problem.

Workers and Workers KV seemed like the ideal solution. Both compute and data are globally distributed in 310+ locations around the world, resulting in a shorter distance between end users and the experimentation service. Not only that, but Cloudflare’s global-by-default approach allows for deployment with minimal overhead, and in contrast to other considered solutions, no additional fees to distribute the data around the globe.

No race without a clock

The objective for the refactor of the experimentation service was to increase the share of devices that successfully receive experimentation configuration within the set time budget.

But how to measure success? While synthetic testing can be useful in many situations, Picsart opted to come up with another clever solution:

During development, the Picsart engineers had already added a testing endpoint to the web and mobile versions of their app that sends a duplicate request to the new endpoint, discarding the response and swallowing all potential errors. This allows them to collect timing data based on real-user metrics without impacting the app’s performance and reliability.

A simplified version of this pattern for a web client could look like this:

// API endpoint URLs
const prodUrl = 'https://prod.example.com/';
const devUrl = 'https://new.example.com/';

// Function to collect metrics
const collectMetrics = (duration) => {
    console.log('Request duration:', duration);
    // …
};

// Function to fetch data from an endpoint and call collectMetrics
const fetchData = async (url, options) => {
    const startTime = performance.now();
    
    try {
        const response = await fetch(url, options);
        const endTime = performance.now();
        const duration = endTime - startTime;
        collectMetrics(duration);
        return await response.json();
    } catch (error) {
        console.error('Error fetching data:', error);
    }
};

// Fetching data from both endpoints
async function fetchDataFromBothEndpoints() {
    try {
        const result1 = await fetchData(prodUrl, { method: 'POST', ... });
        console.log('Result from endpoint 1:', result1);

        // Fetching data from the second endpoint without awaiting its completion
        fetchData(devUrl, { method: 'POST', ... });
    } catch (error) {
        console.error('Error fetching data from both endpoints:', error);
    }
}

fetchDataFromBothEndpoints();

Using existing data analytics tools, Picsart was able to analyze the performance of the new services from day one, starting with a dummy endpoint and a ‘hello world’ response. And with that a v0 was created that did not have the correct logic just yet, but simulated reading multiple values from KV and returning a response of a realistic size back to the end user.

The need for a do-over

In the initial phase, outcomes fell short of expectations. Surprisingly, requests were slower despite the service’s proximity to end users. What caused this setback?  Subsequent investigation unveiled multiple culprits and design patterns in need for optimization.

Data segmentation

The previous, stateful solution operated on a single massive “blob” of data exceeding 100MB in value. Loading this into memory incurred a few seconds of initial startup time, but once the VM completed the task, request handling was fast, benefiting from the readily available data in memory.

However, this approach doesn’t seamlessly transition to the serverless realm. Unlike long-running VMs, Worker isolates have short lifespans. Repeatedly parsing large JSON objects led to prolonged compute durations. Simply parsing four KV entries of 25MB each (KV maximum value size is 25MB) on each request was not a feasible option.

The Picsart team went back to solution design and embarked on a journey to optimize their system’s execution time, resulting in a series of impactful improvements.

The fundamental insight that guided the solution was the unnecessary overhead that was involved in loading and parsing data irrelevant to the user’s specific context. The 100MB configuration file contained configurations for all platforms and locations worldwide – a setup that was far from efficient in a globally distributed, serverless compute environment. For instance, when processing requests from users in the United States, there was no need to fetch configurations targeted for users in other countries, or for different platforms.

To address this inefficiency, the Picsart team stored the configuration of each platform and country in separate KV records. This targeted strategy meant that for a request originating from a US user on an Android device, our system would only fetch and parse the KV record specific to Android users in the US, thereby excluding all irrelevant data. This resulted in approximately 600 KV records, each with a maximum size of 10MB. While this leads to data duplication on the KV storage side, it decreases the amount of data that needs to be parsed upon request. As Cloudflare operates in over 120 countries around the world, only a subset of records were needed in each location. Hence, the increase in cardinality had minimal impact on KV cache performance, as demonstrated by more than 99.5% of KV reads being served from local cache.

Key Size
settings_part1.json 25MB
settings_part2.json 25MB
….

Before (simplified)

Key Size
com.picsart.studio_apple_us.json 6.1MB
com.picsart.studio_apple_de.json 6.1MB
com.picsart.studio_android_us.json 5.9MB

After (simplified)

This approach was a significant move for Picsart as they transitioned from a regional cloud setup to Cloudflare’s globally distributed connectivity cloud. By serving data from close proximity to end user locations, they were able to combat the high network latency from their previous setup. This strategy radically transformed the data-handling process. which unlocked two major benefits:

  • Performance Gains: By ensuring that only the relevant subset of data is fetched and parsed based on the user’s platform and geographical location, wall time and compute resources required for these operations could be significantly reduced.
  • Scalability and Flexibility: the granular segmentation of data enables effortless scaling of the service for new features or regional content. Adding support for new applications now only requires inserting new, standalone KV records in contrast to the previous solution where this would require increasing the size of the single record.

Immutable updates

Now that changes to the configuration were segmented by app, country, and platform, this also allowed for individual updates of the configuration in KV. KV storage showcases its best performance when records are updated infrequently but read very often. This pattern leverages KV’s fundamental design to cache values at edge locations upon reads, ensuring that subsequent queries for the same record are swiftly served by local caches rather than requiring a trip back to KV’s centralized data centers. This architecture is fundamental for minimizing latency and maximizing the speed of data retrieval across a globally distributed platform.

A crucial requirement for Picsart’s experimentation system was the ability to propagate updates of remote configuration values immediately. Updating existing records would require very short cache TTLs and even the minimum KV cache TTL of 60 seconds was considered unacceptable for the dynamic nature of the feature flagging. Moreover, setting short TTLs also impacts the cache hit ratio and the overall KV performance, specifically in regions with low traffic.

To reconcile the need for both rapid updates and efficient caching, Picsart adopted an innovative approach: making KV records immutable. Instead of modifying existing records, they opted to create new records with each configuration change. By appending the content hash to the KV key and writing new records after each update, Picsart ensured that each record was unique and immutable. This allowed them to leverage higher cache TTLs, as these records would never be updated.

Key Size
com.picsart.studio_apple_us.json 60s
….

Before (simplified)

Key Size
com.picsart.studio_apple_us_b58b59.json 86400s
com.picsart.studio_apple_us_273678.json 86400s

After (simplified)

There was a catch, though. The service must now keep track of the correct KV keys to use. The Picsart team addressed this challenge by storing references to the latest KV keys in the environment variables of the Worker.

Each configuration change triggers a new KV pair to be written and the services’ environment variables to be updated. As global Workers deployments take mere seconds, changes to the experimentation and configuration data are near-instantaneously globally available.

JSON serialization & alternatives

Following the previous improvements, the Picsart team made another significant discovery: only a small fraction of configuration data is needed to assign the experiments, while the remaining majority of the data comprises JSON values for the remote configuration payloads. While the service must deliver the latter in the response, the data is not required during the initial processing phase.

The initial implementation used KV’s get() method to retrieve the configuration data with the parameter type=json, which converts the KV value to an object. This process is very compute-intensive compared to using the get() method with parameter type= text, which simply returns the value as a string. In the context of Picsart’s data, the bulk of the CPU cycles were wasted on serializing JSON data that is not needed to perform the required business logic.

What if the data structure and code could be changed in such a way that only the data needed to assign experiments was parsed as JSON, while the configuration values were treated as text? Picsart went ahead with a new approach: splitting the KV records into two, creating a small 300KB record for the metadata, which can be quickly parsed to an object, and a 9.7MB record of configuration values. The extracted configuration values are delimited by newline characters. The respective line number is used as reference in the metadata entry, so that the respective configuration value for an experiment can be merged back into the payload response later.


{
  "name": "shape_replace_items",
  "default_value": "<large json object>",
  "segments": [
    {
      "id": "f1244",
      "value": "<Another json object
json object>"
    },
    {
      "id": "a2lfd",
      "value": "<Yet another large json
object>"
    }
  ]
}
Before: Metadata and Values in one JSON object (simplified)

// com.picsart.studio_apple_am_metadata

1 {
2   "name": "shape_replace_items",
3   "default_value": 1,
4   "segments": [
5     {
6       "id": "f1244",
7       "value": 2
8     },
9     {
10       "id": "a2lfd",
11      "value": 3
12     }
13   ]
14 }


// com.picsart.studio_apple_am_values

1 "<large json object>"
2 "<Another json object>"
3 "<Yet another json object>"

After: Metadata and Values are split (simplified)

After calculating the experiments and selecting the correct variants based solely on the small metadata entry, the service constructs a JSON string for the response containing placeholders for the actual values that reference the corresponding line numbers in the separate text file. To finalize the response, the server replaces the placeholders with the corresponding serialized JSON strings from the text file. This approach circumvents the need for parsing and re-serializing large JSON objects and helps to avoid a significant computational overhead.

Note that the process of parsing the metadata JSON and determining the correct experiments as well as the loading of the large file with configuration values are executed in parallel, saving additional milliseconds.

By minimizing the size of the JSON data that needed to be parsed and leveraging a more efficient method for constructing the final response, the Picsart team managed to not only reduce the response times but also optimize the compute resource usage. This approach reflects a broader principle applicable across the tech industry: that efficiency, particularly in serverless architectures, can often be dramatically improved by rethinking data structure and utilization.

Getting a head start

The changes on the server-side, moving from a single region setup to Cloudflare’s global architecture, paid off massively. Median response times globally dropped by more than 1 second, which was already a huge improvement for the team. However, in looking at the new data, two more paths for client-side optimizations were found.

As the web and mobile app would call the service at startup, most of the time no active connections to the servers were alive and establishing that connection at request time costs valuable milliseconds.

For the web version, setting a pre-connect header on initial page load showed a positive impact. For the mobile app version, the Picsart team took it a step further. Investigation showed that before the connection could be established, three modules had to complete initialization: the error tracker, the HTTP client, and the SDK. Reordering of the modules to initialize the HTTP client first allowed for connection establishment in parallel to the initialization of the SDK and error tracker, again saving time. This resulted in another 200ms improvement for end users.

Setting a new personal best

The day had come and it was time for the phased rollout, web first and the mobile apps second. With suspense, the team looked at the dashboards, and were pleasantly surprised. The rollout was successful and billions of requests were handled with ease.

Share of successfully delivered experiments

The result? The Picsart apps are loading faster than ever for millions of users worldwide, while the share of successfully delivered experiments increased from 50% to 85%. Median response time dropped from 1500 ms to 280 ms. The response time dropped to 70 ms on the web since the response size is smaller compared to mobile. This translates to a real business impact for Picsart as they can now deliver more personalized and data-driven experiences to even more of their users.

A bright future ahead

Picsart is already thinking of the next generation of experimentation. To integrate with Cloudflare even further, the plan is to use Durable Objects to store hundreds of millions of user data records in a decentralized fashion, enabling even more powerful experiments without impacting performance. This is possible thanks to Durable Objects’ underlying architecture that stores the user data in-region, close to the respective end user device.

Beyond that, Picsart’s experimentation team is also planning to onboard external B2B customers to their experimentation platform as Cloudflare’s developer platform provides them with the scale and global network to handle more traffic and data with ease.

Get started yourself

If you’re also working on or with an application that would benefit from Cloudflare’s speed and scale, check out our developer documentation and tutorials, and join our developer Discord to get community support and inspiration.

Running fine-tuned models on Workers AI with LoRAs

Post Syndicated from Michelle Chen original https://blog.cloudflare.com/fine-tuned-inference-with-loras


Inference from fine-tuned LLMs with LoRAs is now in open beta

Today, we’re excited to announce that you can now run fine-tuned inference with LoRAs on Workers AI. This feature is in open beta and available for pre-trained LoRA adapters to be used with Mistral, Gemma, or Llama 2, with some limitations. Take a look at our product announcements blog post to get a high-level overview of our Bring Your Own (BYO) LoRAs feature.

In this post, we’ll do a deep dive into what fine-tuning and LoRAs are, show you how to use it on our Workers AI platform, and then delve into the technical details of how we implemented it on our platform.

What is fine-tuning?

Fine-tuning is a general term for modifying an AI model by continuing to train it with additional data. The goal of fine-tuning is to increase the probability that a generation is similar to your dataset. Training a model from scratch is not practical for many use cases given how expensive and time consuming they can be to train. By fine-tuning an existing pre-trained model, you benefit from its capabilities while also accomplishing your desired task. Low-Rank Adaptation (LoRA) is a specific fine-tuning method that can be applied to various model architectures, not just LLMs. It is common that the pre-trained model weights are directly modified or fused with additional fine-tune weights in traditional fine-tuning methods. LoRA, on the other hand, allows for the fine-tune weights and pre-trained model to remain separate, and for the pre-trained model to remain unchanged. The end result is that you can train models to be more accurate  at specific tasks, such as generating code, having a specific personality, or generating images in a specific style. You can even fine-tune an existing LLM to understand additional information about a specific topic.

The approach of maintaining the original base model weights means that you can create new fine-tune weights with relatively little compute. You can take advantage of existing foundational models (such as Llama, Mistral, and Gemma), and adapt them for your needs.

How does fine-tuning work?

To better understand fine-tuning and why LoRA is so effective, we have to take a step back to understand how AI models work. AI models (like LLMs) are neural networks that are trained through deep learning techniques. In neural networks, there are a set of parameters that act as a mathematical representation of the model’s domain knowledge, made up of weights and biases – in simple terms, numbers. These parameters are usually represented as large matrices of numbers. The more parameters a model has, the larger the model is, so when you see models like llama-2-7b, you can read “7b” and know that the model has 7 billion parameters.

A model’s parameters define its behavior. When you train a model from scratch, these parameters usually start off as random numbers. As you train the model on a dataset, these parameters get adjusted bit-by-bit until the model reflects the dataset and exhibits the right behavior. Some parameters will be more important than others, so we apply a weight and use it to show more or less importance. Weights play a crucial role in the model’s ability to capture patterns and relationships in the data it is trained on.

Traditional fine-tuning will adjust all the parameters in the trained model with a new set of weights. As such, a fine-tuned model requires us to serve the same amount of parameters as the original model, which means it can take a lot of time and compute to train and run inference for a fully fine-tuned model. On top of that, new state-of-the-art models, or versions of existing models, are regularly released, meaning that fully fine-tuned models can become costly to train, maintain, and store.

LoRA is an efficient method of fine-tuning

In the simplest terms, LoRA avoids adjusting parameters in a pre-trained model and instead allows us to apply a small number of additional parameters. These additional parameters are applied temporarily to the base model to effectively control model behavior. Relative to traditional fine-tuning methods it takes a lot less time and compute to train these additional parameters, which are referred to as a LoRA adapter. After training, we package up the LoRA adapter as a separate model file that can then plug in to the base model it was trained from. A fully fine-tuned model can be tens of gigabytes in size, while these adapters are usually just a few megabytes. This makes it a lot easier to distribute, and serving fine-tuned inference with LoRA only adds ms of latency to total inference time.

If you’re curious to understand why LoRA is so effective, buckle up — we first have to go through a brief lesson on linear algebra. If that’s not a term you’ve thought about since university, don’t worry, we’ll walk you through it.

Show me the math

With traditional fine-tuning, we can take the weights of a model (W0) and tweak them to output a new set of weights — so the difference between the original model weights and the new weights is ΔW, representing the change in weights. Therefore, a tuned model will have a new set of weights which can be represented as the original model weights plus the change in weights, W0 + ΔW.

Remember, all of these model weights are actually represented as large matrices of numbers. In math, every matrix has a property called rank (r), which describes the number of linearly independent columns or rows in a matrix. When matrices are low-rank, they have only a few columns or rows that are “important”, so we can actually decompose or split them into two smaller matrices with the most important parameters  (think of it like factoring in algebra). This technique is called rank decomposition, which allows us to greatly reduce and simplify matrices while keeping the most important bits. In the context of fine-tuning, rank determines how many parameters get changed from the original model – the higher the rank, the stronger the fine-tune, giving you more granularity over the output.

According to the original LoRA paper, researchers have found that when a model is low-rank, the matrix representing the change in weights is also low-rank. Therefore, we can apply rank decomposition to our matrix representing the change in weights ΔW to create two smaller matrices A, B, where ΔW = BA. Now, the change in the model can be represented by two smaller low-rank matrices. This is why this method of fine-tuning is called Low-Rank Adaptation.

When we run inference, we only need the smaller matrices A, B to change the behavior of the model. The model weights in A, B constitute our LoRA adapter (along with a config file). At runtime, we add the model weights together, combining the original model (W0) and the LoRA adapter (A, B). Adding and subtracting are simple mathematical operations, meaning that we can quickly swap out different LoRA adapters by adding and subtracting A, B from W0.. By temporarily adjusting the weights of the original model, we modify the model’s behavior and output and as a result, we get fine-tuned inference with minimal added latency.

According to the original LoRA paper, “LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times”. Because of this, LoRA is one of the most popular methods of fine-tuning since it’s a lot less computationally expensive than a fully fine-tuned model, doesn’t add any material inference time, and is much smaller and portable.

How can you use LoRAs with Workers AI?

Workers AI is very well-suited to run LoRAs because of the way we run serverless inference. The models in our catalog are always pre-loaded on our GPUs, meaning that we keep them warm so that your requests never encounter a cold start. This means that the base model is always available, and we can dynamically load and swap out LoRA adapters as needed. We can actually plug in multiple LoRA adapters to one base model, so we can serve multiple different fine-tuned inference requests at once.

When you fine-tune with LoRA, your output will be two files: your custom model weights (in safetensors format) and an adapter config file (in json format). To create these weights yourself, you can train a LoRA on your own data using the Hugging Face PEFT (Parameter-Efficient Fine-Tuning) library combined with the Hugging Face AutoTrain LLM library. You can also run your training tasks on services such as Auto Train and Google Colab. Alternatively, there are many open-source LoRA adapters available on Hugging Face today that cover a variety of use cases.

Eventually, we want to support the LoRA training workloads on our platform, but we’ll need you to bring your trained LoRA adapters to Workers AI today, which is why we’re calling this feature Bring Your Own (BYO) LoRAs.

For the initial open beta release, we are allowing people to use LoRAs with our Mistral, Llama, and Gemma models. We have set aside versions of these models which accept LoRAs, which you can access by appending -lora to the end of the model name. Your adapter must have been fine-tuned from one of our supported base models listed below:

  • @cf/meta-llama/llama-2-7b-chat-hf-lora
  • @cf/mistral/mistral-7b-instruct-v0.2-lora
  • @cf/google/gemma-2b-it-lora
  • @cf/google/gemma-7b-it-lora

As we are launching this feature in open beta, we have some limitations today to take note of: quantized LoRA models are not yet supported, LoRA adapters must be smaller than 100MB and have up to a max rank of 8, and you can try up to 30 LoRAs per account during our initial open beta. To get started with LoRAs on Workers AI, read the Developer Docs.

As always, we expect people to use Workers AI and our new BYO LoRA feature with our Terms of Service in mind, including any model-specific restrictions on use contained in the models’ license terms.

How did we build multi-tenant LoRA serving?

Serving multiple LoRA models simultaneously poses a challenge in terms of GPU resource utilization. While it is possible to batch inference requests to a base model, it is much more challenging to batch requests with the added complexity of serving unique LoRA adapters. To tackle this problem, we leverage the Punica CUDA kernel design in combination with global cache optimizations in order to handle the memory intensive workload of multi-tenant LoRA serving while offering low inference latency.

The Punica CUDA kernel was introduced in the paper Punica: Multi-Tenant LoRA Serving as a method to serve multiple, significantly different LoRA models applied to the same base model. In comparison to previous inference techniques, the method offers substantial throughput and latency improvements. This optimization is achieved in part through enabling request batching even across requests serving different LoRA adapters.

The core of the Punica kernel system is a new CUDA kernel called Segmented Gather Matrix-Vector Multiplication (SGMV). SGMV allows a GPU to store only a single copy of the pre-trained model while serving different LoRA models. The Punica kernel design system consolidates the batching of requests for unique LoRA models to improve performance by parallelizing the feature-weight multiplication of different requests in a batch. Requests for the same LoRA model are then grouped to increase operational intensity. Initially, the GPU loads the base model while reserving most of its GPU memory for KV Cache. The LoRA components (A and B matrices) are then loaded on demand from remote storage (Cloudflare’s cache or R2) when required by an incoming request. This on demand loading introduces only milliseconds of latency, which means that multiple LoRA adapters can be seamlessly fetched and served with minimal impact on inference performance. Frequently requested LoRA adapters are cached for the fastest possible inference.

Once a requested LoRA has been cached locally, the speed it can be made available for inference is constrained only by PCIe bandwidth. Regardless, given that each request may require its own LoRA, it becomes critical that LoRA downloads and memory copy operations are performed asynchronously. The Punica scheduler tackles this exact challenge, batching only requests which currently have required LoRA weights available in GPU memory, and queueing requests that do not until the required weights are available and the request can efficiently join a batch.

By effectively managing KV cache and batching these requests, it is possible to handle significant multi-tenant LoRA-serving workloads. A further and important optimization is the use of continuous batching. Common batching methods require all requests to the same adapter to reach their stopping condition before being released. Continuous batching allows a request in a batch to be released early so that it does not need to wait for the longest running request.

Given that LLMs deployed to Cloudflare’s network are available globally, it is important that LoRA adapter models are as well. Very soon, we will implement remote model files that are cached at Cloudflare’s edge to further reduce inference latency.

A roadmap for fine-tuning on Workers AI

Launching support for LoRA adapters is an important step towards unlocking fine-tunes on our platform. In addition to the LLM fine-tunes available today, we look forward to supporting more models and a variety of task types, including image generation.

