Allegro Trains

ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, ML-Ops and Data-Management
Under Apache License 2.0
By allegroai

deep-learning machine-learning devops ai machinelearning deeplearning k8s control experiment mlops version-control experiment-manager version trains trainsai clearml

**ClearML - Auto-Magical Suite of tools to streamline your ML workflow
Experiment Manager, ML-Ops and Data-Management**

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Formerly known as Allegro Trains

ClearML is a ML/DL development and production suite, it contains three main modules:

Instrumenting these components is the ClearML-server, see Self-Hosting & Free tier Hosting

**[Sign up]( & [Start using]( in under 2 minutes**

ClearML Experiment Manager

Adding only 2 lines to your code gets you the following

Start using ClearML

pip install clearml

Add two lines to your code:
from clearml import Task
task = Task.init(project_name='examples', task_name='hello world')

You are done, everything your process outputs is now automagically logged into ClearML.
Next step automation! Learn more on ClearML two clicks automation here

ClearML Architecture

The ClearML run-time components:

Additional Modules

Why ClearML?

ClearML is our solution to a problem we share with countless other researchers and developers in the machine
learning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.
ClearML tracks and controls the process by associating code version control, research projects,
performance metrics, and model provenance.

We designed ClearML specifically to require effortless integration so that teams can preserve their existing methods
and practices.

We believe ClearML is ground-breaking. We wish to establish new standards of true seamless integration between
experiment management,ML-Ops and data management.

Who We Are

ClearML is supported by the team behind,
where we build deep learning pipelines and infrastructure for enterprise companies.

We built ClearML to track and control the glorious but messy process of training production-grade deep learning models.
We are committed to vigorously supporting and expanding the capabilities of ClearML.

We promise to always be backwardly compatible, making sure all your logs, data and pipelines
will always upgrade with you.


Apache License, Version 2.0 (see the LICENSE for more information)

Documentation, Community & Support

More information in the official documentation and on YouTube.

For examples and use cases, check the examples folder and corresponding documentation.

If you have any questions: post on our Slack Channel, or tag your questions on stackoverflow with 'clearml' tag (previously trains tag).

For feature requests or bug reports, please use GitHub issues.

Additionally, you can always find us at [email protected]


PRs are always welcomed :heart: See more details in the ClearML Guidelines for Contributing.

May the force (and the goddess of learning rates) be with you!