# clearml/clearml

ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution

Repository: https://github.com/clearml/clearml
Canonical: https://ross.abutalabs.com/products/clearml
Homepage: https://clear.ml/docs
Language: Python
License: Apache-2.0
License Family: permissive
Topics: version-control, experiment-manager, version, control, experiment, deeplearning, deep-learning, machine-learning, machinelearning, ai, clearml, k8s, devops, mlops, llmops
Last push: 2026-08-23T11:05:36+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 2641, "days_push": 10, "days_rel": 14, "gap_med": 21, "n_releases_24m": 20}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6840, forks 796 (observed 2026-08-28T04:09:50.543251+00:00)

## What it is
ClearML is an open-source Python SDK and platform for MLOps/LLMOps that provides auto-logged experiment tracking, data versioning, pipeline orchestration, scheduling, and model serving. It integrates with popular ML frameworks like PyTorch, TensorFlow, and Hugging Face, and can run against a hosted or self-hosted ClearML Server.

## Use cases
- track machine learning experiments automatically
- compare model training runs and metrics
- version control datasets on object storage
- orchestrate ML training pipelines on Kubernetes
- schedule and queue GPU training jobs
- deploy and monitor model serving endpoints
- log hyperparameters and artifacts from PyTorch or TensorFlow scripts

## When to choose
- you need end-to-end experiment tracking with minimal code changes
- you want orchestration, data versioning, and serving in one MLOps suite
- you need self-hosted or cloud-hosted experiment management
- you train models with PyTorch, TensorFlow, or Hugging Face and want auto-logging

## When to avoid
- you only need lightweight local metric logging without a server
- your project is not machine-learning related
- you want a fully serverless tool with no backend dependency

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, monitoring, workflow-automation, scheduling, data-science, sdk, analytics
- domain: machine-learning, deep-learning, large-language-models, data-science, developer-tools
- platform: python, windows, self-hosted, cloud
- tags: experiment-tracking, mlops, llmops, model-serving, data-versioning, pipeline-orchestration, auto-logging, hyperparameter-optimization, automation, linux, macos, docker, kubernetes

## Member repositories
- clearml/clearml (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:50.543251+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:41:51.867911+00:00, confidence not recorded.
  - readme: https://github.com/clearml/clearml (fetched 2026-08-28T04:09:50.543251+00:00, sha 99acbce4cb7f)
  - homepage: https://clear.ml/docs (fetched 2026-08-29T08:37:59.931217+00:00, sha 9d1f6196e047)
  - site_page: https://clear.ml/docs/latest/docs/integrations (fetched 2026-08-29T08:37:59.954077+00:00, sha 8f9f81218c77)
  - site_page: https://clear.ml/docs/latest/docs/faq (fetched 2026-08-29T08:37:59.955921+00:00, sha 77cd7ae076a3)
  - site_page: https://clear.ml/docs/latest/docs/clearml_sdk/clearml_sdk_setup (fetched 2026-08-29T08:37:59.940180+00:00, sha 7458a408318d)
  - site_page: https://clear.ml/docs/latest/docs/getting_started/auto_log_exp (fetched 2026-08-29T08:37:59.942104+00:00, sha 1d6984b9a5c2)
  - site_page: https://clear.ml/docs/latest/docs/fundamentals/projects (fetched 2026-08-29T08:37:59.943763+00:00, sha 5bd773037939)
  - site_page: https://clear.ml/docs/latest/docs/references/sdk/task (fetched 2026-08-29T08:37:59.945578+00:00, sha 5658df34a802)
  - site_page: https://clear.ml/docs/latest/docs/best_practices/data_scientist_best_practices (fetched 2026-08-29T08:37:59.950663+00:00, sha 3749a60cc2c5)
  - site_page: https://clear.ml/docs/latest/docs/guides (fetched 2026-08-29T08:37:59.952513+00:00, sha 07cb48a07cdc)
- Data as of 2026-08-30T08:39:29.467469+00:00.