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clearmlData & analytics

clearml

A machine-learning development and MLOps platform for experiment tracking, remote orchestration, data versioning, and model workflows with hosted or self-hosted servers.

  • Data
  • DevOps
  • Data analysis
  • Automation
  • Monitoring / Observability
  • Web
  • CLI
  • Library/framework
  • Browser
  • Self-hostable
  • Runs locally
  • Needs API key
Open websiteView on GitHub
clearml screenshot
Popularity
6.9k Stars
GitHub stars
Recent activity
9/28/2026
Updated in the last 30 days
License
APACHE-2.0
Permissive

Why it matters

We look beyond stars: what problem it solves, whether it creates real utility, and what makes its approach worth noticing.

Last 90 days

Problem

Addresses the fragmentation of experiment tracking, chaotic model versioning, and the difficulty of orchestrating compute resources in ML development.

Practical value

Provides high utility by enabling full-lifecycle monitoring from experiment tracking to model deployment with minimal code integration.

Innovation / differentiation

Integrates GPU resource partitioning and automated orchestration with experiment tracking, tightly coupling MLOps workflows with infrastructure management.

Leverage potential

Supports both self-hosting and managed services, with compatibility for major cloud providers and Kubernetes, lowering the barrier for teams to build ML infrastructure.

Why now

Fills the gap between experimentation and production as AI model development grows in complexity and production requirements increase.

Community activity

In the last 90 days there were 15 new issues and 50 pull requests; the bounded issue/PR samples include 13 issue authors and 9 PR contributors, with at least 2 releases.

Maintainer responsiveness

The 15-issue window sample had a 7% close rate, and the 50-pull-request sample had a 56% merge rate. Maintainer-response observations covered 100% of that issue sample, with a 13% response rate and median first response of 6.4 days.

9 contributors in PR sampleAt least 2 releasesIssue response rate 13% · sample 15 (100% coverage)Median first response 6.4dPR merge rate 56% · 50 sampled in window

Key highlights

  • Capture source state, environments, hyperparameters, logs, resource metrics, and model artifacts
  • Run remote tasks, queues, and training jobs through ClearML Agent
  • Extend workflows with dataset versioning, pipelines, model serving, and compute orchestration

Quick start

How it is installed, how hard it is, and where to start.

Where it runs

Self-hosted (your own server)

Difficulty

Medium — some setup needed

Self-hostableRuns locally
  1. 01Choose the hosted ClearML service or deploy ClearML Server using the official self-hosting guide.
  2. 02Install the Python SDK in the training environment with pip install clearml.
  3. 03Create server credentials and run clearml-init to connect the client.
  4. 04Create a Task in the training script to start experiment tracking, then configure ClearML Agent when remote execution is needed.

Best for

  • Teams that want data on their own servers
  • Developers who want to try it on their machine

More about it

ClearML combines machine-learning experiment tracking, task execution, data management, and MLOps workflows around a shared server. Its Python SDK can capture source-control state, execution environments, hyperparameters, console output, resource metrics, model snapshots, TensorBoard data, and other experiment artifacts, then expose them through the ClearML Server web interface.

Beyond tracking, ClearML uses Agents for remote execution and queue orchestration, while datasets, pipelines, model serving, and compute orchestration are available as related modules in the same ecosystem. Teams can use the hosted ClearML service or deploy their own server.

A typical setup installs the clearml Python package, connects it to a server with clearml-init, and creates a Task in the training code. It is aimed at ML teams that already have Python workloads and want reproducible experiment records, remote execution, and shared operational visibility.

Sources

Each field shows its status and source — expand to review.