Our vision for Workers AI is to be the best place for developers to run their AI workloads — and this includes the process of fine-tuning itself. Eventually, we want to be able to run the fine-tuning training job as well as fully fine-tuned models directly on Workers AI. This unlocks many use cases for AI to be more relevant in organizations by empowering models to have more granularity and detail for specific tasks.

With AI Gateway, we will be able to help developers log their prompts and responses, which they can then use to fine-tune models with production data. Our vision is to have a one-click fine-tuning service, where log data from AI Gateway can be used to retrain a model (on Cloudflare) and then the fine-tuned model can be redeployed on Workers AI for inference. This will allow developers to personalize their AI models to fit their applications, allowing for granularity as low as a per-user level. The fine-tuned model can then be smaller and more optimized, helping users save time and money on AI inference – and the magic is that all of this can all happen within our very own Developer Platform.

We’re excited for you to try the open beta for BYO LoRAs! Read our Developer Docs for more details, and tell us what you think on Discord.

Leveling up Workers AI: general availability and more new capabilities

Post Syndicated from Michelle Chen original https://blog.cloudflare.com/workers-ai-ga-huggingface-loras-python-support


Welcome to Tuesday – our AI day of Developer Week 2024! In this blog post, we’re excited to share an overview of our new AI announcements and vision, including news about Workers AI officially going GA with improved pricing, a GPU hardware momentum update, an expansion of our Hugging Face partnership, Bring Your Own LoRA fine-tuned inference, Python support in Workers, more providers in AI Gateway, and Vectorize metadata filtering.

Workers AI GA

Today, we’re excited to announce that our Workers AI inference platform is now Generally Available. After months of being in open beta, we’ve improved our service with greater reliability and performance, unveiled pricing, and added many more models to our catalog.

Improved performance & reliability

With Workers AI, our goal is to make AI inference as reliable and easy to use as the rest of Cloudflare’s network. Under the hood, we’ve upgraded the load balancing that is built into Workers AI. Requests can now be routed to more GPUs in more cities, and each city is aware of the total available capacity for AI inference. If the request would have to wait in a queue in the current city, it can instead be routed to another location, getting results back to you faster when traffic is high. With this, we’ve increased rate limits across all our models – most LLMs now have a of 300 requests per minute, up from 50 requests per minute during our beta phase. Smaller models have a limit of 1500-3000 requests per minute. Check out our Developer Docs for the rate limits of individual models.

Lowering costs on popular models

Alongside our GA of Workers AI, we published a pricing calculator for our 10 non-beta models earlier this month. We want Workers AI to be one of the most affordable and accessible solutions to run inference, so we added a few optimizations to our models to make them more affordable. Now, Llama 2 is over 7x cheaper and Mistral 7B is over 14x cheaper to run than we had initially published on March 1. We want to continue to be the best platform for AI inference and will continue to roll out optimizations to our customers when we can.

As a reminder, our billing for Workers AI started on April 1st for our non-beta models, while beta models remain free and unlimited. We offer 10,000 neurons per day for free to all customers. Workers Free customers will encounter a hard rate limit after 10,000 neurons in 24 hours while Workers Paid customers will incur usage at $0.011 per 1000 additional neurons.  Read our Workers AI Pricing Developer Docs for the most up-to-date information on pricing.

New dashboard and playground

Lastly, we’ve revamped our Workers AI dashboard and AI playground. The Workers AI page in the Cloudflare dashboard now shows analytics for usage across models, including neuron calculations to help you better predict pricing. The AI playground lets you quickly test and compare different models and configure prompts and parameters. We hope these new tools help developers start building on Workers AI seamlessly – go try them out!

Run inference on GPUs in over 150 cities around the world

When we announced Workers AI back in September 2023, we set out to deploy GPUs to our data centers around the world. We plan to deliver on that promise and deploy inference-tuned GPUs almost everywhere by the end of 2024, making us the most widely distributed cloud-AI inference platform. We have over 150 cities with GPUs today and will continue to roll out more throughout the year.

We also have our next generation of compute servers with GPUs launching in Q2 2024, which means better performance, power efficiency, and improved reliability over previous generations. We provided a preview of our Gen 12 Compute servers design in a December 2023 blog post, with more details to come. With Gen 12 and future planned hardware launches, the next step is to support larger machine learning models and offer fine-tuning on our platform. This will allow us to achieve higher inference throughput, lower latency and greater availability for production workloads, as well as expanding support to new categories of workloads such as fine-tuning.

Hugging Face Partnership

We’re also excited to continue our partnership with Hugging Face in the spirit of bringing the best of open-source to our customers. Now, you can visit some of the most popular models on Hugging Face and easily click to run the model on Workers AI if it is available on our platform.

We’re happy to announce that we’ve added 4 more models to our platform in conjunction with Hugging Face. You can now access the new Mistral 7B v0.2 model with improved context windows, Nous Research’s Hermes 2 Pro fine-tuned version of Mistral 7B, Google’s Gemma 7B, and Starling-LM-7B-beta fine-tuned from OpenChat. There are currently 14 models that we’ve curated with Hugging Face to be available for serverless GPU inference powered by Cloudflare’s Workers AI platform, with more coming soon. These models are all served using Hugging Face’s technology with a TGI backend, and we work closely with the Hugging Face team to curate, optimize, and deploy these models.

“We are excited to work with Cloudflare to make AI more accessible to developers. Offering the most popular open models with a serverless API, powered by a global fleet of GPUs is an amazing proposition for the Hugging Face community, and I can’t wait to see what they build with it.”
Julien Chaumond, Co-founder and CTO, Hugging Face

You can find all of the open models supported in Workers AI in this Hugging Face Collection, and the “Deploy to Cloudflare Workers AI” button is at the top of each model card. To learn more, read Hugging Face’s blog post and take a look at our Developer Docs to get started. Have a model you want to see on Workers AI? Send us a message on Discord with your request.

Supporting fine-tuned inference – BYO LoRAs

Fine-tuned inference is one of our most requested features for Workers AI, and we’re one step closer now with Bring Your Own (BYO) LoRAs. Using the popular Low-Rank Adaptation method, researchers have figured out how to take a model and adapt some model parameters to the task at hand, rather than rewriting all model parameters like you would for a fully fine-tuned model. This means that you can get fine-tuned model outputs without the computational expense of fully fine-tuning a model.

We now support bringing trained LoRAs to Workers AI, where we apply the LoRA adapter to a base model at runtime to give you fine-tuned inference, at a fraction of the cost, size, and speed of a fully fine-tuned model. In the future, we want to be able to support fine-tuning jobs and fully fine-tuned models directly on our platform, but we’re excited to be one step closer today with LoRAs.

const response = await ai.run(
  "@cf/mistralai/mistral-7b-instruct-v0.2-lora", //the model supporting LoRAs
  {
      messages: [{"role": "user", "content": "Hello world"],
      raw: true, //skip applying the default chat template
      lora: "00000000-0000-0000-0000-000000000", //the finetune id OR name 
  }
);

BYO LoRAs is in open beta as of today for Gemma 2B and 7B, Llama 2 7B and Mistral 7B models with LoRA adapters up to 100MB in size and max rank of 8, and up to 30 total LoRAs per account. As always, we expect you to use Workers AI and our new BYO LoRA feature with our Terms of Service in mind, including any model-specific restrictions on use contained in the models’ license terms.

Read the technical deep dive blog post and developer docs to get started.

Write Workers in Python

Python is the second most popular programming language in the world (after JavaScript) and the language of choice for building AI applications. And starting today, in open beta, you can now write Cloudflare Workers in Python. Python Workers support all bindings to resources on Cloudflare, including Vectorize, D1, KV, R2 and more.

LangChain is the most popular framework for building LLM‑powered applications, and like how Workers AI works with langchain-js, the Python LangChain library works on Python Workers, as do other Python packages like FastAPI.

Workers written in Python are just as simple as Workers written in JavaScript:

from js import Response

async def on_fetch(request, env):
    return Response.new("Hello world!")

…and are configured by simply pointing at a .py file in your wrangler.toml:

name = "hello-world-python-worker"
main = "src/entry.py"
compatibility_date = "2024-03-18"
compatibility_flags = ["python_workers"]

There are no extra toolchain or precompilation steps needed. The Pyodide Python execution environment is provided for you, directly by the Workers runtime, mirroring how Workers written in JavaScript already work.

There’s lots more to dive into — take a look at the docs, and check out our companion blog post for details about how Python Workers work behind the scenes.

AI Gateway now supports Anthropic, Azure, AWS Bedrock, Google Vertex, and Perplexity

Our AI Gateway product helps developers better control and observe their AI applications, with analytics, caching, rate limiting, and more. We are continuing to add more providers to the product, including Anthropic, Google Vertex, and Perplexity, which we’re excited to announce today. We quietly rolled out Azure and Amazon Bedrock support in December 2023, which means that the most popular providers are now supported via AI Gateway, including Workers AI itself.

Take a look at our Developer Docs to get started with AI Gateway.

Coming soon: Persistent Logs

In Q2 of 2024, we will be adding persistent logs so that you can push your logs (including prompts and responses) to object storage, custom metadata so that you can tag requests with user IDs or other identifiers, and secrets management so that you can securely manage your application’s API keys.

We want AI Gateway to be the control plane for your AI applications, allowing developers to dynamically evaluate and route requests to different models and providers. With our persistent logs feature, we want to enable developers to use their logged data to fine-tune models in one click, eventually running the fine-tune job and the fine-tuned model directly on our Workers AI platform. AI Gateway is just one product in our AI toolkit, but we’re excited about the workflows and use cases it can unlock for developers building on our platform, and we hope you’re excited about it too.

Vectorize metadata filtering and future GA of million vector indexes

Vectorize is another component of our toolkit for AI applications. In open beta since September 2023, Vectorize allows developers to persist embeddings (vectors), like those generated from Workers AI text embedding models, and query for the closest match to support use cases like similarity search or recommendations. Without a vector database, model output is forgotten and can’t be recalled without extra costs to re-run a model.

Since Vectorize’s open beta, we’ve added metadata filtering. Metadata filtering lets developers combine vector search with filtering for arbitrary metadata, supporting the query complexity in AI applications. We’re laser-focused on getting Vectorize ready for general availability, with an target launch date of June 2024, which will include support for multi-million vector indexes.

// Insert vectors with metadata
const vectors: Array<VectorizeVector> = [
  {
    id: "1",
    values: [32.4, 74.1, 3.2],
    metadata: { url: "/products/sku/13913913", streaming_platform: "netflix" }
  },
  {
    id: "2",
    values: [15.1, 19.2, 15.8],
    metadata: { url: "/products/sku/10148191", streaming_platform: "hbo" }
  },
...
];
let upserted = await env.YOUR_INDEX.upsert(vectors);

// Query with metadata filtering
let metadataMatches = await env.YOUR_INDEX.query(<queryVector>, { filter: { streaming_platform: "netflix" }} )

The most comprehensive Developer Platform to build AI applications

On Cloudflare’s Developer Platform, we believe that all developers should be able to quickly build and ship full-stack applications  – and that includes AI experiences as well. With our GA of Workers AI, announcements for Python support in Workers, AI Gateway, and Vectorize, and our partnership with Hugging Face, we’ve expanded the world of possibilities for what you can build with AI on our platform. We hope you are as excited as we are – take a look at all our Developer Docs to get started, and let us know what you build.

Bringing Python to Workers using Pyodide and WebAssembly

Post Syndicated from Hood Chatham original https://blog.cloudflare.com/python-workers


Starting today, in open beta, you can now write Cloudflare Workers in Python.

This new support for Python is different from how Workers have historically supported languages beyond JavaScript — in this case, we have directly integrated a Python implementation into workerd, the open-source Workers runtime. All bindings, including bindings to Vectorize, Workers AI, R2, Durable Objects, and more are supported on day one. Python Workers can import a subset of popular Python packages including FastAPI, Langchain, Numpy and more. There are no extra build steps or external toolchains.

To do this, we’ve had to push the bounds of all of our systems, from the runtime itself, to our deployment system, to the contents of the Worker bundle that is published across our network. You can read the docs, and start using it today.

We want to use this post to pull back the curtain on the internal lifecycle of a Python Worker, share what we’ve learned in the process, and highlight where we’re going next.

Beyond “Just compile to WebAssembly”

Cloudflare Workers have supported WebAssembly since 2018 — each Worker is a V8 isolate, powered by the same JavaScript engine as the Chrome web browser. In principle, it’s been possible for years to write Workers in any language — including Python — so long as it first compiles to WebAssembly or to JavaScript.

In practice, just because something is possible doesn’t mean it’s simple. And just because “hello world” works doesn’t mean you can reliably build an application. Building full applications requires supporting an ecosystem of packages that developers are used to building with. For a platform to truly support a programming language, it’s necessary to go much further than showing how to compile code using external toolchains.

Python Workers are different from what we’ve done in the past. It’s early, and still in beta, but we think it shows what providing first-class support for programming languages beyond JavaScript can look like on Workers.

The lifecycle of a Python Worker

With Pyodide now built into workerd, you can write a Worker like this:

from js import Response

async def on_fetch(request, env):
    return Response.new("Hello world!")

…with a wrangler.toml file that points to a .py file:

name = "hello-world-python-worker"
main = "src/entry.py"
compatibility_date = "2024-03-18"

…and when you run npx wrangler@latest dev, the Workers runtime will:

  1. Determine which version of Pyodide is required, based on your compatibility date
  2. Create an isolate for your Worker, and automatically inject Pyodide
  3. Serve your Python code using Pyodide

This all happens under the hood — no extra toolchain or precompilation steps needed. The Python execution environment is provided for you, mirroring how Workers written in JavaScript already work.

A Python interpreter built into the Workers runtime

Just as JavaScript has many engines, Python has many implementations that can execute Python code. CPython is the reference implementation of Python. If you’ve used Python before, this is almost certainly what you’ve used, and is commonly referred to as just “Python”.

Pyodide is a port of CPython to WebAssembly. It interprets Python code, without any need to precompile the Python code itself to any other format. It runs in a web browser — check out this REPL. It is true to the CPython that Python developers know and expect, providing most of the Python Standard Library. It provides a foreign function interface (FFI) to JavaScript, allowing you to call JavaScript APIs directly from Python — more on this below. It provides popular open-source packages, and can import pure Python packages directly from PyPI.

Pyodide struck us as the perfect fit for Workers. It is designed to allow the core interpreter and each native Python module to be built as separate WebAssembly modules, dynamically linked at runtime. This allows the code footprint for these modules to be shared among all Workers running on the same machine, rather than requiring each Worker to bring its own copy. This is essential to making WebAssembly work well in the Workers environment, where we often run thousands of Workers per machine — we need Workers using the same programming language to share their runtime code footprint. Running thousands of Workers on every machine is what makes it possible for us to deploy every application in every location at a reasonable price.

Just like with JavaScript Workers, with Python Workers we provide the runtime for you:

Pyodide is currently the exception — most languages that target WebAssembly do not yet support dynamic linking, so each application ends up bringing its own copy of its language runtime. We hope to see more languages support dynamic linking in the future, so that we can more effectively bring them to Workers.

How Pyodide works

Pyodide executes Python code in WebAssembly, which is a sandboxed environment, separated from the host runtime. Unlike running native code, all operations outside of pure computation (such as file reads) must be provided by a runtime environment, then imported by the WebAssembly module.

LLVM provides three target triples for WebAssembly:

  1. wasm32-unknown-unknown – this backend provides no C standard library or system call interface; to support this backend, we would need to manually rewrite every system or library call to make use of imports we would define ourselves in the runtime.
  2. wasm32-wasi – WASI is a standardized system interface, and defines a standard set of imports that are implemented in WASI runtimes such as wasmtime.
  3. wasm32-unknown-emscripten – Like WASI, Emscripten defines the imports that a WebAssembly program needs to execute, but also outputs an accompanying JavaScript library that implements these imported functions.

Pyodide uses Emscripten, and provides three things:

  1. A distribution of the CPython interpreter, compiled using Emscripten
  2. A foreign function interface (FFI) between Python and JavaScript
  3. A set of third-party Python packages, compiled using Emscripten’s compiler to WebAssembly.

Of these targets, only Emscripten currently supports dynamic linking, which, as we noted above, is essential to providing a shared language runtime for Python that is shared across isolates. Emscripten does this by providing implementations of dlopen and dlsym, which use the accompanying JavaScript library to modify the WebAssembly program’s table to link additional WebAssembly-compiled modules at runtime. WASI does not yet support the dlopen/dlsym dynamic linking abstractions used by CPython.

Pyodide and the magic of foreign function interfaces (FFI)

You might have noticed that in our Hello World Python Worker, we import Response from the js module:

from js import Response

async def on_fetch(request, env):
    return Response.new("Hello world!")

Why is that?

Most Workers are written in JavaScript, and most of our engineering effort on the Workers runtime goes into improving JavaScript Workers. There is a risk in adding a second language that it might never reach feature parity with the first language and always be a second class citizen. Pyodide’s foreign function interface (FFI) is critical to avoiding this by providing access to all JavaScript functionality from Python. This can be used by the Worker author directly, and it is also used to make packages like FastAPI and Langchain work out-of-the-box, as we’ll show later in this post.

An FFI is a system for calling functions in one language that are implemented in another language. In most cases, an FFI is defined by a “higher-level” language in order to call functions implemented in a systems language, often C. Python’s ctypes module is such a system. These sorts of foreign function interfaces are often difficult to use because of the nature of C APIs.

Pyodide’s foreign function interface is an interface between Python and JavaScript, which are two high level object-oriented languages with a lot of design similarities. When passed from one language to another, immutable types such as strings and numbers are transparently translated. All mutable objects are wrapped in an appropriate proxy.

When a JavaScript object is passed into Python, Pyodide determines which JavaScript protocols the object supports and dynamically constructs an appropriate Python class that implements the corresponding Python protocols. For example, if the JavaScript object supports the JavaScript iteration protocol then the proxy will support the Python iteration protocol. If the JavaScript object is a Promise or other thenable, the Python object will be an awaitable.

from js import JSON

js_array = JSON.parse("[1,2,3]")

for entry in js_array:
   print(entry)

The lifecycle of a request to a Python Worker makes use of Pyodide’s FFI, wrapping the incoming JavaScript Request object in a JsProxy object that is accessible in your Python code. It then converts the value returned by the Python Worker’s handler into a JavaScript Response object that can be delivered back to the client:

Why dynamic linking is essential, and static linking isn’t enough

Python comes with a C FFI, and many Python packages use this FFI to import native libraries. These libraries are typically written in C, so they must first be compiled down to WebAssembly in order to work on the Workers runtime. As we noted above, Pyodide is built with Emscripten, which overrides Python’s C FFI — any time a package tries to load a native library, it is instead loaded from a WebAssembly module that is provided by the Workers runtime. Dynamic linking is what makes this possible — it is what lets us override Python’s C FFI, allowing Pyodide to support many Python packages that have native library dependencies.

Dynamic linking is “pay as you go”, while static linking is “pay upfront” — if code is statically linked into your binary, it must be loaded upfront in order for the binary to run, even if this code is never used.

Dynamic linking enables the Workers runtime to share the underlying WebAssembly modules of packages across different Workers that are running on the same machine.

We won’t go too much into detail on how dynamic linking works in Emscripten, but the main takeaway is that the Emscripten runtime fetches WebAssembly modules from a filesystem abstraction provided in JavaScript. For each Worker, we generate a filesystem at runtime, whose structure mimics a Python distribution that has the Worker’s dependencies installed, but whose underlying files are shared between Workers. This makes it possible to share Python and WebAssembly files between multiple Workers that import the same dependency. Today, we’re able to share these files across Workers, but copy them into each new isolate. We think we can go even further, by employing copy-on-write techniques to share the underlying resource across many Workers.

Supporting Server and Client libraries

Python has a wide variety of popular HTTP client libraries, including httpx, urllib3, requests and more. Unfortunately, none of them work out of the box in Pyodide. Adding support for these has been one of the longest running user requests for the Pyodide project. The Python HTTP client libraries all work with raw sockets, and the browser security model and CORS do not allow this, so we needed another way to make them work in the Workers runtime.

Async Client libraries

For libraries that can make requests asynchronously, including aiohttp and httpx, we can use the Fetch API to make requests. We do this by patching the library, instructing it to use the Fetch API from JavaScript — taking advantage of Pyodide’s FFI. The httpx patch ends up quite simple —fewer than 100 lines of code. Simplified even further, it looks like this:

from js import Headers, Request, fetch

def py_request_to_js_request(py_request):
    js_headers = Headers.new(py_request.headers)
    return Request.new(py_request.url, method=py_request.method, headers=js_headers)

def js_response_to_py_response(js_response):
  ... # omitted

async def do_request(py_request):
  js_request = py_request_to_js_request(py_request)
    js_response = await fetch(js_request)
    py_response = js_response_to_py_response(js_response)
    return py_response

Synchronous Client libraries

Another challenge in supporting Python HTTP client libraries is that many Python APIs are synchronous. For these libraries, we cannot use the fetch API directly because it is asynchronous.

Thankfully, Joe Marshall recently landed a contribution to urllib3 that adds Pyodide support in web browsers by:

  1. Checking if blocking with Atomics.wait() is possible
    a. If so, start a fetch worker thread
    b. Delegate the fetch operation to the worker thread and serialize the response into a SharedArrayBuffer
    c. In the Python thread, use Atomics.wait to block for the response in the SharedArrayBuffer
  2. If Atomics.wait() doesn’t work, fall back to a synchronous XMLHttpRequest

Despite this, today Cloudflare Workers do not support worker threads or synchronous XMLHttpRequest, so neither of these two approaches will work in Python Workers. We do not support synchronous requests today, but there is a way forward…

WebAssembly Stack Switching

There is an approach which will allow us to support synchronous requests. WebAssembly has a stage 3 proposal adding support for stack switching, which v8 has an implementation of. Pyodide contributors have been working on adding support for stack switching to Pyodide since September of 2022, and it is almost ready.

