tensorboard
- class aitoolbox.torchtrain.callbacks.tensorboard.TensorboardReporterBaseCB(callback_name, log_dir=None, is_project=True, project_name=None, experiment_name=None, local_model_result_folder_path=None, cloud_save_mode=None, bucket_name=None, cloud_dir_prefix=None, **kwargs)[source]
Bases:
AbstractExperimentCallback
Base Tensorboard callback wrapping SummaryWriter
This base callback is intended to be inherited and extended with the more concrete callback geared towards a particular use-case. This callback only setups all the folders needed for local and cloud experiment tracking.
- Parameters:
callback_name (str) – name of the callback
log_dir (str or None) – save directory location
is_project (bool) – set to
True
if the results should be saved into the TrainLoop-created project folder structure or toFalse
if you want to save into a specific full path given in the log_dir parameter.project_name (str or None) – root name of the project
experiment_name (str or None) – name of the particular experiment
local_model_result_folder_path (str or None) – root local path where project folder will be created
cloud_save_mode (str or None) – Storage destination selector. For AWS S3: ‘s3’ / ‘aws_s3’ / ‘aws’ For Google Cloud Storage: ‘gcs’ / ‘google_storage’ / ‘google storage’ Everything else results just in local storage to disk
bucket_name (str) – name of the bucket in the cloud storage
cloud_dir_prefix (str) – path to the folder inside the bucket where the experiments are going to be saved
**kwargs – additional arguments for
torch.utils.tensorboard.SummaryWriter
wrapped inside this callback
- log_mid_train_loss()[source]
Log the training loss at the batch iteration level
Logs current batch loss and the accumulated average loss.
- Returns:
None
- log_train_history_metrics(metric_names)[source]
Log the train history metrics at the end of the epoch
- Parameters:
metric_names (list) – list of train history tracked metrics to be logged
- Returns:
None
- class aitoolbox.torchtrain.callbacks.tensorboard.TensorboardTrainBatchLoss(batch_log_frequency=1, log_dir=None, is_project=True, project_name=None, experiment_name=None, local_model_result_folder_path=None, cloud_save_mode=None, bucket_name=None, cloud_dir_prefix=None, **kwargs)[source]
Bases:
TensorboardReporterBaseCB
Tensorboard training loss logger
- Parameters:
batch_log_frequency (int) – frequency of logging
log_dir (str or None) – save directory location
is_project (bool) – set to
True
if the results should be saved into the TrainLoop-created project folder structure or toFalse
if you want to save into a specific full path given in the log_dir parameter.project_name (str or None) – root name of the project
experiment_name (str or None) – name of the particular experiment
local_model_result_folder_path (str or None) – root local path where project folder will be created
cloud_save_mode (str or None) – Storage destination selector. For AWS S3: ‘s3’ / ‘aws_s3’ / ‘aws’ For Google Cloud Storage: ‘gcs’ / ‘google_storage’ / ‘google storage’ Everything else results just in local storage to disk
bucket_name (str) – name of the bucket in the cloud storage
cloud_dir_prefix (str) – path to the folder inside the bucket where the experiments are going to be saved
**kwargs – additional arguments for
torch.utils.tensorboard.SummaryWriter
wrapped inside this callback
- class aitoolbox.torchtrain.callbacks.tensorboard.TensorboardTrainHistoryMetric(metric_names=None, log_dir=None, is_project=True, project_name=None, experiment_name=None, local_model_result_folder_path=None, cloud_save_mode=None, bucket_name=None, cloud_dir_prefix=None, **kwargs)[source]
Bases:
TensorboardReporterBaseCB
Tensorboard training history values logger
At each end of epoch logs to tensorboard the last value in the training history stored for some tracked metric.
- Parameters:
metric_names (list or None) – list of metric names tracked in the training history. If left to
None
, all the metrics in the training history will be logged.log_dir (str or None) – save directory location
is_project (bool) – set to
True
if the results should be saved into the TrainLoop-created project folder structure or toFalse
if you want to save into a specific full path given in the log_dir parameter.project_name (str or None) – root name of the project
experiment_name (str or None) – name of the particular experiment
local_model_result_folder_path (str or None) – root local path where project folder will be created
cloud_save_mode (str or None) – Storage destination selector. For AWS S3: ‘s3’ / ‘aws_s3’ / ‘aws’ For Google Cloud Storage: ‘gcs’ / ‘google_storage’ / ‘google storage’ Everything else results just in local storage to disk
bucket_name (str) – name of the bucket in the cloud storage
cloud_dir_prefix (str) – path to the folder inside the bucket where the experiments are going to be saved
**kwargs – additional arguments for
torch.utils.tensorboard.SummaryWriter
wrapped inside this callback
- class aitoolbox.torchtrain.callbacks.tensorboard.TensorboardFullTracking(metric_names=None, batch_log_frequency=1, log_dir=None, is_project=True, project_name=None, experiment_name=None, local_model_result_folder_path=None, cloud_save_mode=None, bucket_name=None, cloud_dir_prefix=None, **kwargs)[source]
Bases:
TensorboardReporterBaseCB
Full Tensorboard logger
At each end of epoch logs to tensorboard the last value in the training history stored for some tracked metric. Also logs the training loss at the batch iteration level.
- Parameters:
metric_names (list or None) – list of metric names tracked in the training history. If left to
None
, all the metrics in the training history will be logged.batch_log_frequency (int) – frequency of logging
log_dir (str or None) – save directory location
is_project (bool) – set to
True
if the results should be saved into the TrainLoop-created project folder structure or toFalse
if you want to save into a specific full path given in the log_dir parameter.project_name (str or None) – root name of the project
experiment_name (str or None) – name of the particular experiment
local_model_result_folder_path (str or None) – root local path where project folder will be created
cloud_save_mode (str or None) – Storage destination selector. For AWS S3: ‘s3’ / ‘aws_s3’ / ‘aws’ For Google Cloud Storage: ‘gcs’ / ‘google_storage’ / ‘google storage’ Everything else results just in local storage to disk
bucket_name (str) – name of the bucket in the cloud storage
cloud_dir_prefix (str) – path to the folder inside the bucket where the experiments are going to be saved
**kwargs – additional arguments for
torch.utils.tensorboard.SummaryWriter
wrapped inside this callback