🧠 ClearML: The Open-Source MLOps Suite for Experiment Tracking, Pipelines & More

Machine learning isn’t just about building models — it’s also about managing experiments, tracking data, orchestrating pipelines, and deploying at scale. That’s where ClearML comes in.

ClearML is an open-source, full-stack MLOps platform that helps you track experiments, manage datasets, orchestrate ML workflows, and deploy models — all in one centralized system. It’s designed to work seamlessly with any ML stack, and it’s completely free to use (with enterprise features available for scaling teams).

🚀 What is ClearML?

ClearML is more than just a tracking tool. It’s an end-to-end suite covering:

✅ Experiment Tracking
🧪 Hyperparameter Optimization
🔄 Pipeline Orchestration
🧱 Dataset Management
☁️ Remote Execution (on any cloud or cluster)

Whether you're a solo developer or part of a large ML team, ClearML provides the infrastructure to scale and organize your workflow without friction.

🛠 Installation

pip install clearml

Then connect to the ClearML server (cloud or self-hosted):

clearml-init

You’ll enter your API credentials and choose a workspace. Boom — you’re in.

🔍 Experiment Tracking

ClearML automatically logs:

Code (via git or script snapshot)
Parameters
Scalars (e.g., accuracy, loss)
Artifacts (models, logs, files)
Plots and visualizations

Example (PyTorch):

from clearml import Task

task = Task.init(project_name="MNIST", task_name="Simple CNN", task_type="training")

Now any metric you log with TensorBoard, Matplotlib, or even custom logs will appear on the ClearML dashboard.

🔁 Hyperparameter Optimization

ClearML includes an HPO module called ClearML Optimizer:

Grid, random, Bayesian search
Easy integration with existing scripts
Scales across multiple GPUs or machines

from clearml.automation import UniformParameterRange, HyperParameterOptimizer

You can launch and monitor experiments from a UI or script — no need for manual tracking.

📦 Dataset Versioning

ClearML’s Data Management module allows you to:

Create versioned datasets
Share and reuse datasets across projects
Push and pull datasets via CLI or Python
Store on S3, GCS, Azure, or local disk

from clearml import Dataset

dataset = Dataset.create(dataset_name="cats-vs-dogs", dataset_project="datasets")
dataset.add_files("data/")
dataset.upload()
dataset.finalize()

⚙️ Workflow Orchestration

Use ClearML Pipelines to automate ML workflows — like training → evaluation → deployment.

Define steps as Python functions or scripts. Connect them using the PipelineController:

from clearml import PipelineController

pipe = PipelineController(project="NLP", name="BERT Training Pipeline")
pipe.add_function_step(...)
pipe.start()

Supports caching, parameter passing, artifact transfer, and scheduling.

☁️ Remote Execution

ClearML lets you offload tasks to any connected agent — your local machine, cloud VMs, or Kubernetes.

Schedule jobs from the web UI
Use queues to prioritize workloads
Reuse existing code — no need to rewrite anything

Just connect your compute with:

clearml-agent daemon --queue default

🌐 Cloud & Self-Hosting

ClearML offers:

Free hosted version at app.clear.ml
Docker-based self-hosted server (free)
Enterprise version for scaling teams and security

💼 Use Cases

Track and reproduce thousands of experiments
Automate ML pipelines with conditionals and retries
Manage and version datasets across teams
Run training jobs on any hardware — from a laptop to the cloud
Create dashboards and reports for stakeholders

🎯 Final Thoughts

ClearML is the Swiss Army knife of MLOps. It lets you start simple with experiment tracking, then scale into full pipeline automation and data versioning — all from a single, unified interface.

If you’re looking for a free, powerful, and open-source alternative to other MLOps platforms (like MLflow, WandB, or Kubeflow), ClearML is a must-try.

🔗 Useful Links:

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