Compile Tensorflow on Docker ubuntu22.04

Docker images to compile TensorFlow yourself.

Tensorflow only provide a limited set of build and it can be challenging to compile yourself on certain configuration. With this Dockerfile, you should be able to compile TensorFlow on any Linux platform that run Docker.


This fork includes NVIDIA ``cuda==10.2, NCCL==2.7`` support and will be maintained on forward releases as TensorFlow evolves.

## Requirements

- `docker`
- `docker-compose`

#### Optional GPU Hardware

- Docker CUDA Support `nvidia-container-toolkit`
- CUDA Hardware `tf.test.is_gpu_available()`

## Usage: Compile Wheels in Container

Get started compiling TensorFlow python3 wheels, ensure you have a supported docker container environment. Note that it may take a considerable time to prep image layers with CUDA env and build from source (example: Xeon 24C, build times ~3h).

#### Clone Repo

```bash
$ git clone https://github.com/SarMalik/docker-tensorflow-builder.git
$ cd docker-tensorflow-builder

CPUflags

grep flags -m1 /proc/cpuinfo | cut -d ":" -f 2 | tr '[:upper:]' '[:lower:]' | { read FLAGS; OPT="-march=native"; for flag in $FLAGS; do case "$flag" in "sse4_1" | "sse4_2" | "ssse3" | "fma" | "cx16" | "popcnt" | "avx" | "avx2") OPT+=" -m$flag";; esac; done; MODOPT=${OPT//_/\.}; echo "$MODOPT"; }

Modify Build Params

Clone the build/compile.tf{version}-cuda{version}.sh starter script with bash params for targeting wheel compilation.

Refer to tested configs listed in the Builds section.

$ cat build/compile.*.sh >> vim

Check Dockerfile for change params

export PYTHON_VERSION=3.6.12 export TF_VERSION_GIT_TAG=v1.15.0 export USE_GPU=1 export CUDA_VERSION=10.2 export CUDNN_VERSION=7.6 export NCCL_VERSION=2.7


#### Build

To instantiate the compiler environment, save and run the platform targeting script.

```bash
$ docker ./build/compile.tf{version}-cuda{version}.sh

Build Output

The compiled wheels are written to ${PWD}/wheels/**/*.wheel.

Cleanup Output

Intermediate container layers used for the build process may take up considerable space and can be safely removed from the host using the command: $ sudo docker system prune.

License