experiment_saver
- class aitoolbox.experiment.experiment_saver.AbstractExperimentSaver[source]
Bases:
ABC
- abstract save_experiment(model, result_package, training_history, experiment_timestamp=None, save_true_pred_labels=False, separate_files=False, protect_existing_folder=True)[source]
Method which all the experiment savers need to implement which instructs how the experiment should be saved
- Parameters:
model –
result_package (aitoolbox.ExperimentSave.result_package.AbstractResultPackage) –
training_history (aitoolbox.experiment.training_history.TrainingHistory) –
experiment_timestamp (str) – time stamp of the training start
save_true_pred_labels (bool) – should ground truth labels also be saved
separate_files (bool) – should the results be saved in separate pickle files or should all the results be batched together in a single results file
protect_existing_folder (bool) – can override potentially already existing folder or not
- Returns:
string paths where the experiment files were saved
- Return type:
- class aitoolbox.experiment.experiment_saver.BaseFullExperimentSaver(model_saver, results_saver, project_name, experiment_name)[source]
Bases:
AbstractExperimentSaver
Base full experiment saver functionality used by the underlying experiment saver derivations
- Parameters:
model_saver (aitoolbox.cloud.AWS.model_save.AbstractModelSaver) – selected saver used for model saving
results_saver (aitoolbox.cloud.AWS.results_save.AbstractResultsSaver) – selected saver used for results save
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
- save_experiment(model, result_package, training_history, experiment_timestamp=None, save_true_pred_labels=False, separate_files=False, protect_existing_folder=True)[source]
Save the experiment snapshot formed out of the model and model’s results
- Parameters:
model (dict or keras.Model) – model representation. If used with PyTorch it is a simple dict under the hood. In the case of Keras training this would be the keras Model.
result_package (aitoolbox.experiment.result_package.abstract_result_packages.AbstractResultPackage) –
training_history (aitoolbox.experiment.training_history.TrainingHistory) –
experiment_timestamp (str) – time stamp at the start of training
save_true_pred_labels (bool) – should ground truth labels also be saved
separate_files (bool) – should the results be saved in separate pickle files or should all the results be batched together in a single results file
protect_existing_folder (bool) – can override potentially already existing folder or not
- Returns:
cloud_model_path, cloud_results_path
- Return type:
- class aitoolbox.experiment.experiment_saver.BaseFullExperimentS3Saver(model_saver, project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentSaver
Base experiment saver implementing the S3 saving functionality
This is used by the underlying experiment S3 saver derivations
- Parameters:
model_saver (aitoolbox.cloud.AWS.model_save.AbstractModelSaver) – selected cloud model saver implementing the saving logic for the desired cloud storage provider file saving
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created
- class aitoolbox.experiment.experiment_saver.FullPyTorchExperimentS3Saver(project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentS3Saver
S3 saver for PyTorch experiments
- Parameters:
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created
- class aitoolbox.experiment.experiment_saver.FullKerasExperimentS3Saver(project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentS3Saver
S3 saver for Keras experiments
- Parameters:
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created
- class aitoolbox.experiment.experiment_saver.BaseFullExperimentGoogleStorageSaver(model_saver, project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentSaver
Base experiment saver implementing the Google Storage saving functionality
This is used by the underlying experiment Google Storage saver derivations
- Parameters:
model_saver (aitoolbox.cloud.AWS.model_save.AbstractModelSaver) – selected cloud model saver implementing the saving logic for the desired cloud storage provider file saving
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created
- class aitoolbox.experiment.experiment_saver.FullPyTorchExperimentGoogleStorageSaver(project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentGoogleStorageSaver
Google Storage saver for PyTorch experiments
- Parameters:
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created
- class aitoolbox.experiment.experiment_saver.FullKerasExperimentGoogleStorageSaver(project_name, experiment_name, bucket_name='model-result', cloud_dir_prefix='', local_model_result_folder_path='~/project/model_result')[source]
Bases:
BaseFullExperimentGoogleStorageSaver
Google Storage saver for Keras experiments
- Parameters:
project_name (str) – root name of the project
experiment_name (str) – name of the particular experiment
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
local_model_result_folder_path (str) – root local path where project folder will be created