Use TF SIG Build image to compile Tensorflow without AVX (for Celeron on NAS)

docker_build_tf_for_celeron.sh

#!/bin/bash
# See https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tf_sig_build_dockerfiles

TF_TAG=v2.13.0
BUILDER_TAG=2.13-python3.8

# clonse sources
git clone --depth 1 --branch $TF_TAG https://github.com/tensorflow/tensorflow.git
# output folder
mkdir packages
# cache folder
mkdir bazelcache

# start the builder container
docker run --name tf -w /tf/tensorflow -it -d \
  -v "$(pwd)/packages:/tf/pkg" \
  -v "$(pwd)/tensorflow:/tf/tensorflow" \
  -v "$(pwd)/bazelcache:/tf/cache" \
  tensorflow/build:$BUILDER_TAG \
  bash

# adapt config
docker exec tf /bin/bash cp /usertools/cpu.bazelrc /usertools/cpu.bazelrc.mine
# alt: simply remove avx-related flags
#docker exec tf /bin/bash -c 'sed -i s/build\ --copt=-mavx\ --host_copt=-mavx/build\ --copt=\ --host_copt=/g /usertools/cpu.bazelrc.mine'
# alt: supply specific flags to activate available optimizations e turn off missing ones (i.e. avx instruction set) 
docker exec tf /bin/bash -c 'sed -i s/build\ --copt=-mavx\ --host_copt=-mavx/build\ --copt=-march=core2\ --copt=-mno-avx\ --copt=-mno-avx2\ --copt=-mno-fma\ --copt=-msse4.1\ --copt=-msse4.2\ --copt=-mno-bmi\ --copt=-mno-bmi2\ --host_copt=-march=core2\ --host_copt=-mno-avx\ --host_copt=-mno-avx2\ --host_copt=-mno-fma\ --host_copt=-msse4.1\ --host_copt=-msse4.2\ --host_copt=-mno-bmi\ --host_copt=-mno-bmi2=/g /usertools/cpu.bazelrc.mine'

# cleanup Bazel stuff (if changes result in incompatible cached stuff)
#docker exec tf bazel clean

# build the sources with Bazel
docker exec tf bazel --bazelrc=/usertools/cpu.bazelrc \
  build --config=sigbuild_local_cache \
  tensorflow/tools/pip_package:build_pip_package

# construct the pip package
docker exec tf \
  ./bazel-bin/tensorflow/tools/pip_package/build_pip_package \
  /tf/pkg \
  --cpu

# rename the wheels
docker exec tf /usertools/rename_and_verify_wheels.sh