{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"},{"label":"Monitoring","url":"https://skillfed.io/packages/category/system-monitoring/3"},{"label":"Logging","url":"https://skillfed.io/packages/category/system-logging"},{"label":"Version Control","url":"https://skillfed.io/packages/category/software-development-version-control"}],"enrichment":{"capability":"ClearML is an ML/DL development and production suite that automates experiment tracking, captures environment and hyperparameter data, logs outputs and metrics, and provides orchestration and data management for machine learning workflows.","skillfed_tags":["experiment-tracking","mlops","orchestration"],"use_cases":["Track and compare multiple training runs with automatic capture of code, environment, and hyperparameters without manual logging.","Reproduce past experiments by querying stored environment and parameter snapshots from the ClearML server.","Monitor resource utilization (CPU, GPU, memory) and system metrics across distributed training jobs.","Version and manage datasets with full lineage tracking across S3, GCS, Azure, or local storage.","Orchestrate and schedule remote training jobs on Kubernetes, cloud platforms, or bare-metal machines."],"what_it_does":"ClearML is a comprehensive ML/DL development suite designed to reduce boilerplate in experiment tracking and MLOps workflows. At its core, it captures experiment metadata\u2014source control state, environment packages, hyperparameters, and initial model weights\u2014automatically with just two lines of code. It logs all outputs (stdout, stderr, resource metrics, model snapshots, artifacts, and tensorboard scalars) to a central server, eliminating manual logging and enabling reproducibility.\n\nBeyond experiment tracking, ClearML integrates data management (versioning datasets on S3, GCS, Azure, or NAS), model serving, orchestration dashboards, and remote execution agents. It supports a wide range of ML frameworks and works with Jupyter notebooks and PyCharm. The package requires connection to a ClearML server (either the hosted free tier or self-hosted) to store and visualize experiments, making it a full-stack platform rather than a standalone logging library.","worth_installing":"Yes, if you run ML experiments and want centralized tracking with minimal code changes. The two-line integration is genuinely low-friction, and the platform covers experiment management, data versioning, and orchestration in one package. Requires a ClearML server (free hosted tier available), so it's not a standalone library. Active maintenance, permissive license, and no known vulnerabilities make it a safe choice. Best suited for teams or individuals running multiple experiments who value reproducibility and automation."},"id":"clearml","links":{"html":"https://skillfed.io/packages/clearml","md":"https://skillfed.io/packages/clearml.md","pypi":"https://pypi.org/project/clearml/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"clearml","python_support":"unspecified","summary":"ClearML - Auto-Magical Experiment Manager, Version Control, and MLOps for AI"},"popularity":{"monthly_downloads":630789,"position":5661,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.11"}