clearml: an all-in-one MLOps suite for experiment tracking, orchestration, and data versioning

What it solves

ClearML is designed to handle the "messy process" of training production-grade deep learning models. It provides a unified suite of tools to streamline the AI workflow by integrating experiment tracking, MLOps orchestration, and data management into a single platform, reducing the effort required to preserve research and move models to production.

How it works

ClearML operates through three primary run-time components:

• ClearML Python Package: An SDK that integrates into existing scripts with minimal code changes (often just two lines) to automatically log parameters, metrics, and environment details.
• ClearML Server: A central hub that stores experiment, model, and workflow data, providing a Web UI for management and automation.
• ClearML Agent: A tool for orchestration that enables remote execution, scalability, and reproducibility of experiments and workflows.

Who it’s for

It is built for researchers and developers working with machine learning and deep learning frameworks (such as PyTorch, TensorFlow, Keras, and Scikit-Learn) who need to collaborate, track experiments, and automate their training pipelines.

Highlights

• Automagical Experiment Tracking: Automatically captures source control info, hyper-parameters, stdout/stderr, resource monitoring (CPU/GPU), and model snapshots.
• MLOps/LLMOps Orchestration: Supports remote execution of tasks on Kubernetes, Cloud, or bare-metal, including an AWS Auto-Scaler for EC2 instances.
• Differentiable Data Management: A version control solution for datasets hosted on object storage like S3, GS, Azure, or NAS.
• Model Serving: A scalable solution for deploying model endpoints with GPU support via Nvidia-Triton and built-in monitoring.
• Hyper-Parameter Optimization: Integrated Bayesian optimization algorithms for black-box code optimization.
• Fractional GPUs: Container-based driver-level GPU memory limitation for better resource utilization.