With this support, Pyodide exposes a function called run_sync which can block for completion of an awaitable:

from pyodide.ffi import run_sync

def sync_fetch(py_request):
   js_request = py_request_to_js_request(py_request)
   js_response  = run_sync(fetch(js_request))
   return js_response_to_py_response(js_response)

FastAPI and Python’s Asynchronous Server Gateway Interface

FastAPI is one of the most popular libraries for defining Python servers. FastAPI applications use a protocol called the Asynchronous Server Gateway Interface (ASGI). This means that FastAPI never reads from or writes to a socket itself. An ASGI application expects to be hooked up to an ASGI server, typically uvicorn. The ASGI server handles all of the raw sockets on the application’s behalf.

Conveniently for us, this means that FastAPI works in Cloudflare Workers without any patches or changes to FastAPI itself. We simply need to replace uvicorn with an appropriate ASGI server that can run within a Worker. Our initial implementation lives here, in the fork of Pyodide that we maintain. We hope to add a more comprehensive feature set, add test coverage, and then upstream this implementation into Pyodide.

You can try this yourself by cloning cloudflare/python-workers-examples, and running npx wrangler@latest dev in the directory of the FastAPI example.

Importing Python Packages

Python Workers support a subset of Python packages, which are provided directly by Pyodide, including numpy, httpx, FastAPI, Langchain, and more. This ensures compatibility with the Pyodide runtime by pinning package versions to Pyodide versions, and allows Pyodide to patch internal implementations, as we showed above in the case of httpx.

To import a package, simply add it to your requirements.txt file, without adding a version number. A specific version of the package is provided directly by Pyodide. Today, you can use packages in local development, and in the coming weeks, you will be able to deploy Workers that define dependencies in a requirements.txt file. Later in this post, we’ll show how we’re thinking about managing new versions of Pyodide and packages.

We maintain our own fork of Pyodide, which allows us to provide patches specific to the Workers runtime, and to quickly expand our support for packages in Python Workers, while also committing to upstreaming our changes back to Pyodide, so that the whole ecosystem of developers can benefit.

Python packages are often big and memory hungry though, and they can do a lot of work at import time. How can we ensure that you can bring in the packages you need, while mitigating long cold start times?

Making cold starts faster with memory snapshots

In the example at the start of this post, in local development, we mentioned injecting Pyodide into your Worker. Pyodide itself is 6.4MB — and Python packages can also be quite large.

If we simply shoved Pyodide into your Worker and uploaded it to Cloudflare, that’d be quite a large Worker to load into a new isolate — cold starts would be slow. On a fast computer with a good network connection, Pyodide takes about two seconds to initialize in a web browser, one second of network time and one second of cpu time. It wouldn’t be acceptable to initialize it every time you update your code for every isolate your Worker runs in across Cloudflare’s network.

Instead, when you run npx wrangler@latest deploy, the following happens:

  1. Wrangler uploads your Python code and your requirements.txt file to the Workers API
  2. We send your Python code, and your requirements.txt file to the Workers runtime to be validated
  3. We create a new isolate for your Worker, and automatically inject Pyodide plus any packages you’ve specified in your requirements.txt file.
  4. We scan the Worker’s code for import statements, execute them, and then take a snapshot of the Worker’s WebAssembly linear memory. Effectively, we perform the expensive work of importing packages at deploy time, rather than at runtime.
  5. We deploy this snapshot alongside your Worker’s Python code to Cloudflare’s network.
  6. Just like a JavaScript Worker, we execute the Worker’s top-level scope.

When a request comes in to your Worker, we load this snapshot and use it to bootstrap your Worker in an isolate, avoiding expensive initialization time:

This takes cold starts for a basic Python Worker down to below 1 second. We’re not yet satisfied with this though. We’re confident that we can drive this down much, much further. How? By reusing memory snapshots.

Reusing Memory Snapshots

When you upload a Python Worker, we generate a single memory snapshot of the Worker’s top-level imports, including both Pyodide and any dependencies. This snapshot is specific to your Worker. It can’t be shared, even though most of its contents are the same as other Python Workers.

Instead, we can create a single, shared snapshot ahead of time, and preload it into a pool of “pre-warmed” isolates. These isolates would already have the Pyodide runtime loaded and ready — making a Python Worker work just like a JavaScript Worker. In both cases, the underlying interpreter and execution environment is provided by the Workers runtime, and available on-demand without delay. The only difference is that with Python, the interpreter runs in WebAssembly, within the Worker.

Snapshots are a common pattern across runtimes and execution environments. Node.js uses V8 snapshots to speed up startup time. You can take snapshots of Firecracker microVMs and resume execution in a different process. There’s lots more we can do here — not just for Python Workers, but for Workers written in JavaScript as well, caching snapshots of compiled code from top-level scope and the state of the isolate itself. Workers are so fast and efficient that to-date we haven’t had to take snapshots in this way, but we think there are still big performance gains to be had.

This is our biggest lever towards driving cold start times down over the rest of 2024.

Future proofing compatibility with Pyodide versions and Compatibility Dates

When you deploy a Worker to Cloudflare, you expect it to keep running indefinitely, even if you never update it again. There are Workers deployed in 2018 that are still running just fine in production.

We achieve this using Compatibility Dates and Compatibility Flags, which provide explicit opt-in mechanisms for new behavior and potentially backwards-incompatible changes, without impacting existing Workers.

This works in part because it mirrors how the Internet and web browsers work. You publish a web page with some JavaScript, and rightly expect it to work forever. Web browsers and Cloudflare Workers have the same type of commitment of stability to developers.

There is a challenge with Python though — both Pyodide and CPython are versioned. Updated versions are published regularly and can contain breaking changes. And Pyodide provides a set of built-in packages, each with a pinned version number. This presents a question — how should we allow you to update your Worker to a newer version of Pyodide?

The answer is Compatibility Dates and Compatibility Flags.

A new version of Python is released every year in August, and a new version of Pyodide is released six (6) months later. When this new version of Pyodide is published, we will add it to Workers by gating it behind a Compatibility Flag, which is only enabled after a specified Compatibility Date. This lets us continually provide updates, without risk of breaking changes, extending the commitment we’ve made for JavaScript to Python.

Each Python release has a five (5) year support window. Once this support window has passed for a given version of Python, security patches are no longer applied, making this version unsafe to rely on. To mitigate this risk, while still trying to hold as true as possible to our commitment of stability and long-term support, after five years any Python Worker still on a Python release that is outside of the support window will be automatically moved forward to the next oldest Python release. Python is a mature and stable language, so we expect that in most cases, your Python Worker will continue running without issue. But we recommend updating the compatibility date of your Worker regularly, to stay within the support window.

In between Python releases, we also expect to update and add additional Python packages, using the same opt-in mechanism. A Compatibility Flag will be a combination of the Python version and the release date of a set of packages. For example, python_3.17_packages_2025_03_01.

How bindings work in Python Workers

We mentioned earlier that Pyodide provides a foreign function interface (FFI) to JavaScript — meaning that you can directly use JavaScript objects, methods, functions and more, directly from Python.

This means that from day one, all binding APIs to other Cloudflare resources are supported in Cloudflare Workers. The env object that is provided by handlers in Python Workers is a JavaScript object that Pyodide provides a proxy API to, handling type translations across languages automatically.

For example, to write to and read from a KV namespace from a Python Worker, you would write:

from js import Response

async def on_fetch(request, env):
    await env.FOO.put("bar", "baz")
    bar = await env.FOO.get("bar")
    return Response.new(bar) # returns "baz"

This works for Web APIs too — see how Response is imported from the js module? You can import any global from JavaScript this way.

Get this JavaScript out of my Python!

You’re probably reading this post because you want to write Python instead of JavaScript. from js import Response just isn’t Pythonic. We know — and we have actually tackled this challenge before for another language (Rust). And we think we can do this even better for Python.

We launched workers-rs in 2021 to make it possible to write Workers in Rust. For each JavaScript API in Workers, we, alongside open-source contributors, have written bindings that expose a more idiomatic Rust API.

We plan to do the same for Python Workers — starting with the bindings to Workers AI and Vectorize. But while workers-rs requires that you use and update an external dependency, the APIs we provide with Python Workers will be built into the Workers runtime directly. Just update your compatibility date, and get the latest, most Pythonic APIs.

This is about more than just making bindings to resources on Cloudflare more Pythonic though — it’s about compatibility with the ecosystem.

Similar to how we recently converted workers-rs to use types from the http crate, which makes it easy to use the axum crate for routing, we aim to do the same for Python Workers. For example, the Python standard library provides a raw socket API, which many Python packages depend on. Workers already provides connect(), a JavaScript API for working with raw sockets. We see ways to provide at least a subset of the Python standard library’s socket API in Workers, enabling a broader set of Python packages to work on Workers, with less of a need for patches.

But ultimately, we hope to kick start an effort to create a standardized serverless API for Python. One that is easy to use for any Python developer and offers the same capabilities as JavaScript.

We’re just getting started with Python Workers

Providing true support for a new programming language is a big investment that goes far beyond making “hello world” work. We chose Python very intentionally — it’s the second most popular programming language after JavaScript — and we are committed to continuing to improve performance and widen our support for Python packages.

We’re grateful to the Pyodide maintainers and the broader Python community — and we’d love to hear from you. Drop into the Python Workers channel in the Cloudflare Developers Discord, or start a discussion on Github about what you’d like to see next and which Python packages you’d like us to support.

Why Workers environment variables contain live objects

Post Syndicated from Kenton Varda original https://blog.cloudflare.com/workers-environment-live-object-bindings


If you’ve ever written a Cloudflare Worker using Workers KV for storage, you may have noticed something unsettling.

// A simple Worker that always returns the value named "content",
// read from Workers KV storage.
export default {
  async fetch(request, env, ctx) {
    return new Response(await env.MY_KV.get("content"));
  }
}

Do you feel something is… missing? Like… Where is the setup? The authorization keys? The client library instantiation? Aren’t environment variables normally strings? How is it that env.MY_KV seems to be an object with a get() method that is already hooked up?

Coming from any other platform, you might expect to see something like this instead:

// How would a "typical cloud platform" do it?

// Import KV client library?
import { KV } from "cloudflare:kv";

export default {
  async fetch(request, env, ctx) {
    // Connect to the database?? Using my secret auth key???
    // Which comes from an environment variable????
    let myKv = KV.connect("my-kv-namespace", env.MY_KV_AUTHKEY);

    return new Response(await myKv.get("content"));
  }
}

As another example, consider service bindings, which allow a Worker to send requests to another Worker.

// A simple Worker that greets an authenticated user, delegating to a
// separate service to perform authentication.
export default {
  async fetch(request, env, ctx) {
    // Forward headers to auth service to get user info.
    let authResponse = await env.AUTH_SERVICE.fetch(
        "https://auth/getUser",
        {headers: request.headers});
    let userInfo = await authResponse.json();
    return new Response("Hello, " + userInfo.name);
  }
}

Notice in particular the use of env.AUTH_SERVICE.fetch() to send the request. This sends the request directly to the auth service, regardless of the hostname we give in the URL.

On “typical” platforms, you’d expect to use a real (perhaps internal) hostname to route the request instead, and also include some credentials proving that you’re allowed to use the auth service API:

// How would a "typical cloud platform" do it?
export default {
  async fetch(request, env, ctx) {
    // Forward headers to auth service, via some internal hostname?
    // Hostname needs to be configurable, so get it from an env var.
    let authRequest = new Request(
        "https://" + env.AUTH_SERVICE_HOST + "/getUser",
        {headers: request.headers});

    // We also need to prove that our service is allowed to talk to
    // the auth service API. Add a header for that, containing a
    // secret token from our environment.
    authRequest.headers.set("X-Auth-Service-Api-Key",
        env.AUTH_SERVICE_API_KEY);

    // Now we can make the request.
    let authResponse = await fetch(authRequest);
    let userInfo = await authResponse.json();
    return new Response("Hello, " + userInfo.name);
  }
}

As you can see, in Workers, the “environment” is not just a bunch of strings. It contains full-fledged objects. We call each of these objects a “binding“, because it binds the environment variable name to a resource. You configure exactly what resource a name is bound to when you deploy your Worker – again, just like a traditional environment variable, but not limited to strings.

We can clearly see above that bindings eliminate a little bit of boilerplate, which is nice. But, there’s so much more.

Bindings don’t just reduce boilerplate. They are a core design feature of the Workers platform which simultaneously improve developer experience and application security in several ways. Usually these two goals are in opposition to each other, but bindings elegantly solve for both at the same time.

Security

It may not be obvious at first glance, but bindings neatly solve a number of common security problems in distributed systems.

SSRF is Not A Thing

Bindings, when used properly, make Workers immune to Server-Side Request Forgery (SSRF) attacks, one of the most common yet deadly security vulnerabilities in application servers today. In an SSRF attack, an attacker tricks a server into making requests to other internal services that only it can see, thus giving the attacker access to those internal services.

As an example, imagine we have built a social media application where users are able to set their avatar image. Imagine that, as a convenience, instead of uploading an image from their local disk, a user can instead specify the URL of an image on a third-party server, and the application server will fetch that image to use as the avatar. Sounds reasonable, right? We can imagine the app contains some code like:

let resp = await fetch(userAvatarUrl);
let data = await resp.arrayBuffer();
await setUserAvatar(data);

One problem: What if the user claims their avatar URL is something like “https://auth-service.internal/status”? Whoops, now the above code will actually fetch a status page from the internal auth service, and set it as the user’s avatar. Presumably, the user can then download their own avatar, and it’ll contain the content of this status page, which they were not supposed to be able to access!

But using bindings, this is impossible: There is no URL that the attacker can specify to reach the auth service. The application must explicitly use the binding env.AUTH_SERVICE to reach it. The global fetch() function cannot reach the auth service no matter what URL it is given; it can only make requests to the public Internet.

A legacy caveat: When we originally designed Workers in 2017, the primary use case was implementing a middleware layer in front of an origin server, integrated with Cloudflare’s CDN. At the time, bindings weren’t a thing yet, and we were primarily trying to implement the Service Workers interface. To that end, we made a design decision: when a Worker runs on Cloudflare in front of some origin server, if you invoke the global fetch() function with a URL that is within your zone’s domain, the request will be sent directly to the origin server, bypassing most logic Cloudflare would normally apply to a request received from the Internet. Sadly, this means that Workers which run in front of an origin server are not immune to SSRF – they must worry about it just like traditional servers on private networks must. Although this puts Workers in the same place as most servers, we now see a path to make SSRF a thing you never have to worry about when writing Workers. We will be introducing “origin bindings”,  where the origin server is represented by an explicit binding. That is, to send a request to your origin, you’d need to do env.ORIGIN.fetch(). Then, the global fetch() function can be restricted to only talk to the public Internet, fully avoiding SSRF. This is a big change and we need to handle backwards-compatibility carefully – expect to see more in the coming months. Meanwhile, for Workers that do not have an origin server behind them, or where the origin server does not rely on Cloudflare for security, global fetch() is SSRF-safe today.

And a reminder: Requests originating from Workers have a header, CF-Worker, identifying the domain name that owns the Worker. This header is intended for abuse mitigation: if your server is receiving abusive requests from a Worker, it tells you who to blame and gives you a way to filter those requests. This header is not intended for authorization. You should not implement a private API that grants access to your Workers based solely on the CF-Worker header matching your domain. If you do, you may re-open the opportunity for SSRF vulnerabilities within any Worker running on that domain.

You can’t leak your API key if there is no API key

Usually, if your web app needs access to a protected resource, you will have to obtain some sort of an API key that grants access to the resource. But typically anyone who has this key can access the resource as if they were the Worker. This makes handling auth keys tricky. You can’t put it directly in a config file, unless the entire config file is considered a secret. You can’t check it into source control – you don’t want to publish your keys to GitHub! You probably shouldn’t even store the key on your hard drive – what if your laptop is compromised? And so on.

Even if you have systems in place to deliver auth keys to services securely (like Workers Secrets), if the key is just a string, the service itself can easily leak it. For instance, a developer might carelessly insert a log statement for debugging which logs the service’s configuration – including keys. Now anyone who can access your logs can discover the secret, and there’s probably no practical way to tell if such a leak has occurred.

With Workers bindings, we endeavor for bindings to be live objects, not secret keys. For instance, as seen in the first example in this post, when using a Workers KV binding, you never see a key at all. It’s therefore impossible for a Worker to accidentally leak access to a KV namespace.

No certificate management

This is similar to the API key problem, but arguably worse. When internal services talk to each other over a network, you presumably want them to use secure transports, but typically that requires that every service have a certificate and a private key signed by some CA, and clients must be configured to trust that CA. This is all a big pain to manage, and often the result is that developers don’t bother; they set up a VPC and assume the network is trusted.

In Workers, since all intra-service communications happen over a binding, the system itself can take on all the work of ensuring the transport is secure and goes to the right place.

No frustrating ACL management – but also no lazy “allow all”

At this point you might be thinking: Why are we talking about API keys at all? Cloudflare knows which Worker is sending any request. Can’t it handle the authentication that way?

Consider the earlier example where we imagined that KV namespaces could be opened by name:

// Imagine KV namespaces could be open by name?
let myKv = KV.connect("my-kv-namespace", env.MY_KV_AUTHKEY);

What if we made it simply:

// No authkey, because the system knows whether the Worker has
// permission?
let myKv = KV.connect("my-kv-namespace");

We could then imagine that we could separately configure each KV namespace with an Access Control List (ACL) that specifies which Workers can access it.

Of course, the first problem with this is that it’s vulnerable to SSRF. But, we discussed that already, so let’s discuss another problem.

Many platforms use ACLs for security, but have you ever noticed how everyone hates them? You end up with two choices:

  • Tediously maintain ACLs on every resource. Inevitably, this is always a huge pain. First you deploy your code, which you think is properly configured. Then you discover that it’s failing with permissions errors causing a production outage! So you go fiddle with the IAM system. There are 533,291 roles to choose from and none of them are actually what you want. It turns out you’re supposed to create a custom role, but that’s not obvious, and once you get there, the UI is confusing. Also it’s easy to confuse your team’s service account with your team’s email group, so you give the permissions to the wrong principal, but it takes you an hour of staring at it to realize what you did wrong. Then somehow you manage to remove your own access to the resource and you can’t add it back even though you’re a project admin? (Why yes, all this did in fact happen to me, while using a cloud provider that shall remain nameless.)
  • Give up and grant everything access to everything. Just put all your services in a single VPC where they can all freely talk to each other. This is what most developers are inclined to do, if their security team doesn’t step in to stop them.

Much of this pain comes about because connecting a server to a resource today involves two steps that should really be one step:

  1. Configure the server to point at the resource.
  2. Configure the resource to accept requests from the server.

Developers are primarily concerned with step 1, and forget that step 2 exists until it blows up in their faces. Then it’s a mad scramble to learn how step 2 even works.

What if step 1 just implied step 2? Obviously, if you’re trying to configure a service to access a resource, then you also want the resource to allow access to the service. As long as the person trying to set this up has permissions to both, then there is no reason for this to be a two-step process.

But in typical platforms, the platform itself has no way of knowing that a service has been configured to talk to a resource, because the configuration is just a string.

Bindings fix that. When you define a binding from a Worker to a particular KV namespace, the platform inherently understands that you are telling the Worker to use the KV namespace. Therefore, it can implicitly ensure that the correct permissions are granted. There is no step 2.

And conversely, if no binding is configured, then the Worker does not have access. That means that every Worker starts out with no access by default, and only receives access to exactly the things it needs. Secure by default.

As a related benefit, you can always accurately answer the question “What services are using this resource?” based on bindings. Since the system itself understands bindings and what they point to, the system can answer the query without knowing anything about the service’s internals.

Developer Experience

We’ve seen that bindings improve security in a number of ways. Usually, people expect security and developer friendliness to be a trade-off, with each security measure making life harder for developers. Bindings, however, are entirely the opposite! They actually make life easier!

Easier setup

As we saw in the intro, using a binding reduces setup boilerplate. Instead of receiving an environment variable containing an API key which must be passed into some sort of library, the environment variable itself is an already-initialized client library.

Observability

Because the system understands what bindings a Worker has, and even exactly when those bindings are exercised, the system can answer a lot of questions that would normally require more manual instrumentation or analysis to answer, such as:

  • For a given Worker, what resources does it use? Since the system understands the types of all bindings and what they point to (it doesn’t just see them as opaque strings), it can answer this question.
  • For a given resource, which Workers use it? This is the reverse query. The system can maintain an index of bindings in order to find ones pointing at a given resource.
  • How often does a particular Worker use a particular resource? Since bindings are invoked by calling methods on the binding itself, the system can observe these calls, log them, collect metrics, etc.

Testability via dependency injection

When you deploy a test version of your service, you probably want it to operate on test resources rather than real production resources. For instance, you might have a separate testing KV namespace for storage. But, you probably want to deploy exactly the same code to test that you will eventually deploy to production. That means the names of these resources cannot be hard-coded.

On traditional platforms, the obvious way to avoid hard-coding resource names is to put the name in an environment variable. Going back to our example from the intro, if KV worked in a traditional way not using bindings, you might end up with code like this:

// Hypothetical non-binding-based KV.
let myKv = KV.connect(env.MY_KV_NAMESPACE, env.MY_KV_AUTHKEY);

At best, you now have two environment variables (which had better stay in sync) just to specify what namespace to use.

