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<!-- PROJECT LOGO -->
<br />
<a href="https://zenml.io">
<img alt="ZenML Logo" src="docs/book/.gitbook/assets/header.png" alt="ZenML Logo">
</a>
<br />
[![PyPi][pypi-shield]][pypi-url]
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<div align="center">
<h3 align="center">Build portable, production-ready MLOps pipelines.</h3>
<p align="center">
<div align="center">
Join our <a href="https://zenml.io/slack-invite" target="_blank">
<img width="18" src="https://cdn3.iconfinder.com/data/icons/logos-and-brands-adobe/512/306_Slack-512.png" alt="Slack"/>
<b>Slack Community</b> </a> and be part of the ZenML family.
</div>
<br />
<a href="https://zenml.io/features">Features</a>
·
<a href="https://zenml.io/roadmap">Roadmap</a>
·
<a href="https://github.com/zenml-io/zenml/issues">Report Bug</a>
·
<a href="https://zenml.io/discussion">Vote New Features</a>
·
<a href="https://www.zenml.io/blog">Read Blog</a>
·
<a href="https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22">Contribute to Open Source</a>
·
<a href="https://www.zenml.io/company#team">Meet the Team</a>
<br />
<br />
🎉 Version 0.54.1 is out. Check out the release notes
<a href="https://github.com/zenml-io/zenml/releases">here</a>.
<br />
<br />
</p>
</div>
---
<!-- TABLE OF CONTENTS -->
<details>
<summary>🏁 Table of Contents</summary>
<ol>
<li><a href="#-introduction">Introduction</a></li>
<li><a href="#-quickstart">Quickstart</a></li>
<li>
<a href="#-create-your-own-mlops-platform">Create your own MLOps Platform</a>
<ul>
<li><a href="##-1-deploy-zenml">Deploy ZenML</a></li>
<li><a href="#-2-deploy-stack-components">Deploy Stack Components</a></li>
<li><a href="#-3-create-a-pipeline">Create a Pipeline</a></li>
<li><a href="#-4-start-the-dashboard">Start the Dashboard</a></li>
</ul>
</li>
<li><a href="#-roadmap">Roadmap</a></li>
<li><a href="#-contributing-and-community">Contributing and Community</a></li>
<li><a href="#-getting-help">Getting Help</a></li>
<li><a href="#-license">License</a></li>
</ol>
</details>
<br />
# 🤖 Introduction
🤹 ZenML is an extensible, open-source MLOps framework for creating portable,
production-ready machine learning pipelines. By decoupling infrastructure from
code, ZenML enables developers across your organization to collaborate more
effectively as they develop to production.
- 💼 ZenML gives data scientists the freedom to fully focus on modeling and
experimentation while writing code that is production-ready from the get-go.
- 👨💻 ZenML empowers ML engineers to take ownership of the entire ML lifecycle
end-to-end. Adopting ZenML means fewer handover points and more visibility on
what is happening in your organization.
- 🛫 ZenML enables MLOps infrastructure experts to define, deploy, and manage
sophisticated production environments that are easy to use for colleagues.
ZenML provides a user-friendly syntax designed for ML workflows, compatible with
any cloud or tool. It enables centralized pipeline management, enabling
developers to write code once and effortlessly deploy it to various
infrastructures.
<div align="center">
<img src="docs/book/.gitbook/assets/overview.gif">
</div>
# 🤸 Quickstart
[Install ZenML](https://docs.zenml.io/getting-started/installation) via
[PyPI](https://pypi.org/project/zenml/). Python 3.8 - 3.11 is required:
```bash
pip install "zenml[server]"
```
Take a tour with the guided quickstart by running:
```bash
zenml go
```
# 🖼️ Create your own MLOps Platform
ZenML allows you to create and manage your own MLOps platform using
best-in-class open-source and cloud-based technologies. Here is an example of
how you could set this up for your team:
## 🔋 1. Deploy ZenML
For full functionality ZenML should be deployed on the cloud to
enable collaborative features as the central MLOps interface for teams.
