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 observed · 2026-08-28
Health v2 · maintenance only
99/100
- Activity 99
- Release rhythm 98
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 21
- age_days: 2641
- days_rel: 14
- days_push: 10
- n_releases_24m: 20
Adoption not part of the score
6840 stars · 796 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity stable
machine-learning monitoring workflow-automation scheduling data-science sdk analytics machine-learning deep-learning large-language-models data-science developer-tools python windows self-hosted cloud experiment-tracking mlops llmops model-serving data-versioning pipeline-orchestration auto-logging hyperparameter-optimization automation linux macos docker kubernetes
10 sources
- readme: https://github.com/clearml/clearml · fetched 2026-08-28 · 99acbce4cb7f
- homepage: https://clear.ml/docs · fetched 2026-08-29 · 9d1f6196e047
- site_page: https://clear.ml/docs/latest/docs/integrations · fetched 2026-08-29 · 8f9f81218c77
- site_page: https://clear.ml/docs/latest/docs/faq · fetched 2026-08-29 · 77cd7ae076a3
- site_page: https://clear.ml/docs/latest/docs/clearml_sdk/clearml_sdk_setup · fetched 2026-08-29 · 7458a408318d
- site_page: https://clear.ml/docs/latest/docs/getting_started/auto_log_exp · fetched 2026-08-29 · 1d6984b9a5c2
- site_page: https://clear.ml/docs/latest/docs/fundamentals/projects · fetched 2026-08-29 · 5bd773037939
- site_page: https://clear.ml/docs/latest/docs/references/sdk/task · fetched 2026-08-29 · 5658df34a802
- site_page: https://clear.ml/docs/latest/docs/best_practices/data_scientist_best_practices · fetched 2026-08-29 · 3749a60cc2c5
- site_page: https://clear.ml/docs/latest/docs/guides · fetched 2026-08-29 · 07cb48a07cdc
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| clearml/clearml | main | 99 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem