def setup(self, stage: Optional[str] = None) -> None:
"""
Checks if the dataset folder is present, and the dataset file exists. This is execute on each node in
distributed training.
"""
# Check for existing dataset.csv file in the correct locations. Skip that if a dataset has already been
# loaded (typically only during tests)
if self.config.dataset_data_frame is None:
assert self.config.local_dataset is not None
validate_dataset_paths(self.config.local_dataset,
self.config.dataset_csv)
self.config.read_dataset_if_needed()
self.data_loaders = self.config.create_data_loaders()
def validate_dataset_paths(
model_config: Union[ScalarModelBase, SegmentationModelBase]) -> None:
"""Check that validation of dataset paths is succeeds when csv file exists,
and fails when it's missing."""
assert model_config.local_dataset is not None
ml_util.validate_dataset_paths(model_config.local_dataset, model_config.dataset_csv)
dataset_csv_path = model_config.local_dataset / model_config.dataset_csv
dataset_csv_path.unlink()
ex_message = f"The dataset file {model_config.dataset_csv} is not present"
with pytest.raises(ValueError) as ex:
ml_util.validate_dataset_paths(model_config.local_dataset, model_config.dataset_csv)
assert ex_message in str(ex)
def run(self) -> None:
"""
Driver function to run a ML experiment. If an offline cross validation run is requested, then
this function is recursively called for each cross validation split.
"""
if self.is_offline_cross_val_parent_run():
if self.model_config.is_segmentation_model:
raise NotImplementedError(
"Offline cross validation is only supported for classification models."
)
self.spawn_offline_cross_val_classification_child_runs()
return
# Get the AzureML context in which the script is running
if not self.model_config.is_offline_run and PARENT_RUN_CONTEXT is not None:
logging.info("Setting tags from parent run.")
self.set_run_tags_from_parent()
self.save_build_info_for_dotnet_consumers()
# Set data loader start method
self.set_multiprocessing_start_method()
# configure recovery container if provided
checkpoint_handler = CheckpointHandler(model_config=self.model_config,
azure_config=self.azure_config,
project_root=self.project_root,
run_context=RUN_CONTEXT)
checkpoint_handler.discover_and_download_checkpoints_from_previous_runs(
)
# do training and inference, unless the "only register" switch is set (which requires a run_recovery
# to be valid).
if not self.azure_config.register_model_only_for_epoch:
# Set local_dataset to the mounted path specified in azure_runner.py, if any, or download it if that fails
# and config.local_dataset was not already set.
self.model_config.local_dataset = self.mount_or_download_dataset()
self.model_config.write_args_file()
logging.info(str(self.model_config))
# Ensure that training runs are fully reproducible - setting random seeds alone is not enough!
make_pytorch_reproducible()
# Check for existing dataset.csv file in the correct locations. Skip that if a dataset has already been
# loaded (typically only during tests)
if self.model_config.dataset_data_frame is None:
assert self.model_config.local_dataset is not None
ml_util.validate_dataset_paths(self.model_config.local_dataset)
# train a new model if required
if self.azure_config.train:
with logging_section("Model training"):
model_train(self.model_config, checkpoint_handler)
else:
self.model_config.write_dataset_files()
self.create_activation_maps()
# log the number of epochs used for model training
RUN_CONTEXT.log(name="Train epochs",
value=self.model_config.num_epochs)
# We specify the ModelProcessing as DEFAULT here even if the run_recovery points to an ensemble run, because
# the current run is a single one. See the documentation of ModelProcessing for more details.
best_epoch = self.run_inference_and_register_model(
checkpoint_handler, ModelProcessing.DEFAULT)
# Generate report
if best_epoch:
Runner.generate_report(self.model_config, best_epoch,
ModelProcessing.DEFAULT)
elif self.model_config.is_scalar_model and len(
self.model_config.get_test_epochs()) == 1:
# We don't register scalar models but still want to create a report if we have run inference.
Runner.generate_report(self.model_config,
self.model_config.get_test_epochs()[0],
ModelProcessing.DEFAULT)
def run(self) -> None:
"""
Driver function to run a ML experiment. If an offline cross validation run is requested, then
this function is recursively called for each cross validation split.
"""
if self.is_offline_cross_val_parent_run():
if self.model_config.is_segmentation_model:
raise NotImplementedError("Offline cross validation is only supported for classification models.")
self.spawn_offline_cross_val_classification_child_runs()
return
# Get the AzureML context in which the script is running
if not self.model_config.is_offline_run and PARENT_RUN_CONTEXT is not None:
logging.info("Setting tags from parent run.")
self.set_run_tags_from_parent()
self.save_build_info_for_dotnet_consumers()
# Set data loader start method
self.set_multiprocessing_start_method()
# configure recovery container if provided
checkpoint_handler = CheckpointHandler(model_config=self.model_config,
azure_config=self.azure_config,
project_root=self.project_root,
run_context=RUN_CONTEXT)
checkpoint_handler.download_recovery_checkpoints_or_weights()
# do training and inference, unless the "only register" switch is set (which requires a run_recovery
# to be valid).
if not self.azure_config.only_register_model:
# Set local_dataset to the mounted path specified in azure_runner.py, if any, or download it if that fails
# and config.local_dataset was not already set.
self.model_config.local_dataset = self.mount_or_download_dataset()
# Check for existing dataset.csv file in the correct locations. Skip that if a dataset has already been
# loaded (typically only during tests)
if self.model_config.dataset_data_frame is None:
assert self.model_config.local_dataset is not None
ml_util.validate_dataset_paths(
self.model_config.local_dataset,
self.model_config.dataset_csv)
# train a new model if required
if self.azure_config.train:
with logging_section("Model training"):
model_train(self.model_config, checkpoint_handler, num_nodes=self.azure_config.num_nodes)
else:
self.model_config.write_dataset_files()
self.create_activation_maps()
# log the number of epochs used for model training
RUN_CONTEXT.log(name="Train epochs", value=self.model_config.num_epochs)
# We specify the ModelProcessing as DEFAULT here even if the run_recovery points to an ensemble run, because
# the current run is a single one. See the documentation of ModelProcessing for more details.
self.run_inference_and_register_model(checkpoint_handler, ModelProcessing.DEFAULT)
if self.model_config.generate_report:
self.generate_report(ModelProcessing.DEFAULT)
# If this is an cross validation run, and the present run is child run 0, then wait for the sibling runs,
# build the ensemble model, and write a report for that.
if self.model_config.number_of_cross_validation_splits > 0:
if self.model_config.should_wait_for_other_cross_val_child_runs():
self.wait_for_runs_to_finish()
self.create_ensemble_model()
