Skip to content

Latest commit

 

History

History
110 lines (89 loc) · 4.33 KB

README.md

File metadata and controls

110 lines (89 loc) · 4.33 KB

Intel® Extension for TensorFlow* Docker Container Guide

Description

This document has instructions for running TensorFlow using Intel® Extension for TensorFlow* in a Docker container.

Assumptions:

  • Host machine contains an Intel GPU.
  • Host machine uses a Linux kernel that is compatible with GPU drivers.
  • Host machine has a compatible Intel GPU driver installed
  • Host machine has Docker software installed.

Refer to Install for XPU and Install for CPU for detail.

Binaries Preparation

Download and copy Intel® Extension for TensorFlow* wheel into ./models/binaries directory. You can get the intel-extension-for-tensorflow wheel link from https://pypi.org/project/intel-extension-for-tensorflow/#files, and intel-extension-for-tensorflow-lib wheel link from https://pypi.org/project/intel-extension-for-tensorflow-lib/#files.

To use Intel® Optimization for Horovod* with the Intel® oneAPI Collective Communications Library (oneCCL), copy Horovod wheel into ./models/binaries as well. You can get the intel-optimization-for-horovod wheel link from https://pypi.org/project/intel-optimization-for-horovod/#files.

mkdir -p ./models/binaries
cd ./models/binaries
wget <download link from https://pypi.org/project/intel-extension-for-tensorflow/#files>
wget <download link from https://pypi.org/project/intel-extension-for-tensorflow-lib/#files>
wget <download link from https://pypi.org/project/intel-optimization-for-horovod/#files>

Usage of Docker Container

I. Customize Build Script

We provide build.sh as the Docker container build script. The OS version and some software versions (such as Python and TensorFlow) are hard coded inside the script. If you're using a different version, you can edit this script.

For example, to build a Docker container with Python 3.10 and TensorFlow 2.15.1 on an Ubuntu 22.04 layer, update build.sh as shown below.

IMAGE_NAME=intel-extension-for-tensorflow:cpu-ubuntu
        docker build --build-arg UBUNTU_VERSION=22.04 \
                                --build-arg PYTHON=python3.10 \
                                --build-arg TF_VER=2.15.1 \
                                --build-arg WHEELS=*.whl \
                                -t $IMAGE_NAME \
                                -f itex-cpu.Dockerfile .

II. Build the Container

To build the Docker container, enter into docker folder and run below commands:

./build.sh [xpu/cpu]

III. Running the Container

Run the following commands to start the Docker container. You can use the -v option to mount your local directory into the container. To make the GPU available in the container, attach the GPU to the container using --device /dev/dri option and run the container:

IMAGE_NAME=intel-extension-for-tensorflow:xpu
docker run -v <your-local-dir>:/workspace \
           -v /dev/dri/by-path:/dev/dri/by-path \
           --device /dev/dri \
           --privileged \
           --ipc=host \
           -e http_proxy=$http_proxy \
           -e https_proxy=$https_proxy \
           -e no_proxy=$no_proxy \
           -it \
           $IMAGE_NAME bash

Note: Only for distributed training workloads with Intel® Optimization for Horovod*, the following script should be executed to set the required environment variables after entering the container:

source /opt/intel/horovod-vars.sh

Verify That Intel GPU is Accessible From TensorFlow

You are inside the container now. Run this command to verify the Intel GPU is visible to TensorFlow:

python -c "from tensorflow.python.client import device_lib; print(device_lib.list_local_devices())"

You should see your GPU device in the list of devices. Sample output looks like this:

[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 9266936945121049176
xla_global_id: -1
, name: "/device:XPU:0"
device_type: "XPU"
locality {
bus_id: 1
}
incarnation: 15031084974591766410
physical_device_desc: "device: 0, name: INTEL_XPU, pci bus id: <undefined>"
xla_global_id: -1
, name: "/device:XPU:1"
device_type: "XPU"
locality {
bus_id: 1
}
incarnation: 17448926295332318308
physical_device_desc: "device: 1, name: INTEL_XPU, pci bus id: <undefined>"
xla_global_id: -1
]