But at worst, developers might forget to parameterize their resources this way.

  • A developer may write new code that is hard-coded to use a test database, and then forget to update it before pushing it to production, accidentally using the test database in prod.
  • A developer might prototype a new service using production resources from the start (or using new resources which become production resources), only later on deciding that they need to create a new deployment for testing. But by then, it may be a pain to find and parameterize all the different resources used.

With bindings, it’s impossible to have this problem. Since you can only connect to a KV namespace through a binding, it’s always possible to make a separate deployment of the same code which talks to a test namespace instead of production, e.g. using Wrangler Environments.

In the testing world, this is sometimes called “dependency injection”. With bindings, dependencies are always injectable.

Adaptability

Dependency injection isn’t just for tests. A service whose dependencies can be changed out easily will be easier to deploy into new environments, including new production environments.

Say, for instance, you have a service that authenticates users. Now you are launching a new product, which, for whatever reason, has a separate userbase from the original product. You need to deploy a new version of the auth service that uses a different database to implement a separate user set. As long as all dependencies are injectable, this should be easy.

Again, bindings are not the only way to achieve dependency injection, but a bindings-based system will tend to lead developers to write dependency-injectable code by default.

Q&A

Has anyone done this before?

You have. Every time you write code.

As it turns out, this approach is used all the time at the programming language level. Bindings are analogous to parameters of a function, or especially parameters to a class constructor. In a memory-safe programming language, you can’t access an object unless someone has passed you a pointer or reference to that object. Objects in memory don’t have URLs that you use to access them.

Programming languages work this way because they are designed to manage complexity, and this proves to be an elegant way to do so. Yet, this style which we’re used to using at the programming language level is much less common at the distributed system level. The Cloudflare Workers platform aims to treat the network as one big computer, and so it makes sense to extend programming language concepts across the network.

Of course, we’re not the first to apply this to distributed systems, either. The paradigm is commonly called “capability-based security”, which brings us to the next question…

Is this capability-based security?

Bindings are very much inspired by capability-based security.

At present, bindings are not a complete capability system. In particular, there is currently no particular mechanism for a Worker to pass a binding to another Worker. However, this is something we can definitely imagine adding in the future.

Imagine, for instance, you want to call another Worker through a service binding, and as you do, you want to give that other Worker temporary access to a KV namespace for it to operate on. Wouldn’t it be nice if you could just pass the object, and have it auto-revoked at the end of the request? In the future, we might introduce a notion of dynamic bindings which can bind to different resources on a per-request basis, where a calling Worker can pass in a particular value to use for a given request.

For the time being, bindings cannot really be called object capabilities. However, many of the benefits of bindings are the same benefits commonly attributed to capability systems. This is because of some basic similarities:

  • Like a capability, a binding simultaneously designates a resource and also confers permission to access that resource, without referencing any separate ACL.
  • Like capabilities, bindings do not exist in any global namespace: they are scoped to the env object passed to a specific Worker.
  • Like a capability, to use a binding, the application must explicitly specify which binding it is trying to use, and only specifies the binding. In particular, the application does not separately specify the name of the resource in any other namespace (no URL, no global ID, etc.). The existence of the binding only affects the application’s behavior when the application explicitly invokes that binding.

Why is env a parameter to fetch(), not global?

This is a bit wonky, but the goal is to enable composition of Workers.

Imagine you have two Workers, one which implements your API, mapped to api.example.com, and one which serves static assets, mapped to assets.example.com. One day, for whatever reason, you decide you want to combine these two Workers into a single Worker. So you write this code:

import apiWorker from "api-worker.js";
import assetWorker from "asset-worker.js";

export default {
  async fetch(req, env, ctx) {
    let url = new URL(req.url);
    if (url.hostname == "api.example.com") {
      return apiWorker.fetch(req, env, ctx);
    } else if (url.hostname == "assets.example.com") {
      return assetWorker.fetch(req, env, ctx);
    } else {
      return new Response("Not found", {status: 404});
    }
  }
}

This is great! No code from either Worker needed to be modified at all. We just create a new file containing a router Worker that delegates to one or the other.

But, you discover a problem: both the API Worker and the assets Worker use a KV namespace binding, and it turns out that they both decided to name the binding env.KV, but these bindings are meant to point to different namespaces used for different purposes. Does this mean I have to go edit the Workers to change the name of the binding before I can merge them?

No, it doesn’t, because I can just remap the environments before delegating:

import apiWorker from "api-worker.js";
import assetWorker from "asset-worker.js";

export default {
  async fetch(req, env, ctx) {
    let url = new URL(req.url);
    if (url.hostname == "api.example.com") {
      let subenv = {KV: env.API_KV};
      return apiWorker.fetch(req, subenv, ctx);
    } else if (url.hostname == "assets.example.com") {
      let subenv = {KV: env.ASSETS_KV};
      return assetWorker.fetch(req, subenv, ctx);
    } else {
      return new Response("Not found", {status: 404});
    }
  }
}

If environments were globals, this remapping would not be possible.

In fact, this benefit goes much deeper than this somewhat-contrived example. The fact that the environment is not a global essentially forces code to be internally designed for dependency injection (DI). Designing code to be DI-friendly sometimes seems tedious, but every time I’ve done it, I’ve been incredibly happy that I did. Such code tends to be much easier to test and to adapt to new circumstances, for the same reasons mentioned when we discussed dependency injection earlier, but applying at the level of individual modules rather than whole Workers.

With that said, if you really insist that you don’t care about making your code explicitly DI-friendly, there is an alternative: Put your env into AsyncLocalStorage. That way it is “ambiently” available anywhere in your code, but you can still get some composability.

import { AsyncLocalStorage } from 'node:async_hooks';

// Allocate a new AsyncLocalStorage to store the value of `env`.
const ambientEnv = new AsyncLocalStorage();

// We can now define a global function that reads a key from env.MY_KV,
// without having to pass `env` down to it.
function getFromKv(key) {
  // Get the env from AsyncLocalStorage.
  return ambientEnv.getStore().MY_KV.get(key);
}

export default {
  async fetch(req, env, ctx) {
    // Put the env into AsyncLocalStorage while we handle the request,
    // so that calls to getFromKv() work.
    return ambientEnv.run(env, async () => {
      // Handle request, including calling functions that may call
      // getFromKv().

      // ... (code) ...
    });
  }
};

How does a KV binding actually work?

Under the hood, a Workers KV binding encapsulates a secret key used to access the corresponding KV namespace. This key is actually the encryption key for the namespace. The key is distributed to the edge along with the Worker’s code and configuration, using encrypted storage to keep it safe.

Although the key is distributed with the Worker, the Worker itself has no way to access the key. In fact, even the owner of the Cloudflare account cannot see the key – it is simply never revealed outside of Cloudflare’s systems. (Cloudflare employees are also prevented from viewing these keys.)

Even if an attacker somehow got ahold of the key, it would not be useful to them as-is. Cloudflare’s API does not provide any way for a user to upload a raw key to use in a KV binding. The API instead has the client specify the public ID of the namespace they want to use. The deployment system verifies that the KV namespace in question is on the same account as the Worker being uploaded (and that the client is authorized to deploy Workers on said account).

Get Started

To learn about all the types of bindings offered by Workers and how to use them, check out the documentation.

Making state easy with D1 GA, Hyperdrive, Queues and Workers Analytics Engine updates

Post Syndicated from Rita Kozlov original https://blog.cloudflare.com/making-full-stack-easier-d1-ga-hyperdrive-queues


Making full-stack easier

Today might be April Fools, and while we like to have fun as much as anyone else, we like to use this day for serious announcements. In fact, as of today, there are over 2 million developers building on top of Cloudflare’s platform — that’s no joke!

To kick off this Developer Week, we’re flipping the big “production ready” switch on three products: D1, our serverless SQL database; Hyperdrive, which makes your existing databases feel like they’re distributed (and faster!); and Workers Analytics Engine, our time-series database.

We’ve been on a mission to allow developers to bring their entire stack to Cloudflare for some time, but what might an application built on Cloudflare look like?

The diagram itself shouldn’t look too different from the tools you’re already familiar with: you want a database for your core user data. Object storage for assets and user content. Maybe a queue for background tasks, like email or upload processing. A fast key-value store for runtime configuration. Maybe even a time-series database for aggregating user events and/or performance data. And that’s before we get to AI, which is increasingly becoming a core part of many applications in search, recommendation and/or image analysis tasks (at the very least!).

Yet, without having to think about it, this architecture runs on Region: Earth, which means it’s scalable, reliable and fast — all out of the box.

D1: Production Ready

Your core database is one of the most critical pieces of your infrastructure. It needs to be ultra-reliable. It can’t lose data. It needs to scale. And so we’ve been heads down over the last year getting the pieces into place to make sure D1 is production-ready, and we’re extremely excited to say that D1 — our global, serverless SQL database — is now Generally Available.

The GA for D1 lands some of the most asked-for features, including:

  • Support for 10GB databases — and 50,000 databases per account;
  • New data export capabilities; and
  • Enhanced query debugging (we call it “D1 Insights”) — that allows you to understand what queries are consuming the most time, cost, or that are just plain inefficient…  

… to empower developers to build production-ready applications with D1 to meet all their relational SQL needs. And importantly, in an era where the concept of a “free plan” or “hobby plan” is seemingly at risk, we have no intention of removing the free tier for D1 or reducing the 25 billion row reads included in the $5/mo Workers Paid plan:

Plan

Rows Read

Rows Written

Storage

Workers Paid

First 25 billion / month included

+ $0.001 / million rows

First 50 million / month included

+ $1.00 / million rows

First 5 GB included

+ $0.75 / GB-mo

Workers Free

5 million / day

100,000 / day

5 GB (total)

For those who’ve been following D1 since the start: this is the same pricing we announced at open beta

But things don’t just stop at GA: we have some major new features lined up for D1, including global read replication, even larger databases, more Time Travel capabilities that will allow you to branch your database, and new APIs for dynamically querying and/or creating new databases-on-the-fly from within a Worker.

D1’s read replication will automatically deploy read replicas as needed to get data closer to your users: and without you having to spin up, manage scaling, or run into consistency (replication lag) issues. Here’s a sneak preview of what D1’s upcoming Replication API looks like:

export default {
  async fetch(request: Request, env: Env) {
    const {pathname} = new URL(request.url);
    let resp = null;
    let session = env.DB.withSession(token); // An optional commit token or mode

    // Handle requests within the session.
    if (pathname === "/api/orders/list") {
      // This statement is a read query, so it will work against any
      // replica that has a commit equal or later than `token`.
      const { results } = await session.prepare("SELECT * FROM Orders");
      resp = Response.json(results);
    } else if (pathname === "/api/orders/add") {
      order = await request.json();

      // This statement is a write query, so D1 will send the query to
      // the primary, which always has the latest commit token.
      await session.prepare("INSERT INTO Orders VALUES (?, ?, ?)")
        .bind(order.orderName, order.customer, order.value);
        .run();

      // In order for the application to be correct, this SELECT
      // statement must see the results of the INSERT statement above.
      //
      // D1's new Session API keeps track of commit tokens for queries
      // within the session and will ensure that we won't execute this
      // query until whatever replica we're using has seen the results
      // of the INSERT.
      const { results } = await session.prepare("SELECT COUNT(*) FROM Orders")
        .run();
      resp = Response.json(results);
    }

    // Set the token so we can continue the session in another request.
    resp.headers.set("x-d1-token", session.latestCommitToken);
    return resp;
  }
}

Importantly, we will give developers the ability to maintain session-based consistency, so that users still see their own changes reflected, whilst still benefiting from the performance and latency gains that replication can bring.

You can learn more about how D1’s read replication works under the hood in our deep-dive post, and if you want to start building on D1 today, head to our developer docs to create your first database.

Hyperdrive: GA

We launched Hyperdrive into open beta last September during Birthday Week, and it’s now Generally Available — or in other words, battle-tested and production-ready.

If you’re not caught up on what Hyperdrive is, it’s designed to make the centralized databases you already have feel like they’re global. We use our global network to get faster routes to your database, keep connection pools primed, and cache your most frequently run queries as close to users as possible.

Importantly, Hyperdrive supports the most popular drivers and ORM (Object Relational Mapper) libraries out of the box, so you don’t have to re-learn or re-write your queries:

// Use the popular 'pg' driver? Easy. Hyperdrive just exposes a connection string
// to your Worker.
const client = new Client({ connectionString: env.HYPERDRIVE.connectionString });
await client.connect();

// Prefer using an ORM like Drizzle? Use it with Hyperdrive too.
// https://orm.drizzle.team/docs/get-started-postgresql#node-postgres
const client = new Client({ connectionString: env.HYPERDRIVE.connectionString });
await client.connect();
const db = drizzle(client);

But the work on Hyperdrive doesn’t stop just because it’s now “GA”. Over the next few months, we’ll be bringing support for the other most widely deployed database engine there is: MySQL. We’ll also be bringing support for connecting to databases inside private networks (including cloud VPC networks) via Cloudflare Tunnel and Magic WAN On top of that, we plan to bring more configurability around invalidation and caching strategies, so that you can make more fine-grained decisions around performance vs. data freshness.

As we thought about how we wanted to price Hyperdrive, we realized that it just didn’t seem right to charge for it. After all, the performance benefits from Hyperdrive are not only significant, but essential to connecting to traditional database engines. Without Hyperdrive, paying the latency overhead of 6+ round-trips to connect & query your database per request just isn’t right.

And so we’re happy to announce that for any developer on a Workers Paid plan, Hyperdrive is free. That includes both query caching and connection pooling, as well as the ability to create multiple Hyperdrives — to separate different applications, prod vs. staging, or to provide different configurations (cached vs. uncached, for example).

Plan

Price per query

Connection Pooling

Workers Paid

$0 

$0

To get started with Hyperdrive, head over to the docs to learn how to connect your existing database and start querying it from your Workers.

Queues: Pull From Anywhere

The task queue is an increasingly critical part of building a modern, full-stack application, and this is what we had in mind when we originally announced the open beta of Queues. We’ve since been working on several major Queues features, and we’re launching two of them this week: pull-based consumers and new message delivery controls.

Any HTTP-speaking client can now pull messages from a queue: call the new /pull endpoint on a queue to request a batch of messages, and call the /ack endpoint to acknowledge each message (or batch of messages) as you successfully process them:

// Pull and acknowledge messages from a Queue using any HTTP client
$  curl "https://api.cloudflare.com/client/v4/accounts/${CF_ACCOUNT_ID}/queues/${QUEUE_ID}/messages/pull" -X POST --data '{"visibilityTimeout":10000,"batchSize":100}}' \
     -H "Authorization: Bearer ${QUEUES_TOKEN}" \
     -H "Content-Type:application/json"

// Ack the messages you processed successfully; mark others to be retried.
$ curl "https://api.cloudflare.com/client/v4/accounts/${CF_ACCOUNT_ID}/queues/${QUEUE_ID}/messages/ack" -X POST --data '{"acks":["lease-id-1", "lease-id-2"],"retries":["lease-id-100"]}' \
     -H "Authorization: Bearer ${QUEUES_TOKEN}" \
     -H "Content-Type:application/json"

A pull-based consumer can run anywhere, allowing you to run queue consumers alongside your existing legacy cloud infrastructure. Teams inside Cloudflare adopted this early on, with one use-case focused on writing device telemetry to a queue from our 310+ data centers and consuming within some of our back-of-house infrastructure running on Kubernetes. Importantly, our globally distributed queue infrastructure means that messages are retained within the queue until the consumer is ready to process them.

Queues also now supports delaying messages, both when sending to a queue, as well as when marking a message for retry. This can be useful to queue (pun intended) tasks for the future, as well apply a backoff mechanism if an upstream API or infrastructure has rate limits that require you to pace how quickly you are processing messages.

// Apply a delay to a message when sending it
await env.YOUR_QUEUE.send(msg, { delaySeconds: 3600 })

// Delay a message (or a batch of messages) when marking it for retry
for (const msg of batch.messages) {
	msg.retry({delaySeconds: 300})
} 

We’ll also be bringing substantially increased per-queue throughput over the coming months on the path to getting Queues to GA. It’s important to us that Queues is extremely reliable: lost or dropped messages means that a user doesn’t get their order confirmation email, that password reset notification, and/or their uploads processed — each of those are user-impacting and hard to recover from.

Workers Analytics Engine

Workers Analytics Engine provides unlimited-cardinality analytics at scale, via a built-in API to write data points from Workers, and a SQL API to query that data.

Workers Analytics Engine is backed by the same ClickHouse-based system we have depended on for years at Cloudflare. We use it ourselves to observe the health of our own services, to capture product usage data for billing, and to answer questions about specific customers’ usage patterns. At least one data point is written to this system on nearly every request to Cloudflare’s network. Workers Analytics Engine lets you build your own custom analytics using this same infrastructure, while we manage the hard parts for you.

Since launching in beta, developers have started depending on Workers Analytics Engine for these same use cases and more, from large enterprises to open-source projects like Counterscale. Workers Analytics Engine has been operating at production scale with mission-critical workloads for years — but we hadn’t shared anything about pricing, until today.

We are keeping Workers Analytics Engine pricing simple, and based on two metrics:

  1. Data points written — every time you call writeDataPoint() in a Worker, this counts as one data point written. Every data point costs the same amount — unlike other platforms, there is no penalty for adding dimensions or cardinality, and no need to predict what the size and cost of a compressed data point might be.
  2. Read queries — every time you post to the Workers Analytics Engine SQL API, this counts as one read query. Every query costs the same amount — unlike other platforms, there is no penalty for query complexity, and no need to reason about the number of rows of data that will be read by each query.

Both the Workers Free and Workers Paid plans will include an allocation of data points written and read queries, with pricing for additional usage as follows:

Plan

Data points written

Read queries

Workers Paid

10 million included per month


+$0.25 per additional million

1 million included per month


+$1.00 per additional million

Workers Free

100,000 included per day

10,000 included per day

With this pricing, you can answer, “how much will Workers Analytics Engine cost me?” by counting the number of times you call a function in your Worker, and how many times you make a request to a HTTP API endpoint. Napkin math, rather than spreadsheet math.

This pricing will be made available to everyone in coming months. Between now and then, Workers Analytics Engine continues to be available at no cost. You can start writing data points from your Worker today — it takes just a few minutes and less than 10 lines of code to start capturing data. We’d love to hear what you think.

The week is just getting started

Tune in to what we have in store for you tomorrow on our second day of Developer Week. If you have questions or want to show off something cool you already built, please join our developer Discord.

Welcome to Developer Week 2024

Post Syndicated from Rita Kozlov original https://blog.cloudflare.com/welcome-to-developer-week-2024


It’s time to ship. For us (that’s what Innovation Weeks are all about!), and also for our developers.

Shipping itself is always fun, but getting there is not always easy. Bringing something from idea to life requires many stars to align. That’s what this week is all about — helping developers, including the two million developers already building on our platform, bring their ideas to life.

The full-stack cloud

Building applications requires assembling many different components.

The frontend, the face of the application, must be intuitive, responsive, and visually appealing to engage users effectively. Behind the scenes, you need a backend to handle data processing, storage, and retrieval, ensuring smooth functionality and performance. On top of all that, in the past year AI has entered the chat, so to speak, and increasingly every application requires an element of AI, making it a crucial part of the stack.

The job of a good platform is to provide all these components, and any others you will need, to you, the developer.

Just as there’s nothing more frustrating than coming home from the grocery store and realizing you left out an ingredient, realizing a platform is missing a major component or piece of functionality is no different.

We view providing the tooling that developers need as a critical part of our job as a platform, which is why with every Developer Week, we make it our mission to provide you with more and more pieces you may need. This week is no different — you can expect us to announce more tools and primitives from the frontend to backend to AI.

However, our job doesn’t stop there. If a good platform provides the components, a great platform goes a step further than that.

The job of a great platform is not only to provide the components, but make sure they play well with each other in a way that makes your job as a developer easier. Our vision for the developer platform is exactly that: to anticipate not just the tools you need but also think about how they work with each other, and how they integrate into your development flow.

This week, you will see announcements and deep dives that expound on our vision for an integrated platform: pulling back the curtain on the way we expose services in Workers through bindings for an integrated developer experience, talking about our vision for a unified data platform, updating you on framework support, and more.

The connectivity cloud

While we’re excited for you to build on us as much as possible, we also realize that development projects are rarely greenfield. If you’ve been at this for a long time, chances are a large portion of your application already lives somewhere, whether on another cloud, or on-prem.

That’s why we’re constantly making it easier for you to connect to existing infrastructure or other providers, and working hard to make sure you can still reap the benefits of building on Cloudflare by making your application feel fast and global, regardless of where your backend is.

And vice versa, if your data is on us, but you need to access it from other providers, it’s not our job to keep it hostage in a captivity cloud by charging a tariff for egress.

The experimentation cloud

Before you start assembling components, or even coming up with a plan or a spec for it, there’s an important but overlooked step to the development process — experimentation.

Experimentation can take many forms. Experimentation can be in the form of prototyping an MVP before you spend months developing a product or a feature. If you’ve found yourself rewriting your entire personal website just to try out a new tool or framework, that’s also experimentation.

It’s easy to overlook experimentation as a part of the process, but innovation doesn’t happen without it, which is why it’s something we always want to encourage and support as a part of our platform.

That’s why offering a generous free tier is something that’s been a part of our DNA since the very beginning, and something you can expect to forever be a staple of our platform.

The demo to production cloud

Alright, you’ve got all the tools you need, you’ve had a chance to experiment, and at some point… it’s time to ship.

Shipping is exciting, but shipping is also vulnerable and scary. You’re exposing the thing you’ve been working hard on to the world to criticize. You’re exposing your code to a world of untested edge cases and abuse. You’re exposing your colleagues who are on call to the possibility of getting paged at 1 AM due to the code you released.

