MLCube packing: Bert benchmark [WIP]

[davidjurado]

Project setup

# Create Python environment and install MLCube Docker runner
virtualenv -p python3 ./env && source ./env/bin/activate && pip install mlcube-docker

Clone Training repo and go to Bert directory

git clone https://github.com/mlcommons/training.git && cd ./training
git fetch origin pull/503/head:feature/bert_mlcube && git checkout feature/bert_mlcube
cd ./language_model/tensorflow

Run Bert MLCube on a local machine with Docker runner

# Run Bert tasks: download, extract, preprocess, generate_tfrecords and train
mlcube run --task download
mlcube run --task extract
mlcube run --task preprocess
mlcube run --task generate_tfrecords
mlcube run --task train

We are targeting pull-type installation, so MLCubes should be available on docker hub. If not, try this:

mlcube run ... -Pdocker.build_strategy=auto

Also, users can override the workspace directory by using:

mlcube run --task=download --workspace=absolute_path_to_custom_dir

We are targeting pull-type installation, so MLCube images should be available on docker hub. If not, try this:

mlcube run ... -Pdocker.build_strategy=always

[github-actions[bot]]

MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅

[davidjurado]

Closed this PR because the code was outdated, the new implementation can be found here: 632