---
id: clearml-agent
version: "3.0.3"
license: Apache License 2.0
license_treatment: permissive
maintenance: active
---

# clearml-agent — ClearML Agent - Auto-Magical DevOps for Deep Learning

License: permissive · Maintenance: active · Downloads: 437.5K/mo

## What it is and 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—logging 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.

The 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.

Use it for:

- 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.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

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.

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—the 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.

## Install

pip install clearml-agent
uv add clearml-agent
poetry add clearml-agent

## Installing clearml-agent

Before you install: Low friction: pure Python wheel with standard dependencies (psutil, urllib3, virtualenv, requests, setuptools, pywin32). Active maintenance with recent commits and production-stable status.

License in practice: Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—suitable for enterprise and open-source deployments.

Quickstart:

pip install clearml-agent
clearml-agent init
clearml-agent daemon --help

Requires a ClearML Server instance (self-hosted or free tier at app.clear.ml) to queue and manage jobs; agent alone is an executor without a control plane.

Verify before relying:

- Exact scope of Kubernetes and SLURM integration capabilities and whether they require additional setup beyond the base package.
- Whether the pywin32 dependency on non-Windows systems is optional or causes installation friction.
- Performance characteristics when managing large clusters or high job throughput.

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 437.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

ml job scheduler orchestration, distributed experiment execution, mlops agent cluster management, machine learning devops automation, remote job queue executor, gpu cluster scheduler, experiment environment provisioning, mlops-orchestration, job-scheduler, experiment-execution

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