14 · Expand

  • capability tags

    Verified

    data_analysis, automation, monitoring

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • editor note

    Verified

    {"en":"ClearML goes beyond storing loss curves by connecting experiment metadata, runtime environments, remote execution, and data or model workflows. It becomes more useful once training jobs are spread across machines or team members and simple experiment logging is no longer enough.","zh":"ClearML 的价值不只是记录 loss 曲线，而是把实验元数据、运行环境、远程执行和数据/模型流程连起来。对于训练任务已经开始分散到多台机器或团队成员之间的项目，它比单独的实验日志工具覆盖面更完整。"}

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • how to use

    Verified

    {"steps":[{"en":"Choose the hosted ClearML service or deploy ClearML Server using the official self-hosting guide.","zh":"选择 ClearML 托管服务，或按照官方 Self-Hosting 文档部署 ClearML Server。"},{"en":"Install the Python SDK in the training environment with `pip install clearml`.","zh":"在训练环境执行 `pip install clearml` 安装 Python SDK。"},{"en":"Create server credentials and run `clearml-init` to connect the client.","zh":"创建服务器凭据并运行 `clearml-init` 完成客户端连接。"},{"en":"Create a `Task` in the training script to start experiment tracking, then configure ClearML Agent when remote execution is needed.","zh":"在训练脚本中创建 `Task`，开始记录实验；需要远程执行时再配置 ClearML Agent。"}],"installAt":"self_host","difficulty":"medium"}

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • intro

    Verified

    {"en":"ClearML combines machine-learning experiment tracking, task execution, data management, and MLOps workflows around a shared server. Its Python SDK can capture source-control state, execution environments, hyperparameters, console output, resource metrics, model snapshots, TensorBoard data, and other experiment artifacts, then expose them through the ClearML Server web interface.\n\nBeyond tracking, ClearML uses Agents for remote execution and queue orchestration, while datasets, pipelines, model serving, and compute orchestration are available as related modules in the same ecosystem. Teams can use the hosted ClearML service or deploy their own server.\n\nA typical setup installs the `clearml` Python package, connects it to a server with `clearml-init`, and creates a `Task` in the training code. It is aimed at ML teams that already have Python workloads and want reproducible experiment records, remote execution, and shared operational visibility.","zh":"ClearML 把机器学习实验记录、任务执行、数据管理和后续 MLOps 流程放到同一套平台里。Python SDK 可以记录代码版本、运行环境、超参数、标准输出、资源使用、模型快照和 TensorBoard 等实验结果，并把这些信息汇总到 ClearML Server 的 Web 界面。\n\n在实验追踪之外，ClearML 还提供 Agent 用于远程执行和队列调度，并把数据集版本、流水线、模型服务和计算资源编排作为同一生态的扩展模块。服务器既可以使用 ClearML 的托管服务，也可以自行部署。\n\n典型接入方式是安装 `clearml` Python 包，通过 `clearml-init` 连接服务器，然后在训练代码里创建 `Task`。因此它更适合已经有 Python 训练脚本，希望补上实验可追溯、远程运行和团队共享能力的机器学习项目。"}

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • Latest release

    Verified

    v2.1.12

    Source: GitHub API · latest_release=v2.1.12 · 10/2/2026

  • License

    Verified

    Apache-2.0

    Source: GitHub API · license.spdx_id=Apache-2.0 · 10/2/2026

  • needs api key

    Verified

    Yes

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • One-liner

    Verified

    {"en":"A machine-learning development and MLOps platform for experiment tracking, remote orchestration, data versioning, and model workflows with hosted or self-hosted servers.","zh":"覆盖实验追踪、远程任务编排、数据版本和模型工作流的机器学习开发与 MLOps 平台，可使用托管服务或自建服务器。"}

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • Platforms

    Verified

    browser

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • Category hint

    Inferred from materials

    ai-apps

    Source: Project README · hint=ai-apps · 8/15/2026

  • product forms

    Verified

    web, cli, library_framework

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • role tags

    Verified

    data, devops

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • supports local

    Verified

    Yes

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

  • supports self host

    Verified

    Yes

    Source: manual_curated · Manually curated from the verified project README; no AI draft. · 8/17/2026

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