Currently, there are two main options to deploy ZenML:
- **ZenML Cloud**: With [ZenML Cloud](https://docs.zenml.io/deploying-zenml/zenml-cloud),
you can utilize a control plane to create ZenML servers, also known as tenants.
These tenants are managed and maintained by ZenML's dedicated team, alleviating
the burden of server management from your end.
- **Self-hosted deployment**: Alternatively, you have the flexibility to [deploy
ZenML on your own self-hosted environment](https://docs.zenml.io/deploying-zenml/zenml-self-hosted).
This can be achieved through various methods, including using our CLI, Docker,
Helm, or HuggingFace Spaces.
## 👨🍳 2. Deploy Stack Components
ZenML boasts a ton of [integrations](https://zenml.io/integrations) into
popular MLOps tools. The [ZenML Stack](https://docs.zenml.io/user-guide/starter-guide/understand-stacks)
concept ensures that these tools work nicely together, therefore bringing
structure and standardization into the MLOps workflow.
Deploying and configuring this is super easy with ZenML. For **AWS**, this might
look a bit like this
```bash
# Deploy and register an orchestrator and an artifact store
zenml orchestrator deploy kubernetes_orchestrator --flavor kubernetes --cloud aws
zenml artifact-store deploy s3_artifact_store --flavor s3
# Register this combination of components as a stack
zenml stack register production_stack --orchestrator kubernetes_orchestrator --artifact-store s3_artifact_store --set # Register your production environment
```
When you run a pipeline with this stack set, it will be running on your deployed
Kubernetes cluster.
You can also [deploy your own tooling manually](https://docs.zenml.io/stacks-and-components/stack-deployment).
## 🏇 3. Create a Pipeline
Here's an example of a hello world ZenML pipeline in code:
```python
# run.py
from zenml import pipeline, step
@step
def step_1() -> str:
"""Returns the `world` substring."""
return "world"
@step
def step_2(input_one: str, input_two: str) -> None:
"""Combines the two strings at its input and prints them."""
combined_str = input_one + ' ' + input_two
print(combined_str)
@pipeline
def my_pipeline():
output_step_one = step_1()
step_2(input_one="hello", input_two=output_step_one)
if __name__ == "__main__":
my_pipeline()
```
```bash
python run.py
```
## 👭 4. Start the Dashboard
Open up the ZenML dashboard using this command.
```bash
zenml show
```
# 🗺 Roadmap
ZenML is being built in public. The [roadmap](https://zenml.io/roadmap) is a
regularly updated source of truth for the ZenML community to understand where
the product is going in the short, medium, and long term.
ZenML is managed by a [core team](https://zenml.io/company#CompanyTeam) of
developers that are responsible for making key decisions and incorporating
feedback from the community. The team oversees feedback via various channels,
and you can directly influence the roadmap as follows:
- Vote on your most wanted feature on our [Discussion
board](https://zenml.io/discussion).
- Start a thread in our [Slack channel](https://zenml.io/slack-invite).
- [Create an issue](https://github.com/zenml-io/zenml/issues/new/choose) on our
GitHub repo.
# 🙌 Contributing and Community
We would love to develop ZenML together with our community! The best way to get
started is to select any issue from the [`good-first-issue`
label](https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22)
and open up a Pull Request! If you
would like to contribute, please review our [Contributing
Guide](CONTRIBUTING.md) for all relevant details.
# 🆘 Getting Help
The first point of call should
be [our Slack group](https://zenml.io/slack-invite/).
Ask your questions about bugs or specific use cases, and someone from
the [core team](https://zenml.io/company#CompanyTeam) will respond.
Or, if you
prefer, [open an issue](https://github.com/zenml-io/zenml/issues/new/choose) on
our GitHub repo.
# 📜 License
ZenML is distributed under the terms of the Apache License Version 2.0.
A complete version of the license is available in the [LICENSE](LICENSE) file in
this repository. Any contribution made to this project will be licensed under
the Apache License Version 2.0.
