Run a Deep Learning Reference Stack with PyTorch* on CentOS*

Published: 10/09/2020  

Last Updated: 07/23/2021

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Pull Command for Latest Version

docker pull sysstacks/dlrs-pytorch-centos

 

Tags and Pull Commands for Other Versions

OS Target Version Size Updated Pull
CentOS* x86_64 0.9.1 1.5GB 06/23/2021 docker pull sysstacks/dlrs-pytorch-centos:v0.9.1
CentOS* x86_64 0.9.0 1.06GB 04/23/2021 docker pull sysstacks/dlrs-pytorch-centos:v0.9.0
CentOS* x86_64 0.8.0 974.24 MB 12/16/2020 docker pull sysstacks/dlrs-pytorch-centos:v0.8.0
CentOS* x86_64 0.7.0 1.05 GB 10/08/2020 docker pull sysstacks/dlrs-pytorch-centos:v0.7.0

 

Description

 

The Deep Learning Reference Stack is an integrated, highly performant, open-source stack optimized for Intel® Xeon® Scalable processors. This open-source community release is part of our effort to ensure AI developers have easy access to all of the features and functionality of the Intel® platforms. The Deep Learning Reference Stack is highly tuned and built for cloud native environments. With this stack, we are enabling developers to quickly prototype by reducing the complexity associated with integrating multiple software components, while still giving users the flexibility to customize their solutions. This version includes additional components to provide greater flexibility and a more comprehensive take on the deep learning environment. Highly tuned and built for cloud native environments, the release enables developers to quickly prototype by reducing complexity associated with integrating multiple software components, while still giving users the flexibility to customize their solutions.

The stack includes highly tuned software components across the operating system, deep-learning frameworks (TensorFlow*, PyTorch*), deep learning libraries (oneAPI Deep Neural Network Library (oneDNN)) and other software components. This open source community release is part of an effort to ensure AI developers have easy access to all features and functionality of Intel platforms.To offer more flexibility, there are multiple versions of the Deep Learning Reference Stack.

 

Deep Learning Reference Stack architecture diagram

First Steps

These steps describe using the Deep Learning Reference Stack container to run the PyTorch* benchmarks  for Caffe2. This example provides a template to run other benchmark tests, provided that they can invoke PyTorch.  These steps assume familiarity with using commands from a terminal or command line interface.

  1. Download the PyTorch for CentOS Docker* image from DockerHub*:
    docker pull sysstacks/dlrs-pytorch-centos
    

     

  2. Run the image with Docker:
    docker run --name <image name>  --rm -i -t <sysstacks/dlrs-pytorch-centos> bash
    

    Your terminal prompt should reflect that you are executing commands within the container.  
  3. Clone the benchmark repository:
    git clone https://github.com/pytorch/pytorch.git
    

     

  4. Execute the benchmark script:
    cd pytorch/caffe2/python
    python convnet_benchmarks.py --batch_size 32 \
                          --cpu \
                          --model AlexNet
    

     


Documentation and Sources

Get Started
Docker Hub*
GitHub* Repository
README
Release Announcement
Get Started Guide
Report Issue

Legal Notice

By accessing, downloading or using this software and any required dependent software (the “Software Package”), you agree to the terms and conditions of the software license agreements for the Software Package, which may also include notices, disclaimers, or license terms for third party software included with the Software Package. Please refer to the licenses information for additional details.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.