---
title: "clearml"
type: "tool"
slug: "clearml-clearml"
canonical_url: "https://www.graphcanon.com/tools/clearml-clearml"
github_url: "https://github.com/clearml/clearml"
homepage_url: "https://clear.ml/docs"
stars: 6761
forks: 781
primary_language: "Python"
license: "Apache-2.0"
categories: ["developer-tools", "model-training", "inference-serving", "evaluation-observability"]
tags: ["llmops", "clearml", "deep-learning", "ai", "machine-learning", "k8s", "mlops"]
updated_at: "2026-07-07T19:51:30.78236+00:00"
---

# clearml

> ClearML - Auto-Magical CI/CD for AI workloads

A comprehensive suite that includes experiment management, MLOps/LLMOps orchestration, data management, model serving, and reports to streamline the development and production of ML/DL models.

## Facts

- Repository: https://github.com/clearml/clearml
- Homepage: https://clear.ml/docs
- Stars: 6,761 · Forks: 781 · Open issues: 565 · Watchers: 89
- Primary language: Python
- License: Apache-2.0
- Last pushed: 2026-07-07T11:48:37+00:00

## Categories

- [Developer Tools](/categories/developer-tools.md)
- [Model Training](/categories/model-training.md)
- [Inference & Serving](/categories/inference-serving.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)

## Tags

llmops, clearml, deep-learning, ai, machine-learning, k8s, mlops

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## README (excerpt)

```text
<div align="center" style="text-align: center">

<p style="text-align: center">
  <img align="center" src="docs/clearml-logo.svg#gh-light-mode-only" alt="Clear|ML"><img align="center" src="docs/clearml-logo-dark.svg#gh-dark-mode-only" alt="Clear|ML">
</p>

**[ClearML](https://clear.ml) - Auto-Magical Suite of tools to streamline your AI workflow
</br>Experiment Manager, MLOps/LLMOps and Data-Management**

    <br>
    


`🌟 ClearML is open-source - Leave a star to support the project! 🌟`

</div>

---
### ClearML

ClearML is a ML/DL development and production suite. It contains FIVE main modules:

- [Experiment Manager](#clearml-experiment-manager) - Automagical experiment tracking, environments and results
- [MLOps / LLMOps](https://github.com/clearml/clearml-agent) - Orchestration, Automation & Pipelines solution for ML/DL/GenAI jobs (Kubernetes / Cloud / bare-metal)  
- [Data-Management](https://github.com/clearml/clearml/blob/master/docs/datasets.md) - Fully differentiable data management & version control solution on top of object-storage 
  (S3 / GS / Azure / NAS)  
- [Model-Serving](https://github.com/clearml/clearml-serving) - *cloud-ready* Scalable model serving solution! 
  - **Deploy new model endpoints in under 5 minutes** 
  - Includes optimized GPU serving support backed by Nvidia-Triton 
  - **with out-of-the-box  Model Monitoring** 
- [Reports](https://clear.ml/docs/latest/docs/webapp/webapp_reports) - Create and share rich MarkDown documents supporting embeddable online content 
- :fire: [Orchestration Dashboard](https://clear.ml/docs/latest/docs/webapp/webapp_orchestration_dash/) - Live rich dashboard for your entire compute cluster (Cloud / Kubernetes / On-Prem)
- 🔥 💥 [Fractional GPUs](https://github.com/clearml/clearml-fractional-gpu) - Container based, driver level GPU memory limitation 🙀 !!!
  

Instrumenting these components is the **ClearML-server**, see [Self-Hosting](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) & [Free tier Hosting](https://app.clear.ml)  


---
<div align="center">

**[Sign up](https://app.clear.ml)  &  [Start using](https://clear.ml/docs/) in under 2 minutes**

---
**Friendly tutorials to get you started**

<table>
<tbody>
  <tr>
    <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb"><b>Step 1</b></a> - Experiment Management</td>
    <td><a target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
  </tr>
  <tr>
    <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb"><b>Step 2</b></a> - Remote Execution Agent Setup</td>
    <td><a target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
  </tr>
  <tr>
    <td><a href="https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb"><b>Step 3</b></a> - Remotely Execute Tasks</td>
    <td><a target="_blank" href="https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a></td>
  </tr>
</tbody>
</table>

</div>

---

<table>
<tbody>
  <tr>
    <td>Experiment Management</td>
    <td>Datasets</td>
  </tr>
  <tr>
    <td><a href="https://app.clear.ml"><img src="https://github.com/clearml/clearml/blob/master/docs/experiment_manager.gif?raw=true" width="100%"></a></td>
    <td><a href="https://app.clear.ml/datasets"><img src="https://github.com/clearml/clearml/blob/master/docs/datasets.gif?raw=tru
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/clearml-clearml`](/api/graphcanon/tools/clearml-clearml)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_