AWS Deep Learning Containers v4.4 for Tensorflow

The AWS Deep Learning Containers for TensorFlow include containers for Training and Inference for CPU and GPU, optimized for performance and scale on AWS.

Release Date: March 20, 2020
Created On: March 20, 2020
Last Updated: July 29, 2020

The AWS Deep Learning Containers are available today as version with the latest framework versions of Tensorflow 1.15.2, with newly added SageMaker Python SDK and updates to smexperiments and smdebug. You can launch the new versions of the Deep Learning Containers on Amazon SageMaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, and Amazon Elastic Container Service (Amazon ECS). For a complete list of frameworks and versions supported by the AWS Deep Learning Containers, see release notes below.

The AWS Deep Learning Containers for TensorFlow include containers for Training and Inference for CPU and GPU, optimized for performance and scale on AWS. These Docker images have been tested with Amazon SageMaker, EC2, ECS, and EKS and provide stable versions of NVIDIA CUDA, cuDNN, Intel MKL, Horovod and other required software components to provide a seamless user experience for deep learning workloads. All software components in these images are scanned for security vulnerabilities and updated or patched in accordance with AWS Security best practices.

More details can be found in marketplace, and a list of available containers can be found in our documentation. Get started quickly with the AWS Deep Learning Containers using the getting-started guides and beginner to advanced level tutorials in our developer guide. You can also subscribe to our discussion forum to get launch announcements and post your questions.

Release Notes

Security Advisory

  1. AWS recommends that customers monitor critical security updates in the AWS Security Bulletin
  2. Issue: pycrypto security vulnerability - pycrypto cannot be upgraded beyond v2.6.1 in Python 2 containers

Highlights of the Release

  • Introduced SageMaker Python SDK for Tensorflow 1.15.2 Training for Py3
  • Updated smexperiments package for Tensorflow 1.15.2 Training for Py3
  • Updated smdebug package for Tensorflow 1.15.2 Training for Py3

Prepackaged Deep Learning Frameworks Included

  • TensorFlow: TensorFlow is an open source software library for numerical computation using data flow graphs.
    • branch/tag used : v1.15.2
    • Supported with CUDA 10.0 and Intel MKL-DNN v0.20-rc
  • Keras: Deep Learning Library for Python
    • Tensorflow integration with Keras v2.3.1
  • Horovod: Horovod is a distributed training framework. The goal of Horovod is to easily take single-GPU deep learning program and train it on multiple GPUs. Horovod nodes communicate directly with each other instead of going through a centralized node and average gradients using the ring-allreduce algorithm.
    • branch/tag used : v0.18.2
  • SageMaker Python SDK: SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well.
    • branch/tag used: v1.50.17

Bill of Materials: List of all components

  • CPU: Training container
    • sagemaker-tensorflow-training==2.2.6.dev0
    • sagemaker-tensorflow==1.15.0.1.1.0
    • sagemaker-containers==2.8.1
    • sagemaker==1.50.17 (for py3 only)
    • numpy==1.16.6 (for py2)
    • numpy==1.18.1 (for py3)
    • OpenMPI=4.0.1
    • Horovod=0.18.2
    • scipy==1.2.2
    • scikit-learn==0.20.3
    • pandas==0.24.2
    • Pillow==6.2.1
    • h5py==2.10.0
    • keras_applications==1.0.8
    • keras_preprocessing==1.1.0
    • keras==2.3.1
    • requests==2.22.0
    • awscli==1.18.21
    • smdebug==0.7.1(for py3 only)
    • smexperiments==0.1.7 (for py3 only)
  • CPU: Inference container
    • tensorflow-model-server=1.15.2
    • tensorflow-serving-api-gpu==1.15.0
    • awscli==1.16.296
  • GPU: Training Container
    • sagemaker-tensorflow-training==2.2.6.dev0
    • sagemaker-tensorflow==1.15.0.1.1.0
    • sagemaker-containers==2.8.1
    • sagemaker==1.50.17 (for py3 only)
    • cuda-command-line-tools-10-0
    • cuda-cublas-10-0
    • cuda-cufft-10-0
    • cuda-curand-10-0
    • cuda-cusolver-10-0
    • cuda-cusparse-10-0
    • libcudnn7=7.5.1.10-1+cuda10.0
    • libnccl2=2.4.7-1+cuda10.0
    • libnccl-dev=2.4.7-1+cuda10.0
    • OpenMPI=4.0.1
    • Horovod=0.18.2
    • numpy==1.16.6 (for py2)
    • numpy==1.18.1 (for py3)
    • scipy==1.2.2
    • scikit-learn==0.20.3
    • pandas==0.24.2
    • Pillow==6.2.1
    • h5py==2.10.0
    • keras_applications==1.0.8
    • keras_preprocessing==1.1.0
    • keras==2.3.1
    • requests==2.22.0
    • awscli==1.18.21
    • smdebug==0.7.1(for py3 only)
    • smexperiments==0.1.7 (for py3 only)
  • GPU: Inference Container
    • tensorflow-model-server=1.15.2
    • cuda-command-line-tools-10-0
    • cuda-cublas-10-0
    • cuda-cufft-10-0
    • cuda-curand-10-0
    • cuda-cusolver-10-0
    • cuda-cusparse-10-0
    • libcudnn7: 7.5.1.10-1+cuda10.0
    • libnccl2: 2.4.7-1+cuda10.0
    • libnvinfer5=5.0.2-1+cuda10.0
    • tensorflow-serving-api-gpu==1.15.0
    • awscli==1.16.296

Python Support

Python 2.7 and Python 3.6 are supported in the containers for all of the installed deep learning frameworks.
 

CPU Instance Type Support

The containers supports CPU instance types. TensorFlow is built with support for Intel MKL2019 DNN library support.
 

GPU Instance Type support

The containers support GPU instance types and contain the following software components for GPU support.
 

  • CUDA 10.0 / cuDNN 7.5.1.10-1+cuda10.0 / NCCL 2.4.7-1+cuda10.0

AWS Regions support

Available in the following regions:
 


Region
Code

US East (Ohio)
us-east-2

US East (N. Virginia)
us-east-1

US West (Oregon)
us-west-2

US West (SFO)
us-west-1

Asia Pacific (Mumbai)
ap-south-1

Asia Pacific (Seoul)
ap-northeast-2

Asia Pacific (Singapore)
ap-southeast-1

Asia Pacific (Sydney)
ap-southeast-2

Asia Pacific (Tokyo)
ap-northeast-1

Central (Canada)
ca-central-1

EU (Frankfurt)
eu-central-1

EU (Ireland)
eu-west-1

EU (London)
eu-west-2

EU(Paris)
eu-west-3

SA (Sau Paulo)
sa-east-1

EU (Stockholm)
eu-north-1
AP East (Hong Kong) ap-east-1
ME South (Bahrain) me-south-1

Build and Test

  • Built on: c5.18xlarge
  • Tested on: c4.8xlarge, c5.18xlarge, g3.16xlarge, m4.16xlarge, p2.16xlarge, p3.16xlarge, p3dn.24xlarge
  • Tested with MNIST and Resnet50/ImageNet datasets on EC2, ECS AMI (Amazon Linux AMI 2.0.20190614) and EKS AMI (1.11-v20190614) and Amazon Sagemaker.

Known Issue

  1. Issue: pycrypto security vulnerability - pycrypto cannot be upgraded beyond v2.6.1 in Python 2 containers