def test_resource_monitor_store_to_file(test_output_dirs: OutputFolderForTests) -> None:
"""
Test if storing metrics to a file works correctly.
"""
tensorboard_folder = test_output_dirs.root_dir
r = ResourceMonitor(interval_seconds=5,
tensorboard_folder=tensorboard_folder,
csv_results_folder=tensorboard_folder)
r.gpu_aggregates = {
1: GpuUtilization(id=1, mem_util=1, load=2, mem_reserved_gb=30.0, mem_allocated_gb=40.0, count=10),
}
r.gpu_max = {
1: GpuUtilization(id=1, mem_util=0.4, load=0.5, mem_reserved_gb=6.0, mem_allocated_gb=7.0, count=10),
}
r.store_to_file()
# Write a second time - we expect that to overwrite and only produce one set of metrics
r.store_to_file()
parsed_metrics = r.read_aggregate_metrics()
assert parsed_metrics == {
"GPU1": {
"MemUtil_Percent": 10.0,
"Load_Percent": 20.0,
"MemReserved_GB": 3.0,
"MemAllocated_GB": 4.0,
"MaxMemUtil_Percent": 40.0,
"MaxLoad_Percent": 50.0,
"MaxMemReserved_GB": 6.0,
"MaxMemAllocated_GB": 7.0,
}}
def start_resource_monitor(config: LightningContainer) -> ResourceMonitor:
# initialize and start GPU monitoring
gpu_tensorboard = config.logs_folder / "gpu_utilization"
# Result file in CSV format should NOT live in the logs folder, the streaming upload that is
# used for this folder might corrupt the file.
gpu_csv = config.outputs_folder / "gpu_utilization"
gpu_csv.mkdir(parents=True, exist_ok=True)
logging.info(f"Starting resource monitor. GPU utilization will be written to Tensorboard in "
f"{gpu_tensorboard}, aggregate metrics to {gpu_csv}")
resource_monitor = ResourceMonitor(interval_seconds=config.monitoring_interval_seconds,
tensorboard_folder=gpu_tensorboard,
csv_results_folder=gpu_csv)
resource_monitor.start()
return resource_monitor
def test_resource_monitor(test_output_dirs: OutputFolderForTests) -> None:
"""
Test if metrics are correctly updated in the ResourceMonitor class.
"""
results_folder = test_output_dirs.root_dir
r = ResourceMonitor(interval_seconds=5, tensorboard_folder=results_folder, csv_results_folder=results_folder)
def create_gpu(id: int, load: float, mem_total: float, mem_used: float) -> GPU:
return GPU(ID=id, uuid=None, load=load, memoryTotal=mem_total, memoryUsed=mem_used,
memoryFree=None, driver=None, gpu_name=None,
serial=None, display_mode=None, display_active=None, temp_gpu=None)
# Fake objects coming from GPUtil: Two entries for GPU1, 1 entry only for GPU2
gpu1 = create_gpu(1, 0.1, 10, 2) # memUti=0.2
gpu2 = create_gpu(2, 0.2, 10, 3) # memUti=0.3
gpu3 = create_gpu(1, 0.3, 10, 5) # memUti=0.5
# Mock torch calls so that we can run on CPUs. memory allocated: 2GB, reserved: 1GB
with mock.patch("torch.cuda.memory_allocated", return_value=2 ** 31):
with mock.patch("torch.cuda.memory_reserved", return_value=2 ** 30):
# Update with results for both GPUs
r.update_metrics([gpu1, gpu2])
# Next update with data for GPU2 missing
r.update_metrics([gpu3])
# Element-wise maximum of metrics
assert r.gpu_max == {
1: GpuUtilization(id=1, load=0.3, mem_util=0.5, mem_allocated_gb=2.0, mem_reserved_gb=1.0, count=2),
2: GpuUtilization(id=2, load=0.2, mem_util=0.3, mem_allocated_gb=2.0, mem_reserved_gb=1.0, count=1),
