A wrapper to run and monitor absl app.
Project description
ABSL-Extra
A collection of utils I commonly use for running my experiments. It will:
- Notify on execution start, finish or failed.
- By default, Notifier will just log those out to
stdout
. - I prefer receiving those in Slack, though (see example below).
- By default, Notifier will just log those out to
- Log parsed CLI flags from
absl.flags.FLAGS
and config values fromconfig_file:get_config()
- Inject
ml_collections.ConfigDict
fromconfig_file
, if kwarg provided. - Inject
pymongo.collection.Collection
ifmongo_config
kwarg provided. - Select registered task to run based on --task= CLI argument.
Minimal example
import os
from pymongo.collection import Collection
from ml_collections import ConfigDict
from absl import logging
import tensorflow as tf
from absl_extra import tf_utils, tasks, notifier
@tasks.register_task(
config_file="config.py",
mongo_config=dict(uri=os.environ["MONGO_URI"], db_name="my_project", collection="experiment_1"),
notifier=notifier.SlackNotifier(slack_token=os.environ["SLACK_BOT_TOKEN"], channel_id=os.environ["CHANNEL_ID"])
)
@tf_utils.requires_gpu
def main(config: ConfigDict, db: Collection) -> None:
if tf_utils.supports_mixed_precision():
tf.keras.mixed_precision.set_global_policy("mixed_float16")
with tf_utils.make_gpu_strategy().scope():
logging.info("Doing some heavy lifting...")
if __name__ == "__main__":
tasks.run()
Planned for:
- global app state for different tasks
- list of pre/post hooks
- keras callback with notifier
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