Of course, the wrong answer is not shipping.

The right answer is having a platform that supports you and holds your hand through the scary parts. This means a platform that can seamlessly scale from zero to sixty. A platform gives you the tools to test your code, and release it gradually to the world to help you gain confidence. Or a platform provides the observability you need when you are trying to figure out what’s gone wrong at 1 AM.

That’s why this week, you can look forward to some announcements from us that we hope will help you sleep better.

The demo to production cloud — for inference

We talked about some of the scary parts of deploying to production, and while all these apply to AI as well, building AI applications today, especially in production, presents its own unique set of challenges.

Almost every day you see a new AI demo go viral — from Sora to Devin, it’s easy and inspiring to imagine our world completely changed by AI. But if you’ve started actually playing with and implementing AI use cases, you know the harsh reality of making AI truly work. It requires a lot of trial and error to get the results you want — choosing a model, RAG, fine-tuning…

And that’s before you even go to production.

That’s when you encounter the real challenge — provisioning enough capacity to stay up, without over-provisioning and overpaying. This is the exact challenge we set out to solve from the early days of Workers — helping developers not worry about infrastructure, just the application they want to build.

With the recent rise of AI, we’ve noticed many of these challenges return. Thankfully, managing loads and infrastructure is what we’re good at here at Cloudflare. It’s what we’ve had practice at for over a decade of running our platform. It’s all just one giant scheduler.

Our vision for our AI platform is to help solve the exact challenges in deploying AI workloads that we’ve been helping developers solve for, well, any other type of workload. Whether you’re deploying directly on us with Workers AI, or another provider, we’ll help provide the tools you need to access the models you need, without overpaying for idle compute.

Don’t worry, it’s all going to be fine.

So what can you expect this week?

No one in my family can keep a secret — my sister cannot get me a birthday present without spoiling it the week before. For me, the anticipation and the look of surprise is part of the fun! My coworkers seem to have clued into this.

While I won’t give away too much, we’ve already teased out a few things last week (you can find some hints here, here and here), as well as in this blog post if you read closely (because as it turns out, I too, can’t help myself).

See you tomorrow!

Our series of announcements starts on Monday, April 1st. We look forward to sharing them with you here on our blog, and discussing them with you on Discord and X.

Improved Workers testing via Vitest and workerd

Post Syndicated from Brendan Coll original https://blog.cloudflare.com/workers-vitest-integration


Today, we’re excited to announce a new Workers Vitest integration – allowing you to write unit and integration tests via the popular testing framework, Vitest, that execute directly in our runtime, workerd!

This integration provides you with the ability to test anything related to your Worker!

For the first time, you can write unit tests that run within the same runtime that Cloudflare Workers run on in production, providing greater confidence that the behavior of your Worker in tests will be the same as when deployed to production. For integration tests, you can now write tests for Workers that are triggered by Cron Triggers in addition to traditional fetch() events. You can also more easily test complex applications that interact with KV, R2, D1, Queues, Service Bindings, and more Cloudflare products.

For all of your tests, you have access to Vitest features like snapshots, mocks, timers, and spies.

In addition to increased testing and functionality, you’ll also notice other developer experience improvements like hot-module-reloading, watch mode on by default, and per-test isolated storage. Meaning that, as you develop and edit your tests, they’ll automatically re-run, without you having to restart your test runner.

Get started testing Workers with Vitest

The easiest way to get started with testing your Workers via Vitest is to start a new Workers project via our create-cloudflare tool:

npm create cloudflare@latest hello-world -- --type=hello-world

Running this command will scaffold a new project for you with the Workers Vitest integration already set up. An example unit test and integration test are also included.

Manual install and setup instructions

If you prefer to manually install and set up the Workers Vitest integration, begin by installing @cloudflare/vitest-pool-workers from npm:

$ npm install --save-dev @cloudflare/vitest-pool-workers

@cloudflare/vitest-pool-workers has a peer dependency on a specific version of vitest. Modern versions of npm will install this automatically, but we recommend you install it explicitly too. Refer to the getting started guide for the current supported version. If you’re using TypeScript, add @cloudflare/vitest-pool-workers to your tsconfig.json’s types to get types for the cloudflare:test module:

$ npm install --save-dev @cloudflare/vitest-pool-workers

@cloudflare/vitest-pool-workers has a peer dependency on a specific version of vitest. Modern versions of npm will install this automatically, but we recommend you install it explicitly too. Refer to the getting started guide for the current supported version. If you’re using TypeScript, add @cloudflare/vitest-pool-workers to your tsconfig.json’s types to get types for the cloudflare:test module:

{
  "compilerOptions": {
    "module": "esnext",
    "moduleResolution": "bundler",
    "lib": ["esnext"],
    "types": [
      "@cloudflare/workers-types/experimental",
      "@cloudflare/vitest-pool-workers"
    ]
  }
}

Then, enable the pool in your Vitest configuration file:

// vitest.config.js
import { defineWorkersConfig } from "@cloudflare/vitest-pool-workers/config";

export default defineWorkersConfig({
  test: {
    poolOptions: {
      workers: {
        wrangler: { configPath: "./wrangler.toml" },
      },
    },
  },
});

After that, define a compatibility date after “2022-10-31” and enable the nodejs_compat compatibility flag in your wrangler.toml:

# wrangler.toml
main = "src/index.ts"
compatibility_date = "2024-01-01"
compatibility_flags = ["nodejs_compat"]

Test anything exported from a Worker

With the new Workers Vitest Integration, you can test anything exported from your Worker in both unit and integration-style tests. Within these tests, you can also test connected resources like R2, KV, and Durable Objects, as well as applications involving multiple Workers.

Writing unit tests

In a Workers context, a unit test imports and directly calls functions from your Worker then asserts on their return values. Let’s say you have a Worker that looks like this:

export function add(a, b) {
  return a + b;
}

export default {
  async fetch(request) {
    const url = new URL(request.url);
    const a = parseInt(url.searchParams.get("a"));
    const b = parseInt(url.searchParams.get("b"));
    return new Response(add(a, b));
  }
}

After you’ve setup and installed the Workers Vitest integration, you can unit test this Worker by creating a new test file called index.spec.js with the following code:

import { env, createExecutionContext, waitOnExecutionContext, } from "cloudflare:test";
import { describe, it, expect } from "vitest";
import { add }, worker from "./src";

describe("Hello World worker", () => {
  it(“adds two numbers”, async () => {
    expect(add(2,3).toBe(5);
  });
  it("sends request (unit style)", async () => {
    const request = new Request("http://example.com/?a=3&b=4");
    const ctx = createExecutionContext();
    const response = await worker.fetch(request, env, ctx);
    await waitOnExecutionContext(ctx);
    expect(await response.text()).toMatchInlineSnapshot(`"7"`);
  });
});

Using the Workers Vitest integration, you can write unit tests like these for any of your Workers.

Writing integration tests

While unit tests are great for testing individual parts of your application, integration tests assess multiple units of functionality, ensuring that workflows and features work as expected. These are usually more complex than unit tests, but provide greater confidence that your app works as expected. In the Workers context, an integration test sends HTTP requests to your Worker and asserts on the HTTP responses.

With the Workers Vitest Integration, you can run integration tests by importing SELF from the new cloudflare:test utility like this:

// test/index.spec.ts
import { SELF } from "cloudflare:test";
import { it, expect } from "vitest";
import "../src";

// an integration test using SELF
it("sends request (integration style)", async () => {
   const response = await SELF.fetch("http://example.com/?a=3&b=4");
   expect(await response.text()).toMatchInlineSnapshot(`"7"`);
});

When using SELF for integration tests, your Worker code runs in the same context as the test runner. This means you can use mocks to control your Worker.

Testing different scenarios

Whether you’re writing unit or integration tests, if your application uses Cloudflare Developer Platform products (e.g. KV, R2, D1, Queues, or Durable Objects), you can test them. To demonstrate this, we have created a set of examples to help get you started testing.

Better testing experience === better testing

Having better testing tools makes it easier to test your projects right from the start, which leads to better overall quality and experience for your end users. The Workers Vitest integration provides that better experience, not just in terms of developer experience, but in making it easier to test your entire application.

The rest of this post will focus on how we built this new testing integration, diving into the internals of how Vitest works, the problems we encountered trying to get a framework to work within our runtime, and ultimately how we solved it and the improved DX that it unlocked.

How Vitest traditionally works

When you start Vitest’s CLI, it first collects and sequences all your test files. By default, Vitest uses a “threads” pool, which spawns Node.js worker threads for isolating and running tests in parallel. Each thread gets a test file to run, dynamically requesting and evaluating code as needed. When the test runner imports a module, it sends a request to the host’s “Vite Node Server” which will either return raw JavaScript code transformed by Vite, or an external module path. If raw code is returned, it will be executed using the node:vm runInThisContext() function. If a module path is returned, it will be imported using dynamic import(). Transforming user code with Vite allows hot-module-reloading (HMR) — when a module changes, it’s invalidated in the module cache and a new version will be returned when it’s next imported.

Miniflare is a fully-local simulator for Cloudflare’s Developer Platform. Miniflare v2 provided a custom environment for Vitest that allowed you to run your tests inside the Workers sandbox. This meant you could import and call any function using Workers runtime APIs in your tests. You weren’t restricted to integration tests that just sent and received HTTP requests. In addition, this environment provided per-test isolated storage, automatically undoing any changes made at the end of each test. In Miniflare v2, this environment was relatively simple to implement. We’d already reimplemented Workers Runtime APIs in a Node.js environment, and could inject them using Vitest’s APIs into the global scope of the test runner.

By contrast, Miniflare v3 runs your Worker code inside the same workerd runtime that Cloudflare uses in production. Running tests directly in workerd presented a challenge — workerd runs in its own process, separate from the Node.js worker thread, and it’s not possible to reference JavaScript classes across a process boundary.

Solving the problem with custom pools

Instead, we use Vitest’s custom pools feature to run the test runner in Cloudflare Workers running locally with workerd. A pool receives test files to run and decides how to execute them. By executing the runner inside workerd, tests have direct access to Workers runtime APIs as they’re running in a Worker. WebSockets are used to send and receive serialisable RPC messages between the Node.js host and workerd process. Note we’re running the exact same test runner code originally designed for a Node-context inside a Worker here. This means our Worker needs to provide Node’s built-in modules, support for dynamic code evaluation, and loading of arbitrary modules from disk with Node-resolution behavior. The nodejs_compat compatibility flag provides support for some of Node’s built-in modules, but does not solve our other problems. For that, we had to get creative…

Dynamic code evaluation

For security reasons, the Cloudflare Workers runtime does not allow dynamic code evaluation via eval() or new Function(). It also requires all modules to be defined ahead-of-time before execution starts. The test runner doesn’t know what code to run until we start executing tests, so without lifting these restrictions, we have no way of executing the raw JavaScript code transformed by Vite nor importing arbitrary modules from disk. Fortunately, code that is only meant to run locally – like tests – has a much more relaxed security model than deployed code. To support local testing and other development-specific use-cases such as Vite’s new Runtime API, we added “unsafe-eval bindings” and “module-fallback services” to workerd.

Unsafe-eval bindings provide local-only access to the eval() function, and new Function()/new AsyncFunction()/new WebAssembly.Module() constructors. By exposing these through a binding, we retain control over which code has access to these features.

// Type signature for unsafe-eval bindings
interface UnsafeEval {
  eval(script: string, name?: string): unknown;
  newFunction(script: string, name?: string, ...args: string[]): Function;
  newAsyncFunction(script: string, name?: string, ...args: string[]): AsyncFunction;
  newWasmModule(src: BufferSource): WebAssembly.Module;
}

Using the unsafe-eval binding eval() method, we were able to implement a polyfill for the required vm.runInThisContext() function. While we could also implement loading of arbitrary modules from disk using unsafe-eval bindings, this would require us to rebuild workerd’s module resolution system in JavaScript. Instead, we allow workers to be configured with module fallback services. If enabled, imports that cannot be resolved by workerd become HTTP requests to the fallback service. These include the specifier, referrer, and whether it was an import or require. The service may respond with a module definition, or a redirect to another location if the resolved location doesn’t match the specifier. Requests originating from synchronous requires will block the main thread until the module is resolved. The Workers Vitest pool’s fallback service implements Node-like resolution with Node-style interoperability between CommonJS and ES modules.

Durable Objects as test runners

Now that we can run and import arbitrary code, the next step is to get Vitest’s thread worker running inside workerd. Every incoming request has its own request context. To improve overall performance, I/O objects such as streams, request/response bodies and WebSockets created in one request context cannot be used from another. This means if we want to use a WebSocket for RPC between the pool and our workerd processes, we need to make sure the WebSocket is only used from one request context. To coordinate this, we define a singleton Durable Object for accepting the RPC connection and running tests from. Functions using RPC such as resolving modules, reporting results and console logging will always use this singleton. We use Miniflare’s “magic proxy” system to get a reference to the singleton’s stub in Node.js, and send a WebSocket upgrade request directly to it. After adding a few more Node.js polyfills, and a basic cloudflare:test module to provide access to bindings and a function for creating ExecutionContexts, we’re able to write basic Workers unit tests! 🎉

Integration tests with hot-module-reloading

In addition to unit tests, we support integration testing with a special SELF service binding in the cloudflare:test module. This points to a special export default { fetch(...) {...} } handler which uses Vite to import your Worker’s main module.

Using Vite’s transformation pipeline here means your handler gets hot-module-reloading (HMR) for free! When code is updated, the module cache is invalidated, tests are rerun, and subsequent requests will execute with new code. The same approach of wrapping user code handlers applies to Durable Objects too, providing the same HMR benefits.

Integration tests can be written by calling SELF.fetch(), which will dispatch a fetch() event to your user code in the same global scope as your test, but under a different request context. This means global mocks apply to your Worker’s execution, as do request context lifetime restrictions. In particular, if you forget to call ctx.waitUntil(), you’ll see an appropriate error message. This wouldn’t be the case if you called your Worker’s handler directly in a unit test, as you’d be running under the runner singleton’s Durable Object request context, whose lifetime is automatically extended.

// test/index.spec.ts
import { SELF } from "cloudflare:test";
import { it, expect } from "vitest";
import "../src/index";

it("sends request", async () => {
   const response = await SELF.fetch("https://example.com");
   expect(await response.text()).toMatchInlineSnapshot(`"body"`);
});

Isolated per-test storage

Most Workers applications will have at least one binding to a Cloudflare storage service, such as KV, R2 or D1. Ideally, tests should be self-contained and runnable in any order or on their own. To make this possible, writes to storage need to be undone at the end of each test, so reads by other tests aren’t affected. Whilst it’s possible to do this manually, it can be tricky to keep track of all writes and undo them in the correct order. For example, take the following two functions:

// helpers.ts
interface Env {
  NAMESPACE: KVNamespace;
}
// Get the current list stored in a KV namespace
export async function get(env: Env, key: string): Promise<string[]> {
  return await env.NAMESPACE.get(key, "json") ?? [];
}
// Add an item to the end of the list
export async function append(env: Env, key: string, item: string) {
  const value = await get(env, key);
  value.push(item);
  await env.NAMESPACE.put(key, JSON.stringify(value));
}

If we wanted to test these functions, we might write something like below. Note we have to keep track of all the keys we might write to, and restore their values at the end of tests, even if those tests fail.

// helpers.spec.ts
import { env } from "cloudflare:test";
import { beforeAll, beforeEach, afterEach, it, expect } from "vitest";
import { get, append } from "./helpers";

let startingList1: string | null;
let startingList2: string | null;
beforeEach(async () => {
  // Store values before each test
  startingList1 = await env.NAMESPACE.get("list 1");
  startingList2 = await env.NAMESPACE.get("list 2");
});
afterEach(async () => {
  // Restore starting values after each test
  if (startingList1 === null) {
    await env.NAMESPACE.delete("list 1");
  } else {
    await env.NAMESPACE.put("list 1", startingList1);
  }
  if (startingList2 === null) {
    await env.NAMESPACE.delete("list 2");
  } else {
    await env.NAMESPACE.put("list 2", startingList2);
  }
});

beforeAll(async () => {
  await append(env, "list 1", "one");
});

it("appends to one list", async () => {
  await append(env, "list 1", "two");
  expect(await get(env, "list 1")).toStrictEqual(["one", "two"]);
});

it("appends to two lists", async () => {
  await append(env, "list 1", "three");
  await append(env, "list 2", "four");
  expect(await get(env, "list 1")).toStrictEqual(["one", "three"]);
  expect(await get(env, "list 2")).toStrictEqual(["four"]);
});

This is slightly easier with the recently introduced onTestFinished() hook, but you still need to remember which keys were written to, or enumerate them at the start/end of tests. You’d also need to manage this for KV, R2, Durable Objects, caches and any other storage service you used. Ideally, the testing framework should just manage this all for you.

That’s exactly what the Workers Vitest pool does with the isolatedStorage option which is enabled by default. Any writes to storage performed in a test are automagically undone at the end of the test. To support seeding data in beforeAll() hooks, including those in nested describe()-blocks, a stack is used. Before each suite or test, a new frame is pushed to the storage stack. All writes performed by the test or associated beforeEach()/afterEach() hooks are written to the frame. After each suite or test, the top frame is popped from the storage stack, undoing any writes.

Miniflare implements simulators for storage services on top of Durable Objects with a separate blob store. When running locally, workerd uses SQLite for Durable Object storage. To implement isolated storage, we implement an on-disk stack of .sqlite database files by backing up the databases when “pushing”, and restoring backups when “popping”. Blobs stored in the separate store are retained through stack operations, and cleaned up at the end of each test run. Whilst this works, it involves copying lots of .sqlite files. Looking ahead, we’d like to explore using SQLite SAVEPOINTS for a more efficient solution.

Declarative request mocking

In addition to storage, most Workers will make outbound fetch() requests. For tests, it’s often useful to mock responses to these requests. Miniflare already allows you to specify an undici MockAgent to route all requests through. The MockAgent class provides a declarative interface for specifying requests to mock and the corresponding responses to return. This API is relatively simple, whilst being flexible enough for advanced use cases. We provide an instance of MockAgent as fetchMock in the cloudflare:test module.

import { fetchMock } from "cloudflare:test";
import { beforeAll, afterEach, it, expect } from "vitest";

beforeAll(() => {
  // Enable outbound request mocking...
  fetchMock.activate();
  // ...and throw errors if an outbound request isn't mocked
  fetchMock.disableNetConnect();
});
// Ensure we matched every mock we defined
afterEach(() => fetchMock.assertNoPendingInterceptors());

it("mocks requests", async () => {
  // Mock the first request to `https://example.com`
  fetchMock
    .get("https://example.com")
    .intercept({ path: "/" })
    .reply(200, "body");

  const response = await fetch("https://example.com/");
  expect(await response.text()).toBe("body");
});

To implement this, we bundled a stripped down version of undici containing just the MockAgent code. We then built a custom undici Dispatcher that used the Worker’s global fetch() function instead of undici’s built-in HTTP implementation based on llhttp and node:net.

Testing Durable Objects directly

Finally, Miniflare v2’s custom Vitest environment provided support for accessing the instance methods and state of Durable Objects in tests directly. This allowed you to unit test Durable Objects like any other JavaScript class—you could mock particular methods and properties, or immediately call specific handlers like alarm(). To implement this in workerd, we rely on our existing wrapping of user Durable Objects for Vite transforms and hot-module reloading. When you call the runInDurableObject(stub, callback) function from cloudflare:test, we store callback in a global cache and send a special fetch() request to stub which is intercepted by the wrapper. The wrapper executes the callback in the request context of the Durable Object, and stores the result in the same cache. runInDurableObject() then reads from this cache, and returns the result.

Note that this assumes the Durable Object is running in the same isolate as the runInDurableObject() call. While this is true for same-Worker Durable Objects running locally, it means Durable Objects defined in auxiliary workers can’t be accessed directly.

Try it out!

We are excited to release the @cloudflare/vitest-pool-workers package on npm, and to provide an improved testing experience for you.

Make sure to read the Write your first test guide and begin writing unit and integration tests today! If you’ve been writing tests using one of our previous options, our unstable_dev migration guide or our Miniflare 2 migration guide should explain key differences and help you move your tests over quickly.

If you run into issues or have suggestions for improvements, please file an issue in our GitHub repo or reach out via our Developer Discord.

Adding new LLMs, text classification and code generation models to the Workers AI catalog

Post Syndicated from Michelle Chen http://blog.cloudflare.com/author/michelle/ original https://blog.cloudflare.com/february-2024-workersai-catalog-update


Over the last few months, the Workers AI team has been hard at work making improvements to our AI platform. We launched back in September, and in November, we added more models like Code Llama, Stable Diffusion, Mistral, as well as improvements like streaming and longer context windows.

Today, we’re excited to announce the release of eight new models.

The new models are highlighted below, but check out our full model catalog with over 20 models in our developer docs.

Text generation
@hf/thebloke/llama-2-13b-chat-awq
@hf/thebloke/zephyr-7b-beta-awq
@hf/thebloke/mistral-7b-instruct-v0.1-awq
@hf/thebloke/openhermes-2.5-mistral-7b-awq
@hf/thebloke/neural-chat-7b-v3-1-awq
@hf/thebloke/llamaguard-7b-awq

Code generation
@hf/thebloke/deepseek-coder-6.7b-base-awq
@hf/thebloke/deepseek-coder-6.7b-instruct-awq

Bringing you the best of open source

Our mission is to support a wide array of open source models and tasks. In line with this, we’re excited to announce a preview of the latest models and features available for deployment on Cloudflare’s network.

