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The ClearML Python Package supports the automatic logging that documents the experiment for you, and an extensive set of powerful features and functionality you can use to improve experimentation, other workflows, and get more out of ClearML.

The ClearML Python Package collects data from scripts including the Git repository (branch, commit ID, and uncommitted changes), working directory and entry point, hyperparameters, initial weights model, model snapshots (checkpoints), output model, other artifacts, metrics, logs, other reported data (from libraries and visualization toolkits), and debug samples.

In conjunction with the ClearML Hosted Service (or self-hosted ClearML Server) and ClearML Agent, the ClearML Python Package allows you and your teammates to collaborate programmatically and using the ClearML Web UI.


  • Task - The task module contains the task.Task class which is the code template for all Task features and functionality, including collecting data from scripts, storing that data in a Task object, automatic bindings with frameworks (TensorFlow/TensorBoard, PyTorch, Keras, Fastai, scikit-learn), libraries (Pandas, Plotly, AutoKeras), and visualization tools (Matplotlib, Seaborn), and a robust set of methods for Task execution, cloning, connecting parameter dictionaries, configurations, models, working with storage, and more.

  • Logger - The logger module contains the logger.Logger class which is the ClearML console log and metric statistics interface, and contains methods for explicit reporting, setting an upload destination in storage for debug samples, logger cache control, and TensorBoard support in addition to ClearML automatic TensorBoard logging.

  • Model - The model module contains three classes: model.Model which represents an existing model in ClearML that can be loaded and connected to a Task, model.InputModel which represents an existing model that you can load into ClearML, and model.OutputModel which represents the experiment output model that is always connected to the Task.

  • Automation - The automation module contains classes supporting hyperparameter optimization, including Optuna, HpBandSter, grid searching, random searching, you own customized search strategies, and resource budgeting for searches; the AWS autoscaler; pipeline controllers; and Task monitoring.

  • StorageManager - The storage module contains the storage.manager.StorageManager class which provides support for downloading and uploading from storage, including folders, S3, Google Cloud Storage, Azure Storage, and http(s).

  • Dataset - The dataset module contains classes that helps manage Dataset. Users can create, modify and delete datasets as well as retrieve them for use in their code


ClearML example scripts in the examples folder of the clearml GitHub repository. They are pre-loaded in the ClearML Hosted Service, and can be viewed, cloned, and edited in the ClearML Web UI, ClearML Examples project. The examples are each explained in this documentation's examples section.