Skip to content

Latest commit

 

History

History
88 lines (59 loc) · 2.9 KB

build_from_source.md

File metadata and controls

88 lines (59 loc) · 2.9 KB
Raw

Build from source

This document describes how to build XLA components.

If you did not clone the XLA repository or install Bazel, please check out the "Get started" section of the README document.

Linux

Configure

XLA builds are configured by the .bazelrc file in the repository's root directory. The ./configure or ./configure.py scripts can be used to adjust common settings.

If you need to change the configuration, run the ./configure script from the repository's root directory. This script will prompt you for the location of XLA dependencies and asks for additional build configuration options (compiler flags, for example). Refer to the Sample session section for details.

./configure

There is also a python version of this script, ./configure.py. If using a virtual environment, python configure.py prioritizes paths within the environment, whereas ./configure prioritizes paths outside the environment. In both cases you can change the default.

CPU support

We recommend using a suitable docker container to build/test XLA, such as TensorFlow's docker container:

docker run --name xla -w /xla -it -d --rm -v $PWD:/xla tensorflow/tensorflow:latest-gpu bash

Using a docker container you can build XLA with CPU support using the following commands:

docker exec xla ./configure
docker exec xla bazel build //xla/...  --spawn_strategy=sandboxed --test_output=all

If you want to build XLA targets with CPU support without Docker you need to install gcc-10:

apt install gcc-10 g++-10

Then configure and build targets using the following commands:

yes '' | GCC_HOST_COMPILER_PATH=/usr/bin/gcc-10 CC=/usr/bin/gcc-10 TF_NEED_ROCM=0 TF_NEED_CUDA=0 TF_CUDA_CLANG=0 ./configure

bazel build --test_output=all --spawn_strategy=sandboxed //xla/...

GPU support

We recommend using a GPU docker container to build XLA with GPU support, such as:

docker run --name xla_gpu -w /xla -it -d --rm -v $PWD:/xla tensorflow/tensorflow:devel-gpu bash

To build XLA with GPU support use the following command:

docker exec -e TF_NEED_CUDA=1 xla_gpu ./configure
docker exec xla_gpu bazel build --test_output=all --spawn_strategy=sandboxed //xla/...

If you want to build XLA targets with GPU support without Docker you need to install the following dependencies additional to CPU dependencies: cuda-11.2, cuDNN-8.1.

Then configure and build targets using the following commands:

yes '' | GCC_HOST_COMPILER_PATH=/usr/bin/gcc-10 CC=/usr/bin/gcc-10 TF_NEED_ROCM=0 TF_NEED_CUDA=1 TF_CUDA_CLANG=0 ./configure

bazel build --test_output=all --spawn_strategy=sandboxed //xla/...

For more details regarding TensorFlow's GPU docker images you can check out this document.