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

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

  • Experiment Manager - Automagical experiment tracking, environments and results
  • ML-Ops - Automation, Pipelines & Orchestration solution for ML/DL jobs (K8s / Cloud / bare-metal)
  • Data-Management - Fully differentiable data management & version control solution on top of object-storage


ClearML Experiment Manager

Adding only 2 lines to your code gets you the following

  • Complete experiment setup log
    • Full source control info including non-committed local changes
    • Execution environment (including specific packages & versions)
    • Hyper-parameters
      • ArgParser for command line parameters with currently used values
      • Explicit parameters dictionary
      • Tensorflow Defines (absl-py)
      • Hydra configuration and overrides
    • Initial model weights file
  • Full experiment output automatic capture
    • stdout and stderr
    • Resource Monitoring (CPU/GPU utilization, temperature, IO, network, etc.)
    • Model snapshots (With optional automatic upload to central storage: Shared folder, S3, GS, Azure, Http)
    • Artifacts log & store (Shared folder, S3, GS, Azure, Http)
    • Tensorboard/TensorboardX scalars, metrics, histograms, images, audio and video samples
    • Matplotlib & Seaborn
    • ClearML Explicit Logging interface for complete flexibility.
  • Extensive platform support and integrations

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:

  • The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods.
  • The ClearML Server storing experiment, model, and workflow data, and supporting the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. It is available as a hosted service and open source for you to deploy your own ClearML Server.
  • The ClearML Agent for ML-Ops orchestration, experiment and workflow reproducibility, and scalability.