clearml

What it is and what it does

ClearML is a comprehensive ML/DL development suite designed to reduce boilerplate in experiment tracking and MLOps workflows. At its core, it captures experiment metadata—source control state, environment packages, hyperparameters, and initial model weights—automatically with just two lines of code. It logs all outputs (stdout, stderr, resource metrics, model snapshots, artifacts, and tensorboard scalars) to a central server, eliminating manual logging and enabling reproducibility.

Beyond experiment tracking, ClearML integrates data management (versioning datasets on S3, GCS, Azure, or NAS), model serving, orchestration dashboards, and remote execution agents. It supports a wide range of ML frameworks and works with Jupyter notebooks and PyCharm. The package requires connection to a ClearML server (either the hosted free tier or self-hosted) to store and visualize experiments, making it a full-stack platform rather than a standalone logging library.

Use it for:

• Track and compare multiple training runs with automatic capture of code, environment, and hyperparameters without manual logging.
• Reproduce past experiments by querying stored environment and parameter snapshots from the ClearML server.
• Monitor resource utilization (CPU, GPU, memory) and system metrics across distributed training jobs.
• Version and manage datasets with full lineage tracking across S3, GCS, Azure, or local storage.
• Orchestrate and schedule remote training jobs on Kubernetes, cloud platforms, or bare-metal machines.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

ClearML is an ML/DL development and production suite that automates experiment tracking, captures environment and hyperparameter data, logs outputs and metrics, and provides orchestration and data management for machine learning workflows.

Yes, if you run ML experiments and want centralized tracking with minimal code changes. The two-line integration is genuinely low-friction, and the platform covers experiment management, data versioning, and orchestration in one package. Requires a ClearML server (free hosted tier available), so it's not a standalone library. Active maintenance, permissive license, and no known vulnerabilities make it a safe choice. Best suited for teams or individuals running multiple experiments who value reproducibility and automation.

Install

clearml on PyPI

pip install clearml

uv add clearml

poetry add clearml

Installing clearml

Before you install

Low install friction with a pure Python wheel distribution. Active maintenance with a recent release (7 days ago) and substantial community engagement (6820 GitHub stars). Depends on 15 runtime packages including common utilities like requests, numpy, and PyYAML, all widely available.

License in practice

Licensed under Apache License 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions—suitable for proprietary projects.

Quickstart

pip install clearml

from clearml import Task
task = Task.init(project_name='examples', task_name='hello world')

Requires a ClearML server connection; sign up at app.clear.ml or deploy your own server, then run `clearml-init` to configure credentials.

Verify before relying

• Whether the package supports all advertised ML frameworks (PyTorch, TensorFlow, Keras, XGBoost, etc.) equally well or if some integrations are more mature than others.
• Performance overhead of automatic logging and monitoring on training speed or resource consumption.
• Scalability limits for very large datasets or high-frequency metric logging.

Package facts

Evidence: clearml-2.1.11-py2.py3-none-any.whl

Keywords: clearml, trains, development, machine, deep, learning, version, control, machine-learning, machinelearning, deeplearning, deep-learning, experiment-manager, experimentmanager

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