zenml/llms.txt · Datasets at Hugging Face

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or through Python:

```python
import zenml

print(zenml.__version__)
```

If you would like to learn more about the current release, please visit our [PyPi package page.](https://pypi.org/project/zenml)

## Running with Docker

`zenml` is also available as a Docker image hosted publicly on [DockerHub](https://hub.docker.com/r/zenmldocker/zenml). Use the following command to get started in a bash environment with `zenml` available:

```shell
docker run -it zenmldocker/zenml /bin/bash
```

If you would like to run the ZenML server with Docker:

```shell
docker run -it -d -p 8080:8080 zenmldocker/zenml-server
```

<figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>

## Deploying the server

Though ZenML can run entirely as a pip package on a local system, complete with the dashboard. You can do this easily:

```shell
pip install "zenml[server]"
zenml login --local # opens the dashboard locally
```

However, advanced ZenML features are dependent on a centrally-deployed ZenML server accessible to other MLOps stack components. You can read more about it [here](deploying-zenml/README.md).

For the deployment of ZenML, you have the option to either [self-host](deploying-zenml/README.md) it or register for a free [ZenML Pro](https://cloud.zenml.io/signup?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link) account.

================
File: docs/book/getting-started/system-architectures.md
================
---
icon: building-columns
description: Different variations of the ZenML architecture depending on your needs.
---

# System Architecture

This guide walks through the various ways that ZenML can be deployed, from self-hosted OSS, to
SaaS, to self-hosted ZenML Pro!

## ZenML OSS (Self-hosted)

{% hint style="info" %}
This page is intended as a high level overview. To learn more about how about to deploy ZenML OSS,
read [this guide](../getting-started/deploying-zenml/README.md).
{% endhint %}

A ZenML OSS deployment consists of the following moving pieces:

* ZenML OSS Server: This is a FastAPI app that manages metadata of pipelines, artifacts, stacks etc.
Note: In ZenML Pro, the notion of a ZenML server is replaced with what is known as a "Tenant". For
all intents and purposes, consider a ZenML Tenant to be a ZenML OSS server that comes with more functionality.
* OSS Metadata Store: This is where all ZenML tenant metadata is stored, including
ML metadata such as tracking and versioning information about pipelines and
models.
* OSS Dashboard: This is a ReactJS app that shows pipelines, runs, etc.
* Secrets Store: All secrets and credentials required to access customer
infrastructure services are stored in a secure secrets store. The ZenML Pro
API has access to these secrets and uses them to access customer
infrastructure services on behalf of the ZenML Pro. The secrets store can be
hosted either by the ZenML Pro or by the customer.

![ZenML OSS server deployment architecture](../.gitbook/assets/oss_simple_deployment.png)

ZenML OSS is free with Apache 2.0 license. Learn how to deploy it [here](./deploying-zenml/README.md).

{% hint style="info" %}
To learn more about the core concepts for ZenML OSS, go [here](../getting-started/core-concepts.md).
{% endhint %}

## ZenML Pro (SaaS or Self-hosted)

{% hint style="info" %}
If you're interested in assessing ZenML Pro SaaS, you can create
a [free account](https://cloud.zenml.io/?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link).

If would like to self-host ZenML Pro, please [book a demo](https://zenml.io/book-a-demo).
{% endhint %}

The above deployment can be augmented with the ZenML Pro components:

* ZenML Pro Control Plane: This is the central controlling entity of all tenants.
* Pro Dashboard: This is a dashboard that builds on top of the OSS dashboard, and
add further functionality.
* Pro Metadata Store: This is a PostgreSQL database where all ZenML Pro related metadata is stored such
as roles, permissions, teams, and tenant management related data.
* Pro Add-ons: These are Python modules injected into the OSS Server for enhanced functionality.

or through Python:

```python

import zenml

print(zenml.__version__)

```

If you would like to learn more about the current release, please visit our [PyPi package page.](https://pypi.org/project/zenml)

## Running with Docker

`zenml` is also available as a Docker image hosted publicly on [DockerHub](https://hub.docker.com/r/zenmldocker/zenml). Use the following command to get started in a bash environment with `zenml` available:

```shell

docker run -it zenmldocker/zenml /bin/bash

```

If you would like to run the ZenML server with Docker:

```shell

docker run -it -d -p 8080:8080 zenmldocker/zenml-server

```

## Deploying the server

Though ZenML can run entirely as a pip package on a local system, complete with the dashboard. You can do this easily:

```shell

pip install "zenml[server]"

zenml login --local # opens the dashboard locally

```

However, advanced ZenML features are dependent on a centrally-deployed ZenML server accessible to other MLOps stack components. You can read more about it [here](deploying-zenml/README.md).

For the deployment of ZenML, you have the option to either [self-host](deploying-zenml/README.md) it or register for a free [ZenML Pro](https://cloud.zenml.io/signup?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link) account.

================

File: docs/book/getting-started/system-architectures.md

================

---

icon: building-columns

description: Different variations of the ZenML architecture depending on your needs.

---

# System Architecture

This guide walks through the various ways that ZenML can be deployed, from self-hosted OSS, to

SaaS, to self-hosted ZenML Pro!

## ZenML OSS (Self-hosted)

{% hint style="info" %}

This page is intended as a high level overview. To learn more about how about to deploy ZenML OSS,

read [this guide](../getting-started/deploying-zenml/README.md).

{% endhint %}

A ZenML OSS deployment consists of the following moving pieces:

* **ZenML OSS Server**: This is a FastAPI app that manages metadata of pipelines, artifacts, stacks etc.

Note: In ZenML Pro, the notion of a ZenML server is replaced with what is known as a "Tenant". For

all intents and purposes, consider a ZenML Tenant to be a ZenML OSS server that comes with more functionality.

* **OSS Metadata Store**: This is where all ZenML tenant metadata is stored, including

ML metadata such as tracking and versioning information about pipelines and

models.

* **OSS Dashboard**: This is a ReactJS app that shows pipelines, runs, etc.

* **Secrets Store**: All secrets and credentials required to access customer

infrastructure services are stored in a secure secrets store. The ZenML Pro

API has access to these secrets and uses them to access customer

infrastructure services on behalf of the ZenML Pro. The secrets store can be

hosted either by the ZenML Pro or by the customer.

![ZenML OSS server deployment architecture](../.gitbook/assets/oss_simple_deployment.png)

ZenML OSS is free with Apache 2.0 license. Learn how to deploy it [here](./deploying-zenml/README.md).

{% hint style="info" %}

To learn more about the core concepts for ZenML OSS, go [here](../getting-started/core-concepts.md).

{% endhint %}

## ZenML Pro (SaaS or Self-hosted)

{% hint style="info" %}

If you're interested in assessing ZenML Pro SaaS, you can create

a [free account](https://cloud.zenml.io/?utm\_source=docs\&utm\_medium=referral\_link\&utm\_campaign=cloud\_promotion\&utm\_content=signup\_link).

If would like to self-host ZenML Pro, please [book a demo](https://zenml.io/book-a-demo).

{% endhint %}

The above deployment can be augmented with the ZenML Pro components:

* **ZenML Pro Control Plane**: This is the central controlling entity of all tenants.

* **Pro Dashboard**: This is a dashboard that builds on top of the OSS dashboard, and

add further functionality.

* **Pro Metadata Store**: This is a PostgreSQL database where all ZenML Pro related metadata is stored such

as roles, permissions, teams, and tenant management related data.

* **Pro Add-ons**: These are Python modules injected into the OSS Server for enhanced functionality.