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Intel® Extension for TensorFlow*🔗

Intel® Extension for TensorFlow* extends TensorFlow* with up-to-date feature optimizations for an extra performance boost on Intel hardware.

Intel® Extension for TensorFlow* is based on the TensorFlow PluggableDevice interface to bring Intel XPU(GPU, CPU, etc.) devices into TensorFlow* with flexibility for on-demand performance on the following Intel GPUs:

Note: There are two dockerhub repositories (intel/intel-extension-for-tensorflow and intel/intel-optimized-tensorflow) that are routinely updated with the latest images, however, some legacy images have not be published to both repositories.

XPU images🔗

The images below include support for both CPU and GPU optimizations:

Tag(s) TensorFlow ITEX Driver Dockerfile
2.15.0.1-xpu-pip-base, xpu v2.15.1 v2.15.0.1 803.63 v0.4.0-Beta
2.15.0.0-xpu v2.15.0 v2.15.0.0 803 v0.4.0-Beta
2.14.0.1-xpu v2.14.1 v2.14.0.1 736 v0.3.4
2.13.0.0-xpu v2.13.0 v2.13.0.0 647 v0.2.3

Run the XPU Container🔗

docker run -it --rm \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --ipc=host \
    intel/intel-extension-for-tensorflow:xpu

The images below additionally include Jupyter Notebook server:

Tag(s) TensorFlow IPEX Driver Dockerfile
2.15.0.1-xpu-pip-jupyter v2.15.1 v2.15.0.1 803.63 v0.4.0-Beta
xpu-jupyter v2.14.1 v2.14.0.1 736 v0.3.4

Run the XPU Jupyter Container🔗

docker run -it --rm \
    -p 8888:8888 \
    --net=host \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    --ipc=host \
    intel/intel-extension-for-tensorflow:2.15.0.1-xpu-pip-jupyter

After running the command above, copy the URL (something like http://127.0.0.1:$PORT/?token=***) into your browser to access the notebook server.


The images below are TensorFlow* Serving with GPU Optimizations:

Tag(s) TensorFlow IPEX
2.14.0.1-serving-gpu, serving-gpu v2.14.1 v2.14.0.1
2.13.0.0-serving-gpu, v2.13.0 v2.13.0.0

Run the Serving GPU Container🔗

docker run -it --rm \
    -p 8500:8500 \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    -e MODEL_NAME=<your-model-name> \
    -e MODEL_DIR=<your-model-dir> \
    intel/intel-extension-for-tensorflow:serving-gpu

For more details, follow the procedure in the Intel® Extension for TensorFlow* Serving instructions.

CPU only images🔗

The images below are built only with CPU optimizations (GPU acceleration support was deliberately excluded):

Tag(s) TensorFlow ITEX Dockerfile
2.15.1-pip-base, latest v2.15.1 v2.15.0.1 v0.4.0-Beta
2.15.0-pip-base v2.15.0 v2.15.0.0 v0.4.0-Beta
2.14.0-pip-base v2.14.1 v2.14.0.1 v0.3.4
2.13-pip-base v2.13.0 v2.13.0.0 v0.2.3

The images below additionally include Jupyter Notebook server:

Tag(s) TensorFlow ITEX Dockerfile
2.15.1-pip-jupyter v2.15.1 v2.15.0.1 v0.4.0-Beta
2.15.0-pip-jupyter v2.15.0 v2.15.0.0 v0.4.0-Beta
2.14.0-pip-jupyter v2.14.1 v2.14.0.1 v0.3.4
2.13-pip-jupyter v2.13.0 v2.13.0.0 v0.2.3

Run the CPU Jupyter Container🔗

docker run -it --rm \
    -p 8888:8888 \
    --net=host \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    intel/intel-extension-for-tensorflow:2.15.1-pip-jupyter

After running the command above, copy the URL (something like http://127.0.0.1:$PORT/?token=***) into your browser to access the notebook server.


The images below additionally include Horovod:

Tag(s) Tensorflow ITEX Horovod Dockerfile
2.15.1-pip-multinode v2.15.1 v2.15.0.1 v0.28.1 v0.4.0-Beta
2.15.0-pip-multinode v2.15.0 v2.15.0.0 v0.28.1 v0.4.0-Beta
2.14.0-pip-openmpi-multinode v2.14.1 v2.14.0.1 v0.28.1 v0.3.4
2.13-pip-openmpi-mulitnode v2.13.0 v2.13.0.0 v0.28.0 v0.2.3

Note

Passwordless SSH connection is also enabled in the image, but the container does not contain any SSH ID keys. The user needs to mount those keys at /root/.ssh/id_rsa and /etc/ssh/authorized_keys.

Tip

Before mounting any keys, modify the permissions of those files with chmod 600 authorized_keys; chmod 600 id_rsa to grant read access for the default user account.

Setup and Run ITEX Multi-Node Container🔗

Important

Maintainence, Bug Fixes, and Releases of Intel® Extension for TensorFlow* Multi-Node Container for Xeon Processors have ceased development. The last supported version is 2.15.1. For future releases, please use the Intel® Extension for TensorFlow* Multi-Node Container for XPU.

Some additional assembly is required to utilize this container with OpenSSH. To perform any kind of DDP (Distributed Data Parallel) execution, containers are assigned the roles of launcher and worker respectively:

SSH Server (Worker)

  1. Authorized Keys : /etc/ssh/authorized_keys

SSH Client (Launcher)

  1. Private User Key : /root/.ssh/id_rsa

To add these files correctly please follow the steps described below.