}
# Aggregates should contain the sum of metrics that were observed.
assert r.gpu_aggregates == {
1: GpuUtilization(id=1, load=0.4, mem_util=0.7, mem_allocated_gb=4.0, mem_reserved_gb=2.0, count=2),
2: GpuUtilization(id=2, load=0.2, mem_util=0.3, mem_allocated_gb=2.0, mem_reserved_gb=1.0, count=1),
}
r.writer.flush()
r.store_to_file()
tb_file = list(results_folder.rglob("*tfevents*"))[0]
assert os.path.getsize(str(tb_file)) > 100
assert r.aggregate_metrics_file.is_file
assert len(r.aggregate_metrics_file.read_text().splitlines()) == 17
parsed_metrics = r.read_aggregate_metrics()
# There should be one entry per GPU
assert len(parsed_metrics) == 2
# Each GPU has 4 averages, 4 max.
assert len(parsed_metrics["GPU1"]) == 8
assert len(parsed_metrics["GPU2"]) == 8
def test_resource_monitor_store_to_file(
test_output_dirs: TestOutputDirectories) -> None:
"""
Test if storing metrics to a file works correctly.
"""
tensorboard_folder = Path(test_output_dirs.root_dir)
r = ResourceMonitor(interval_seconds=5,
tensorboard_folder=tensorboard_folder)
r.gpu_aggregates = {
1:
GpuUtilization(id=1,
mem_util=1,
load=2,
mem_reserved_gb=30.0,
mem_allocated_gb=40.0,
count=10),
}
r.gpu_max = {
1:
GpuUtilization(id=1,
mem_util=0.4,
load=0.5,
mem_reserved_gb=6.0,
mem_allocated_gb=7.0,
count=10),
}
r.store_to_file()
# Write a second time - we expect that to overwrite and only produce one set of metrics
r.store_to_file()
parsed_metrics = r.read_aggregate_metrics()
assert parsed_metrics == [
("GPU1/MemUtil_Percent", 10.0),
("GPU1/Load_Percent", 20.0),
("GPU1/MemReserved_GB", 3.0),
("GPU1/MemAllocated_GB", 4.0),
("GPU1/MaxMemUtil_Percent", 40.0),
("GPU1/MaxLoad_Percent", 50.0),
("GPU1/MaxMemReserved_GB", 6.0),
("GPU1/MaxMemAllocated_GB", 7.0),
]
def model_train(config: ModelConfigBase,
checkpoint_handler: CheckpointHandler) -> ModelTrainingResults:
"""
The main training loop. It creates the model, dataset, optimizer_type, and criterion, then proceeds
to train the model. If a checkpoint was specified, then it loads the checkpoint before resuming training.
:param config: The arguments which specify all required information.
:param checkpoint_handler: Checkpoint handler object to find checkpoint paths for model initialization
:raises TypeError: If the arguments are of the wrong type.
:raises ValueError: When there are issues loading a previous checkpoint.
"""
# Save the dataset files for later use in cross validation analysis
config.write_dataset_files()
# set the random seed for all libraries
ml_util.set_random_seed(config.get_effective_random_seed(),
"Patch visualization")
# Visualize how patches are sampled for segmentation models. This changes the random generator, but we don't
# want training to depend on how many patients we visualized, and hence set the random seed again right after.
with logging_section(
"Visualizing the effect of sampling random crops for training"):
visualize_random_crops_for_dataset(config)
ml_util.set_random_seed(config.get_effective_random_seed(),
"Model training")
logging.debug("Creating the PyTorch model.")
# Create the train loader and validation loader to load images from the dataset
data_loaders = config.create_data_loaders()
# Get the path to the checkpoint to recover from
checkpoint_path = checkpoint_handler.get_recovery_path_train()
models_and_optimizer = ModelAndInfo(
config=config,
model_execution_mode=ModelExecutionMode.TRAIN,
checkpoint_path=checkpoint_path)
# Create the main model
# If continuing from a previous run at a specific epoch, then load the previous model.
model_loaded = models_and_optimizer.try_create_model_and_load_from_checkpoint(
)
if not model_loaded:
raise ValueError(
"There was no checkpoint file available for the model for given start_epoch {}"
.format(config.start_epoch))