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Check out the release notes\n <a href=\"https://github.com/zenml-io/zenml/releases\">here</a>.\n <br />\n <br />\n </p>\n</div>\n\n---\n\n<!-- TABLE OF CONTENTS -->\n<details>\n <summary>\ud83c\udfc1 Table of Contents</summary>\n <ol>\n <li><a href=\"#-introduction\">Introduction</a></li>\n <li><a href=\"#-quickstart\">Quickstart</a></li>\n <li>\n <a href=\"#-create-your-own-mlops-platform\">Create your own MLOps Platform</a>\n <ul>\n <li><a href=\"##-1-deploy-zenml\">Deploy ZenML</a></li>\n <li><a href=\"#-2-deploy-stack-components\">Deploy Stack Components</a></li>\n <li><a href=\"#-3-create-a-pipeline\">Create a Pipeline</a></li>\n <li><a href=\"#-4-start-the-dashboard\">Start the Dashboard</a></li>\n </ul>\n </li>\n <li><a href=\"#-roadmap\">Roadmap</a></li>\n <li><a href=\"#-contributing-and-community\">Contributing and Community</a></li>\n <li><a href=\"#-getting-help\">Getting Help</a></li>\n <li><a href=\"#-license\">License</a></li>\n </ol>\n</details>\n\n<br />\n\n# \ud83e\udd16 Introduction\n\n\ud83e\udd39 ZenML is an extensible, open-source MLOps framework for creating portable,\nproduction-ready machine learning pipelines. By decoupling infrastructure from\ncode, ZenML enables developers across your organization to collaborate more\neffectively as they develop to production.\n\n- \ud83d\udcbc ZenML gives data scientists the freedom to fully focus on modeling and\nexperimentation while writing code that is production-ready from the get-go.\n\n- \ud83d\udc68\u200d\ud83d\udcbb ZenML empowers ML engineers to take ownership of the entire ML lifecycle\n end-to-end. Adopting ZenML means fewer handover points and more visibility on\n what is happening in your organization.\n\n- \ud83d\udeeb ZenML enables MLOps infrastructure experts to define, deploy, and manage\nsophisticated production environments that are easy to use for colleagues.\n\n\n\nZenML provides a user-friendly syntax designed for ML workflows, compatible with\nany cloud or tool. It enables centralized pipeline management, enabling\ndevelopers to write code once and effortlessly deploy it to various\ninfrastructures.\n\n<div align=\"center\">\n <img src=\"docs/book/.gitbook/assets/overview.gif\">\n</div>\n\n# \ud83e\udd38 Quickstart\n\n[Install ZenML](https://docs.zenml.io/getting-started/installation) via\n[PyPI](https://pypi.org/project/zenml/). Python 3.8 - 3.11 is required:\n\n```bash\npip install \"zenml[server]\"\n```\n\nTake a tour with the guided quickstart by running:\n\n```bash\nzenml go\n```\n\n# \ud83d\uddbc\ufe0f Create your own MLOps Platform\n\nZenML allows you to create and manage your own MLOps platform using \nbest-in-class open-source and cloud-based technologies. Here is an example of \nhow you could set this up for your team:\n\n## \ud83d\udd0b 1. Deploy ZenML\n\nFor full functionality ZenML should be deployed on the cloud to\nenable collaborative features as the central MLOps interface for teams.\n\n\n\nCurrently, there are two main options to deploy ZenML:\n\n- **ZenML Cloud**: With [ZenML Cloud](https://docs.zenml.io/deploying-zenml/zenml-cloud), \nyou can utilize a control plane to create ZenML servers, also known as tenants. \nThese tenants are managed and maintained by ZenML's dedicated team, alleviating \nthe burden of server management from your end. \n\n- **Self-hosted deployment**: Alternatively, you have the flexibility to [deploy \nZenML on your own self-hosted environment](https://docs.zenml.io/deploying-zenml/zenml-self-hosted). \nThis can be achieved through various methods, including using our CLI, Docker, \nHelm, or HuggingFace Spaces.