Tag Archives: deep learning

Building deep learning inference with AWS Lambda and Amazon EFS

Post Syndicated from James Beswick original https://aws.amazon.com/blogs/compute/building-deep-learning-inference-with-aws-lambda-and-amazon-efs/

This post is courtesy of Giuseppe Angelo Porcelli, Principal ML Specialist SA, and Diego Natali, Solutions Architect.

Amazon EFS for AWS Lambda makes it easier for serverless applications requiring persistent file storage or access to large amounts of reference data. Previously, applications had to download data from an object store or database to local ephemeral storage in 512-MB chunks for processing. This creates more code, causes slower startup behavior, and slower data processing. Customers also faced challenges when loading large code packages and models for ML inference.

Recently, AWS announced Amazon EFS support for AWS Lambda. It enables customers to easily share data across function invocations. It also allows you to read large reference data files, and write function output to a persistent and shared data store. Customers can now use Lambda to build data-intensive applications, and load larger libraries and models. They can process larger amounts of data in a highly distributed manner, and share data across functions, containers, and instances.

In this blog post, we show how you can use EFS to store deep learning (DL) framework libraries and models to load from Lambda to execute inferences. We provide a code example on executing serverless inferences with TensorFlow 2.

Using EFS and Lambda for deep learning inference requires to execute two steps:

  1. Storing the deep learning libraries and model on EFS
  2. Creating a Lambda function for inference, which loads the libraries and model from the EFS file system

In the next sections, we share some best practices to implement these steps, and then discuss a full, working example.

Prerequisites

This post assumes experience with Lambda, EFS, plus general knowledge of Python programming, DL, and DL frameworks. To help you get started, read the blog post and documentation.

1. Storing the deep learning libraries and model on Amazon EFS

To populate EFS with DL framework Python libraries and the DL model, there are different options. You can use EC2 instances, third-party tools like lambdci or AWS CodeBuild. AWS CodeBuild is a fully managed continuous integration service that compiles source code, runs tests, and produces software packages for deployment.

This blog post uses an AWS CodeBuild project, configured as follows:

  • The build environment is a Docker container replicating the Lambda runtime environment. To make sure that the packages work in Lambda, it uses the lambci/lambda build container images on Docker Hub.
  • The EFS file system is mounted to the CodeBuild environment.
  • Build commands are used to install the DL framework and download the model to specific paths of the file system.

After the build completes, the EFS file system contains the Python libraries and the model in specific paths. It is attached to the Lambda function for loading those libraries at runtime and execute inference.

For this example, these are the CodeBuild commands to install the TensorFlow 2 framework and an SSD (Single Shot MultiBox Detector) pre-trained object detection model from TensorFlow Hub:

'echo "Downloading and copying model..."',
'mkdir -p $CODEBUILD_EFS1/lambda/model',
'curl https://storage.googleapis.com/tfhub-modules/google/openimages_v4/ssd/mobilenet_v2/1.tar.gz --output /tmp/1.tar.gz',
'tar zxf /tmp/1.tar.gz -C $CODEBUILD_EFS1/lambda/model',

'echo "Installing virtual environment..."',
'mkdir -p $CODEBUILD_EFS1/lambda',
'python3 -m venv $CODEBUILD_EFS1/lambda/tensorflow',
'echo "Installing Tensorflow..."',
'source $CODEBUILD_EFS1/lambda/tensorflow/bin/activate && pip3 install ' +
              (props.installPackages ? props.installPackages : "tensorflow"),

'echo "Changing folder permissions..."',
'chown -R 1000:1000 $CODEBUILD_EFS1/lambda/'

Considerations

  • The approach described can also work for other ML/DL frameworks
  • The EFS file system can be attached to multiple Lambda functions. This means it can share the DL framework libraries with multiple inference functions (up to 25000 connections for each file system).
  • There are alternatives to using EFS for model storage. If the model size fits in the Lambda package deployment, then you could optimize the first invocation since it doesn’t need to download the model. You can also use the function’s initializer to load the model since the first mount to EFS only takes a few hundred milliseconds.

2. Creating a Lambda function for inference

After attaching the EFS file system, you may structure the Lambda code as follows:

Lambda code structure

The code outside the handler method first adds the local mount path to the Python path. It then imports the frameworks, and loads the model into memory. Executing those operations outside of the function’s handler ensures that those objects remain initialized and reused in subsequent invocations of the same Lambda function instance. The code inside the handler runs the inference flow by reading inputs, executing the actual inference, and returning the results to the caller.

For hosting the TensorFlow 2 object detection model in the example, this is the function code:

import sys
import os

# Setting library paths.
efs_path = "/mnt/python"
python_pkg_path = os.path.join(efs_path, "tensorflow/lib/python3.8/site-packages")
sys.path.append(python_pkg_path)

import json
import string
import time
import io
import requests

# Importing TensorFlow
import tensorflow as tf

# Loading model
model_path = os.path.join(efs_path, 'model/')
loaded_model = tf.saved_model.load(model_path)
detector = loaded_model.signatures['default']

def lambda_handler(event, context):
    r = requests.get(event['url'])
    img = tf.image.decode_jpeg(r.content, channels=3)

    # Executing inference.
    converted_img  = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...]
    start_time = time.time()
    result = detector(converted_img)
    end_time = time.time()

    obj = {
        'detection_boxes' : result['detection_boxes'].numpy().tolist(),
        'detection_scores': result['detection_scores'].numpy().tolist(),
        'detection_class_entities': [el.decode('UTF-8') for el in result['detection_class_entities'].numpy()] 
    }    

    return {
        'statusCode': 200,
        'body': json.dumps(obj)
    }

When invoked, the response is like:

{
    "statusCode": 200,
    "body": "{
    \"detection_boxes\": This field contains the relative position of the bounding boxes,
    \"detection_class_entities\": This field returns the class labels,
    \"detection_scores\": This field returns the detection confidences
    }"
}

Running the example

This working example is provided to set up and run ML/AI inference on Lambda using EFS. To run it, you must have the AWS CDK installed. Execute the following commands:

# clone repository
$ git clone https://github.com/aws-samples/lambda-efs-deep-learning-inference.git
$ cd lambda-efs-ml-demo

# Install the CDK and bootstrap the target account (if this was never done before)
$ npm install -g aws-cdk
$ cdk bootstrap aws://{account_id}/{region}

# Install packages for the project, build and deploy
$ cd cdk/
$ npm install
$ npm run build
$ cdk deploy

After deployment, note the output:

Outputs:
LambdaEFSMLDemo.LambdaFunctionName = LambdaEFSMLDemo-LambdaEFSMLExecuteInference17332C2-0546aa45dfXXXXXX

It takes a few minutes for AWS CodeBuild to deploy the libraries and framework to EFS. To test the Lambda function, run this command, replacing the function name:

$ aws lambda invoke \
    --function-name LambdaEFSMLDemo-LambdaEFSMLExecuteInference17332C2-0546aa45dfXXXXXX \
    --region us-east-1 \
    --cli-binary-format raw-in-base64-out \
    --payload '{"url": "https://images.pexels.com/photos/310983/pexels-photo-310983.jpeg?auto=compress&cs=tinysrgb&dpr=2&h=650&w=940"}' \
    --region us-east-1 \
    /tmp/return.json    

This is the output:

{
    "StatusCode": 200,
    "ExecutedVersion": "$LATEST"
}

Here you can check the inference’s result:
$ tail /tmp/return.json

The following image shows the bounding boxes created from the inference output.

Inference result

The following image shows the bounding boxes created from the inference output.

Image with bounding boxes

To generate this image with the bounding boxes, use the Jupyter notebook from the repository. We reduce the number of bounding boxes to the most relevant classes:

  • Bicycle: 91%
  • Wheel: 48%
  • Person: 45%
  • Wheel: 44%
  • Man: 40%
  • Bicycle wheel: 37%
  • Bicycle wheel: 30%

To clean up the deployment, run:

$ cdk destroy

Performance considerations

When planning for ML inference, you must keep three main aspects in mind: the type of compute resources required for inference, model size and memory footprint, function initialization and cold start.

Lambda is best suited for CPU-based inferencing, which meets the needs for most ML/DL inference use cases. Lambda’s memory can be set between 128 MB and 3008 MB. This means that large models (for example, FasterRCNN models) that may require more memory or dedicated GPUs are not a good fit.

It’s important to understand how Lambda invokes affect performance. The first request to a function instance is called a “cold-start”. This is where the function is provisioned, code downloaded, and the initializer is executed to download the code and load libraries. In this example, it takes about 40 seconds to load the full TensorFlow 2 libraries from EFS, and another 8 seconds to load the model into memory.

Subsequent calls to the same Lambda function instance don’t incur cold start latency if the request is handled by an existing execution environment. Customers who want to reduce this one-time cold start can use Provisioned Concurrency. This feature provides customers with greater control over performance of their serverless applications at any scale.

The EFS mount operation only takes a few hundred milliseconds and only happens once during the function provisioning. EFS supports up to 25,000 connections so is ideal for functions that scale up. We recommend you use EFS provisioned throughput with Provisioned Concurrency for better performance. To learn more, read the documentation about Amazon EFS performance and monitoring Amazon EFS.

Conclusion

This post shows how you can use EFS for Lambda to deploy large DL libraries and models into a function for synchronous invocations. The same approach can be applied to asynchronous invokes. For example, you could perform object detection on images stored in Amazon S3, or streaming invokes on data in Amazon Kinesis and Amazon DynamoDB.

EFS for Lambda enables many new use cases. To learn more about how to use EFS for Lambda, see the AWS News Blog post and read the documentation.

Deep learning cat prey detector

Post Syndicated from Ashley Whittaker original https://www.raspberrypi.org/blog/deep-learning-cat-prey-detector/

We’ve all been able to check on our kitties’ outdoor activities for a while now, thanks to motion-activated cameras. And the internet’s favourite cat flap even live-tweets when it senses paws through the door.

A nightvision image of a cat approaching a cat flap with a mouse in its mouth

“Did you already make dinner? I stopped on the way home to pick this up for you.”

