AI Frameworks · clearml

clearml

ClearML is an open-source MLOps platform that automates experiment tracking, model management, and orchestration for AI/ML workloads. It provides centralized tracking, data versioning, pipeline orchestration, and model serving with support for Kubernetes and cloud deployment.

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Source: GitHub — github.com/clearml/clearml

6.8k

GitHub stars

781

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	clearml/clearml
Owner	clearml
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	6.8k
Forks	781
Open issues	565
Latest release	v2.1.10 (2026-07-01)
Last updated	2026-07-07
Source	https://github.com/clearml/clearml

What clearml is

Python-based MLOps suite with five core modules: experiment manager (auto-capture of code, environment, metrics), data management with versioning on object storage (S3, GCS, Azure), orchestration for distributed job scheduling, model serving with GPU optimization via Triton, and reporting. Integrates with PyTorch, TensorFlow, Keras, XGBoost, scikit-learn, and configuration frameworks (Hydra, argparse, Click).

Quickstart

Get the clearml source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/clearml/clearml.git2cd clearml3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Multi-framework Experiment Tracking at Scale

Teams running diverse ML experiments across PyTorch, TensorFlow, XGBoost, and LightGBM benefit from automatic capture of hyperparameters, code snapshots, resource metrics, and artifacts with minimal code instrumentation (2 lines).

Distributed Job Orchestration on Kubernetes or Cloud

Organizations needing centralized scheduling and monitoring of ML pipelines across on-prem, Kubernetes, and multi-cloud environments can use ClearML agent for remote execution and the orchestration dashboard for real-time cluster visibility.

Data Versioning and Lineage for Production Pipelines

Data-intensive workflows requiring version control, reproducibility, and integration with S3/GCS/Azure storage can leverage ClearML's data management layer with object-storage backend and linked experiment/dataset tracking.

Implementation considerations

Requires ClearML server deployment (cloud-hosted free tier available or self-hosted option); integration with existing ML infrastructure (Jupyter, PyCharm, CI/CD) varies by setup.
Agent-based orchestration needs configuration for compute pools, worker queues, and resource limits; Kubernetes deployment documented but requires cluster access and networking setup.
Data management layer depends on external object storage (S3, GCS, Azure); ensure credentials, IAM policies, and network access are pre-configured before data versioning workflows.
Experiment auto-capture works best with instrumentation-light codebases; legacy scripts with hardcoded paths or embedded config may require refactoring for full tracking benefit.
GPU serving via Triton integration and fractional GPU support require Nvidia runtime; validate container and driver compatibility in target deployment environment.

When to avoid it — and what to weigh

Lightweight, Stateless Logging Only — If you need only simple metric logging without infrastructure overhead, ClearML's full-stack approach (server + agents + orchestration) may be over-architected; consider lighter alternatives like Weights & Biases or Neptune.
Non-Python or Legacy Language Workflows — ClearML is Python-first; support for R, Java, or Scala ML frameworks is not clearly documented. If primary workload is non-Python, integration effort is unknown.
Highly Restricted Security Environments — Self-hosted deployments require managing a ClearML server (backend infrastructure). If air-gapped or zero-internet deployments with strict approval processes are mandatory, deployment complexity and security review burden are significant.
Minimal Budget for Infrastructure Management — Running ClearML server on-prem or self-hosted requires DevOps effort; free tier is cloud-hosted. Hidden costs: agent infrastructure, storage backend setup, monitoring/logging systems integration.

License & commercial use

ClearML is licensed under Apache License 2.0, a permissive OSI-approved license allowing commercial use, modification, and distribution under the same license terms.

Apache 2.0 permits commercial use in proprietary products and services. However, any distributions of modified ClearML code must retain Apache 2.0 license notices. Use of the free ClearML-hosted tier (app.clear.ml) is subject to separate cloud service terms; self-hosted deployments have no usage restrictions from the license. Review terms of service for hosted tier and any commercial support agreements separately.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	High
DEV.co fit	Good
Assessment confidence	High

Security considerations

ClearML handles sensitive data (code, credentials, model weights, training data). No formal security audit or CVE history provided in data; self-hosted deployments require securing MongoDB, API endpoints, and inter-service communication. Hosted tier relies on Clear.ML's infrastructure security (unknown posture). Credentials (storage keys, API tokens) must be securely injected into agents and audit-logged. Data encryption at rest/transit not explicitly documented in data provided—requires review.

Alternatives to consider

Weights & Biases (W&B)

SaaS-first, lighter instrumentation, similar multi-framework support; better for teams wanting managed infrastructure without self-hosting; higher per-user cost at scale.

MLflow

Simpler, lightweight experiment tracking; better for small teams or academic projects; lacks built-in orchestration and data versioning; less opinionated architecture.

Kubeflow

Kubernetes-native, stronger on ML pipeline orchestration; steeper learning curve, less experiment-tracking-first; better for enterprises already invested in Kubernetes.

Software development agency

Build on clearml with DEV.co software developers

Evaluate ClearML for your team by reviewing self-hosting requirements, security posture, and agent deployment topology. Start with the free cloud tier or proof-of-concept on Kubernetes.

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clearml FAQ

Does ClearML require a server?

Yes. ClearML operates as a client-server system. You can use the free cloud-hosted tier (app.clear.ml) or self-host the server using Docker. Demo server mode is available but experiments are public.

What Python versions are supported?

Data indicates PyPI and Conda distributions exist; specific version range not provided in data. Check PyPI (pypi.org/project/clearml) for current version matrix.

Can I use ClearML with Jupyter notebooks?

Yes. README explicitly mentions seamless Jupyter integration with version control and automatic environment capture.

Is GPU fractional support production-ready?

Fractional GPU feature exists (clearml-fractional-gpu) but maturity/stability not stated in data. Requires Nvidia runtime; validation in target environment recommended before production use.

Software development & web development with DEV.co

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If clearml is part of your ai frameworks roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Streamline Your ML Workflow?

Evaluate ClearML for your team by reviewing self-hosting requirements, security posture, and agent deployment topology. Start with the free cloud tier or proof-of-concept on Kubernetes.

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