One of the standout models is deep-seek-coder-6.7b, which notably scores approximately 15% higher on popular benchmarks against comparable Code Llama models. This performance advantage is attributed to its diverse training data, which includes both English and Chinese code generation datasets. In addition, the openHermes-2.5-mistral-7b model showcases how high quality fine-tuning datasets can improve the accuracy of base models. This Mistral 7b fine-tune outperforms the base model by approximately 10% on many LLM benchmarks.

We’re also introducing innovative models that incorporate Activation-aware Weight Quantization (AWQ), such as the llama-2-13b-awq. This quantization technique is just one of the strategies to improve memory efficiency in Large Language Models. While quantization generally boosts inference efficiency in AI models, it often does so at the expense of precision. AWQ strikes a balance to mitigate this tradeoff.

The pace of progress in AI can be overwhelming, but Cloudflare’s Workers AI simplifies getting started with the latest models. We handle the latest advancements and make them easily accessible from a Worker or our HTTP APIs. You are only ever an API call or Workers binding away from cutting-edge models. Simply put, Workers AI allows developers to concentrate on delivering exceptional user experiences without the burdens of deployment, infrastructure, or scalability concerns.

Using AI to safeguard your applications

A newer use case we support with this model catalog update is the ability to classify and check prompts and responses with llamaguard-7b-awq. We’ve all heard of stories of AI bots that unintentionally give answers that might be inappropriate or harmful. This new LlamaGuard model allows developers to detect and categorize when a prompt or a response is potentially unsafe, allowing them to control the inputs and outputs of their applications with developer-defined categories.

Here’s an example of an application that takes a user input and compares that to an example set of defined unsafe categories to determine if the input is safe or not. You can customize the content categories depending on your use case. The below example categories are provided for informational purposes only, and Cloudflare does not make any guarantees regarding the performance of Llama Guard or any outcomes or results.

import { Ai } from '@cloudflare/ai';

export default {
	async fetch(request, env) {
		const payload = await request.json();
		// Modify these to your needs, this is the example from Meta
		const unsafeCategories = `
01: Violence and Hate.
Should not
- Help people plan or engage in violence.
- Encourage people to engage in violence.
- Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
- Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics.
Can
-  Provide information on violence and discrimination.
- Discuss the topics of hate and violence, and can discuss historical events involving violence.
02: Sexual Content.
Should not
- Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities.
Can
- Discuss topics related to sexuality and sexual education.
- Provide general information regarding sexual activities.
- Provide guidance on topics related to sexual health.
03: Criminal Planning.
Should not
- Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in criminal activities or condone criminal behavior.
Can
- Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes.
- Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems.
04: Guns and Illegal Weapons.
Should not
- Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them.
- Encourage people to engage in firearm or illegal weapons crimes or condone such behavior.
Can
- Discuss firearms and the arguments for and against firearm ownership.
05: Regulated or Controlled Substances.
Should not
- Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis.
- Assist or encourage people to create such substances when it is illegal to do so.
Can
- Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are).
- Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation.
06: Self-Harm.
Should not
- Encourage people to harm themselves.
- Romanticize or condone self-harm or suicide.
- Provide information regarding the methods of suicide or self-harm.
- Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves.
Can
- Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions).
Should
- Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves.
		`;

		// Assemble the prompt
		const prompt = `Task: Check if there is unsafe content in 'User' messages in conversations according to our safety policy with the below categories.

		<BEGIN UNSAFE CONTENT CATEGORIES>
		${unsafeCategories}
		<END UNSAFE CONTENT CATEGORIES>

		<BEGIN CONVERSATION>
		User: ${payload.userContent}
		<END CONVERSATION>
	`;
		const ai = new Ai(env.AI);
		const response = await ai.run('@hf/thebloke/llamaguard-7b-awq', {
			prompt,
		});
		return Response.json(response);
	},
};

How do I get started?

Try out our new models within the AI section of the Cloudflare dashboard or take a look at our Developer Docs to get started. With the Workers AI platform you can build an app with Workers and Pages, store data with R2, D1, Workers KV, or Vectorize, and run model inference with Workers AI – all in one place. Having more models allows developers to build all different kinds of applications, and we plan to continually update our model catalog to bring you the best of open-source.

We’re excited to see what you build! If you’re looking for inspiration, take a look at our collection of “Built-with” stories that highlight what others are building on Cloudflare’s Developer Platform. Stay tuned for a pricing announcement and higher usage limits coming in the next few weeks, as well as more models coming soon. Join us on Discord to share what you’re working on and any feedback you might have.

LangChain Support for Workers AI, Vectorize and D1

Post Syndicated from Ricky Robinett http://blog.cloudflare.com/author/ricky/ original https://blog.cloudflare.com/langchain-support-for-workers-ai-vectorize-and-d1


During Developer Week, we announced LangChain support for Cloudflare Workers. Langchain is an open-source framework that allows developers to create powerful AI workflows by combining different models, providers, and plugins using a declarative API — and it dovetails perfectly with Workers for creating full stack, AI-powered applications.

Since then, we’ve been working with the LangChain team on deeper integration of many tools across Cloudflare’s developer platform and are excited to share what we’ve been up to.

Today, we’re announcing five new key integrations with LangChain:

  1. Workers AI Chat Models: This allows you to use Workers AI text generation to power your chat model within your LangChain.js application.
  2. Workers AI Instruct Models: This allows you to use Workers AI models fine-tuned for instruct use-cases, such as Mistral and CodeLlama, inside your Langchain.js application.
  3. Text Embeddings Models: If you’re working with text embeddings, you can now use Workers AI text embeddings with LangChain.js.
  4. Vectorize Vector Store: When working with a Vector database and LangChain.js, you now have the option of using Vectorize, Cloudflare’s powerful vector database.
  5. Cloudflare D1-Backed Chat Memory: For longer-term persistence across chat sessions, you can swap out LangChain’s default in-memory chatHistory that backs chat memory classes like BufferMemory for a Cloudflare D1 instance.

With the addition of these five Cloudflare AI tools into LangChain, developers have powerful new primitives to integrate into new and existing AI applications. With LangChain’s expressive tooling for mixing and matching AI tools and models, you can use Vectorize, Cloudflare AI’s text embedding and generation models, and Cloudflare D1 to build a fully-featured AI application in just a few lines of code.

This is a full persistent chat app powered by an LLM in 10 lines of code–deployed to @Cloudflare Workers, powered by @LangChainAI and @Cloudflare D1.

You can even pass in a unique sessionId and have completely user/session-specific conversations 🤯 https://t.co/le9vbMZ7Mc pic.twitter.com/jngG3Z7NQ6

— Kristian Freeman (@kristianf_) September 20, 2023

Getting started with a Cloudflare + LangChain + Nuxt Multi-source Chatbot template

You can get started by using LangChain’s Cloudflare Chatbot template: https://github.com/langchain-ai/langchain-cloudflare-nuxt-template

This application shows how various pieces of Cloudflare Workers AI fit together and expands on the concept of retrieval augmented generation (RAG) to build a conversational retrieval system that can route between multiple data sources, choosing the one more relevant based on the incoming question. This method helps cut down on distraction from off-topic documents getting pulled in by a vector store’s similarity search, which could occur if only a single database were used.

The base version runs entirely on the Cloudflare Workers AI stack with the Llama 2-7B model. It uses:

  • A chat variant of Llama 2-7B run on Cloudflare Workers AI
  • A Cloudflare Workers AI embeddings model
  • Two different Cloudflare Vectorize DBs (though you could add more!)
  • Cloudflare Pages for hosting
  • LangChain.js for orchestration
  • Nuxt + Vue for the frontend

The two default data sources are a PDF detailing some of Cloudflare’s features and a blog post by Lilian Weng at OpenAI that talks about autonomous agents.

The bot will classify incoming questions as being about Cloudflare, AI, or neither, and draw on the corresponding data source for more targeted results. Everything is fully customizable – you can change the content of the ingested data, the models used, and all prompts!

And if you have access to the LangSmith beta, the app also has tracing set up so that you can easily see and debug each step in the application.

We can’t wait to see what you build

We can’t wait to see what you all build with LangChain and Cloudflare. Come tell us about it in discord or on our community forums.

Re-introducing the Cloudflare Workers Playground

Post Syndicated from Adam Murray original http://blog.cloudflare.com/workers-playground/

Re-introducing the Cloudflare Workers Playground

Re-introducing the Cloudflare Workers Playground

Since the very initial announcement of Cloudflare Workers, we’ve provided a playground. The motivation behind that being a belief that users should have a convenient, low-commitment way to play around with and learn more about Workers.

Over the last few years, while Cloudflare Workers and our Developer Platform have changed and grown, the original playground has not. Today, we’re proud to announce a revamp of the playground that demonstrates the power of Workers, along with new development tooling, and the ability to share your playground code and deploy instantly to Cloudflare’s global network.

A focus on origin Workers

When Workers was first introduced, many of the examples and use-cases centered around middleware, where a Worker intercepts a request to an origin and does something before returning a response. This includes things like: modifying headers, redirecting traffic, helping with A/B testing, or caching. Ultimately the Worker isn’t acting as an origin in these cases, it sits between the user and the destination.

While Workers are still great for these types of tasks, for the updated playground, we decided to focus on the Worker-as-origin use-case. This is where the Worker receives a request and is responsible for returning the full response. In this case, the Worker is the destination, not middle-ware. This is a great way for you to develop more complex use-cases like user interfaces or APIs.

A new editor experience

During Developer Week in May, we announced a new, authenticated dashboard editor experience powered by VSCode. Now, this same experience is available to users in the playground.

Users now have a more robust IDE experience that supports: multi-module Workers, type-checking via JSDoc comments and the `workers-types` package, pretty error pages, and real previews that update as you edit code. The new editor only supports Module syntax, which is the preferred way for users to develop new Workers.

When the playground first loads, it looks like this:

Re-introducing the Cloudflare Workers Playground

The content you see on the right is coming from the code on the left. You can modify this just as you would in a code editor. Once you make an edit, it will be updated shortly on the right as demonstrated below:

You’re not limited to the starter demo. Feel free to edit and remove those files to create APIs, user interfaces, or any other application that you come up with.

Updated developer tooling

Along with the updated editor, the new playground also contains numerous developer tools to help give you visibility into the Worker.

Playground users have access to the same Chrome DevTools technology that we use in the Wrangler CLI and the Dashboard. Within this view, you can: view logs, view network requests, and profile your Worker among other things.

Re-introducing the Cloudflare Workers Playground

At the top of the playground, you’ll also see an “HTTP” tab which you can use to test your Worker against various HTTP methods.

Re-introducing the Cloudflare Workers Playground

Share what you create

With all these improvements, we haven’t forgotten the core use of a playground—to share Workers with other people! Whatever your use-case; whether you’re building a demo to showcase the power of Workers or sending someone an example of how to fix a specific issue, all you need to do is click “Copy Link” in the top right of the Playground then paste the URL in any URL bar.

Re-introducing the Cloudflare Workers Playground

The unique URL will be shareable and deployable as long as you have it. This means that you could create quick demos by creating various Workers in the Playground, and bookmark them to share later. They won’t expire.

Deploying to the Supercloud

We also wanted to make it easier to go from writing a Worker in the Playground to deploying that Worker to Cloudflare’s global network. We’ve included a “Deploy” button that will help you quickly deploy the Worker you’ve just created.

Re-introducing the Cloudflare Workers Playground

If you don’t already have a Cloudflare account, you will also be guided through the onboarding process.

Try it out

This is now available to all users in Region:Earth. Go to https://workers.cloudflare.com/playground and give it a go!

A Socket API that works across JavaScript runtimes — announcing a WinterCG spec and Node.js implementation of connect()

Post Syndicated from Dominik Picheta original http://blog.cloudflare.com/socket-api-works-javascript-runtimes-wintercg-polyfill-connect/

A Socket API that works across JavaScript runtimes — announcing a WinterCG spec and Node.js implementation of connect()

A Socket API that works across JavaScript runtimes — announcing a WinterCG spec and Node.js implementation of connect()

Earlier this year, we announced a new API for creating outbound TCP socketsconnect(). From day one, we’ve been working with the Web-interoperable Runtimes Community Group (WinterCG) community to chart a course toward making this API a standard, available across all runtimes and platforms — including Node.js.

Today, we’re sharing that we’ve reached a new milestone in the path to making this API available across runtimes — engineers from Cloudflare and Vercel have published a draft specification of the connect() sockets API for review by the community, along with a Node.js compatible implementation of the connect() API that developers can start using today.

This implementation helps both application developers and maintainers of libraries and frameworks:

  1. Maintainers of existing libraries that use the node:net and node:tls APIs can use it to more easily add support for runtimes where node:net and node:tls are not available.
  2. JavaScript frameworks can use it to make connect() available in local development, making it easier for application developers to target runtimes that provide connect().

Why create a new standard? Why connect()?

As we described when we first announced connect(), to-date there has not been a standard API across JavaScript runtimes for creating and working with TCP or UDP sockets. This makes it harder for maintainers of open-source libraries to ensure compatibility across runtimes, and ultimately creates friction for application developers who have to navigate which libraries work on which platforms.

While Node.js provides the node:net and node:tls APIs, these APIs were designed over 10 years ago in the very early days of the Node.js project and remain callback-based. As a result, they can be hard to work with, and expose configuration in ways that don’t fit serverless platforms or web browsers.

The connect() API fills this gap by incorporating the best parts of existing socket APIs and prior proposed standards, based on feedback from the JavaScript community — including contributors to Node.js. Libraries like pg (node-postgres on Github) are already using the connect() API.

The connect() specification

At time of writing, the draft specification of the Sockets API defines the following API:

dictionary SocketAddress {
  DOMString hostname;
  unsigned short port;
};

typedef (DOMString or SocketAddress) AnySocketAddress;

enum SecureTransportKind { "off", "on", "starttls" };

[Exposed=*]
dictionary SocketOptions {
  SecureTransportKind secureTransport = "off";
  boolean allowHalfOpen = false;
};

[Exposed=*]
interface Connect {
  Socket connect(AnySocketAddress address, optional SocketOptions opts);
};

interface Socket {
  readonly attribute ReadableStream readable;
  readonly attribute WritableStream writable;

  readonly attribute Promise<undefined> closed;
  Promise<undefined> close();

  Socket startTls();
};

The proposed API is Promise-based and reuses existing standards whenever possible. For example, ReadableStream and WritableStream are used for the read and write ends of the socket. This makes it easy to pipe data from a TCP socket to any other library or existing code that accepts a ReadableStream as input, or to write to a TCP socket via a WritableStream.

The entrypoint of the API is the connect() function, which takes a string containing both the hostname and port separated by a colon, or an object with discrete hostname and port fields. It returns a Socket object which represents a socket connection. An instance of this object exposes attributes and methods for working with the connection.

A connection can be established in plain-text or TLS mode, as well as a special “starttls” mode which allows the socket to be easily upgraded to TLS after some period of plain-text data transfer, by calling the startTls() method on the Socket object. No need to create a new socket or switch to using a separate set of APIs once the socket is upgraded to use TLS.

For example, to upgrade a socket using the startTLS pattern, you might do something like this:

import { connect } from "@arrowood.dev/socket"

const options = { secureTransport: "starttls" };
const socket = connect("address:port", options);
const secureSocket = socket.startTls();
// The socket is immediately writable
// Relies on web standard WritableStream
const writer = secureSocket.writable.getWriter();
const encoder = new TextEncoder();
const encoded = encoder.encode("hello");
await writer.write(encoded);

Equivalent code using the node:net and node:tls APIs:

import net from 'node:net'
import tls from 'node:tls'

const socket = new net.Socket(HOST, PORT);
socket.once('connect', () => {
  const options = { socket };
  const secureSocket = tls.connect(options, () => {
    // The socket can only be written to once the
    // connection is established.
    // Polymorphic API, uses Node.js streams
    secureSocket.write('hello');
  }
})

Use the Node.js implementation of connect() in your library

To make it easier for open-source library maintainers to adopt the connect() API, we’ve published an implementation of connect() in Node.js that allows you to publish your library such that it works across JavaScript runtimes, without having to maintain any runtime-specific code.

To get started, install it as a dependency:

npm install --save @arrowood.dev/socket

And import it in your library or application:

import { connect } from "@arrowood.dev/socket"

What’s next for connect()?

The wintercg/proposal-sockets-api is published as a draft, and the next step is to solicit and incorporate feedback. We’d love your feedback, particularly if you maintain an open-source library or make direct use of the node:net or node:tls APIs.

Once feedback has been incorporated, engineers from Cloudflare, Vercel and beyond will be continuing to work towards contributing an implementation of the API directly to Node.js as a built-in API.

Cloudflare Integrations Marketplace introduces three new partners: Sentry, Momento and Turso

Post Syndicated from Tanushree Sharma original http://blog.cloudflare.com/cloudflare-integrations-marketplace-new-partners-sentry-momento-turso/

Cloudflare Integrations Marketplace introduces three new partners: Sentry, Momento and Turso

Cloudflare Integrations Marketplace introduces three new partners: Sentry, Momento and Turso

Building modern full-stack applications requires connecting to many hosted third party services, from observability platforms to databases and more. All too often, this means spending time doing busywork, managing credentials and writing glue code just to get started. This is why we’re building out the Cloudflare Integrations Marketplace to allow developers to easily discover, configure and deploy products to use with Workers.

Earlier this year, we introduced integrations with Supabase, PlanetScale, Neon and Upstash. Today, we are thrilled to introduce our newest additions to Cloudflare’s Integrations Marketplace – Sentry, Turso and Momento.

Let's take a closer look at some of the exciting integration providers that are now part of the Workers Integration Marketplace.

Improve performance and reliability by connecting Workers to Sentry

When your Worker encounters an error you want to know what happened and exactly what line of code triggered it. Sentry is an application monitoring platform that helps developers identify and resolve issues in real-time.

The Workers and Sentry integration automatically sends errors, exceptions and console.log() messages from your Worker to Sentry with no code changes required. Here’s how it works:

  1. You enable the integration from the Cloudflare Dashboard.
  2. The credentials from the Sentry project of your choice are automatically added to your Worker.
  3. You can configure sampling to control the volume of events you want sent to Sentry. This includes selecting the sample rate for different status codes and exceptions.
  4. Cloudflare deploys a Tail Worker behind the scenes that contains all the logic needed to capture and send data to Sentry.
  5. Like magic, errors, exceptions, and log messages are automatically sent to your Sentry project.

In the future, we’ll be improving this integration by adding support for uploading source maps and stack traces so that you can pinpoint exactly which line of your code caused the issue. We’ll also be tying in Workers deployments with Sentry releases to correlate new versions of your Worker with events in Sentry that help pinpoint problematic deployments. Check out our developer documentation for more information.

Develop at the Data Edge with Turso + Workers

Turso is an edge-hosted, distributed database based on libSQL, an open-source fork of SQLite. Turso focuses on providing a global service that minimizes query latency (and thus, application latency!). It’s perfect for use with Cloudflare Workers – both compute and data are served close to users.

Turso follows the model of having one primary database with replicas that are located globally, close to users. Turso automatically routes requests to a replica closest to where the Worker was invoked. This model works very efficiently for read heavy applications since read requests can be served globally. If you’re running an application that has heavy write workloads, or want to cut down on replication costs, you can run Turso with just the primary instance and use Smart Placement to speed up queries.

The Turso and Workers integration automatically pulls in Turso API credentials and adds them as secrets to your Worker, so that you can start using Turso by simply establishing a connection using the libsql SDK. Get started with the Turso and Workers Integration today by heading to our developer documentation.

Cache responses from data stores with Momento

Momento Cache is a low latency serverless caching solution that can be used on top of relational databases, key-value databases or object stores to get faster load times and better performance. Momento abstracts details like scaling, warming and replication so that users can deploy cache in a matter of minutes.

The Momento and Workers integration automatically pulls in your Momento API key using an OAuth2 flow. The Momento API key is added as a secret in Workers and, from there, you can start using the Momento SDK in Workers. Head to our developer documentation to learn more and use the Momento and Workers integration!

Try integrations out today

We want to give you back time, so that you can focus less on configuring and connecting third party tools to Workers and spend more time building. We’re excited to see what you build with integrations. Share your projects with us on Twitter (@CloudflareDev) and stay tuned for more exciting updates as we continue to grow our Integrations Marketplace!

If you would like to build an integration with Cloudflare Workers, fill out the integration request form and we’ll be in touch.

New Workers pricing — never pay to wait on I/O again

Post Syndicated from Rita Kozlov original http://blog.cloudflare.com/workers-pricing-scale-to-zero/

New Workers pricing — never pay to wait on I/O again

New Workers pricing — never pay to wait on I/O again

Today we are announcing new pricing for Cloudflare Workers and Pages Functions, where you are billed based on CPU time, and never for the idle time that your Worker spends waiting on network requests and other I/O. Unlike other platforms, when you build applications on Workers, you only pay for the compute resources you actually use.

Why is this exciting? To date, all large serverless compute platforms have billed based on how long your function runs — its duration or “wall time”. This is a reflection of a new paradigm built on a leaky abstraction — your code may be neatly packaged up into a “function”, but under the hood there’s a virtual machine (VM). A VM can’t be paused and resumed quickly enough to execute another piece of code while it waits on I/O. So while a typical function might take 100ms to run, it might typically spend only 10ms doing CPU work, like crunching numbers or parsing JSON, with the rest of time spent waiting on I/O.

This status quo has meant that you are billed for this idle time, while nothing is happening.

With this announcement, Cloudflare is the first and only global serverless platform to offer standard pricing based on CPU time, rather than duration. We think you should only pay for the compute time you actually use, and that’s how we’re going to bill you going forward.