But what’s eluded us “owners” of felines up until now is the ability to stop our furry companions from bringing home mauled presents we neither want nor asked for.

A cat flap bouncer powered by deep learning

Now this Raspberry Pi–powered machine learning build, shared by reddit user u/eee_bume, can help us out: at its heart, there’s a convolutional neural network cascade that detects whether a cat is trying to enter a cat flap with something in its maw. (No word from the creators on how many half-consumed rodents the makers had to dispose of while training the machine learning model.)

The neural network first detects the whole cat in an image; then it hones in on the cat’s maw. Image classification is performed to detect whether there is anything in or around the maw. If the network thinks the cat is trying to smuggle caught contraband into the house, it’s a “no” from this virtual door bouncer.

The system runs on Raspberry Pi 4 with an infrared camera at an average detection rate of  around 1 FPS. The PC-Val value, representing the certainty of the prey classification => prey/no_prey certainty threshold, is 0.5.

The home made set up including small camera lights and sensors

The infrared camera setup, powered by Raspberry Pi

How to get enough training data

This project formed Nicolas Baumann’s and Michael Ganz’s spring semester thesis at the Swiss Federal Institute of Technology. One of the problems they ran into while trying to train their device is that cats are only expected to enter the cat flap carrying prey 3% of the time, which leads to a largely imbalanced classification problem. It would have taken a loooong time if they had just waited for Nicolas and Michael’s pets to bring home enough decomposing gifts.

Lots of different cats faces close up, some with prey in their mouths, some without

The cutest mugshots you ever did see

To get around this, they custom-built a scalable image data gathering network to simplify and maximise the collection of training data. It features multiple distributed Camera Nodes (CN), a centralised main archive, and a custom labeling tool. As a result of the data gathering network, 40GB of training data have been amassed.

What is my cat eating?!

The makers also took the time to train their neural network to classify different types of prey. So far, it recognises mice, lizards, slow-worms, and birds.

Infrared shots of one cat while the camera decides if it has prey in its mouth or not

“Come ooooon, it’s not even a *whole* mouse, let me in!”

It’s still being tweaked, but at the moment the machine learning model correctly detects when a cat has prey in its mouth 93% of the time. But it still falsely accuses kitties 28% of the time. We’ll leave it to you to decide whether your feline companion will stand for that kind of false positive rate, or whether it’s more than your job’s worth.

The post Deep learning cat prey detector appeared first on Raspberry Pi.

Lerner — using RL agents for test case scheduling

Post Syndicated from Netflix Technology Blog original https://medium.com/netflix-techblog/lerner-using-rl-agents-for-test-case-scheduling-3e0686211198?source=rss----2615bd06b42e---4

Lerner — using RL agents for test case scheduling

By: Stanislav Kirdey, Kevin Cureton, Scott Rick, Sankar Ramanathan

Introduction

Netflix brings delightful customer experiences to homes on a variety of devices that continues to grow each day. The device ecosystem is rich with partners ranging from Silicon-on-Chip (SoC) manufacturers, Original Design Manufacturer (ODM) and Original Equipment Manufacturer (OEM) vendors.

Partners across the globe leverage Netflix device certification process on a continual basis to ensure that quality products and experiences are delivered to their customers. The certification process involves the verification of partner’s implementation of features provided by the Netflix SDK.

The Partner Device Ecosystem organization in Netflix is responsible for ensuring successful integration and testing of the Netflix application on all partner devices. Netflix engineers run a series of tests and benchmarks to validate the device across multiple dimensions including compatibility of the device with the Netflix SDK, device performance, audio-video playback quality, license handling, encryption and security. All this leads to a plethora of test cases, most of them automated, that need to be executed to validate the functionality of a device running Netflix.

Problem

With a collection of tests that, by nature, are time consuming to run and sometimes require manual intervention, we need to prioritize and schedule test executions in a way that will expedite detection of test failures. There are several problems efficient test scheduling could help us solve:

  1. Quickly detect a regression in the integration of the Netflix SDK on a consumer electronic or MVPD (multichannel video programming distributor) device.
  2. Detect a regression in a test case. Using the Netflix Reference Application and known good devices, ensure the test case continues to function and tests what is expected.
  3. When code many test cases are dependent on has changed, choose the right test cases among thousands of affected tests to quickly validate the change before committing it and running extensive, and expensive, tests.
  4. Choose the most promising subset of tests out of thousands of test cases available when running continuous integration against a device.
  5. Recommend a set of test cases to execute against the device that would increase the probability of failing the device in real-time.

Solving the above problems could help Netflix and our Partners save time and money during the entire lifecycle of device design, build, test, and certification.

These problems could be solved in several different ways. In our quest to be objective, scientific, and inline with the Netflix philosophy of using data to drive solutions for intriguing problems, we proceeded by leveraging machine learning.

Our inspiration was the findings in a research paper “Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration” by Helge Spieker, et. al. We thought that reinforcement learning would be a promising approach that could provide great flexibility in the training process. Likewise it has very low requirements on the initial amount of training data.

In the case of continuously testing a Netflix SDK integration on a new device, we usually lack relevant data for model training in the early phases of integration. In this situation training an agent is a great fit as it allows us to start with very little input data and let the agent explore and exploit the patterns it learns in the process of SDK integration and regression testing. The agent in reinforcement learning is an entity that performs a decision on what action to take considering the current state of the environment, and gets a reward based on the quality of the action.

Solution

We built a system called Lerner that consists of a set of microservices and a python library that allows scalable agent training and inference for test case scheduling. We also provide an API client in Python.

Lerner works in tandem with our continuous integration framework that executes on-device tests using the Netflix Test Studio platform. Tests are run on Netflix Reference Applications (running as containers on Titus), as well as on physical devices.

There were several motivations that led to building a custom solution:

  1. We wanted to keep the APIs and integrations as simple as possible.
  2. We needed a way to run agents and tie the runs to the internal infrastructure for analytics, reporting, and visualizations.
  3. We wanted the to tool be available as a standalone library as well as scalable API service.

Lerner provides ability to setup any number of agents making it the first component in our re-usable reinforcement learning framework for device certification.

Lerner, as a web-service, relies on Amazon Web Services (AWS) and Netflix’s Open Source Software (OSS) tools. We use Spinnaker to deploy instances and host the API containers on Titus — which allows fast deployment times and rapid scalability. Lerner uses AWS services to store binary versions of the agents, agent configurations, and training data. To maintain the quality of Lerner APIs, we are using the server-less paradigm for Lerner’s own integration testing by utilizing AWS Lambda.

The agent training library is written in Python and supports versions 2.7, 3.5, 3.6, and 3.7. The library is available in the artifactory repository for easy installation. It can be used in Python notebooks — allowing for rapid experimentation in isolated environments without a need to perform API calls. The agent training library exposes different types of learning agents that utilize neural networks to approximate action.

The neural network (NN)-based agent uses a deep net with fully connected layers. The NN gets the state of a particular test case (the input) and outputs a continuous value, where a higher number means an earlier position in a test execution schedule. The inputs to the neural network include: general historical features such as the last N executions and several domain specific features that provide meta-information about a test case.

The Lerner APIs are split into three areas:

  1. Storing execution results.
  2. Getting recommendations based on the current state of the environment.
  3. Assign reward to the agent based on the execution result and predicted recommendations.

A process of getting recommendations and rewarding the agent using APIs consists of 4 steps:

  1. Out of all available test cases for a particular job — form a request that can be interpreted by Lerner. This involves aggregation of historical results and additional features.
  2. Lerner returns a recommendation identified with a unique episode id.
  3. A CI system can execute the recommendation and submit the execution results to Lerner based on the episode id.
  4. Call an API to assign a reward based on the agent id and episode id.

Below is a diagram of the services and persistence layers that support the functionality of the Lerner API.

The self-service nature of the tool makes it easy for service owners to integrate with Lerner, create agents, ask agents for recommendations and reward them after execution results are available.

The metrics relevant to the training and recommendation process are reported to Atlas and visualized using Netflix’s Lumen. Users of the service can track the statistics specific to the agents they setup and deploy, which allows them to build their own dashboards.

We have identified some interesting patterns while doing online reinforcement learning.

  • The recommendation/execution reward cycle can happen without any prior training data.
  • We can bootstrap several CI jobs that would use agents with different reward functions, and gain additional insight based on agents performance. It could help us design and implement more targeted reward functions.
  • We can keep a small amount of historical data to train agents. The data can be truncated after each execution and offloaded to a long-term storage for further analysis.

Some of the downsides:

  • It might take time for an agent to stop exploring and start exploiting the accumulated experience.
  • As agents stored in a binary format in the database, an update of an agent from multiple jobs could cause a race condition in its state. Handling concurrency in the training process is cumbersome and requires trade offs. We achieved the desired state by relying on the locking mechanisms of the underlying persistence layer that stores and serves agent binaries.

Thus, we have the luxury of training as many agents as we want that could prioritize and recommend test cases based on their unique learning experiences.

Outcome

We are currently piloting the system and have live agents serving predictions for various CI runs. At the moment we run Lerner-based CIs in parallel with CIs that either execute test cases in random order or use simple heuristics as sorting test cases by time and execute everything that previously failed.

The system was built with simplicity and performance in mind, so the set of APIs are minimal. We developed client libraries that allow seamless, but opinionated, integration with Lerner.

We collect several metrics to evaluate the performance of a recommendation, with main metrics being time taken to first failure and time taken to complete a whole scheduled run.

Lerner-based recommendations are proving to be different and more insightful than random runs, as they allow us to fit a particular time budget and detect patterns such as cases that tend to fail together in a cluster, cases that haven’t been run in a long time, and so on.

The below graphs shows more or less an artificial case when a schedule of 100+ test cases would contain several flaky tests. The Y-axis represents how many minutes it took to complete the schedule or reach a first failed test case. In blue, we have random recommendations with no time budget constraints. In green you can see executions based on Lerner recommendations under a time constraint of 60 minutes. The green spikes represent Lerner exploring the environment, where the wiggly lines around 0 are the executions that failed quickly as Lerner was exploiting its policy.