# Print out a detailed breakdown of layers, memory consumption and time.
generate_and_print_model_summary(config, models_and_optimizer.model)
# Move model to GPU and adjust for multiple GPUs
models_and_optimizer.adjust_model_for_gpus()
# Create the mean teacher model and move to GPU
if config.compute_mean_teacher_model:
mean_teacher_model_loaded = models_and_optimizer.try_create_mean_teacher_model_load_from_checkpoint_and_adjust(
)
if not mean_teacher_model_loaded:
raise ValueError(
"There was no checkpoint file available for the mean teacher model "
f"for given start_epoch {config.start_epoch}")
# Create optimizer
models_and_optimizer.create_optimizer()
if checkpoint_handler.should_load_optimizer_checkpoint():
optimizer_loaded = models_and_optimizer.try_load_checkpoint_for_optimizer(
)
if not optimizer_loaded:
raise ValueError(
f"There was no checkpoint file available for the optimizer for given start_epoch "
f"{config.start_epoch}")
# Create checkpoint directory for this run if it doesn't already exist
logging.info(f"Models are saved at {config.checkpoint_folder}")
if not config.checkpoint_folder.is_dir():
config.checkpoint_folder.mkdir()
# Create the SummaryWriters for Tensorboard
writers = create_summary_writers(config)
config.create_dataframe_loggers()
# Create LR scheduler
l_rate_scheduler = SchedulerWithWarmUp(config,
models_and_optimizer.optimizer)
# Training loop
logging.info("Starting training")
train_results_per_epoch, val_results_per_epoch, learning_rates_per_epoch = [], [], []
resource_monitor = None
if config.monitoring_interval_seconds > 0:
# initialize and start GPU monitoring
diagnostics_events = config.logs_folder / "diagnostics"
logging.info(
f"Starting resource monitor, outputting to {diagnostics_events}")
resource_monitor = ResourceMonitor(
interval_seconds=config.monitoring_interval_seconds,
tensorboard_folder=diagnostics_events)
resource_monitor.start()
gradient_scaler = GradScaler(
) if config.use_gpu and config.use_mixed_precision else None
optimal_temperature_scale_values = []
for epoch in config.get_train_epochs():
logging.info("Starting epoch {}".format(epoch))
save_epoch = config.should_save_epoch(
epoch) and models_and_optimizer.optimizer is not None
# store the learning rates used for each epoch
epoch_lrs = l_rate_scheduler.get_last_lr()
learning_rates_per_epoch.append(epoch_lrs)
train_val_params: TrainValidateParameters = \
TrainValidateParameters(data_loader=data_loaders[ModelExecutionMode.TRAIN],
model=models_and_optimizer.model,
mean_teacher_model=models_and_optimizer.mean_teacher_model,
epoch=epoch,
optimizer=models_and_optimizer.optimizer,
gradient_scaler=gradient_scaler,
epoch_learning_rate=epoch_lrs,
summary_writers=writers,
dataframe_loggers=config.metrics_data_frame_loggers,
in_training_mode=True)
training_steps = create_model_training_steps(config, train_val_params)
train_epoch_results = train_or_validate_epoch(training_steps)
train_results_per_epoch.append(train_epoch_results.metrics)
metrics.validate_and_store_model_parameters(writers.train, epoch,
models_and_optimizer.model)
# Run without adjusting weights on the validation set
train_val_params.in_training_mode = False
train_val_params.data_loader = data_loaders[ModelExecutionMode.VAL]
