To get started with the ZenML Dashboard, follow these steps:
Install Node.js
Ensure you have Node.js installed. You can either install version 18 directly or use nvm (Node Version Manager) with the following commands:
nvm install --lts
nvm use --lts
Install Pnpm:
The project uses pnpm v10 as the package manager. pnpm v11 is not yet supported (it introduced trust-downgrade checks that conflict with some dependencies). Install it with:
npm install -g pnpm@10
Alternatively, if you use corepack, run
corepack enable and it will pick up the required version from package.json
automatically.
Install Dependencies:
Navigate to the project directory and install dependencies:
pnpm install
Set Environment Variable:
Configure the environment variable VITE_API_BASE_URL by replacing <YOUR_ZENML_SERVER_DEPLOYMENT_URL> with your ZenML Server deployment URL. Example:
export VITE_API_BASE_URL="https://your-zenml-server-url/api/v1"
[!IMPORTANT]
The frontend and the server-url needs to be on the same domain, e.g.localhostfor local development
[!NOTE]
You can set theVITE_FRONTEND_VERSIONto a version number, e.gv0.17.0. This value is used to read the UI Version from the environment
Run Development Server:
For development, run the server with:
pnpm dev
Alternatively, build the project for production with:
pnpm build
Using Docker (Optional):
ZenML provides Docker images for the server and dashboard. Run the following command to serve both:
docker run -it -d -p 8080:8080 --name zenml zenmldocker/zenml-server
Access the dashboard at http://localhost:8080
For detailed deployment instructions and additional options, refer to the deployment guide.
Now you're ready to explore and visualize your ML pipelines, stacks, and artifacts with the ZenML Dashboard!
The ZenML Dashboard is a Typescript React-based application designed to seamlessly integrate with the main ZenML Python package. It serves as a unified platform for managing and visualizing your ML pipelines, stacks, and artifacts in one centralized location.
The ZenML Dashboard is intricately connected with the ZenML Server as its backend and is not intended for standalone use. Here's a brief overview of how it fits into the ZenML ecosystem:
Sister Repository:
Bundled Build Files:
yarn build, correspond to specific versions of the dashboard.Local Deployment:
Serve the static build files locally using the ZenML Python package:
zenml login --local
This creates a local daemon that serves the files in a FastAPI server.
Logging into the ZenML Dashboard is a simple process. Navigate to the login page and enter your credentials.
Explore your machine learning artifacts effortlessly. The dashboard offers a structured view of your pipelines, stacks, components, etc... making it easy to manage and visualize your workflows.
This combination of a user-friendly interface and visualizations enhances your experience, helping you navigate and understand your machine learning processes efficiently.
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.
If you would like to contribute, please review our Contributing Guide for all relevant details.
Need a helping hand? We've got you covered! Getting assistance with ZenML is quick and easy.
Join our Slack Community:
Open an Issue:
Check the Documentation:
ZenML Dashboard is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in the LICENSE file in this repository. Any contribution made to this workspace will be licensed under the Apache License Version 2.0.
TypeScript
99.6%
To get started with the ZenML Dashboard, follow these steps:
Install Node.js
Ensure you have Node.js installed. You can either install version 18 directly or use nvm (Node Version Manager) with the following commands:
nvm install --lts
nvm use --lts
Install Pnpm:
The project uses pnpm v10 as the package manager. pnpm v11 is not yet supported (it introduced trust-downgrade checks that conflict with some dependencies). Install it with:
npm install -g pnpm@10
Alternatively, if you use corepack, run
corepack enable and it will pick up the required version from package.json
automatically.
Install Dependencies:
Navigate to the project directory and install dependencies:
pnpm install
Set Environment Variable:
Configure the environment variable VITE_API_BASE_URL by replacing <YOUR_ZENML_SERVER_DEPLOYMENT_URL> with your ZenML Server deployment URL. Example:
export VITE_API_BASE_URL="https://your-zenml-server-url/api/v1"
[!IMPORTANT]
The frontend and the server-url needs to be on the same domain, e.g.localhostfor local development
[!NOTE]
You can set theVITE_FRONTEND_VERSIONto a version number, e.gv0.17.0. This value is used to read the UI Version from the environment
Run Development Server:
For development, run the server with:
pnpm dev
Alternatively, build the project for production with:
pnpm build
Using Docker (Optional):
ZenML provides Docker images for the server and dashboard. Run the following command to serve both:
docker run -it -d -p 8080:8080 --name zenml zenmldocker/zenml-server
Access the dashboard at http://localhost:8080
For detailed deployment instructions and additional options, refer to the deployment guide.
Now you're ready to explore and visualize your ML pipelines, stacks, and artifacts with the ZenML Dashboard!
The ZenML Dashboard is a Typescript React-based application designed to seamlessly integrate with the main ZenML Python package. It serves as a unified platform for managing and visualizing your ML pipelines, stacks, and artifacts in one centralized location.
The ZenML Dashboard is intricately connected with the ZenML Server as its backend and is not intended for standalone use. Here's a brief overview of how it fits into the ZenML ecosystem:
Sister Repository:
Bundled Build Files:
yarn build, correspond to specific versions of the dashboard.Local Deployment:
Serve the static build files locally using the ZenML Python package:
zenml login --local
This creates a local daemon that serves the files in a FastAPI server.
Logging into the ZenML Dashboard is a simple process. Navigate to the login page and enter your credentials.
Explore your machine learning artifacts effortlessly. The dashboard offers a structured view of your pipelines, stacks, components, etc... making it easy to manage and visualize your workflows.
This combination of a user-friendly interface and visualizations enhances your experience, helping you navigate and understand your machine learning processes efficiently.
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.
If you would like to contribute, please review our Contributing Guide for all relevant details.
Need a helping hand? We've got you covered! Getting assistance with ZenML is quick and easy.
Join our Slack Community:
Open an Issue:
Check the Documentation:
ZenML Dashboard is distributed under the terms of the Apache License Version 2.0. A complete version of the license is available in the LICENSE file in this repository. Any contribution made to this workspace will be licensed under the Apache License Version 2.0.
TypeScript
99.6%