Execution of schedules that were randomly generated. Y-axis represents time to finish execution or reach first failure.
Execution of Lerner based schedules. You can see moments when Lerner was exploring the environment, and the wiggly lines represent when the schedule was generated based on exploiting existing knowledge.

Next Steps

The next phases of the project will focus on:

  • Reward functions that are aware of a comprehensive domain context, such as assigning appropriate rewards to states where infrastructure is fragile and test case could not be run appropriately.
  • Administrative user-interface to manage agents.
  • More generic, simple, and user-friendly framework for reinforcement learning and agent deployment.
  • Using Lerner on all available CIs jobs against all SDK versions.
  • Experiment with different neural network architectures.

If you would like to be a part of our team, come join us.


Lerner — using RL agents for test case scheduling was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.

GPU workloads on AWS Batch

Post Syndicated from Josh Rad original https://aws.amazon.com/blogs/compute/gpu-workloads-on-aws-batch/

Contributed by Manuel Manzano Hoss, Cloud Support Engineer

I remember playing around with graphics processing units (GPUs) workload examples in 2017 when the Deep Learning on AWS Batch post was published by my colleague Kiuk Chung. He provided an example of how to train a convolutional neural network (CNN), the LeNet architecture, to recognize handwritten digits from the MNIST dataset using Apache MXNet as the framework. Back then, to run such jobs with GPU capabilities, I had to do the following:

  • Create a custom GPU-enabled AMI that had installed Docker, the ECS agent, NVIDIA driver and container runtime, and CUDA.
  • Identify the type of P2 EC2 instance that had the required amount of GPU for my job.
  • Check the amount of vCPUs that it offered (even if I was not interested on using them).
  • Specify that number of vCPUs for my job.

All that, when I didn’t have any certainty that the instance was going to have the GPU required available when my job was already running. Back then, there was no GPU pinning. Other jobs running on the same EC2 instance were able to use that GPU, making the orchestration of my jobs a tricky task.

Fast forward two years. Today, AWS Batch announced integrated support for Amazon EC2 Accelerated Instances. It is now possible to specify an amount of GPU as a resource that AWS Batch considers in choosing the EC2 instance to run your job, along with vCPU and memory. That allows me to take advantage of the main benefits of using AWS Batch, the compute resource selection algorithm and job scheduler. It also frees me from having to check the types of EC2 instances that have enough GPU.

Also, I can take advantage of the Amazon ECS GPU-optimized AMI maintained by AWS. It comes with the NVIDIA drivers and all the necessary software to run GPU-enabled jobs. When I allow the P2 or P3 instance types on my compute environment, AWS Batch launches my compute resources using the Amazon ECS GPU-optimized AMI automatically.

In other words, now I don’t worry about the GPU task list mentioned earlier. I can focus on deciding which framework and command to run on my GPU-accelerated workload. At the same time, I’m now sure that my jobs have access to the required performance, as physical GPUs are pinned to each job and not shared among them.

A GPU race against the past

As a kind of GPU-race exercise, I checked a similar example to the one from Kiuk’s post, to see how fast it could be to run a GPU-enabled job now. I used the AWS Management Console to demonstrate how simple the steps are.

In this case, I decided to use the deep neural network architecture called multilayer perceptron (MLP), not the LeNet CNN, to compare the validation accuracy between them.

To make the test even simpler and faster to implement, I thought I would use one of the recently announced AWS Deep Learning containers, which come pre-packed with different frameworks and ready-to-process data. I chose the container that comes with MXNet and Python 2.7, customized for Training and GPU. For more information about the Docker images available, see the AWS Deep Learning Containers documentation.

In the AWS Batch console, I created a managed compute environment with the default settings, allowing AWS Batch to create the required IAM roles on my behalf.

On the configuration of the compute resources, I selected the P2 and P3 families of instances, as those are the type of instance with GPU capabilities. You can select On-Demand Instances, but in this case I decided to use Spot Instances to take advantage of the discounts that this pricing model offers. I left the defaults for all other settings, selecting the AmazonEC2SpotFleetRole role that I created the first time that I used Spot Instances:

Finally, I also left the network settings as default. My compute environment selected the default VPC, three subnets, and a security group. They are enough to run my jobs and at the same time keep my environment safe by limiting connections from outside the VPC:

I created a job queue, GPU_JobQueue, attaching it to the compute environment that I just created:

Next, I registered the same job definition that I would have created following Kiuk’s post. I specified enough memory to run this test, one vCPU, and the AWS Deep Learning Docker image that I chose, in this case mxnet-training:1.4.0-gpu-py27-cu90-ubuntu16.04. The amount of GPU required was in this case, one. To have access to run the script, the container must run as privileged, or using the root user.

Finally, I submitted the job. I first cloned the MXNet repository for the train_mnist.py Python script. Then I ran the script itself, with the parameter –gpus 0 to indicate that the assigned GPU should be used. The job inherits all the other parameters from the job definition:

sh -c 'git clone -b 1.3.1 https://github.com/apache/incubator-mxnet.git && python /incubator-mxnet/example/image-classification/train_mnist.py --gpus 0'

That’s all, and my GPU-enabled job was running. It took me less than two minutes to go from zero to having the job submitted. This is the log of my job, from which I removed the iterations from epoch 1 to 18 to make it shorter:

14:32:31     Cloning into 'incubator-mxnet'...

14:33:50     Note: checking out '19c501680183237d52a862e6ae1dc4ddc296305b'.

14:33:51     INFO:root:start with arguments Namespace(add_stn=False, batch_size=64, disp_batches=100, dtype='float32', gc_threshold=0.5, gc_type='none', gpus='0', initializer='default', kv_store='device', load_epoch=None, loss='', lr=0.05, lr_factor=0.1, lr_step_epochs='10', macrobatch_size=0, model_prefix=None, mom=0.9, monitor=0, network='mlp', num_classes=10, num_epochs=20, num_examples=60000, num_layers=No

14:33:51     DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): yann.lecun.com:80

14:33:54     DEBUG:urllib3.connectionpool:http://yann.lecun.com:80 "GET /exdb/mnist/train-labels-idx1-ubyte.gz HTTP/1.1" 200 28881

14:33:55     DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): yann.lecun.com:80

14:33:55     DEBUG:urllib3.connectionpool:http://yann.lecun.com:80 "GET /exdb/mnist/train-images-idx3-ubyte.gz HTTP/1.1" 200 9912422

14:33:59     DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): yann.lecun.com:80

14:33:59     DEBUG:urllib3.connectionpool:http://yann.lecun.com:80 "GET /exdb/mnist/t10k-labels-idx1-ubyte.gz HTTP/1.1" 200 4542

14:33:59     DEBUG:urllib3.connectionpool:Starting new HTTP connection (1): yann.lecun.com:80

14:34:00     DEBUG:urllib3.connectionpool:http://yann.lecun.com:80 "GET /exdb/mnist/t10k-images-idx3-ubyte.gz HTTP/1.1" 200 1648877

14:34:04     INFO:root:Epoch[0] Batch [0-100] Speed: 37038.30 samples/sec accuracy=0.793472

14:34:04     INFO:root:Epoch[0] Batch [100-200] Speed: 36457.89 samples/sec accuracy=0.906719

14:34:04     INFO:root:Epoch[0] Batch [200-300] Speed: 36981.20 samples/sec accuracy=0.927500

14:34:04     INFO:root:Epoch[0] Batch [300-400] Speed: 36925.04 samples/sec accuracy=0.935156

14:34:04     INFO:root:Epoch[0] Batch [400-500] Speed: 37262.36 samples/sec accuracy=0.940156

14:34:05     INFO:root:Epoch[0] Batch [500-600] Speed: 37729.64 samples/sec accuracy=0.942813

14:34:05     INFO:root:Epoch[0] Batch [600-700] Speed: 37493.55 samples/sec accuracy=0.949063

14:34:05     INFO:root:Epoch[0] Batch [700-800] Speed: 37320.80 samples/sec accuracy=0.953906

14:34:05     INFO:root:Epoch[0] Batch [800-900] Speed: 37705.85 samples/sec accuracy=0.958281

14:34:05     INFO:root:Epoch[0] Train-accuracy=0.924024

14:34:05     INFO:root:Epoch[0] Time cost=1.633

...  LOGS REMOVED

14:34:44     INFO:root:Epoch[19] Batch [0-100] Speed: 36864.44 samples/sec accuracy=0.999691

14:34:44     INFO:root:Epoch[19] Batch [100-200] Speed: 37088.35 samples/sec accuracy=1.000000

14:34:44     INFO:root:Epoch[19] Batch [200-300] Speed: 36706.91 samples/sec accuracy=0.999687

14:34:44     INFO:root:Epoch[19] Batch [300-400] Speed: 37941.19 samples/sec accuracy=0.999687

14:34:44     INFO:root:Epoch[19] Batch [400-500] Speed: 37180.97 samples/sec accuracy=0.999844

14:34:44     INFO:root:Epoch[19] Batch [500-600] Speed: 37122.30 samples/sec accuracy=0.999844

14:34:45     INFO:root:Epoch[19] Batch [600-700] Speed: 37199.37 samples/sec accuracy=0.999687

14:34:45     INFO:root:Epoch[19] Batch [700-800] Speed: 37284.93 samples/sec accuracy=0.999219

14:34:45     INFO:root:Epoch[19] Batch [800-900] Speed: 36996.80 samples/sec accuracy=0.999844

14:34:45     INFO:root:Epoch[19] Train-accuracy=0.999733

14:34:45     INFO:root:Epoch[19] Time cost=1.617

14:34:45     INFO:root:Epoch[19] Validation-accuracy=0.983579

Summary

As you can see, after AWS Batch launched the instance, the job took slightly more than two minutes to run. I spent roughly five minutes from start to finish. That was much faster than the time that I was previously spending just to configure the AMI. Using the AWS CLI, one of the AWS SDKs, or AWS CloudFormation, the same environment could be created even faster.