# if temperature scaling is enabled then do not save validation metrics for the checkpoint epochs
# as these will be re-computed after performing temperature scaling on the validation set.
if isinstance(config, SequenceModelBase):
train_val_params.save_metrics = not (
save_epoch and config.temperature_scaling_config)
training_steps = create_model_training_steps(config, train_val_params)
val_epoch_results = train_or_validate_epoch(training_steps)
val_results_per_epoch.append(val_epoch_results.metrics)
if config.is_segmentation_model:
metrics.store_epoch_stats_for_segmentation(
config.outputs_folder, epoch, epoch_lrs,
train_epoch_results.metrics, val_epoch_results.metrics)
if save_epoch:
# perform temperature scaling if required
if isinstance(
config,
SequenceModelBase) and config.temperature_scaling_config:
optimal_temperature, scaled_val_results = \
temperature_scaling_steps(config, train_val_params, val_epoch_results)
optimal_temperature_scale_values.append(optimal_temperature)
# overwrite the metrics for the epoch with the metrics from the temperature scaled model
val_results_per_epoch[-1] = scaled_val_results.metrics
models_and_optimizer.save_checkpoint(epoch)
# Updating the learning rate should happen at the end of the training loop, so that the
# initial learning rate will be used for the very first epoch.
l_rate_scheduler.step()
model_training_results = ModelTrainingResults(
train_results_per_epoch=train_results_per_epoch,
val_results_per_epoch=val_results_per_epoch,
learning_rates_per_epoch=learning_rates_per_epoch,
optimal_temperature_scale_values_per_checkpoint_epoch=
optimal_temperature_scale_values)
logging.info("Finished training")
# Since we have trained the model further, let the checkpoint_handler object know so it can handle
# checkpoints correctly.
checkpoint_handler.additional_training_done()
# Upload visualization directory to AML run context to be able to see it
# in the Azure UI.
if config.max_batch_grad_cam > 0 and config.visualization_folder.exists():
RUN_CONTEXT.upload_folder(name=VISUALIZATION_FOLDER,
path=str(config.visualization_folder))
writers.close_all()
config.metrics_data_frame_loggers.close_all()
if resource_monitor:
# stop the resource monitoring process
logging.info(
"Shutting down the resource monitor process. Aggregate resource utilization:"
)
for name, value in resource_monitor.read_aggregate_metrics():
logging.info(f"{name}: {value}")
if not is_offline_run_context(RUN_CONTEXT):
RUN_CONTEXT.log(name, value)
resource_monitor.kill()
return model_training_results
def model_train(config: ModelConfigBase, run_recovery: Optional[RunRecovery] = None) -> ModelTrainingResults:
"""
The main training loop. It creates the model, dataset, optimizer_type, and criterion, then proceeds
to train the model. If a checkpoint was specified, then it loads the checkpoint before resuming training.
:param config: The arguments which specify all required information.
:param run_recovery: Recovery information to restart training from an existing run.
:raises TypeError: If the arguments are of the wrong type.
:raises ValueError: When there are issues loading a previous checkpoint.
"""
# Save the dataset files for later use in cross validation analysis
config.write_dataset_files()
# set the random seed for all libraries
ml_util.set_random_seed(config.get_effective_random_seed(), "Model Training")
logging.debug("Creating the PyTorch model.")
# Create the train loader and validation loader to load images from the dataset
data_loaders = config.create_data_loaders()
# Create models, optimizers, and whether is_mean_teacher
checkpoint_path = get_recovery_path_train(run_recovery=run_recovery,
is_mean_teacher=False,
epoch=config.start_epoch)
models_and_optimizers = [ModelAndInfo(config=config,
model_execution_mode=ModelExecutionMode.TRAIN,
is_mean_teacher=False,
checkpoint_path=checkpoint_path if config.should_load_checkpoint_for_training() else None)]
if config.compute_mean_teacher_model:
checkpoint_path = get_recovery_path_train(run_recovery=run_recovery,
is_mean_teacher=True,
epoch=config.start_epoch)
models_and_optimizers.append(ModelAndInfo(config=config,
model_execution_mode=ModelExecutionMode.TRAIN,
is_mean_teacher=True,
checkpoint_path=checkpoint_path if config.should_load_checkpoint_for_training() else None))
