main.py

# Copyright 2024 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Main file for running the ogbg-molpcba example.

This file is intentionally kept short. The majority for logic is in libraries
that can be easily tested and imported in Colab.
"""

from absl import app
from absl import flags
from absl import logging
from clu import platform
import jax
from ml_collections import config_flags
import tensorflow as tf

import train


FLAGS = flags.FLAGS

flags.DEFINE_string('workdir', None, 'Directory to store model data.')
config_flags.DEFINE_config_file(
    'config',
    None,
    'File path to the training hyperparameter configuration.',
    lock_config=True,
)


def main(argv):
  if len(argv) > 1:
    raise app.UsageError('Too many command-line arguments.')

  # Hide any GPUs from TensorFlow. Otherwise TF might reserve memory and make
  # it unavailable to JAX.
  tf.config.experimental.set_visible_devices([], 'GPU')

  # This example only supports single-host training on a single device.
  logging.info('JAX host: %d / %d', jax.process_index(), jax.process_count())
  logging.info('JAX local devices: %r', jax.local_devices())

  # Add a note so that we can tell which task is which JAX host.
  # (Depending on the platform task 0 is not guaranteed to be host 0)
  platform.work_unit().set_task_status(
      f'process_index: {jax.process_index()}, '
      f'process_count: {jax.process_count()}'
  )
  platform.work_unit().create_artifact(
      platform.ArtifactType.DIRECTORY, FLAGS.workdir, 'workdir'
  )

  train.train_and_evaluate(FLAGS.config, FLAGS.workdir)


if __name__ == '__main__':
  flags.mark_flags_as_required(['config', 'workdir'])
  app.run(main)