From a training point of view, I lost on the validation accuracy, as the results obtained using the LeNet CNN are higher than when using an MLP network. On the other hand, my job was faster, with a time cost of 1.6 seconds in average for each epoch. As the software stack evolves, and increased hardware capabilities come along, these numbers keep improving, but that shouldn’t mean extra complexity. Using managed primitives like the one presented in this post enables a simpler implementation.

I encourage you to test this example and see for yourself how just a few clicks or commands lets you start running GPU jobs with AWS Batch. Then, it is just a matter of replacing the Docker image that I used for one with the framework of your choice, TensorFlow, Caffe, PyTorch, Keras, etc. Start to run your GPU-enabled machine learning, deep learning, computational fluid dynamics (CFD), seismic analysis, molecular modeling, genomics, or computational finance workloads. It’s faster and easier than ever.

If you decide to give it a try, have any doubt or just want to let me know what you think about this post, please write in the comments section!

Yoga training with YogAI and a Raspberry Pi smart mirror | The MagPi issue 80

Post Syndicated from Rob Zwetsloot original https://www.raspberrypi.org/blog/yoga-training-with-yogai-and-a-raspberry-pi-smart-mirror-the-magpi-issue-80/

Running on a smart mirror, YogAI uses a database of postures, image recognition software, and the magic of mirrors to not only show users their current posture but to also teach them how to correct their posture to reach peak yogi-ness. Here’s Rob Zwetsloot from The MagPi magazine with more.

yogai

We’ve seen many ‘magic mirror’ projects over the past few years, featuring a TV screen behind the glass to show useful information, but YogAI takes the concept to a whole new level by providing an AI personal trainer to guide and correct your yoga positions.

Self-confessed fitness nuts Salma Mayorquin and Terry Rodriguez thought that having a personal trainer could be a way to keep track of their fitness progress, so why not try to make a virtual one? “With [deep learning] models like pose estimation, we figured there was a way we could make a program that could track how we were exercising and started experimenting from there,” says Terry.

“YogAI guides users through a flow of yoga poses, offering generally helpful advice when the camera senses a user not in the correct pose,” explains Salma. “At the heart, YogAI uses pose estimation to find reference key points on the body. This is used to understand and classify common yoga poses.”

Users interact with YogAI through both visual feedback via the mirror display, and a voice interface — using the Snips AIR voice assistant — which enables the user to give spoken commands to start, stop, pause, and restart a yoga session. YogAI also talks back through the Flite voice synthesiser to guide the yogi to achieve the correct poses.

While a prototype magic mirror only took the experienced makers a week to build, training the AI to recognise yoga poses in real time was a trickier task. “We need our computer vision models to run quickly so that we have enough resolution in time to identify the move,” reveals Terry.

Strike a pose

A Raspberry Pi 3 interprets the camera images in real time, detecting key body points to display the pose on the mirror and classify it using a deep-learning model trained with a dataset of around 35000 samples.

However, the pair found that the Pi could only run image inference at one frame every 4–5 seconds, resulting in lag. A workaround was soon found: “Shrinking our pose estimation models down using TensorFlow Lite, we were able to bring our frame rate from 0.2 fps to 2.5 fps,” says Salma. “For faster inference, we will look for ways to reduce the model further. We also believe upgrading to the Raspberry Pi Compute Module 3 will increase the performance significantly.”


“Overall, the accuracy across a dozen common poses is roughly 80%,” divulges Terry. “Not surprisingly, we find similar pose variants, e.g. warrior poses, can be a source of confusion. When the head/face is blocked, the pose estimates degrade, which impacts our classification of poses like downward dog.”

More intense exercise

As well as using the system for yoga, Salma and Terry are planning to adapt YogAI to monitor more energetic workouts. “We’re interested in strength training, and others have suggested dance and karate katas,” says Terry. “We think YogAI is well-positioned to perform more general health and personal wellness tasks.”

“We want to integrate with popular health wearables,” adds Salma. “A smart watch with an accelerometer and heart rate monitor can introduce a lot of important context to bring YogAI closer to our vision for a smart mirror yoga instructor and toward a personal wellness platform.”

More from The MagPi magazine

The MagPi magazine issue 80 is out today. Buy your copy now from the Raspberry Pi Press store, major newsagents in the UK, or Barnes & Noble, Fry’s, or Micro Center in the US. Or, download your free PDF copy from The MagPi magazine website.

Subscribe now

Subscribe to The MagPi magazine on a monthly, quarterly, or twelve-month basis to save money against newsstand prices!

Twelve-month print subscribers get a free Raspberry Pi 3A+, the perfect Raspberry Pi to try your hand at some of the latest projects covered in The MagPi magazine.

The post Yoga training with YogAI and a Raspberry Pi smart mirror | The MagPi issue 80 appeared first on Raspberry Pi.

Scheduling GPUs for deep learning tasks on Amazon ECS

Post Syndicated from Anuneet Kumar original https://aws.amazon.com/blogs/compute/scheduling-gpus-for-deep-learning-tasks-on-amazon-ecs/

This post is contributed by Brent Langston – Sr. Developer Advocate, Amazon Container Services

Last week, AWS announced enhanced Amazon Elastic Container Service (Amazon ECS) support for GPU-enabled EC2 instances. This means that now GPUs are first class resources that can be requested in your task definition, and scheduled on your cluster by ECS.

Previously, to schedule a GPU workload, you had to maintain your own custom configured AMI, with a custom configured Docker runtime. You also had to use custom vCPU logic as a stand-in for assigning your GPU workloads to GPU instances. Even when all that was in place, there was still no pinning of a GPU to a task. One task might consume more GPU resources than it should. This could cause other tasks to not have a GPU available.

Now, AWS maintains an ECS-optimized AMI that includes the correct NVIDIA drivers and Docker customizations. You can use this AMI to provision your GPU workloads. With this enhancement, GPUs can also be requested directly in the task definition. Like allocating CPU or RAM to a task, now you can explicitly request a number of GPUs to be allocated to your task. The scheduler looks for matching resources on the cluster to place those tasks. The GPUs are pinned to the task for as long as the task is running, and can’t be allocated to any other tasks.

I thought I’d see how easy it is to deploy GPU workloads to my ECS cluster. I’m working in the US-EAST-2 (Ohio) region, from my AWS Cloud9 IDE, so these commands work for Amazon Linux. Feel free to adapt to your environment as necessary.

If you’d like to run this example yourself, you can find all the code in this GitHub repo. If you run this example in your own account, be aware of the instance pricing, and clean up your resources when your experiment is complete.

Clone the repo using the following command:

git clone https://github.com/brentley/tensorflow-container.git

Setup

You need the latest version of the AWS CLI (for this post, I used 1.16.98):

echo “export PATH=$HOME/.local/bin:$HOME/bin:$PATH” >> ~/.bash_profile
source ~/.bash_profile
pip install --user -U awscli

Provision an ECS cluster, with two C5 instances, and two P3 instances:

aws cloudformation deploy --stack-name tensorflow-test --template-file cluster-cpu-gpu.yml --capabilities CAPABILITY_IAM                            

While AWS CloudFormation is provisioning resources, examine the template used to build your infrastructure. Open `cluster-cpu-gpu.yml`, and you see that you are provisioning a test VPC with two c5.2xlarge instances, and two p3.2xlarge instances. This gives you one NVIDIA Tesla V100 GPU per instance, for a total of two GPUs to run training tasks.

I adapted the TensorFlow benchmark Docker container to create a training workload. I use this container to compare the GPU scheduling and runtime.

When the CloudFormation stack is deployed, register a task definition with the ECS service:

aws ecs register-task-definition --cli-input-json file://gpu-1-taskdef.json

To request GPU resources in the task definition, the only change needed is to include a GPU resource requirement in the container definition:

            "resourceRequirements": [
                {
                    "type": "GPU",
                    "value": "1"
                }
            ],

Including this resource requirement ensures that the ECS scheduler allocates the task to an instance with a free GPU resource.

Launch a single-GPU training workload

Now you’re ready to launch the first GPU workload.

export cluster=$(aws cloudformation describe-stacks --stack-name tensorflow-test --query 
'Stacks[0].Outputs[?OutputKey==`ClusterName`].OutputValue' --output text) 
echo $cluster
aws ecs run-task --cluster $cluster --task-definition tensorflow-1-gpu

When you launch the task, the output shows the `guIds` values that are assigned to the task. This GPU is pinned to this task, and can’t be shared with any other tasks. If all GPUs are allocated, you can’t schedule additional GPU tasks until a running task with a GPU completes. That frees the GPU to be scheduled again.

When you look at the log output in Amazon CloudWatch Logs, you see that the container discovered one GPU: `/gpu0` and the training benchmark trained at a rate of 321.16 images/sec.

With your two p3.2xlarge nodes in the cluster, you are limited to two concurrent single GPU based workloads. To scale horizontally, you could add additional p3.2xlarge nodes. This would limit your workloads to a single GPU each.  To scale vertically, you could bump up the instance type,  which would allow you to assign multiple GPUs to a single task.  Now, let’s see how fast your TensorFlow container can train when assigned multiple GPUs.

Launch a multiple-GPU training workload

To begin, replace the p3.2xlarge instances with p3.16xlarge instances. This gives your cluster two instances that each have eight GPUs, for a total of 16 GPUs that can be allocated.

aws cloudformation deploy --stack-name tensorflow-test --template-file cluster-cpu-gpu.yml --parameter-overrides GPUInstanceType=p3.16xlarge --capabilities CAPABILITY_IAM

When the CloudFormation deploy is complete, register two more task definitions to launch your benchmark container requesting more GPUs:

aws ecs register-task-definition --cli-input-json file://gpu-4-taskdef.json  
aws ecs register-task-definition --cli-input-json file://gpu-8-taskdef.json 

Next, launch two TensorFlow benchmark containers, one requesting four GPUs, and one requesting eight GPUs:

aws ecs run-task --cluster $cluster --task-definition tensorflow-4-gpu
aws ecs run-task --cluster $cluster --task-definition tensorflow-8-gpu

With each task request, GPUs are allocated: four in the first request, and eight in the second request. Again, these GPUs are pinned to the task, and not usable by any other task until these tasks are complete.