# Create the models.
# If continuing from a previous run at a specific epoch, then load the previous model.
for model_and_info in models_and_optimizers:
model_loaded = model_and_info.try_create_model_and_load_from_checkpoint()
if not model_loaded:
raise ValueError("There was no checkpoint file available for the model for given start_epoch {}"
.format(config.start_epoch))
# Print out a detailed breakdown of layers, memory consumption and time.
generate_and_print_model_summary(config, models_and_optimizers[0].model)
# Move model to GPU and adjust for multiple GPUs
models_and_optimizers[0].adjust_model_for_gpus()
if len(models_and_optimizers) > 1:
models_and_optimizers[1].create_summary_and_adjust_model_for_gpus()
# Create optimizer
optimizer_loaded = models_and_optimizers[0].try_create_optimizer_and_load_from_checkpoint()
if not optimizer_loaded:
raise ValueError("There was no checkpoint file available for the optimizer for given start_epoch {}"
.format(config.start_epoch))
# Create checkpoint directory for this run if it doesn't already exist
logging.info("Models are saved at {}".format(config.checkpoint_folder))
if not os.path.isdir(config.checkpoint_folder):
os.makedirs(config.checkpoint_folder)
# Create the SummaryWriters for Tensorboard
writers = create_summary_writers(config)
config.create_dataframe_loggers()
model = models_and_optimizers[0].model
optimizer = models_and_optimizers[0].optimizer
mean_teacher_model = models_and_optimizers[1].model if len(models_and_optimizers) > 1 else None
# Create LR scheduler
l_rate_scheduler = SchedulerWithWarmUp(config, optimizer)
# Training loop
logging.info("Starting training")
train_results_per_epoch, val_results_per_epoch, learning_rates_per_epoch = [], [], []
resource_monitor = None
if config.monitoring_interval_seconds > 0:
# initialize and start GPU monitoring
resource_monitor = ResourceMonitor(interval_seconds=config.monitoring_interval_seconds,
tb_log_file_path=str(config.logs_folder / "diagnostics"))
resource_monitor.start()
gradient_scaler = GradScaler() if config.use_gpu and config.use_mixed_precision else None
optimal_temperature_scale_values = []
for epoch in config.get_train_epochs():
logging.info("Starting epoch {}".format(epoch))
save_epoch = config.should_save_epoch(epoch) and optimizer is not None
# store the learning rates used for each epoch
epoch_lrs = l_rate_scheduler.get_last_lr()
learning_rates_per_epoch.append(epoch_lrs)
train_val_params: TrainValidateParameters = \
TrainValidateParameters(data_loader=data_loaders[ModelExecutionMode.TRAIN],
model=model,
mean_teacher_model=mean_teacher_model,
epoch=epoch,
optimizer=optimizer,
gradient_scaler=gradient_scaler,
epoch_learning_rate=epoch_lrs,
summary_writers=writers,
dataframe_loggers=config.metrics_data_frame_loggers,
in_training_mode=True)
training_steps = create_model_training_steps(config, train_val_params)
train_epoch_results = train_or_validate_epoch(training_steps)
train_results_per_epoch.append(train_epoch_results.metrics)
metrics.validate_and_store_model_parameters(writers.train, epoch, model)
# Run without adjusting weights on the validation set
train_val_params.in_training_mode = False
train_val_params.data_loader = data_loaders[ModelExecutionMode.VAL]
# if temperature scaling is enabled then do not save validation metrics for the checkpoint epochs
# as these will be re-computed after performing temperature scaling on the validation set.
if isinstance(config, SequenceModelBase):
train_val_params.save_metrics = not (save_epoch and config.temperature_scaling_config)
training_steps = create_model_training_steps(config, train_val_params)
val_epoch_results = train_or_validate_epoch(training_steps)
val_results_per_epoch.append(val_epoch_results.metrics)
if config.is_segmentation_model:
metrics.store_epoch_stats_for_segmentation(config.outputs_folder, epoch, epoch_lrs,
train_epoch_results.metrics,
val_epoch_results.metrics)
if save_epoch:
# perform temperature scaling if required
if isinstance(config, SequenceModelBase) and config.temperature_scaling_config:
optimal_temperature, scaled_val_results = \
temperature_scaling_steps(config, train_val_params, val_epoch_results)
optimal_temperature_scale_values.append(optimal_temperature)
# overwrite the metrics for the epoch with the metrics from the temperature scaled model
val_results_per_epoch[-1] = scaled_val_results.metrics
assert optimizer is not None
save_checkpoint(model, optimizer, epoch, config)
if config.compute_mean_teacher_model:
assert mean_teacher_model is not None
save_checkpoint(mean_teacher_model, optimizer, epoch, config, mean_teacher_model=True)