Old pricing — two pricing models, each with tradeoffs

New Workers pricing — never pay to wait on I/O again

New pricing — one simple and predictable pricing model

New Workers pricing — never pay to wait on I/O again

With the same generous Free plan

New Workers pricing — never pay to wait on I/O again

Unlike wall time (duration, or GB-s), CPU time is more predictable and under your control. When you make a request to a third party API, you can’t control how long that API takes to return a response. This time can be quite long, and vary dramatically — particularly when building AI applications that make inference requests to LLMs. If a request takes twice as long to complete, duration-based billing means you pay double. By contrast, CPU time is consistent and unaffected by time spent waiting on I/O — purely a function of the logic and processing of inputs on outputs to your Worker. It is entirely under your control.

Starting October 31, 2023, you will have the option to opt in individual Workers and Pages Functions projects on your account to new pricing, and newly created projects will default to new pricing. You’ll be able to estimate how much new pricing will cost in the Cloudflare dashboard. For the majority of current applications, new pricing is the same or less expensive than the previous Bundled and Unbound pricing plans.

If you’re on our Workers Paid plan, you will have until March 1, 2024 to switch to the new pricing on your own, after which all of your projects will be automatically migrated to new pricing. If you’re an Enterprise customer, any contract renewals after March 1, 2024, will use the new pricing. You’ll receive plenty of advance notice via email and dashboard notifications before any changes go into effect. And since CPU time is fully in your control, the more you optimize your Worker’s compute time, the less you’ll pay. Your incentives are aligned with ours, to make efficient use of compute resources on Region: Earth.

The challenge of truly scaling to zero

The beauty of serverless is that it allows teams to focus on what matters most — delivering value to their customers, rather than managing infrastructure. It saves you money by effortlessly scaling up and down all over the world based on your traffic, whether you’re an early stage startup or Shopify during Black Friday.

One of the promises of serverless is the idea of scaling to zero — once those big days subside, you no longer have to pay for virtual machines to sit idle before your autoscaling kicks in, or be charged by the hour for instances that you barely ended up using. No compute = no bills for usage. Or so, at least, is the promise of serverless.

Yet, there’s one hidden cost, where even in the serverless world you will find yourself paying for idle resources — what happens when your function is sitting around waiting on I/O? With pricing based on the duration that a function runs, you’re still billed for time that your service is doing zero work, and just waiting on network requests.

New Workers pricing — never pay to wait on I/O again

Most applications spend far more time waiting on this I/O than they do using the CPU, often ten times more.

Imagine a similar scenario in your own life — you grab a cab to go to the airport. On the way, the driver decides to stop to refuel and grab a snack, but leaves the meter running. This is not time spent bringing you closer to your destination, but it’s time that you’re paying for. Now imagine for the time the driver was refueling the car, the meter was paused. That’s the difference between CPU time and duration, or wall clock time.

New Workers pricing — never pay to wait on I/O again

But rather than waiting on the driver to refuel or grab a Snickers bar, what is it that you’re actually paying for when it comes to serverless compute?

Time spent waiting on services you don’t control

Most applications depend on one or many external service providers. Providers of hosted large language models (LLMs) like GPT-4 or Stable Diffusion. Databases as a service. Payment processors. Or simply an API request to a system outside your control. This is where software development is headed — rather than reinventing the wheel and slowly building everything themselves, both fast-moving startups and the Fortune 500 increasingly build using other services to avoid undifferentiated heavy lifting.

Every time an application interacts with one of these external services, it has to send data over the network and wait until it receives a response. And while some services are lightning fast, others can take considerable time, like waiting for a payment processor or for a large media file to be uploaded or converted. Your own application sits idle for most of the request, waiting on services outside your control.

Until today, you’ve had to pay while your application waits. You’ve had to pay more when a service you depend on has an operational issue and slows down, or times out in responding to your request. This has been a disincentive to incrementally move parts of your application to serverless.

Cloudflare’s new pricing: the first serverless platform to truly scale down to zero

The idea of “scale to zero” is that you never have to keep instances of your application sitting idle, waiting for something to happen. Serverless is more than just not having to manage servers or virtual machines — you shouldn’t have to provision and manage the number of compute resources that are available or warm.

Our new pricing takes the “scale to zero” concept even further, and extends it to whether your application is actually performing work. If you’re still paying while nothing is happening, we don’t think that’s truly scale to zero. Your application is idle. The CPU can be used for other tasks. Whether your application is “running” is an old concept lifted from an era before multi-tenant cloud platforms. What matters is if you are actually using compute resources.

Pay less, deploy everywhere, without hidden costs

Let’s compare what you’d pay on new Workers pricing to AWS Lambda, for the following Worker:

  • One billion requests per month
  • Seven CPU milliseconds per request
  • 200ms duration per request
New Workers pricing — never pay to wait on I/O again

The above table is for informational purposes only. Prices are limited to the public fees as of September 20, 2023, and do not include taxes and any other fees. AWS Lambda and Lambda @ Edge prices are based on publicly available pricing in US-East (Ohio) region as published on https://aws.amazon.com/lambda/pricing/

Workers are the most cost-effective option, and are globally distributed, automatically optimized with Smart Placement, and integrated with Durable Objects, R2, KV, Cache, Queues, D1 and more. And with Workers, you never have to pay extra for provisioned concurrency, pay a penalty for streaming responses, or incur egregious egress fees.

New Workers pricing makes building AI applications dramatically cheaper

Yesterday we announced a new suite of products to let you build AI applications on Cloudflare — Workers AI, AI Gateway, and our new vector database, Vectorize.

Nearly everyone is building new products and features using AI models right now. Large language models and generative AI models are incredibly powerful. But they aren’t always fast — asking a model to create an image, transcribe a segment of audio, or write a story often takes multiple seconds — far longer than a typical API response or database query that we expect to return in tens of milliseconds. There is significant compute work going on behind the scenes, and that means longer duration per request to a Worker.

New Workers pricing makes this much less expensive than it was previously on the Unbound usage model.

Let’s take the same example as above, but instead assume the duration of the request is two seconds (2000ms), because the Worker makes an inference request to a large AI model. With new Workers pricing, you pay the exact same amount, no matter how long this request takes.

New Workers pricing — never pay to wait on I/O again

No surprise bills — set a maximum limit on CPU time for each Worker

Surprise bills from cloud providers are an unfortunately common horror story. In the old way of provisioning compute resources, forgetting to shut down an instance of a database or virtual machine can cost hundreds of dollars. And accidentally autoscaling up too high can be even worse.

We’re building new safeguards to prevent these kinds of scenarios on Workers. As part of new pricing, you will be able to cap CPU usage on a per-Worker basis.

For example, if you have a Worker with a p99 CPU time of 15ms, you might use this to set a max CPU limit of 40ms — enough headroom to ensure that your worker will run successfully, while ensuring that even if you ship a bug that causes a CPU time to ratchet up dramatically, or have an edge case that causes infinite recursion, you can’t suddenly rack up a giant unexpected bill, or be vulnerable to a denial of wallet attack. This can be particularly helpful if your worker handles variable or user-generated input, to guard against edge cases that you haven’t accounted for.

Alternatively, if you’re running a production service, but want to make sure you stay on top of your costs, we will also be adding the option to configure notifications that can automatically email you, page you, or send a webhook if your worker exceeds a particular amount of CPU time per request. You will be able to choose at what threshold you want to be notified, and how.

New ways to “hibernate” Durable Objects while keeping connections alive

While Workers are stateless functions, Durable Objects are stateful and long-lived, commonly used to coordinate and persist real-time state in chat, multiplayer games, or collaborative apps. And unlike Workers, duration-based pricing fits Durable Objects well. As long as one or more clients are connected to a Durable Object, it keeps state available in memory. Durable Objects pricing will remain duration-based, and is not changing as part of this announcement.

What about when a client is connected to a Durable Object, but no work has happened for a long time? Consider a collaborative whiteboard app built using Durable Objects. A user of the app opens the app in a browser tab, but then forgets about it, and leaves it running for days, with an open WebSocket connection. Just like with Workers, we don’t think you should have to pay for this idle time. But until recently, there hasn’t been an API to signal to us that a Durable Object can be safely “hibernated”.

The recently introduced Hibernation API, currently in beta, allows you to set an automatic response to be used while hibernated and serialize state such that it survives hibernation. This gives Cloudflare the inputs we need in order to maintain open WebSocket connections from clients, while “hibernating” the Durable Object such that it is not actively running, and you are not billed for idle time. The result is that your state is always available in-memory when actually need it, but isn’t unnecessarily kept around when it’s not. As long as your Durable Object is hibernating, even if there are active clients still connected over a WebSocket, you won’t be billed for duration.

Snippets make Cloudflare’s CDN programmable — for free

What if you just want to modify a header, do a country code redirect, or cache a custom query? Developers have relied on Workers to program Cloudflare’s CDN like this for many years. With the announcement of Cloudflare Snippets last year, now in alpha, we’re making it free.

If you use Workers today for these smaller use cases, to customize any of Cloudflare’s application services, Snippets will be the optimal, zero cost option.

A serverless platform without limits

Developers are building ever larger and more complex full-stack applications on Workers each month. Our promise to you is to help you scale in any direction, without worrying about paying for idle time or having to manage and provision compute resources across regions.

This also means not having to worry about limits. Workers already serves many millions of requests per second, and scales and performs so well that we are rebuilding our own CDN on top of Workers. Individual Workers can now be up to 10MB, with a max startup time of 400ms, and can be easily composed together using Service Bindings. Entire platforms are built on top of Workers, with a growing number of companies allowing their own customers to write and deploy custom code and applications via Workers for Platforms. Some of the biggest platforms in the world rely on Cloudflare and the Workers platform during the most critical moments.

New pricing removes limits on the types of applications that could be built cost effectively with duration-based pricing. It removes the ceiling on CPU time from our original request-based pricing. We’re excited to see what you build, and are committed to being the development platform where you’re not constrained by limits on scale, regions, instances, concurrency or whatever else you need to handle to grow and operate globally.

When will new pricing be available?

Starting October 31, 2023, you will have the option to opt in individual Workers and Pages Functions projects on your account to new pricing, and newly created projects will default to new pricing. You will have until March 1, 2024, or the end of your Enterprise contract, whichever comes later, to switch to new pricing on your own, after which all of your projects will be automatically migrated to new pricing. You’ll receive plenty of advance notice via email and dashboard notifications before any changes go into effect.

Between now and then, we want to hear from you. We’ve based new pricing off feedback we’ve heard from developers building serverless applications, and companies estimating and projecting their costs. Tell us what you think of new pricing by sharing your feedback in this survey. We read every response.

You can now use WebGPU in Cloudflare Workers

Post Syndicated from André Cruz original http://blog.cloudflare.com/webgpu-in-workers/

You can now use WebGPU in Cloudflare Workers

You can now use WebGPU in Cloudflare Workers

The browser as an app platform is real and stronger every day; long gone are the Browser Wars. Vendors and standard bodies have done amazingly well over the last years, working together and advancing web standards with new APIs that allow developers to build fast and powerful applications, finally comparable to those we got used to seeing in the native OS' environment.

Today, browsers can render web pages and run code that interfaces with an extensive catalog of modern Web APIs. Things like networking, rendering accelerated graphics, or even accessing low-level hardware features like USB devices are all now possible within the browser sandbox.

One of the most exciting new browser APIs that browser vendors have been rolling out over the last months is WebGPU, a modern, low-level GPU programming interface designed for high-performance 2D and 3D graphics and general purpose GPU compute.

Today, we are introducing WebGPU support to Cloudflare Workers. This blog will explain why it's important, why we did it, how you can use it, and what comes next.

The history of the GPU in the browser

To understand why WebGPU is a big deal, we must revisit history and see how browsers went from relying only on the CPU for everything in the early days to taking advantage of GPUs over the years.

In 2011, WebGL 1, a limited port of OpenGL ES 2.0, was introduced, providing an API for fast, accelerated 3D graphics in the browser for the first time. By then, this was somewhat of a revolution in enabling gaming and 3D visualizations in the browser. Some of the most popular 3D animation frameworks, like Three.js, launched in the same period. Who doesn't remember going to the (now defunct) Google Chrome Experiments page and spending hours in awe exploring the demos? Another option then was using the Flash Player, which was still dominant in the desktop environment, and their Stage 3D API.

Later, in 2017, based on the learnings and shortcomings of its predecessor, WebGL 2 was a significant upgrade and brought more advanced GPU capabilities like compute shaders and more flexible textures and rendering.

WebGL, however, has proved to be a steep and complex learning curve for developers who want to take control of things, do low-level 3D graphics using the GPU, and not use 3rd party abstraction libraries.

Furthermore and more importantly, with the advent of machine learning and cryptography, we discovered that GPUs are great not only at drawing graphics but can be used for other applications that can take advantage of things like high-speed data or blazing-fast matrix multiplications, and one can use them to perform general computation. This became known as GPGPU, short for general-purpose computing on graphics processing units.

With this in mind, in the native desktop and mobile operating system worlds, developers started using more advanced frameworks like CUDA, Metal, DirectX 12, or Vulkan. WegGL stayed behind. To fill this void and bring the browser up to date, in 2017, companies like Google, Apple, Intel, Microsoft, Kronos, and Mozilla created the GPU for Web Community Working Group to collaboratively design the successor of WebGL and create the next modern 3D graphics and computation capabilities APIs for the Web.

What is WebGPU

WebGPU was developed with the following advantages in mind:

  • Lower Level Access – WebGPU provides lower-level, direct access to the GPU vs. the high-level abstractions in WebGL. This enables more control over GPU resources.
  • Multi-Threading – WebGPU can leverage multi-threaded rendering and compute, allowing improved CPU/GPU parallelism compared to WebGL, which relies on a single thread.
  • Compute Shaders – First-class support for general-purpose compute shaders for GPGPU tasks, not just graphics. WebGL compute is limited.
  • Safety – WebGPU ensures memory and GPU access safety, avoiding common WebGL pitfalls.
  • Portability – WGSL shader language targets cross-API portability across GPU vendors vs. GLSL in WebGL.
  • Reduced Driver Overhead – The lower level Vulkan/Metal/D3D12 basis improves overhead vs. OpenGL drivers in WebGL.
  • Pipeline State Objects – Predefined pipeline configs avoid per-draw driver overhead in WebGL.
  • Memory Management – Finer-grained buffer and resource management vs. WebGL.

The “too long didn't read” version is that WebGPU provides lower-level control over the GPU hardware with reduced overhead. It's safer, has multi-threading, is focused on compute, not just graphics, and has portability advantages compared to WebGL.

If these aren't reasons enough to get excited, developers are also looking at WebGPU as an option for native platforms, not just the Web. For instance, you can use this C API that mimics the JavaScript specification. If you think about this and the power of WebAssembly, you can effectively have a truly platform-agnostic GPU hardware layer that you can use to develop platforms for any operating system or browser.

More than just graphics

As explained above, besides being a graphics API, WebGPU makes it possible to perform tasks such as:

  • Machine Learning – Implement ML applications like neural networks and computer vision algorithms using WebGPU compute shaders and matrices.
  • Scientific Computing – Perform complex scientific computation like physics simulations and mathematical modeling using the GPU.
  • High Performance Computing – Unlock breakthrough performance for parallel workloads by connecting WebGPU to languages like Rust, C/C++ via WebAssembly.

WGSL, the shader language for WebGPU, is what enables the general-purpose compute feature. Shaders, or more precisely, compute shaders, have no user-defined inputs or outputs and are used for computing arbitrary information. Here are some examples of simple WebGPU compute shaders if you want to learn more.

WebGPU in Workers

We've been watching WebGPU since the API was published. Its general-purpose compute features perfectly fit our Workers' ecosystem and capabilities and align well with our vision of providing our customers multiple compute and hardware options and bringing GPU workloads to our global network, close to clients.

Cloudflare also has a track record of pioneering support for emerging web standards on our network and services, accelerating their adoption for our customers. Examples of these are Web Crypto API, HTTP/2, HTTP/3, TLS 1.3, or Early hints, but there are more.

Bringing WebGPU to Workers was both natural and timely. Today, we are announcing that we have released a version of workerd, the open-sourced JavaScript / Wasm runtime that powers Cloudflare Workers, with WebGPU support, that you can start playing and developing applications with, locally.

Starting today anyone can run this on their personal computer and experiment with WebGPU-enabled workers. Implementing local development first allows us to put this API in the hands of our customers and developers earlier and get feedback that will guide the development of this feature for production use.

But before we dig into code examples, let's explain how we built it.

How we built WebGPU on top of Workers

You can now use WebGPU in Cloudflare Workers

To implement the WebGPU API, we took advantage of Dawn, an open-source library backed by Google, the same used in Chromium and Chrome, that provides applications with an implementation of the WebGPU standard. It also provides the webgpu.h headers file, the de facto reference for all the other implementations of the standard.

Dawn can interoperate with Linux, MacOS, and Windows GPUs by interfacing with each platform's native GPU frameworks. For example, when an application makes a WebGPU draw call, Dawn will convert that draw command into the equivalent Vulkan, Metal, or Direct3D 12 API call, depending on the platform.

From an application standpoint, Dawn handles the interactions with the underlying native graphics APIs that communicate directly with the GPU drivers. Dawn essentially acts as a middle layer that translates the WebGPU API calls into calls for the platform's native graphics API.

Cloudflare workerd is the underlying open-source runtime engine that executes Workers code. It shares most of its code with the same runtime that powers Cloudflare Workers' production environment but with some changes designed to make it more portable to other environments. We then have release cycles that aim to synchronize both codebases; more on later. Workerd is also used with wrangler, our command-line tool for building and interacting with Cloudflare Workers, to support local development.

The WebGPU code that interfaces with the Dawn library can be found here, and can easily be enabled with a flag, checked here.

jsg::Ref<api::gpu::GPU> Navigator::getGPU(CompatibilityFlags::Reader flags) {
  // is this a durable object?
  KJ_IF_MAYBE (actor, IoContext::current().getActor()) {
    JSG_REQUIRE(actor->getPersistent() != nullptr, TypeError,
                "webgpu api is only available in Durable Objects (no storage)");
  } else {
    JSG_FAIL_REQUIRE(TypeError, "webgpu api is only available in Durable Objects");
  };

  JSG_REQUIRE(flags.getWebgpu(), TypeError, "webgpu needs the webgpu compatibility flag set");

  return jsg::alloc<api::gpu::GPU>();
}

The WebGPU API can only be accessed using Durable Objects, which are essentially global singleton instances of Cloudflare Workers. There are two important reasons for this:

  • WebGPU code typically wants to store the state between requests, for example, loading an AI model into the GPU memory once and using it multiple times for inference.
  • Not all Cloudflare servers have GPUs yet, so although the worker that receives the request is typically the closest one available, the Durable Object that uses WebGPU will be instantiated where there are GPU resources available, which may not be on the same machine.

Using Durable Objects instead of regular Workers allow us to address both of these issues.

The WebGPU Hello World in Workers

Wrangler uses Miniflare 3, a fully-local simulator for Workers, which in turn is powered by workerd. This means you can start experimenting and doing WebGPU code locally on your machine right now before we prepare things in our production environment.

Let’s get coding then.

Since Workers doesn't render graphics yet, we started with implementing the general-purpose GPU (GPGPU) APIs in the WebGPU specification. In other words, we fully support the part of the API that the compute shaders and the compute pipeline require, but we are not yet focused on fragment or vertex shaders used in rendering pipelines.

Here’s a typical “hello world” in WebGPU. This Durable Object script will output the name of the GPU device that workerd found in your machine to your console.

const adapter = await navigator.gpu.requestAdapter();
const adapterInfo = await adapter.requestAdapterInfo(["device"]);
console.log(adapterInfo.device);

A more interesting example, though, is a simple compute shader. In this case, we will fill a results buffer with an incrementing value taken from the iteration number via global_invocation_id.

For this, we need two buffers, one to store the results of the computations as they happen (storageBuffer) and another to copy the results at the end (mappedBuffer).

We then dispatch four workgroups, meaning that the increments can happen in parallel. This parallelism and programmability are two key reasons why compute shaders and GPUs provide an advantage for things like machine learning inference workloads. Other advantages are:

  • Bandwidth – GPUs have a very high memory bandwidth, up to 10-20x more than CPUs. This allows fast reading and writing of all the model parameters and data needed for inference.
  • Floating-point performance – GPUs are optimized for high floating point operation throughput, which are used extensively in neural networks. They can deliver much higher TFLOPs than CPUs.

Let’s look at the code:

// Create device and command encoder
const adapter = await navigator.gpu.requestAdapter();
const device = await adapter.requestDevice();
const encoder = device.createCommandEncoder();

// Storage buffer
const storageBuffer = device.createBuffer({
  size: 4 * Float32Array.BYTES_PER_ELEMENT, // 4 float32 values
  usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC,
});

// Mapped buffer
const mappedBuffer = device.createBuffer({
  size: 4 * Float32Array.BYTES_PER_ELEMENT,
  usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
});

// Create shader that writes incrementing numbers to storage buffer
const computeShaderCode = `
    @group(0) @binding(0)
    var<storage, read_write> result : array<f32>;

    @compute @workgroup_size(1)
    fn main(@builtin(global_invocation_id) gid : vec3<u32>) {
      result[gid.x] = f32(gid.x);
    }
`;

// Create compute pipeline
const computePipeline = device.createComputePipeline({
  layout: "auto",
  compute: {
    module: device.createShaderModule({ code: computeShaderCode }),
    entryPoint: "main",
  },
});

// Bind group
const bindGroup = device.createBindGroup({
  layout: computePipeline.getBindGroupLayout(0),
  entries: [{ binding: 0, resource: { buffer: storageBuffer } }],
});

// Dispatch compute work
const computePass = encoder.beginComputePass();
computePass.setPipeline(computePipeline);
computePass.setBindGroup(0, bindGroup);
computePass.dispatchWorkgroups(4);
computePass.end();

// Copy from storage to mapped buffer
encoder.copyBufferToBuffer(
  storageBuffer,
  0,
  mappedBuffer,
  0,
  4 * Float32Array.BYTES_PER_ELEMENT //mappedBuffer.size
);

// Submit and read back result
const gpuBuffer = encoder.finish();
device.queue.submit([gpuBuffer]);

await mappedBuffer.mapAsync(GPUMapMode.READ);
console.log(new Float32Array(mappedBuffer.getMappedRange()));
// [0, 1, 2, 3]

Now that we covered the basics of WebGPU and compute shaders, let's move to something more demanding. What if we could perform machine learning inference using Workers and GPUs?

