{"categories":[{"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":"Image Recognition","url":"https://skillfed.io/packages/category/scientific-engineering-image-recognition"}],"enrichment":{"capability":"ClearML Agent is a job scheduler and orchestration service that runs machine learning experiments on local or cloud resources, managing virtual environments, dependencies, and execution monitoring across Linux, macOS, and Windows.","skillfed_tags":["mlops-orchestration","job-scheduler","experiment-execution"],"use_cases":["Run hyperparameter tuning or AutoML pipelines across a cluster of GPUs without manual environment setup.","Deploy a multi-machine experiment queue where researchers enqueue jobs from a web UI and agents execute them automatically.","Integrate existing Kubernetes or SLURM clusters with ClearML for unified job scheduling and monitoring.","Execute long-running deep learning training jobs with automatic cleanup, resource monitoring, and abort capability via the UI.","Combine on-premises and cloud resources into a single logical cluster for cost-optimized experiment execution."],"what_it_does":"ClearML Agent is a background service that polls job queues from a ClearML Server, pulls scheduled experiments, and executes them in isolated environments. It handles environment setup (virtualenv or Docker), dependency installation, code cloning, and execution monitoring\u2014logging all output back to the ClearML UI for visibility and control. The agent runs on any machine (GPU or CPU) and can be deployed across on-premises, cloud, or hybrid infrastructure to form a distributed execution cluster.\n\nThe package is designed for teams running machine learning workflows at scale. Rather than managing job submission manually, users enqueue experiments from the ClearML UI, and agents pick them up and run them with automatic environment provisioning. It supports optional Kubernetes and SLURM integration, cloud autoscaling, and resource monitoring. The core workflow requires a ClearML Server instance to coordinate jobs; the agent itself is the execution worker.","worth_installing":"Yes, if you are running a ClearML Server and need distributed experiment execution. The agent is production-stable, actively maintained, has no known vulnerabilities, and low install friction. It is worth installing only as part of a ClearML deployment\u2014the agent alone provides no value without a server to queue jobs. If you already use ClearML, this is the standard way to execute jobs remotely."},"id":"clearml-agent","links":{"html":"https://skillfed.io/packages/clearml-agent","md":"https://skillfed.io/packages/clearml-agent.md","pypi":"https://pypi.org/project/clearml-agent/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-02","license_spdx":null,"license_treatment":"permissive","name":"clearml-agent","python_support":"unspecified","summary":"ClearML Agent - Auto-Magical DevOps for Deep Learning"},"popularity":{"monthly_downloads":437538,"position":6668,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.0.3"}