Comet
Experiment tracking, model registry & production monitoring for ML teams
ClearML
Automagical suite to streamline AI experiment, orchestration, and serving
ClearML provides experiment tracking, data versioning, pipeline orchestration, and scalable model serving with just two lines of code, supporting major ML frameworks and cloud/on‑prem deployments.
Overview
Overview
ClearML is a unified platform that turns messy machine‑learning workflows into reproducible, observable pipelines. By adding only two lines of Python, data scientists instantly capture code versions, hyper‑parameters, environment details, and output artifacts, while engineers gain a web UI to monitor resources and compare runs.
Capabilities & Deployment
The suite covers five core modules: Experiment Manager, Data‑Management, MLOps/LLMOps orchestration, Model‑Serving, and Reporting. It integrates with PyTorch, TensorFlow, Scikit‑Learn and others, and works on cloud, Kubernetes, or bare‑metal clusters. Model serving leverages Nvidia‑Triton for GPU‑accelerated inference and includes built‑in monitoring. Users can self‑host the ClearML Server for full data control or use the hosted free tier. Together, these tools enable teams to move from prototype to production in minutes while maintaining full provenance and scalability.
Highlights
Zero‑code experiment tracking with automatic environment capture
Unified data versioning across S3, GCS, Azure, and NAS
Scalable model serving with Nvidia‑Triton and built‑in monitoring
Orchestration dashboard for cloud, Kubernetes, and on‑prem clusters
Pros
- Effortless integration – only two lines of code
- Comprehensive suite covering experiments to serving
- Supports all major ML/DL frameworks
- Self‑hostable server for full data control
Considerations
- Feature‑rich UI can be overwhelming for beginners
- Self‑hosting requires managing ClearML‑Server components
- Advanced orchestration may need Kubernetes expertise
- Community support varies compared to commercial MLOps platforms
Managed products teams compare with
When teams consider ClearML, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Research teams needing reproducible experiment tracking
- Enterprises deploying models on hybrid cloud/on‑prem environments
- Data scientists who want built‑in dataset versioning
- DevOps groups automating ML pipelines with minimal code changes
Not ideal when
- Small scripts with no need for orchestration or serving
- Teams requiring out‑of‑the‑box SaaS with dedicated support
- Projects that only need simple hyper‑parameter tuning without full MLOps stack
- Organizations preferring a single‑purpose tool over an integrated suite
How teams use it
Rapid prototyping to production
Track experiments, version datasets, and deploy a model endpoint in under 5 minutes, ensuring reproducibility.
Hybrid cloud training pipelines
Orchestrate GPU jobs across on‑prem clusters and cloud providers, auto‑scaling resources via ClearML Agent.
Dataset governance for regulated industries
Version and audit data stored in S3 or Azure with full lineage linked to experiments.
Continuous model monitoring
Serve models with Nvidia‑Triton and receive real‑time performance metrics and drift alerts via built‑in monitoring.
Tech snapshot
Python100%
Frequently asked questions
How do I start using ClearML?
Install the `clearml` Python package, run `clearml-init` to configure credentials, and add two lines of code to initialize a Task.
Can I host ClearML myself?
Yes, the ClearML Server is open source and can be deployed on‑prem or in any cloud environment.
Which ML frameworks are supported?
PyTorch, TensorFlow, Keras, FastAI, XGBoost, LightGBM, MegEngine, Scikit‑Learn and more.
Is model serving GPU‑accelerated?
ClearML integrates with Nvidia‑Triton, providing optimized GPU inference out of the box.
What storage backends are compatible with ClearML‑Data?
Object storage services such as Amazon S3, Google Cloud Storage, Azure Blob, as well as NAS file systems.
Project at a glance
Active- Stars
- 6,461
- Watchers
- 6,461
- Forks
- 728
LicenseApache-2.0
Repo age6 years old
Last commit4 days ago
Primary languagePython
Last synced 3 hours ago