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clearml
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LICENSE
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LICENSE
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same "printed page" as the copyright notice for easier
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identification within third-party archives.
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Copyright 2019 allegro.ai
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Copyright 2025 ClearML Inc
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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34
README.md
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README.md
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<div align="center">
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<img src="https://github.com/allegroai/clearml-agent/blob/master/docs/clearml_agent_logo.png?raw=true" width="250px">
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<img src="https://github.com/clearml/clearml-agent/blob/master/docs/clearml_agent_logo.png?raw=true" width="250px">
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**ClearML Agent - MLOps/LLMOps made easy
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MLOps/LLMOps scheduler & orchestration solution supporting Linux, macOS and Windows**
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[](https://img.shields.io/github/license/allegroai/clearml-agent.svg)
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[](https://img.shields.io/github/license/clearml/clearml-agent.svg)
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[](https://img.shields.io/pypi/pyversions/clearml-agent.svg)
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[](https://img.shields.io/pypi/v/clearml-agent.svg)
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[](https://pypi.org/project/clearml-agent/)
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[](https://artifacthub.io/packages/search?repo=allegroai)
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[](https://artifacthub.io/packages/search?repo=clearml)
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`🌟 ClearML is open-source - Leave a star to support the project! 🌟`
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@ -33,21 +33,21 @@ It is a zero configuration fire-and-forget execution agent, providing a full ML/
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**Full Automation in 5 steps**
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1. ClearML Server [self-hosted](https://github.com/allegroai/clearml-server)
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1. ClearML Server [self-hosted](https://github.com/clearml/clearml-server)
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or [free tier hosting](https://app.clear.ml)
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2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any GPU machine:
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on-premises / cloud / ...)
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3. Create a [job](https://clear.ml/docs/latest/docs/apps/clearml_task) or
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add [ClearML](https://github.com/allegroai/clearml) to your code with just 2 lines of code
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add [ClearML](https://github.com/clearml/clearml) to your code with just 2 lines of code
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4. Change the [parameters](#using-the-clearml-agent) in the UI & schedule for [execution](#using-the-clearml-agent) (or
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automate with an [AutoML pipeline](#automl-and-orchestration-pipelines-))
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5. :chart_with_downwards_trend: :chart_with_upwards_trend: :eyes: :beer:
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"All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that"
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**Try ClearML now** [Self Hosted](https://github.com/allegroai/clearml-server)
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**Try ClearML now** [Self Hosted](https://github.com/clearml/clearml-server)
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or [Free tier Hosting](https://app.clear.ml)
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<a href="https://app.clear.ml"><img src="https://github.com/allegroai/clearml-agent/blob/master/docs/screenshots.gif?raw=true" width="100%"></a>
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<a href="https://app.clear.ml"><img src="https://github.com/clearml/clearml-agent/blob/master/docs/screenshots.gif?raw=true" width="100%"></a>
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### Simple, Flexible Experiment Orchestration
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@ -71,7 +71,7 @@ or [Free tier Hosting](https://app.clear.ml)
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We think Kubernetes is awesome, but it is not a must to get started with remote execution agents and cluster management.
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We designed `clearml-agent` so you can run both bare-metal and on top of Kubernetes, in any combination that fits your environment.
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You can find the Dockerfiles in the [docker folder](./docker) and the helm Chart in https://github.com/allegroai/clearml-helm-charts
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You can find the Dockerfiles in the [docker folder](./docker) and the helm Chart in https://github.com/clearml/clearml-helm-charts
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#### Benefits of integrating existing Kubernetes cluster with ClearML
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@ -86,8 +86,8 @@ You can find the Dockerfiles in the [docker folder](./docker) and the helm Chart
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- **Enterprise Features**: RBAC, vault, multi-tenancy, scheduler, quota management, fractional GPU support
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**Run the agent in Kubernetes Glue mode an map ClearML jobs directly to K8s jobs:**
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- Use the [ClearML Agent Helm Chart](https://github.com/allegroai/clearml-helm-charts/tree/main/charts/clearml-agent) to spin an agent pod acting as a controller
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- Or run the [clearml-k8s glue](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py) on
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- Use the [ClearML Agent Helm Chart](https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml-agent) to spin an agent pod acting as a controller
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- Or run the [clearml-k8s glue](https://github.com/clearml/clearml-agent/blob/master/examples/k8s_glue_example.py) on
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a Kubernetes cpu node
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- The clearml-k8s glue pulls jobs from the ClearML job execution queue and prepares a Kubernetes job (based on provided
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yaml template)
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@ -151,7 +151,7 @@ The ClearML Agent executes experiments using the following process:
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#### System Design & Flow
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<img src="https://github.com/allegroai/clearml-agent/blob/master/docs/clearml_architecture.png" width="100%" alt="clearml-architecture">
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<img src="https://github.com/clearml/clearml-agent/blob/master/docs/clearml_architecture.png" width="100%" alt="clearml-architecture">
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#### Installing the ClearML Agent
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@ -279,7 +279,7 @@ clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11
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### How do I create an experiment on the ClearML Server? <a name="from-scratch"></a>
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* Integrate [ClearML](https://github.com/allegroai/clearml) with your code
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* Integrate [ClearML](https://github.com/clearml/clearml) with your code
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* Execute the code on your machine (Manually / PyCharm / Jupyter Notebook)
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* As your code is running, **ClearML** creates an experiment logging all the necessary execution information:
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- Git repository link and commit ID (or an entire jupyter notebook)
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@ -326,21 +326,21 @@ The ClearML Agent can also be used to implement AutoML orchestration and Experim
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ClearML package.
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Sample AutoML & Orchestration examples can be found in the
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ClearML [example/automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder.
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ClearML [example/automation](https://github.com/clearml/clearml/tree/master/examples/automation) folder.
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AutoML examples:
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- [Toy Keras training experiment](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py)
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- [Toy Keras training experiment](https://github.com/clearml/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py)
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- In order to create an experiment-template in the system, this code must be executed once manually
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- [Random Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
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- [Random Search over the above Keras experiment-template](https://github.com/clearml/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
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- This example will create multiple copies of the Keras experiment-template, with different hyperparameter
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combinations
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Experiment Pipeline examples:
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- [First step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/task_piping_example.py)
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- [First step experiment](https://github.com/clearml/clearml/blob/master/examples/automation/task_piping_example.py)
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- This example will "process data", and once done, will launch a copy of the 'second step' experiment-template
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- [Second step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/toy_base_task.py)
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- [Second step experiment](https://github.com/clearml/clearml/blob/master/examples/automation/toy_base_task.py)
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- In order to create an experiment-template in the system, this code must be executed once manually
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### License
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4
setup.py
4
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"""
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ClearML Inc.
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CLEARML-AGENT DevOps for machine/deep learning
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https://github.com/allegroai/clearml-agent
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https://github.com/clearml/clearml-agent
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"""
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import os.path
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@ -39,7 +39,7 @@ setup(
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long_description=long_description,
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long_description_content_type='text/markdown',
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# The project's main homepage.
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url='https://github.com/allegroai/clearml-agent',
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url='https://github.com/clearml/clearml-agent',
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author='clearml',
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author_email='clearml@clearml.ai',
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license='Apache License 2.0',
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