\n\n## \ud83d\udc68\u200d\ud83c\udf73 2. Deploy Stack Components\n\nZenML boasts a ton of [integrations](https://zenml.io/integrations) into \npopular MLOps tools. The [ZenML Stack](https://docs.zenml.io/user-guide/starter-guide/understand-stacks) \nconcept ensures that these tools work nicely together, therefore bringing\nstructure and standardization into the MLOps workflow.\n\nDeploying and configuring this is super easy with ZenML. For **AWS**, this might \nlook a bit like this\n\n```bash\n# Deploy and register an orchestrator and an artifact store\nzenml orchestrator deploy kubernetes_orchestrator --flavor kubernetes --cloud aws\nzenml artifact-store deploy s3_artifact_store --flavor s3\n\n# Register this combination of components as a stack\nzenml stack register production_stack --orchestrator kubernetes_orchestrator --artifact-store s3_artifact_store --set # Register your production environment\n```\n\nWhen you run a pipeline with this stack set, it will be running on your deployed\nKubernetes cluster.\n\nYou can also [deploy your own tooling manually](https://docs.zenml.io/stacks-and-components/stack-deployment).\n\n## \ud83c\udfc7 3. Create a Pipeline\n\nHere's an example of a hello world ZenML pipeline in code:\n\n```python\n# run.py\nfrom zenml import pipeline, step\n\n\n@step\ndef step_1() -> str:\n \"\"\"Returns the `world` substring.\"\"\"\n return \"world\"\n\n\n@step\ndef step_2(input_one: str, input_two: str) -> None:\n \"\"\"Combines the two strings at its input and prints them.\"\"\"\n combined_str = input_one + ' ' + input_two\n print(combined_str)\n\n\n@pipeline\ndef my_pipeline():\n output_step_one = step_1()\n step_2(input_one=\"hello\", input_two=output_step_one)\n\n\nif __name__ == \"__main__\":\n my_pipeline()\n```\n\n```bash\npython run.py\n```\n\n## \ud83d\udc6d 4. Start the Dashboard\n\nOpen up the ZenML dashboard using this command.\n\n```bash\nzenml show\n```\n\n# \ud83d\uddfa Roadmap\n\nZenML is being built in public. The [roadmap](https://zenml.io/roadmap) is a\nregularly updated source of truth for the ZenML community to understand where\nthe product is going in the short, medium, and long term.\n\nZenML is managed by a [core team](https://zenml.io/company#CompanyTeam) of\ndevelopers that are responsible for making key decisions and incorporating\nfeedback from the community. The team oversees feedback via various channels,\nand you can directly influence the roadmap as follows:\n\n- Vote on your most wanted feature on our [Discussion\n board](https://zenml.io/discussion).\n- Start a thread in our [Slack channel](https://zenml.io/slack-invite).\n- [Create an issue](https://github.com/zenml-io/zenml/issues/new/choose) on our\n GitHub repo.\n\n# \ud83d\ude4c Contributing and Community\n\nWe would love to develop ZenML together with our community! The best way to get\nstarted is to select any issue from the [`good-first-issue`\nlabel](https://github.com/issues?q=is%3Aopen+is%3Aissue+archived%3Afalse+user%3Azenml-io+label%3A%22good+first+issue%22)\nand open up a Pull Request! If you\nwould like to contribute, please review our [Contributing\nGuide](CONTRIBUTING.md) for all relevant details.\n\n# \ud83c\udd98 Getting Help\n\nThe first point of call should\nbe [our Slack group](https://zenml.io/slack-invite/).\nAsk your questions about bugs or specific use cases, and someone from\nthe [core team](https://zenml.io/company#CompanyTeam) will respond.\nOr, if you\nprefer, [open an issue](https://github.com/zenml-io/zenml/issues/new/choose) on\nour GitHub repo.\n\n# \ud83d\udcdc License\n\nZenML is distributed under the terms of the Apache License Version 2.0.\nA complete version of the license is available in the [LICENSE](LICENSE) file in\nthis repository. Any contribution made to this project will be licensed under\nthe Apache License Version 2.0.\n",
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