Check the log output in CloudWatch Logs:

On the “devices” lines, you can see that the container discovered and used four (or eight) GPUs. Also, the total images/sec improved to 1297.41 with four GPUs, and 1707.23 with eight GPUs.

Because you can pin single or multiple GPUs to a task, running advanced GPU based training tasks on Amazon ECS is easier than ever!

Cleanup

To clean up your running resources, delete the CloudFormation stack:

aws cloudformation delete-stack --stack-name tensorflow-test

Conclusion

For more information, see Working with GPUs on Amazon ECS.

If you want to keep up on the latest container info from AWS, please follow me on Twitter and tweet any questions! @brentContained

Some quick thoughts on the public discussion regarding facial recognition and Amazon Rekognition this past week

Post Syndicated from Dr. Matt Wood original https://aws.amazon.com/blogs/aws/some-quick-thoughts-on-the-public-discussion-regarding-facial-recognition-and-amazon-rekognition-this-past-week/

We have seen a lot of discussion this past week about the role of Amazon Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

Amazon Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of Amazon Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). Amazon Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

There have always been and will always be risks with new technology capabilities. Each organization choosing to employ technology must act responsibly or risk legal penalties and public condemnation. AWS takes its responsibilities seriously. But we believe it is the wrong approach to impose a ban on promising new technologies because they might be used by bad actors for nefarious purposes in the future. The world would be a very different place if we had restricted people from buying computers because it was possible to use that computer to do harm. The same can be said of thousands of technologies upon which we all rely each day. Through responsible use, the benefits have far outweighed the risks.

Customers are off to a great start with Amazon Rekognition; the evidence of the positive impact this new technology can provide is strong (and growing by the week), and we’re excited to continue to support our customers in its responsible use.

-Dr. Matt Wood, general manager of artificial intelligence at AWS

Amazon SageMaker Updates – Tokyo Region, CloudFormation, Chainer, and GreenGrass ML

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/sagemaker-tokyo-summit-2018/

Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.

Amazon SageMaker Chainer Estimator


Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.

Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.


import argparse
import os

if __name__ =='__main__':

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument('--epochs', type=int, default=10)
    parser.add_argument('--batch-size', type=int, default=64)
    parser.add_argument('--learning-rate', type=float, default=0.05)

    # Data, model, and output directories
    parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
    parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
    parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])

    args, _ = parser.parse_known_args()

    # ... load from args.train and args.test, train a model, write model to args.model_dir.

Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.


from sagemaker.chainer.estimator import Chainer
# Create my estimator
chainer_estimator = Chainer(
    entry_point='example.py',
    train_instance_count=1,
    train_instance_type='ml.p3.2xlarge',
    hyperparameters={'epochs': 10, 'batch-size': 64}
)
# Train my estimator
chainer_estimator.fit({'train': train_input, 'test': test_input})

# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = chainer_estimator.deploy(
    instance_type="ml.m4.xlarge",
    initial_instance_count=1
)

Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.

There’s a lot more documentation and information available in both the README and the example notebooks.

AWS GreenGrass ML with Chainer

AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML.

JAWS UG

I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.

Randall

Own your own working Pokémon Pokédex!

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/deep-learning-pokedex/

Squeal with delight as your inner Pokémon trainer witnesses the wonder of Adrian Rosebrock’s deep learning Pokédex.

Creating a real-life Pokedex with a Raspberry Pi, Python, and Deep Learning

This video demos a real-like Pokedex, complete with visual recognition, that I created using a Raspberry Pi, Python, and Deep Learning. You can find the entire blog post, including code, using this link: https://www.pyimagesearch.com/2018/04/30/a-fun-hands-on-deep-learning-project-for-beginners-students-and-hobbyists/ Music credit to YouTube user “No Copyright” for providing royalty free music: https://www.youtube.com/watch?v=PXpjqURczn8

The history of Pokémon in 30 seconds

The Pokémon franchise was created by video game designer Satoshi Tajiri in 1995. In the fictional world of Pokémon, Pokémon Trainers explore the vast landscape, catching and training small creatures called Pokémon. To date, there are 802 different types of Pokémon. They range from the ever recognisable Pikachu, a bright yellow electric Pokémon, to the highly sought-after Shiny Charizard, a metallic, playing-card-shaped Pokémon that your mate Alex claims she has in mint condition, but refuses to show you.

Pokemon GIF

In the world of Pokémon, children as young as ten-year-old protagonist and all-round annoyance Ash Ketchum are allowed to leave home and wander the wilderness. There, they hunt vicious, deadly creatures in the hope of becoming a Pokémon Master.

Adrian’s deep learning Pokédex

Adrian is a bit of a deep learning pro, as demonstrated by his Santa/Not Santa detector, which we wrote about last year. For that project, he also provided a great explanation of what deep learning actually is. In a nutshell:

…a subfield of machine learning, which is, in turn, a subfield of artificial intelligence (AI).While AI embodies a large, diverse set of techniques and algorithms related to automatic reasoning (inference, planning, heuristics, etc), the machine learning subfields are specifically interested in pattern recognition and learning from data.

As with his earlier Raspberry Pi project, Adrian uses the Keras deep learning model and the TensorFlow backend, plus a few other packages such as Adrian’s own imutils functions and OpenCV.

Adrian trained a Convolutional Neural Network using Keras on a dataset of 1191 Pokémon images, obtaining 96.84% accuracy. As Adrian explains, this model is able to identify Pokémon via still image and video. It’s perfect for creating a Pokédex – an interactive Pokémon catalogue that should, according to the franchise, be able to identify and read out information on any known Pokémon when captured by camera. More information on model training can be found on Adrian’s blog.

Adrian Rosebeck deep learning pokemon pokedex

For the physical build, a Raspberry Pi 3 with camera module is paired with the Raspberry Pi 7″ touch display to create a portable Pokédex. And while Adrian comments that the same result can be achieved using your home computer and a webcam, that’s not how Adrian rolls as a Raspberry Pi fan.

Adrian Rosebeck deep learning pokemon pokedex

Plus, the smaller size of the Pi is perfect for one of you to incorporate this deep learning model into a 3D-printed Pokédex for ultimate Pokémon glory, pretty please, thank you.

Adrian Rosebeck deep learning pokemon pokedex

Adrian has gone into impressive detail about how the project works and how you can create your own on his blog, pyimagesearch. So if you’re interested in learning more about deep learning, and making your own Pokédex, be sure to visit.

The post Own your own working Pokémon Pokédex! appeared first on Raspberry Pi.

Amazon SageMaker Now Supports Additional Instance Types, Local Mode, Open Sourced Containers, MXNet and Tensorflow Updates

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/amazon-sagemaker-roundup-sf/

Amazon SageMaker continues to iterate quickly and release new features on behalf of customers. Starting today, SageMaker adds support for many new instance types, local testing with the SDK, and Apache MXNet 1.1.0 and Tensorflow 1.6.0. Let’s take a quick look at each of these updates.

New Instance Types

Amazon SageMaker customers now have additional options for right-sizing their workloads for notebooks, training, and hosting. Notebook instances now support almost all T2, M4, P2, and P3 instance types with the exception of t2.micro, t2.small, and m4.large instances. Model training now supports nearly all M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large, c4.large, and c5.large instances. Finally, model hosting now supports nearly all T2, M4, M5, C4, C5, P2, and P3 instances with the exception of m4.large instances. Many customers can take advantage of the newest P3, C5, and M5 instances to get the best price/performance for their workloads. Customers also take advantage of the burstable compute model on T2 instances for endpoints or notebooks that are used less frequently.

Open Sourced Containers, Local Mode, and TensorFlow 1.6.0 and MXNet 1.1.0

Today Amazon SageMaker has open sourced the MXNet and Tensorflow deep learning containers that power the MXNet and Tensorflow estimators in the SageMaker SDK. The ability to write Python scripts that conform to simple interface is still one of my favorite SageMaker features and now those containers can be additionally customized to include any additional libraries. You can download these containers locally to iterate and experiment which can accelerate your debugging cycle. When you’re ready go from local testing to production training and hosting you just change one line of code.

These containers launch with support for Tensorflow 1.6.0 and MXNet 1.1.0 as well. Tensorflow has a number of new 1.6.0 features including support for CUDA 9.0, cuDNN 7, and AVX instructions which allows for significant speedups in many training applications. MXNet 1.1.0 adds a number of new features including a Text API mxnet.text with support for text processing, indexing, glossaries, and more. Two of the really cool pre-trained embeddings included are GloVe and fastText.
<

Available Now
All of the features mentioned above are available today. As always please let us know on Twitter or in the comments below if you have any questions or if you’re building something interesting. Now, if you’ll excuse me I’m going to go experiment with some of those new MXNet APIs!

Randall

Auto Scaling is now available for Amazon SageMaker

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/auto-scaling-is-now-available-for-amazon-sagemaker/

Kumar Venkateswar, Product Manager on the AWS ML Platforms Team, shares details on the announcement of Auto Scaling with Amazon SageMaker.


With Amazon SageMaker, thousands of customers have been able to easily build, train and deploy their machine learning (ML) models. Today, we’re making it even easier to manage production ML models, with Auto Scaling for Amazon SageMaker. Instead of having to manually manage the number of instances to match the scale that you need for your inferences, you can now have SageMaker automatically scale the number of instances based on an AWS Auto Scaling Policy.

SageMaker has made managing the ML process easier for many customers. We’ve seen customers take advantage of managed Jupyter notebooks and managed distributed training. We’ve seen customers deploying their models to SageMaker hosting for inferences, as they integrate machine learning with their applications. SageMaker makes this easy –  you don’t have to think about patching the operating system (OS) or frameworks on your inference hosts, and you don’t have to configure inference hosts across Availability Zones. You just deploy your models to SageMaker, and it handles the rest.

