ClearML Python Package¶
This reference section provides detailed information about the ClearML Python Package classes and their usage. This section also includes ClearML Python Package Extras which are additional ClearML components that require installation and configuration separate from the ClearML Python Package. These classes and extras allow you to implement the many features of ClearML for enhanced experimentation and automation. This is in addition to the automagical capturing of experiment inputs and outputs.
Task class contains methods to manage all the parts of experiments and integrate them with ClearML for tracking, storage, coding, analysis, comparison, and experiment management, including:
Task.init- Initialize a Task (experiment).
connect- Connect the parts of an experiment to the experiment in ClearML, including hyperparameter dictionaries, input and output models, configuration dictionaries, and label enumeration.
get_logger- Create a logger object which you can use for explicit reporting.
get_task- Get a reference to any experiment in ClearML by Task Id, task name and project name, or task name only.
register_artifact- Register an artifact, and ClearML logs changes to it.
get_registered_artifacts- After you register an artifact, retrieve a reference to it, manipulate the artifact in your code, and the changes are updated in ClearML.
upload_artifact- Upload an artifact, but ClearML does not logs changes to it.
get_model_config_dict- Get the configuration as a dictionary and then easily work with it in your code.
set_model_config- Set the model configuration before creating an output model so that the output model inherits the configuration properties.
set_model_label_enumeration- Similar to set_model_config(), but for label enumeration.
clone- Create an editable copy of an experiment. For example, clone an experiment for hyperparameter tuning or multiple times for automation.
enqueue- Add an experiment to a queue. Similar to dequeue() above, enqueue() allows you to develop scripts which can automate your experimentation.
dequeue- Remove an enqueued experiment from a queue.
reset- Make an experiment editable and allow remote execution of the experiment. This deletes output of the previous.
set_initial_iteration- To continue a previous experiment run, set the initial iteration (set to a non-zero).
get_initial_iteration- Get the value of the initial iteration.
Logger class and methods to extend ClearML automagical capturing of stdout and stderr with ClearML’ own explicit reporting for scalar metrics, any plot data in a variety of chart types, text messages, errors, warnings, debug messages, uploading tables, and uploading debug samples (images, audio, and video), as well as other methods.
Use the Model class’s
OutputModel classes and methods to augment control of input models and output using methods such as:
InputModel.import_model- Import a pre-trained model for your experiment.
InputModel.connect- Connect the current model the Task (experiment).
InputModel.config_dict- Get the input model’s configuration as a dictionary.
InputModel.get_weights- Download the base model.
OutputModel.connect- Connect the output model to the Task (experiment).
OutputModel.set_upload_destination- Specify a destination for uploading debug samples, including images, audio, and video.
The Automation Module reference page provides detailed information for the following:
To list, download, and upload files to / from storage, as well as manage cache, use the
StoreHelper class methods.
ClearML Python Package Extras
ClearML Python Package Extras are components of the ClearML Python Package which provide additional features, but are not installed when you install ClearML (meaning, they are not installed when you execute,
pip install clearml). They are referred to as extras, because they are defined in the ClearML Python Package setup.py
extras_require dictionary. Python package components in the
extras_require dictionary require separate installation.
The ClearML Python Package Extras include extras for experiment storage of model checkpoints (snapshots) and other artifacts. The supported Cloud storage types include:
Google Cloud Storage
These extras for storage also require that you add your storage bucket names, credentials, and related information to your ClearML configuration file.
To install and configure any of these, follow the instructions on the ClearML Python Package Extras page.
After installing and configuring, you can use the Cloud storage in your Python experiment scripts by calling the
Task.init method, and specifying the