# Updating the learning rate should happen at the end of the training loop, so that the
# initial learning rate will be used for the very first epoch.
l_rate_scheduler.step()
model_training_results = ModelTrainingResults(
train_results_per_epoch=train_results_per_epoch,
val_results_per_epoch=val_results_per_epoch,
learning_rates_per_epoch=learning_rates_per_epoch,
optimal_temperature_scale_values_per_checkpoint_epoch=optimal_temperature_scale_values
)
logging.info("Finished training")
# Upload visualization directory to AML run context to be able to see it
# in the Azure UI.
if config.max_batch_grad_cam > 0 and config.visualization_folder.exists():
RUN_CONTEXT.upload_folder(name=VISUALIZATION_FOLDER, path=str(config.visualization_folder))
writers.close_all()
config.metrics_data_frame_loggers.close_all()
if resource_monitor:
# stop the resource monitoring process
resource_monitor.kill()
return model_training_results
def model_train(config: ModelConfigBase,
checkpoint_handler: CheckpointHandler,
num_nodes: int = 1) -> ModelTrainingResults:
"""
The main training loop. It creates the Pytorch model based on the configuration options passed in,
creates a Pytorch Lightning trainer, and trains the model.
If a checkpoint was specified, then it loads the checkpoint before resuming training.
:param config: The arguments which specify all required information.
:param checkpoint_handler: Checkpoint handler object to find checkpoint paths for model initialization
:param num_nodes: The number of nodes to use in distributed training.
"""
# Get the path to the checkpoint to recover from
checkpoint_path = checkpoint_handler.get_recovery_path_train()
# This reads the dataset file, and possibly sets required pre-processing objects, like one-hot encoder
# for categorical features, that need to be available before creating the model.
config.read_dataset_if_needed()
# Create the trainer object. Backup the environment variables before doing that, in case we need to run a second
# training in the unit tests.d
old_environ = dict(os.environ)
seed_everything(config.get_effective_random_seed())
trainer, storing_logger = create_lightning_trainer(config, checkpoint_path, num_nodes=num_nodes)
logging.info(f"GLOBAL_RANK: {os.getenv('GLOBAL_RANK')}, LOCAL_RANK {os.getenv('LOCAL_RANK')}. "
f"trainer.global_rank: {trainer.global_rank}")
logging.debug("Creating the PyTorch model.")
lightning_model = create_lightning_model(config)
lightning_model.storing_logger = storing_logger
resource_monitor = None
# Execute some bookkeeping tasks only once if running distributed:
if is_rank_zero():
config.write_args_file()
logging.info(str(config))
# Save the dataset files for later use in cross validation analysis
config.write_dataset_files()
logging.info(f"Model checkpoints are saved at {config.checkpoint_folder}")
# set the random seed for all libraries
ml_util.set_random_seed(config.get_effective_random_seed(), "Patch visualization")
# Visualize how patches are sampled for segmentation models. This changes the random generator, but we don't
# want training to depend on how many patients we visualized, and hence set the random seed again right after.
with logging_section("Visualizing the effect of sampling random crops for training"):
visualize_random_crops_for_dataset(config)
# Print out a detailed breakdown of layers, memory consumption and time.
generate_and_print_model_summary(config, lightning_model.model)
if config.monitoring_interval_seconds > 0:
# initialize and start GPU monitoring
diagnostics_events = config.logs_folder / "diagnostics"
logging.info(f"Starting resource monitor, outputting to {diagnostics_events}")
resource_monitor = ResourceMonitor(interval_seconds=config.monitoring_interval_seconds,
tensorboard_folder=diagnostics_events)
resource_monitor.start()
# Training loop
logging.info("Starting training")
lightning_data = TrainingAndValidationDataLightning(config) # type: ignore
# When trying to store the config object in the constructor, it does not appear to get stored at all, later
# reference of the object simply fail. Hence, have to set explicitly here.
lightning_data.config = config
trainer.fit(lightning_model, datamodule=lightning_data)
trainer.logger.close() # type: ignore
lightning_model.close_all_loggers()
world_size = getattr(trainer, "world_size", 0)