Until now, you have needed to specify the number and type of instances per endpoint (or production variant) to provide the scale that you need for your inferences. If your inference volume changes, you can change the number and/or type of instances that back each endpoint to accommodate that change, without incurring any downtime. In addition to making it easy to change provisioning, customers have asked us how we can make managing capacity for SageMaker even easier.

With Auto Scaling for Amazon SageMaker, in the SageMaker console, the AWS Auto Scaling API, and the AWS SDK, this becomes much easier. Now, instead of having to closely monitor inference volume, and change the endpoint configuration in response, customers can configure a scaling policy to be used by AWS Auto Scaling. Auto Scaling adjusts the number of instances up or down in response to actual workloads, determined by using Amazon CloudWatch metrics and target values defined in the policy. In this way, customers can automatically adjust their inference capacity to maintain predictable performance at a low cost. You simply specify the target inference throughput per instance and provide upper and lower bounds for the number of instances for each production variant. SageMaker will then monitor throughput per instance using Amazon CloudWatch alarms, and then it will adjust provisioned capacity up or down as needed.

After you configure the endpoint with Auto Scaling, SageMaker will continue to monitor your deployed models to automatically adjust the instance count. SageMaker will keep throughput within desired levels, in response to changes in application traffic. This makes it easier to manage models in production, and it can help reduce the cost of deployed models, as you no longer have to provision sufficient capacity in order to manage your peak load. Instead, you configure the limits to accommodate your minimum expected traffic and the maximum peak, and Amazon SageMaker will work within those limits to minimize cost.

How do you get started? Open the SageMaker console. For existing endpoints, you first access the endpoint to modify the settings.


Then, scroll to the Endpoint runtime settings section, select the variant, and choose Configure auto scaling.


First, configure the minimum and maximum number of instances.

Next, choose the throughput per instance at which you want to add an additional instance, given previous load testing.

You can optionally set cool down periods for scaling in or out, to avoid oscillation during periods of wide fluctuation in workload. If not, SageMaker will assume default values.

And that’s it! You now have an endpoint that will automatically scale with increasing inferences.

You pay for the capacity used at regular SageMaker pay-as-you-go pricing, so you no longer have to pay for unused capacity during relative idle periods!

Auto Scaling in Amazon SageMaker is available today in the US East (N. Virginia & Ohio), EU (Ireland), and U.S. West (Oregon) AWS regions. To learn more, see the Amazon SageMaker Auto Scaling documentation.


Kumar Venkateswar is a Product Manager in the AWS ML Platforms team, which includes Amazon SageMaker, Amazon Machine Learning, and the AWS Deep Learning AMIs. When not working, Kumar plays the violin and Magic: The Gathering.

 

 

 

 


 

 

AWS Online Tech Talks – January 2018

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-january-2018/

Happy New Year! Kick of 2018 right by expanding your AWS knowledge with a great batch of new Tech Talks. We’re covering some of the biggest launches from re:Invent including Amazon Neptune, Amazon Rekognition Video, AWS Fargate, AWS Cloud9, Amazon Kinesis Video Streams, AWS PrivateLink, AWS Single-Sign On and more!

January 2018– Schedule

Noted below are the upcoming scheduled live, online technical sessions being held during the month of January. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.

Webinars featured this month are:

Monday January 22

Analytics & Big Data
11:00 AM – 11:45 AM PT Analyze your Data Lake, Fast @ Any Scale  Lvl 300

Database
01:00 PM – 01:45 PM PT Deep Dive on Amazon Neptune Lvl 200

Tuesday, January 23

Artificial Intelligence
9:00 AM – 09:45 AM PT  How to get the most out of Amazon Rekognition Video, a deep learning based video analysis service Lvl 300

Containers

11:00 AM – 11:45 AM Introducing AWS Fargate Lvl 200

Serverless
01:00 PM – 02:00 PM PT Overview of Serverless Application Deployment Patterns Lvl 400

Wednesday, January 24

DevOps
09:00 AM – 09:45 AM PT Introducing AWS Cloud9  Lvl 200

Analytics & Big Data
11:00 AM – 11:45 AM PT Deep Dive: Amazon Kinesis Video Streams
Lvl 300
Database
01:00 PM – 01:45 PM PT Introducing Amazon Aurora with PostgreSQL Compatibility Lvl 200

Thursday, January 25

Artificial Intelligence
09:00 AM – 09:45 AM PT Introducing Amazon SageMaker Lvl 200

Mobile
11:00 AM – 11:45 AM PT Ionic and React Hybrid Web/Native Mobile Applications with Mobile Hub Lvl 200

IoT
01:00 PM – 01:45 PM PT Connected Product Development: Secure Cloud & Local Connectivity for Microcontroller-based Devices Lvl 200

Monday, January 29

Enterprise
11:00 AM – 11:45 AM PT Enterprise Solutions Best Practices 100 Achieving Business Value with AWS Lvl 100

Compute
01:00 PM – 01:45 PM PT Introduction to Amazon Lightsail Lvl 200

Tuesday, January 30

Security, Identity & Compliance
09:00 AM – 09:45 AM PT Introducing Managed Rules for AWS WAF Lvl 200

Storage
11:00 AM – 11:45 AM PT  Improving Backup & DR – AWS Storage Gateway Lvl 300

Compute
01:00 PM – 01:45 PM PT  Introducing the New Simplified Access Model for EC2 Spot Instances Lvl 200

Wednesday, January 31

Networking
09:00 AM – 09:45 AM PT  Deep Dive on AWS PrivateLink Lvl 300

Enterprise
11:00 AM – 11:45 AM PT Preparing Your Team for a Cloud Transformation Lvl 200

Compute
01:00 PM – 01:45 PM PT  The Nitro Project: Next-Generation EC2 Infrastructure Lvl 300

Thursday, February 1

Security, Identity & Compliance
09:00 AM – 09:45 AM PT  Deep Dive on AWS Single Sign-On Lvl 300

Storage
11:00 AM – 11:45 AM PT How to Build a Data Lake in Amazon S3 & Amazon Glacier Lvl 300

The deep learning Santa/Not Santa detector

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/deep-learning-santa-detector/

Did you see Mommy kissing Santa Claus? Or was it simply an imposter? The Not Santa detector is here to help solve the mystery once and for all.

Building a “Not Santa” detector on the Raspberry Pi using deep learning, Keras, and Python

The video is a demo of my “Not Santa” detector that I deployed to the Raspberry Pi. I trained the detector using deep learning, Keras, and Python. You can find the full source code and tutorial here: https://www.pyimagesearch.com/2017/12/18/keras-deep-learning-raspberry-pi/

Ho-ho-how does it work?

Note: Adrian Rosebrock is not Santa. But he does a good enough impression of the jolly old fellow that his disguise can fool a Raspberry Pi into thinking otherwise.

Raspberry Pi 'Not Santa' detector

We jest, but has anyone seen Adrian and Santa in the same room together?
Image c/o Adrian Rosebrock

But how is the Raspberry Pi able to detect the Santa-ness or Not-Santa-ness of people who walk into the frame?

Two words: deep learning

If you’re not sure what deep learning is, you’re not alone. It’s a hefty topic, and one that Adrian has written a book about, so I grilled him for a bluffers’ guide. In his words, deep learning is:

…a subfield of machine learning, which is, in turn a subfield of artificial intelligence (AI). While AI embodies a large, diverse set of techniques and algorithms related to automatic reasoning (inference, planning, heuristics, etc), the machine learning subfields are specifically interested in pattern recognition and learning from data.

Artificial Neural Networks (ANNs) are a class of machine learning algorithms that can learn from data. We have been using ANNs successfully for over 60 years, but something special happened in the past 5 years — (1) we’ve been able to accumulate massive datasets, orders of magnitude larger than previous datasets, and (2) we have access to specialized hardware to train networks faster (i.e., GPUs).

Given these large datasets and specialized hardware, deeper neural networks can be trained, leading to the term “deep learning”.

So now we have a bird’s-eye view of deep learning, how does the detector detect?

Cameras and twinkly lights

Adrian used a model he had trained on two datasets to detect whether or not an image contains Santa. He deployed the Not Santa detector code to a Raspberry Pi, then attached a camera, speakers, and The Pi Hut’s 3D Xmas Tree.

Raspberry Pi 'Not Santa' detector

Components for Santa detection
Image c/o Adrian Rosebrock

The camera captures footage of Santa in the wild, while the Christmas tree add-on provides a twinkly notification, accompanied by a resonant ho, ho, ho from the speakers.

A deeper deep dive into deep learning

A full breakdown of the project and the workings of the Not Santa detector can be found on Adrian’s blog, PyImageSearch, which includes links to other deep learning and image classification tutorials using TensorFlow and Keras. It’s an excellent place to start if you’d like to understand more about deep learning.

Build your own Santa detector

Santa might catch on to Adrian’s clever detector and start avoiding the camera, and for that eventuality, we have our own Santa detector. It uses motion detection to notify you of his presence (and your presents!).

Raspberry Pi Santa detector

Check out our Santa Detector resource here and use a passive infrared sensor, Raspberry Pi, and Scratch to catch the big man in action.

The post The deep learning Santa/Not Santa detector appeared first on Raspberry Pi.

AWS Contributes to Milestone 1.0 Release and Adds Model Serving Capability for Apache MXNet

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-contributes-to-milestone-1-0-release-and-adds-model-serving-capability-for-apache-mxnet/

Post by Dr. Matt Wood

Today AWS announced contributions to the milestone 1.0 release of the Apache MXNet deep learning engine including the introduction of a new model-serving capability for MXNet. The new capabilities in MXNet provide the following benefits to users:

1) MXNet is easier to use: The model server for MXNet is a new capability introduced by AWS, and it packages, runs, and serves deep learning models in seconds with just a few lines of code, making them accessible over the internet via an API endpoint and thus easy to integrate into applications. The 1.0 release also includes an advanced indexing capability that enables users to perform matrix operations in a more intuitive manner.