  1. Setup ID Keys

    You can use the commands provided below to generate the identity keys for OpenSSH.

    ssh-keygen -q -N "" -t rsa -b 4096 -f ./id_rsa
    touch authorized_keys
    cat id_rsa.pub >> authorized_keys
    
  2. Configure the permissions and ownership for all of the files you have created so far

    chmod 600 id_rsa config authorized_keys
    chown root:root id_rsa.pub id_rsa config authorized_keys
    
  3. Create a hostfile for horovod. (Optional)

    Host host1
        HostName <Hostname of host1>
        IdentitiesOnly yes
        IdentityFile ~/.root/id_rsa
        Port <SSH Port>
    Host host2
        HostName <Hostname of host2>
        IdentitiesOnly yes
        IdentityFile ~/.root/id_rsa
        Port <SSH Port>
    ...
    
  4. Configure Horovod in your python script

    import horovod.torch as hvd
    
    hvd.init()
    
  5. Now start the workers and execute DDP on the launcher

    1. Worker run command:

      docker run -it --rm \
          --net=host \
          -v $PWD/authorized_keys:/etc/ssh/authorized_keys \
          -v $PWD/tests:/workspace/tests \
          -w /workspace \
          intel/intel-optimized-tensorflow:2.15.1-pip-multinode \
          bash -c '/usr/sbin/sshd -D'
      
    2. Launcher run command:

      docker run -it --rm \
          --net=host \
          -v $PWD/id_rsa:/root/.ssh/id_rsa \
          -v $PWD/tests:/workspace/tests \
          -v $PWD/hostfile:/root/ssh/config \
          -w /workspace \
          intel/intel-optimized-tensorflow:2.15.1-pip-multinode \
          bash -c 'horovodrun --verbose -np 2 -H host1:1,host2:1 /workspace/tests/tf_base_test.py'
      

Note

Intel® MPI can be configured based on your machine settings. If the above commands do not work for you, see the documentation for how to configure based on your network.


The images below are TensorFlow* Serving with CPU Optimizations:

Tag(s) TensorFlow ITEX
2.14.0.1-serving-cpu, serving-cpu v2.14.1 v2.14.0.1
2.13.0.0-serving-cpu v2.13.0 v2.13.0.0

Run the Serving CPU Container🔗

docker run -it --rm \
    -p 8500:8500 \
    --device /dev/dri \
    -v /dev/dri/by-path:/dev/dri/by-path \
    -v $PWD/workspace:/workspace \
    -w /workspace \
    -e MODEL_NAME=<your-model-name> \
    -e MODEL_DIR=<your-model-dir> \
    intel/intel-extension-for-tensorflow:serving-cpu

For more details, follow the procedure in the Intel® Extension for TensorFlow* Serving instructions.

CPU only images with Intel® Distribution for Python*🔗

The images below are built only with CPU optimizations (GPU acceleration support was deliberately excluded) and include Intel® Distribution for Python*:

Tag(s) TensorFlow ITEX Dockerfile
2.15.1-idp-base v2.15.1 v2.15.0.1 v0.4.0-Beta
2.15.0-idp-base v2.15.0 v2.15.0.0 v0.4.0-Beta
2.14.0-idp-base v2.14.1 v2.14.0.1 v0.3.4
2.13-idp-base v2.13.0 v2.13.0.0 v0.2.3

The images below additionally include Jupyter Notebook server:

Tag(s) TensorFlow ITEX Dockerfile
2.15.1-idp-jupyter v2.15.1 v2.15.0.1 v0.4.0-Beta
2.15.0-idp-jupyter v2.15.0 v2.15.0.0 v0.4.0-Beta
2.14.0-idp-jupyter v2.14.1 v2.14.0.1 v0.3.4
2.13-idp-jupyter v2.13.0 v2.13.0.0 v0.2.3

The images below additionally include Horovod:

Tag(s) Tensorflow ITEX Horovod Dockerfile
2.15.1-idp-multinode v2.15.1 v2.15.0.1 v0.28.1 v0.4.0-Beta
2.15.0-idp-multinode v2.15.0 v2.15.0.0 v0.28.1 v0.4.0-Beta
2.14.0-idp-openmpi-multinode v2.14.1 v2.14.0.1 v0.28.1 v0.3.4
2.13-idp-openmpi-mulitnode v2.13.0 v2.13.0.0 v0.28.0 v0.2.3

XPU images with Intel® Distribution for Python*🔗

The images below are built only with CPU and GPU optimizations and include Intel® Distribution for Python*:

Tag(s) Pytorch ITEX Driver Dockerfile
2.15.0.1-xpu-idp-base v2.15.1 v2.15.0.1 803 v0.4.0-Beta
2.15.0-xpu-idp-base v2.15.0 v2.15.0.0 803 v0.4.0-Beta

The images below additionally include Jupyter Notebook server:

Tag(s) Pytorch IPEX Driver Jupyter Port Dockerfile
2.15.0.1-xpu-idp-jupyter v2.15.1 v2.15.0.1 803 8888 v0.4.0-Beta
2.15.0-xpu-idp-jupyter [v2.1.0] v2.15.0.0 803 8888 v0.4.0-Beta

Build from Source🔗

To build the images from source, clone the AI Containers repository, follow the main README.md file to setup your environment, and run the following command:

cd pytorch
docker compose build tf-base
docker compose run tf-base

You can find the list of services below for each container in the group:

Service Name Description
tf-base Base image with Intel® Extension for TensorFlow*
jupyter Adds Jupyter Notebook server
multinode Adds Intel® MPI, Horovod and INC
xpu Adds Intel GPU Support
xpu-jupyter Adds Jupyter notebook server to GPU image

License🔗

View the License for the Intel® Extension for TensorFlow*.

The images below also contain other software which may be under other licenses (such as TensorFlow, Jupyter, Bash, etc. from the base).

It is the image user's responsibility to ensure that any use of The images below comply with any relevant licenses for all software contained within.

* Other names and brands may be claimed as the property of others.