is_azureml_run = not config.is_offline_run
# Per-subject model outputs for regression models are written per rank, and need to be aggregated here.
# Each thread per rank will come here, and upload its files to the run outputs. Rank 0 will later download them.
if is_azureml_run and world_size > 1 and isinstance(lightning_model, ScalarLightning):
upload_output_file_as_temp(lightning_model.train_subject_outputs_logger.csv_path, config.outputs_folder)
upload_output_file_as_temp(lightning_model.val_subject_outputs_logger.csv_path, config.outputs_folder)
# DDP will start multiple instances of the runner, one for each GPU. Those should terminate here after training.
# We can now use the global_rank of the Lightining model, rather than environment variables, because DDP has set
# all necessary properties.
if lightning_model.global_rank != 0:
logging.info(f"Terminating training thread with rank {lightning_model.global_rank}.")
sys.exit()
logging.info("Choosing the best checkpoint and removing redundant files.")
cleanup_checkpoint_folder(config.checkpoint_folder)
# Lightning modifies a ton of environment variables. If we first run training and then the test suite,
# those environment variables will mislead the training runs in the test suite, and make them crash.
# Hence, restore the original environment after training.
os.environ.clear()
os.environ.update(old_environ)
if world_size and isinstance(lightning_model, ScalarLightning):
if is_azureml_run and world_size > 1:
# In a DDP run on the local box, all ranks will write to local disk, hence no download needed.
# In a multi-node DDP, each rank would upload to AzureML, and rank 0 will now download all results and
# concatenate
for rank in range(world_size):
for mode in [ModelExecutionMode.TRAIN, ModelExecutionMode.VAL]:
file = mode.value + "/" + get_subject_output_file_per_rank(rank)
RUN_CONTEXT.download_file(name=TEMP_PREFIX + file, output_file_path=config.outputs_folder / file)
# Concatenate all temporary file per execution mode
for mode in [ModelExecutionMode.TRAIN, ModelExecutionMode.VAL]:
temp_files = (config.outputs_folder / mode.value).rglob(SUBJECT_OUTPUT_PER_RANK_PREFIX + "*")
result_file = config.outputs_folder / mode.value / SUBJECT_METRICS_FILE_NAME
for i, file in enumerate(temp_files):
temp_file_contents = file.read_text()
if i == 0:
# Copy the first file as-is, including the first line with the column headers
result_file.write_text(temp_file_contents)
else:
# For all files but the first one, cut off the header line.
result_file.write_text(os.linesep.join(temp_file_contents.splitlines()[1:]))
model_training_results = ModelTrainingResults(
train_results_per_epoch=list(storing_logger.to_metrics_dicts(prefix_filter=TRAIN_PREFIX).values()),
val_results_per_epoch=list(storing_logger.to_metrics_dicts(prefix_filter=VALIDATION_PREFIX).values()),
train_diagnostics=lightning_model.train_diagnostics,
val_diagnostics=lightning_model.val_diagnostics,
optimal_temperature_scale_values_per_checkpoint_epoch=[]
)
logging.info("Finished training")
# Since we have trained the model further, let the checkpoint_handler object know so it can handle
# checkpoints correctly.
checkpoint_handler.additional_training_done()
# Upload visualization directory to AML run context to be able to see it
# in the Azure UI.
if config.max_batch_grad_cam > 0 and config.visualization_folder.exists():
RUN_CONTEXT.upload_folder(name=VISUALIZATION_FOLDER, path=str(config.visualization_folder))
if resource_monitor:
# stop the resource monitoring process
logging.info("Shutting down the resource monitor process. Aggregate resource utilization:")
for name, value in resource_monitor.read_aggregate_metrics():
logging.info(f"{name}: {value}")
if not config.is_offline_run:
RUN_CONTEXT.log(name, value)
resource_monitor.kill()
return model_training_results