  • Model Serving enables set up of an API endpoint for prediction: It saves developers time and effort by condensing the task of setting up an API endpoint for running and integrating prediction functionality into an application to just a few lines of code. It bridges the barrier between Python-based deep learning frameworks and production systems through a Docker container-based deployment model.
  • Advanced indexing for array operations in MXNet: It is now more intuitive for developers to leverage the powerful array operations in MXNet. They can use the advanced indexing capability by leveraging existing knowledge of NumPy/SciPy arrays. For example, it supports MXNet NDArray and Numpy ndarray as index, e.g. (a[mx.nd.array([1,2], dtype = ‘int32’]).

2) MXNet is faster: The 1.0 release includes implementation of cutting-edge features that optimize the performance of training and inference. Gradient compression enables users to train models up to five times faster by reducing communication bandwidth between compute nodes without loss in convergence rate or accuracy. For speech recognition acoustic modeling like the Alexa voice, this feature can reduce network bandwidth by up to three orders of magnitude during training. With the support of NVIDIA Collective Communication Library (NCCL), users can train a model 20% faster on multi-GPU systems.

  • Optimize network bandwidth with gradient compression: In distributed training, each machine must communicate frequently with others to update the weight-vectors and thereby collectively build a single model, leading to high network traffic. Gradient compression algorithm enables users to train models up to five times faster by compressing the model changes communicated by each instance.
  • Optimize the training performance by taking advantage of NCCL: NCCL implements multi-GPU and multi-node collective communication primitives that are performance optimized for NVIDIA GPUs. NCCL provides communication routines that are optimized to achieve high bandwidth over interconnection between multi-GPUs. MXNet supports NCCL to train models about 20% faster on multi-GPU systems.

3) MXNet provides easy interoperability: MXNet now includes a tool for converting neural network code written with the Caffe framework to MXNet code, making it easier for users to take advantage of MXNet’s scalability and performance.

  • Migrate Caffe models to MXNet: It is now possible to easily migrate Caffe code to MXNet, using the new source code translation tool for converting Caffe code to MXNet code.

MXNet has helped developers and researchers make progress with everything from language translation to autonomous vehicles and behavioral biometric security. We are excited to see the broad base of users that are building production artificial intelligence applications powered by neural network models developed and trained with MXNet. For example, the autonomous driving company TuSimple recently piloted a self-driving truck on a 200-mile journey from Yuma, Arizona to San Diego, California using MXNet. This release also includes a full-featured and performance optimized version of the Gluon programming interface. The ease-of-use associated with it combined with the extensive set of tutorials has led significant adoption among developers new to deep learning. The flexibility of the interface has driven interest within the research community, especially in the natural language processing domain.

Getting started with MXNet
Getting started with MXNet is simple. To learn more about the Gluon interface and deep learning, you can reference this comprehensive set of tutorials, which covers everything from an introduction to deep learning to how to implement cutting-edge neural network models. If you’re a contributor to a machine learning framework, check out the interface specs on GitHub.

To get started with the Model Server for Apache MXNet, install the library with the following command:

$ pip install mxnet-model-server

The Model Server library has a Model Zoo with 10 pre-trained deep learning models, including the SqueezeNet 1.1 object classification model. You can start serving the SqueezeNet model with just the following command:

$ mxnet-model-server \
  --models squeezenet=https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model \
  --service dms/model_service/mxnet_vision_service.py

Learn more about the Model Server and view the source code, reference examples, and tutorials here: https://github.com/awslabs/mxnet-model-server/

-Dr. Matt Wood

New – Amazon EC2 Instances with Up to 8 NVIDIA Tesla V100 GPUs (P3)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-amazon-ec2-instances-with-up-to-8-nvidia-tesla-v100-gpus-p3/

Driven by customer demand and made possible by on-going advances in the state-of-the-art, we’ve come a long way since the original m1.small instance that we launched in 2006, with instances that are emphasize compute power, burstable performance, memory size, local storage, and accelerated computing.

The New P3
Today we are making the next generation of GPU-powered EC2 instances available in four AWS regions. Powered by up to eight NVIDIA Tesla V100 GPUs, the P3 instances are designed to handle compute-intensive machine learning, deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics workloads.

P3 instances use customized Intel Xeon E5-2686v4 processors running at up to 2.7 GHz. They are available in three sizes (all VPC-only and EBS-only):

ModelNVIDIA Tesla V100 GPUsGPU MemoryNVIDIA NVLinkvCPUsMain MemoryNetwork BandwidthEBS Bandwidth
p3.2xlarge116 GiBn/a861 GiBUp to 10 Gbps1.5 Gbps
p3.8xlarge464 GiB200 GBps32244 GiB10 Gbps7 Gbps
p3.16xlarge8128 GiB300 GBps64488 GiB25 Gbps14 Gbps

Each of the NVIDIA GPUs is packed with 5,120 CUDA cores and another 640 Tensor cores and can deliver up to 125 TFLOPS of mixed-precision floating point, 15.7 TFLOPS of single-precision floating point, and 7.8 TFLOPS of double-precision floating point. On the two larger sizes, the GPUs are connected together via NVIDIA NVLink 2.0 running at a total data rate of up to 300 GBps. This allows the GPUs to exchange intermediate results and other data at high speed, without having to move it through the CPU or the PCI-Express fabric.

What’s a Tensor Core?
I had not heard the term Tensor core before starting to write this post. According to this very helpful post on the NVIDIA Blog, Tensor cores are designed to speed up the training and inference of large, deep neural networks. Each core is able to quickly and efficiently multiply a pair of 4×4 half-precision (also known as FP16) matrices together, add the resulting 4×4 matrix to another half or single-precision (FP32) matrix, and store the resulting 4×4 matrix in either half or single-precision form. Here’s a diagram from NVIDIA’s blog post:

This operation is in the innermost loop of the training process for a deep neural network, and is an excellent example of how today’s NVIDIA GPU hardware is purpose-built to address a very specific market need. By the way, the mixed-precision qualifier on the Tensor core performance means that it is flexible enough to work with with a combination of 16-bit and 32-bit floating point values.

Performance in Perspective
I always like to put raw performance numbers into a real-world perspective so that they are easier to relate to and (hopefully) more meaningful. This turned out to be surprisingly difficult, given that the eight NVIDIA Tesla V100 GPUs on a single p3.16xlarge can do 125 trillion single-precision floating point multiplications per second.

Let’s go back to the dawn of the microprocessor era, and consider the Intel 8080A chip that powered the MITS Altair that I bought in the summer of 1977. With a 2 MHz clock, it was able to do about 832 multiplications per second (I used this data and corrected it for the faster clock speed). The p3.16xlarge is roughly 150 billion times faster. However, just 1.2 billion seconds have gone by since that summer. In other words, I can do 100x more calculations today in one second than my Altair could have done in the last 40 years!

What about the innovative 8087 math coprocessor that was an optional accessory for the IBM PC that was announced in the summer of 1981? With a 5 MHz clock and purpose-built hardware, it was able to do about 52,632 multiplications per second. 1.14 billion seconds have elapsed since then, p3.16xlarge is 2.37 billion times faster, so the poor little PC would be barely halfway through a calculation that would run for 1 second today.

Ok, how about a Cray-1? First delivered in 1976, this supercomputer was able to perform vector operations at 160 MFLOPS, making the p3.x16xlarge 781,000 times faster. It could have iterated on some interesting problem 1500 times over the years since it was introduced.

Comparisons between the P3 and today’s scale-out supercomputers are harder to make, given that you can think of the P3 as a step-and-repeat component of a supercomputer that you can launch on as as-needed basis.

Run One Today
In order to take full advantage of the NVIDIA Tesla V100 GPUs and the Tensor cores, you will need to use CUDA 9 and cuDNN7. These drivers and libraries have already been added to the newest versions of the Windows AMIs and will be included in an updated Amazon Linux AMI that is scheduled for release on November 7th. New packages are already available in our repos if you want to to install them on your existing Amazon Linux AMI.

The newest AWS Deep Learning AMIs come preinstalled with the latest releases of Apache MxNet, Caffe2, and Tensorflow (each with support for the NVIDIA Tesla V100 GPUs), and will be updated to support P3 instances with other machine learning frameworks such as Microsoft Cognitive Toolkit and PyTorch as soon as these frameworks release support for the NVIDIA Tesla V100 GPUs. You can also use the NVIDIA Volta Deep Learning AMI for NGC.

P3 instances are available in the US East (Northern Virginia), US West (Oregon), EU (Ireland), and Asia Pacific (Tokyo) Regions in On-Demand, Spot, Reserved Instance, and Dedicated Host form.

Jeff;

 

Bringing Datacenter-Scale Hardware-Software Co-design to the Cloud with FireSim and Amazon EC2 F1 Instances

Post Syndicated from Mia Champion original https://aws.amazon.com/blogs/compute/bringing-datacenter-scale-hardware-software-co-design-to-the-cloud-with-firesim-and-amazon-ec2-f1-instances/

The recent addition of Xilinx FPGAs to AWS Cloud compute offerings is one way that AWS is enabling global growth in the areas of advanced analytics, deep learning and AI. The customized F1 servers use pooled accelerators, enabling interconnectivity of up to 8 FPGAs, each one including 64 GiB DDR4 ECC protected memory, with a dedicated PCIe x16 connection. That makes this a powerful engine with the capacity to process advanced analytical applications at scale, at a significantly faster rate. For example, AWS commercial partner Edico Genome is able to achieve an approximately 30X speedup in analyzing whole genome sequencing datasets using their DRAGEN platform powered with F1 instances.

While the availability of FPGA F1 compute on-demand provides clear accessibility and cost advantages, many mainstream users are still finding that the “threshold to entry” in developing or running FPGA-accelerated simulations is too high. Researchers at the UC Berkeley RISE Lab have developed “FireSim”, powered by Amazon FPGA F1 instances as an open-source resource, FireSim lowers that entry bar and makes it easier for everyone to leverage the power of an FPGA-accelerated compute environment. Whether you are part of a small start-up development team or working at a large datacenter scale, hardware-software co-design enables faster time-to-deployment, lower costs, and more predictable performance. We are excited to feature FireSim in this post from Sagar Karandikar and his colleagues at UC-Berkeley.