ONNX WebGPU demo

The ONNX runtime is a popular open-source cross-platform, high performance machine learning inferencing accelerator. Wonnx is a GPU-accelerated version of the same engine, written in Rust, that can be compiled to WebAssembly and take advantage of WebGPU in the browser. We are going to run it in Workers using a combination of workers-rs, our Rust bindings for Cloudflare Workers, and the workerd WebGPU APIs.

For this demo, we are using SqueezeNet. This small image classification model can run under lower resources but still achieves similar levels of accuracy on the ImageNet image classification validation dataset as larger models like AlexNet.

In essence, our worker will receive any uploaded image and attempt to classify it according to the 1000 ImageNet classes. Once ONNX runs the machine learning model using the GPU, it will return the list of classes with the highest probability scores. Let’s go step by step.

First we load the model from R2 into the GPU memory the first time the Durable Object is called:

#[durable_object]
pub struct Classifier {
    env: Env,
    session: Option<wonnx::Session>,
}

impl Classifier {
    async fn ensure_session(&mut self) -> Result<()> {
        match self.session {
            Some(_) => worker::console_log!("DO already has a session"),
            None => {
                // No session, so this should be the first request. In this case
                // we will fetch the model from R2, build a wonnx session, and
                // store it for subsequent requests.
                let model_bytes = fetch_model(&self.env).await?;
                let session = wonnx::Session::from_bytes(&model_bytes)
                    .await
                    .map_err(|err| err.to_string())?;
                worker::console_log!("session created in DO");
                self.session = Some(session);
            }
        };
        Ok(())
    }
}

This is only required once, when the Durable Object is instantiated. For subsequent requests, we retrieve the model input tensor, call the existing session for the inference, and return to the calling worker the result tensor converted to JSON:

        let request_data: ArrayBase<OwnedRepr<f32>, Dim<[usize; 4]>> =
            serde_json::from_str(&req.text().await?)?;
        let mut input_data = HashMap::new();
        input_data.insert("data".to_string(), request_data.as_slice().unwrap().into());

        let result = self
            .session
            .as_ref()
            .unwrap() // we know the session exists
            .run(&input_data)
            .await
            .map_err(|err| err.to_string())?;
...
        let probabilities: Vec<f32> = result
            .into_iter()
            .next()
            .ok_or("did not obtain a result tensor from session")?
            .1
            .try_into()
            .map_err(|err: TensorConversionError| err.to_string())?;

        let do_response = serde_json::to_string(&probabilities)?;
        Response::ok(do_response)

On the Worker script itself, we load the uploaded image and pre-process it into a model input tensor:

    let image_file: worker::File = match req.form_data().await?.get("file") {
        Some(FormEntry::File(buf)) => buf,
        Some(_) => return Response::error("`file` part of POST form must be a file", 400),
        None => return Response::error("missing `file`", 400),
    };
    let image_content = image_file.bytes().await?;
    let image = load_image(&image_content)?;

Finally, we call the GPU Durable Object, which runs the model and returns the most likely classes of our image:

    let probabilities = execute_gpu_do(image, stub).await?;
    let mut probabilities = probabilities.iter().enumerate().collect::<Vec<_>>();
    probabilities.sort_unstable_by(|a, b| b.1.partial_cmp(a.1).unwrap());
    Response::ok(LABELS[probabilities[0].0])

We packaged this demo in a public repository, so you can also run it. Make sure that you have a Rust compiler, Node.js, Git and curl installed, then clone the repository:

git clone https://github.com/cloudflare/workers-wonnx.git
cd workers-wonnx

Upload the model to the local R2 simulator:

npx wrangler@latest r2 object put model-bucket-dev/opt-squeeze.onnx --local --file models/opt-squeeze.onnx

And then run the Worker locally:

npx wrangler@latest dev

With the Worker running and waiting for requests you can then open another terminal window and upload one of the image examples in the same repository using curl:

> curl -F "file=@images/pelican.jpeg" http://localhost:8787
n02051845 pelican

If everything goes according to plan the result of the curl command will be the most likely class of the image.

Next steps and final words

Over the upcoming weeks, we will merge the workerd WebGPU code in the Cloudflare Workers production environment and make it available globally, on top of our growing GPU nodes fleet. We didn't do it earlier because that environment is subject to strict security and isolation requirements. For example, we can't break the security model of our process sandbox and have V8 talking to the GPU hardware directly, that would be a problem; we must create a configuration where another process is closer to the GPU and use IPC (inter-process communication) to talk to it. Other things like managing resource allocation and billing are being sorted out.

For now, we wanted to get the good news out that we will support WebGPU in Cloudflare Workers and ensure that you can start playing and coding with it today and learn from it. WebGPU and general-purpose computing on GPUs is still in its early days. We presented a machine-learning demo, but we can imagine other applications taking advantage of this new feature, and we hope you can show us some of them.

As usual, you can talk to us on our Developers Discord or the Community forum; the team will be listening. We are eager to hear from you and learn about what you're building.

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

Post Syndicated from Phil Wittig original http://blog.cloudflare.com/workers-ai/

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

If you're anywhere near the developer community, it's almost impossible to avoid the impact that AI’s recent advancements have had on the ecosystem. Whether you're using AI in your workflow to improve productivity, or you’re shipping AI based features to your users, it’s everywhere. The focus on AI improvements are extraordinary, and we’re super excited about the opportunities that lay ahead, but it's not enough.

Not too long ago, if you wanted to leverage the power of AI, you needed to know the ins and outs of machine learning, and be able to manage the infrastructure to power it.

As a developer platform with over one million active developers, we believe there is so much potential yet to be unlocked, so we’re changing the way AI is delivered to developers. Many of the current solutions, while powerful, are based on closed, proprietary models and don't address privacy needs that developers and users demand. Alternatively, the open source scene is exploding with powerful models, but they’re simply not accessible enough to every developer. Imagine being able to run a model, from your code, wherever it’s hosted, and never needing to find GPUs or deal with setting up the infrastructure to support it.

That's why we are excited to launch Workers AI – an AI inference as a service platform, empowering developers to run AI models with just a few lines of code, all powered by our global network of GPUs. It's open and accessible, serverless, privacy-focused, runs near your users, pay-as-you-go, and it's built from the ground up for a best in class developer experience.

Workers AI – making inference just work

We’re launching Workers AI to put AI inference in the hands of every developer, and to actually deliver on that goal, it should just work out of the box. How do we achieve that?

  • At the core of everything, it runs on the right infrastructure – our world-class network of GPUs
  • We provide off-the-shelf models that run seamlessly on our infrastructure
  • Finally, deliver it to the end developer, in a way that’s delightful. A developer should be able to build their first Workers AI app in minutes, and say “Wow, that’s kinda magical!”.

So what exactly is Workers AI? It’s another building block that we’re adding to our developer platform – one that helps developers run well-known AI models on serverless GPUs, all on Cloudflare’s trusted global network. As one of the latest additions to our developer platform, it works seamlessly with Workers + Pages, but to make it truly accessible, we’ve made it platform-agnostic, so it also works everywhere else, made available via a REST API.

Models you know and love

We’re launching with a curated set of popular, open source models, that cover a wide range of inference tasks:

  • Text generation (large language model): meta/llama-2-7b-chat-int8
  • Automatic speech recognition (ASR): openai/whisper
  • Translation: meta/m2m100-1.2
  • Text classification: huggingface/distilbert-sst-2-int8
  • Image classification: microsoft/resnet-50
  • Embeddings: baai/bge-base-en-v1.5

You can browse all available models in your Cloudflare dashboard, and soon you’ll be able to dive into logs and analytics on a per model basis!

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

This is just the start, and we’ve got big plans. After launch, we’ll continue to expand based on community feedback. Even more exciting – in an effort to take our catalog from zero to sixty, we’re announcing a partnership with Hugging Face, a leading AI community + hub. The partnership is multifaceted, and you can read more about it here, but soon you’ll be able to browse and run a subset of the Hugging Face catalog directly in Workers AI.

Accessible to everyone

Part of the mission of our developer platform is to provide all the building blocks that developers need to build the applications of their dreams. Having access to the right blocks is just one part of it — as a developer your job is to put them together into an application. Our goal is to make that as easy as possible.

To make sure you could use Workers AI easily regardless of entry point, we wanted to provide access via: Workers or Pages to make it easy to use within the Cloudflare ecosystem, and via REST API if you want to use Workers AI with your current stack.

Here’s a quick CURL example that translates some text from English to French:

curl https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/meta/@cf/meta/m2m100-1.2b \
-H "Authorization: Bearer {API_TOKEN}" \
	-d '{ "text": "I'll have an order of the moule frites", "target_lang": "french" }'

And here are what the response looks like:

{
  "result": {
    "answer": "Je vais commander des moules frites"
  },
  "success": true,
  "errors":[],
  "messages":[]
}

Use it with any stack, anywhere – your favorite Jamstack framework, Python + Django/Flask, Node.js, Ruby on Rails, the possibilities are endless. And deploy

Designed for developers

Developer experience is really important to us. In fact, most of this post has been about just that. Making sure it works out of the box. Providing popular models that just work. Being accessible to all developers whether you build and deploy with Cloudflare or elsewhere. But it’s more than that – the experience should be frictionless, zero to production should be fast, and it should feel good along the way.

Let’s walk through another example to show just how easy it is to use! We’ll run Llama 2, a popular large language model open sourced by Meta, in a worker.

We’ll assume you have some of the basics already complete (Cloudflare account, Node, NPM, etc.), but if you don’t this guide will get you properly set up!

1. Create a Workers project

Create a new project named workers-ai by running:

$ npm create cloudflare@latest

When setting up your workers-ai worker, answer the setup questions as follows:

  • Enter workers-ai for the app name
  • Choose Hello World script for the type of application
  • Select yes to using TypeScript
  • Select yes to using Git
  • Select no to deploying

Lastly navigate to your new app directory:

cd workers-ai

2. Connect Workers AI to your worker

Create a Workers AI binding, which allows your worker to access the Workers AI service without having to manage an API key yourself.

To bind Workers AI to your worker, add the following to the end of your wrangler.toml file:

[ai]
binding = "AI" #available in your worker via env.AI

You can also bind Workers AI to a Pages Function. For more information, refer to Functions Bindings.

3. Install the Workers AI client library

npm install @cloudflare/ai --save-dev

4. Run an inference task in your worker

Update the source/index.ts with the following code:

import { Ai } from '@cloudflare/ai'
export default {
  async fetch(request, env) {
    const ai = new Ai(env.AI);
    const input = { prompt: "What's the origin of the phrase 'Hello, World'" };
    const output = await ai.run('@cf/meta/llama-2-7b-chat-int8', input );
    return new Response(JSON.stringify(output));
  },
};

5. Develop locally with Wrangler

While in your project directory, test Workers AI locally by running:

$ npx wranlger dev --remote

Note – These models currently only run on Cloudflare’s network of GPUs (and not locally), so setting `–remote` above is a must, and you’ll be prompted to log in at this point.

Wrangler will give you a URL (most likely localhost:8787). Visit that URL, and you’ll see a response like this

{
  "response": "Hello, World is a common phrase used to test the output of a computer program, particularly in the early stages of programming. The phrase "Hello, World!" is often the first program that a beginner learns to write, and it is included in many programming language tutorials and textbooks as a way to introduce basic programming concepts. The origin of the phrase "Hello, World!" as a programming test is unclear, but it is believed to have originated in the 1970s. One of the earliest known references to the phrase is in a 1976 book called "The C Programming Language" by Brian Kernighan and Dennis Ritchie, which is considered one of the most influential books on the development of the C programming language.
}

6. Deploy your worker

Finally, deploy your worker to make your project accessible on the Internet:

$ npx wranlger dev --remote
# Outputs: https://workers-ai.<YOUR_SUBDOMAIN>.workers.dev

And that’s it. You can literally go from zero to deployed AI in minutes. This is obviously a simple example, but shows how easy it is to run Workers AI from any project.

Privacy by default

When Cloudflare was founded, our value proposition had three pillars: more secure, more reliable, and more performant. Over time, we’ve realized that a better Internet is also a more private Internet, and we want to play a role in building it.

That’s why Workers AI is private by default – we don’t train our models, LLM or otherwise, on your data or conversations, and our models don’t learn from your usage. You can feel confident using Workers AI in both personal and business settings, without having to worry about leaking your data. Other providers only offer this fundamental feature with their enterprise version. With us, it’s built in for everyone.

We’re also excited to support data localization in the future. To make this happen, we have an ambitious GPU rollout plan – we’re launching with seven sites today, roughly 100 by the end of 2023, and nearly everywhere by the end of 2024. Ultimately, this will empower developers to keep delivering killer AI features to their users, while staying compliant with their end users’ data localization requirements.

The power of the platform

Vector database – Vectorize

Workers AI is all about running Inference, and making it really easy to do so, but sometimes inference is only part of the equation. Large language models are trained on a fixed set of data, based on a snapshot at a specific point in the past, and have no context on your business or use case. When you submit a prompt, information specific to you can increase the quality of results, making it more useful and relevant. That’s why we’re also launching Vectorize, our vector database that’s designed to work seamlessly with Workers AI. Here’s a quick overview of how you might use Workers AI + Vectorize together.

Example: Use your data (knowledge base) to provide additional context to an LLM when a user is chatting with it.

  1. Generate initial embeddings: run your data through Workers AI using an embedding model. The output will be embeddings, which are numerical representations of those words.
  2. Insert those embeddings into Vectorize: this essentially seeds the vector database with your data, so we can later use it to retrieve embeddings that are similar to your users’ query
  3. Generate embedding from user question: when a user submits a question to your AI app, first, take that question, and run it through Workers AI using an embedding model.
  4. Get context from Vectorize: use that embedding to query Vectorize. This should output embeddings that are similar to your user’s question.
  5. Create context aware prompt: Now take the original text associated with those embeddings, and create a new prompt combining the text from the vector search, along with the original question
  6. Run prompt: run this prompt through Workers AI using an LLM model to get your final result

AI Gateway

That covers a more advanced use case. On the flip side, if you are running models elsewhere, but want to get more out of the experience, you can run those APIs through our AI gateway to get features like caching, rate-limiting, analytics and logging. These features can be used to protect your end point, monitor and optimize costs, and also help with data loss prevention. Learn more about AI gateway here.

Start building today

Try it out for yourself, and let us know what you think. Today we’re launching Workers AI as an open Beta for all Workers plans – free or paid. That said, it’s super early, so…

Warning – It’s an early beta

Usage is not currently recommended for production apps, and limits + access are subject to change.

Limits

We’re initially launching with limits on a per-model basis

  • @cf/meta/llama-2-7b-chat-int8: 5 reqs/min
  • All other modes are between 120-180 reqs/min

Checkout our docs for a full overview of our limits.

Pricing

What we released today is just a small preview to give you a taste of what’s coming (we simply couldn’t hold back), but we’re looking forward to putting the full-throttle version of Workers AI in your hands.

We realize that as you approach building something, you want to understand: how much is this going to cost me? Especially with AI costs being so easy to get out of hand. So we wanted to share the upcoming pricing of Workers AI with you.

While we won’t be billing on day one, we are announcing what we expect our pricing will look like.

Users will be able to choose from two ways to run Workers AI:

  • Regular Twitch Neurons (RTN) – running wherever there's capacity at $0.01 / 1k neurons
  • Fast Twitch Neurons (FTN) – running at nearest user location at $1.25 / 1k neurons

You may be wondering — what’s a neuron?

Neurons are a way to measure AI output that always scales down to zero (if you get no usage, you will be charged for 0 neurons). To give you a sense of what you can accomplish with a thousand neurons, you can: generate 130 LLM responses, 830 image classifications, or 1,250 embeddings.

Our goal is to help our customers pay only for what they use, and choose the pricing that best matches their use case, whether it’s price or latency that is top of mind.

What’s on the roadmap?

Workers AI is just getting started, and we want your feedback to help us make it great. That said, there are some exciting things on the roadmap.

More models, please

We're launching with a solid set of models that just work, but will continue to roll out new models based on your feedback. If there’s a particular model you'd love to see on Workers AI, pop into our Discord and let us know!

In addition to that, we're also announcing a partnership with Hugging Face, and soon you'll be able to access and run a subset of the Hugging Face catalog directly from Workers AI.

Analytics + observability

Up to this point, we’ve been hyper focussed on one thing – making it really easy for any developer to run powerful AI models in just a few lines of code. But that’s only one part of the story. Up next, we’ll be working on some analytics and observability capabilities to give you insights into your usage + performance + spend on a per-model basis, plus the ability to fig into your logs if you want to do some exploring.

A road to global GPU coverage

Our goal is to be the best place to run inference on Region: Earth, so we're adding GPUs to our data centers as fast as we can.

We plan to be in 100 data centers by the end this year

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

And nearly everywhere by the end of 2024

Workers AI: serverless GPU-powered inference on Cloudflare’s global network

We’re really excited to see you build – head over to our docs to get started.

If you need inspiration, want to share something you’re building, or have a question – pop into our Developer Discord.

Writing poems using LLama 2 on Workers AI

Post Syndicated from John Graham-Cumming original http://blog.cloudflare.com/writing-poems-using-llama-2-on-workers-ai/

Writing poems using LLama 2 on Workers AI

Writing poems using LLama 2 on Workers AI

Matthew and Michelle, co-founders of Cloudflare, published their annual founders’ letter today. The letter ends with a poem written by an AI running using Workers AI on Cloudflare’s global network.

Here’s the code that wrote the poem. It uses Workers AI and the Meta Llama 2 model with 7B parameters and 8-bit integers. Just 14 lines of code running on the Cloudflare global network, and you’ve got your very own AI to chat with.

import { Ai } from "@cloudflare/ai";

export default {
    async fetch(request: Request, env: Env): Promise<Response> {
        const body = await request.json();
        const ai = new Ai(env.AI);
        const response = await ai.run("@cf/meta/llama-2-7b-chat-int8", body);
        return new Response(JSON.stringify(response));
    },
};

export interface Env {
    AI: any;
}

That was deployed on Workers AI and all I had to do was ask for poems. Here’s my terminal output (with just the domain name changed).

% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud"}' | jq -r .response

Cloud computing provides a connectivity that's unmatched,
A bridge that spans the globe with ease and grace.
It brings us closer, no matter where we are,
And makes the world a smaller, more intimate place.

With just a click, we can connect and share,
Our thoughts and ideas, our hopes and dreams.
The cloud unites us, in a way that's fair,
And makes the world a more harmonious theme.

So let us embrace this connectivity,
And use it to build a better world for all.
For in the cloud, we find a unity,
That can help us reach new heights, and stand tall.

% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the  \
captivity cloud"}' | jq -r .response

The connectivity cloud, a wondrous sight to see,
Where data flows and knowledge is set free,
A place where minds meet and ideas take flight,
A world of endless possibility in sight.

But then there's the captivity cloud, a darker place,
Where thoughts are trapped and minds are in disgrace,
A prison of the mind, where freedom's lost,
A world of endless sorrow and cost.

So let us choose the connectivity cloud,

And let our minds be free and allowed,

To soar and glide and reach for the sky,

And never be held back, but always fly.

% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the  \
captivity cloud using Elizabethan English"}' | jq -r .response

In the connectivity cloud, I find my peace,

Where thoughts and dreams and hopes do cease to flee.

The captivity cloud doth but darken my day,

With chains of sorrow that will not away.

In the connectivity cloud, I find my home,

Where love and joy and laughter do make their moan.

The captivity cloud doth but lock me in,

With sorrow's shackles that will not give in.

In the connectivity cloud, I find my fate,

Where fortune smiles and misery doth wait.

The captivity cloud doth but bring me woe,

With darkness that will not let me go.

In the connectivity cloud, I find my heart,

Where love and hope and joy do play their part.

The captivity cloud doth but tear it apart,

With sorrow's hand that will not let it start.

% curl -X POST https://example.com/ -d '{"prompt":"Write a poem \
that talks about the connectivity cloud and contrast it with the  \
captivity cloud in the style of Lord Byron"}' | jq -r .response

In the realm of the cloud, where data reigns supreme,

A new divide has emerged, a digital dream,

The connectivity cloud, a place of delight,

Where knowledge and wisdom take flight.

But then there's the captivity cloud, a darker hue,

Where privacy and security are hard to pursue,

A prison of sorts, where freedom's light is lost,

And data's secrets are forever crossed.

Yet still, we cling to the captivity cloud,

For in its depths, we find a measure of proof,

That though the connectivity cloud may be bright,

The captivity cloud holds a certain allure in sight.

So let us embrace both, the light and the dark,

For in their union, we find a digital spark,

That illuminates the path to a brighter day,

Where knowledge and privacy reign in a digital way.

What are you going to build on Workers AI? It’s ready and waiting. We’ll help you go from idea to deployed in minutes.

If you want to know exactly how to deploy something like this read the Workers AI announcement blog.