―Mia Champion, Sr. Data Scientist, AWS

Mapping an 8-node FireSim cluster simulation to Amazon EC2 F1

As traditional hardware scaling nears its end, the data centers of tomorrow are trending towards heterogeneity, employing custom hardware accelerators and increasingly high-performance interconnects. Prototyping new hardware at scale has traditionally been either extremely expensive, or very slow. In this post, I introduce FireSim, a new hardware simulation platform under development in the computer architecture research group at UC Berkeley that enables fast, scalable hardware simulation using Amazon EC2 F1 instances.

FireSim benefits both hardware and software developers working on new rack-scale systems: software developers can use the simulated nodes with new hardware features as they would use a real machine, while hardware developers have full control over the hardware being simulated and can run real software stacks while hardware is still under development. In conjunction with this post, we’re releasing the first public demo of FireSim, which lets you deploy your own 8-node simulated cluster on an F1 Instance and run benchmarks against it. This demo simulates a pre-built “vanilla” cluster, but demonstrates FireSim’s high performance and usability.

Why FireSim + F1?

FPGA-accelerated hardware simulation is by no means a new concept. However, previous attempts to use FPGAs for simulation have been fraught with usability, scalability, and cost issues. FireSim takes advantage of EC2 F1 and open-source hardware to address the traditional problems with FPGA-accelerated simulation:
Problem #1: FPGA-based simulations have traditionally been expensive, difficult to deploy, and difficult to reproduce.
FireSim uses public-cloud infrastructure like F1, which means no upfront cost to purchase and deploy FPGAs. Developers and researchers can distribute pre-built AMIs and AFIs, as in this public demo (more details later in this post), to make experiments easy to reproduce. FireSim also automates most of the work involved in deploying an FPGA simulation, essentially enabling one-click conversion from new RTL to deploying on an FPGA cluster.

Problem #2: FPGA-based simulations have traditionally been difficult (and expensive) to scale.
Because FireSim uses F1, users can scale out experiments by spinning up additional EC2 instances, rather than spending hundreds of thousands of dollars on large FPGA clusters.

Problem #3: Finding open hardware to simulate has traditionally been difficult. Finding open hardware that can run real software stacks is even harder.
FireSim simulates RocketChip, an open, silicon-proven, RISC-V-based processor platform, and adds peripherals like a NIC and disk device to build up a realistic system. Processors that implement RISC-V automatically support real operating systems (such as Linux) and even support applications like Apache and Memcached. We provide a custom Buildroot-based FireSim Linux distribution that runs on our simulated nodes and includes many popular developer tools.

Problem #4: Writing hardware in traditional HDLs is time-consuming.
Both FireSim and RocketChip use the Chisel HDL, which brings modern programming paradigms to hardware description languages. Chisel greatly simplifies the process of building large, highly parameterized hardware components.

How to use FireSim for hardware/software co-design

FireSim drastically improves the process of co-designing hardware and software by acting as a push-button interface for collaboration between hardware developers and systems software developers. The following diagram describes the workflows that hardware and software developers use when working with FireSim.

Figure 2. The FireSim custom hardware development workflow.

The hardware developer’s view:

  1. Write custom RTL for your accelerator, peripheral, or processor modification in a productive language like Chisel.
  2. Run a software simulation of your hardware design in standard gate-level simulation tools for early-stage debugging.
  3. Run FireSim build scripts, which automatically build your simulation, run it through the Vivado toolchain/AWS shell scripts, and publish an AFI.
  4. Deploy your simulation on EC2 F1 using the generated simulation driver and AFI
  5. Run real software builds released by software developers to benchmark your hardware

The software developer’s view:

  1. Deploy the AMI/AFI generated by the hardware developer on an F1 instance to simulate a cluster of nodes (or scale out to many F1 nodes for larger simulated core-counts).
  2. Connect using SSH into the simulated nodes in the cluster and boot the Linux distribution included with FireSim. This distribution is easy to customize, and already supports many standard software packages.
  3. Directly prototype your software using the same exact interfaces that the software will see when deployed on the real future system you’re prototyping, with the same performance characteristics as observed from software, even at scale.

FireSim demo v1.0

Figure 3. Cluster topology simulated by FireSim demo v1.0.

This first public demo of FireSim focuses on the aforementioned “software-developer’s view” of the custom hardware development cycle. The demo simulates a cluster of 1 to 8 RocketChip-based nodes, interconnected by a functional network simulation. The simulated nodes work just like “real” machines:  they boot Linux, you can connect to them using SSH, and you can run real applications on top. The nodes can see each other (and the EC2 F1 instance on which they’re deployed) on the network and communicate with one another. While the demo currently simulates a pre-built “vanilla” cluster, the entire hardware configuration of these simulated nodes can be modified after FireSim is open-sourced.

In this post, I walk through bringing up a single-node FireSim simulation for experienced EC2 F1 users. For more detailed instructions for new users and instructions for running a larger 8-node simulation, see FireSim Demo v1.0 on Amazon EC2 F1. Both demos walk you through setting up an instance from a demo AMI/AFI and booting Linux on the simulated nodes. The full demo instructions also walk you through an example workload, running Memcached on the simulated nodes, with YCSB as a load generator to demonstrate network functionality.

Deploying the demo on F1

In this release, we provide pre-built binaries for driving simulation from the host and a pre-built AFI that contains the FPGA infrastructure necessary to simulate a RocketChip-based node.

Starting your F1 instances

First, launch an instance using the free FireSim Demo v1.0 product available on the AWS Marketplace on an f1.2xlarge instance. After your instance has booted, log in using the user name centos. On the first login, you should see the message “FireSim network config completed.” This sets up the necessary tap interfaces and bridge on the EC2 instance to enable communicating with the simulated nodes.

AMI contents

The AMI contains a variety of tools to help you run simulations and build software for RISC-V systems, including the riscv64 toolchain, a Buildroot-based Linux distribution that runs on the simulated nodes, and the simulation driver program. For more details, see the AMI Contents section on the FireSim website.

Single-node demo

First, you need to flash the FPGA with the FireSim AFI. To do so, run:

[[email protected]_ADDR ~]$ sudo fpga-load-local-image -S 0 -I agfi-00a74c2d615134b21

To start a simulation, run the following at the command line:

[[email protected]_ADDR ~]$ boot-firesim-singlenode

This automatically calls the simulation driver, telling it to load the Linux kernel image and root filesystem for the Linux distro. This produces output similar to the following:

Simulations Started. You can use the UART console of each simulated node by attaching to the following screens:

There is a screen on:

2492.fsim0      (Detached)

1 Socket in /var/run/screen/S-centos.

You could connect to the simulated UART console by connecting to this screen, but instead opt to use SSH to access the node instead.

First, ping the node to make sure it has come online. This is currently required because nodes may get stuck at Linux boot if the NIC does not receive any network traffic. For more information, see Troubleshooting/Errata. The node is always assigned the IP address 192.168.1.10:

[[email protected]_ADDR ~]$ ping 192.168.1.10

This should eventually produce the following output:

PING 192.168.1.10 (192.168.1.10) 56(84) bytes of data.

From 192.168.1.1 icmp_seq=1 Destination Host Unreachable

64 bytes from 192.168.1.10: icmp_seq=1 ttl=64 time=2017 ms

64 bytes from 192.168.1.10: icmp_seq=2 ttl=64 time=1018 ms

64 bytes from 192.168.1.10: icmp_seq=3 ttl=64 time=19.0 ms

At this point, you know that the simulated node is online. You can connect to it using SSH with the user name root and password firesim. It is also convenient to make sure that your TERM variable is set correctly. In this case, the simulation expects TERM=linux, so provide that:

[[email protected]_ADDR ~]$ TERM=linux ssh [email protected]

The authenticity of host ‘192.168.1.10 (192.168.1.10)’ can’t be established.

ECDSA key fingerprint is 63:e9:66:d0:5c:06:2c:1d:5c:95:33:c8:36:92:30:49.

Are you sure you want to continue connecting (yes/no)? yes

Warning: Permanently added ‘192.168.1.10’ (ECDSA) to the list of known hosts.

[email protected]’s password:

#

At this point, you’re connected to the simulated node. Run uname -a as an example. You should see the following output, indicating that you’re connected to a RISC-V system:

# uname -a

Linux buildroot 4.12.0-rc2 #1 Fri Aug 4 03:44:55 UTC 2017 riscv64 GNU/Linux

Now you can run programs on the simulated node, as you would with a real machine. For an example workload (running YCSB against Memcached on the simulated node) or to run a larger 8-node simulation, see the full FireSim Demo v1.0 on Amazon EC2 F1 demo instructions.

Finally, when you are finished, you can shut down the simulated node by running the following command from within the simulated node:

# poweroff

You can confirm that the simulation has ended by running screen -ls, which should now report that there are no detached screens.

Future plans

At Berkeley, we’re planning to keep improving the FireSim platform to enable our own research in future data center architectures, like FireBox. The FireSim platform will eventually support more sophisticated processors, custom accelerators (such as Hwacha), network models, and peripherals, in addition to scaling to larger numbers of FPGAs. In the future, we’ll open source the entire platform, including Midas, the tool used to transform RTL into FPGA simulators, allowing users to modify any part of the hardware/software stack. Follow @firesimproject on Twitter to stay tuned to future FireSim updates.

Acknowledgements

FireSim is the joint work of many students and faculty at Berkeley: Sagar Karandikar, Donggyu Kim, Howard Mao, David Biancolin, Jack Koenig, Jonathan Bachrach, and Krste Asanović. This work is partially funded by AWS through the RISE Lab, by the Intel Science and Technology Center for Agile HW Design, and by ASPIRE Lab sponsors and affiliates Intel, Google, HPE, Huawei, NVIDIA, and SK hynix.