https://github.com/gaocegege/modelz-moss
https://github.com/OpenLMLab/MOSS
https://github.com/gaocegege/modelz-moss
Last synced: 2 months ago
JSON representation
https://github.com/OpenLMLab/MOSS
- Host: GitHub
- URL: https://github.com/gaocegege/modelz-moss
- Owner: gaocegege
- Created: 2023-04-22T07:18:31.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-04-22T07:22:52.000Z (about 2 years ago)
- Last Synced: 2025-02-08T15:48:05.337Z (4 months ago)
- Language: Python
- Size: 7.81 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Modelz Gradio Template
This is a template for creating a [Gradio](https://gradio.app/) app on [Modelz](https://modelz.ai/).
Building an Gradio app could be straightforward. You will need to provide three key components:
- A `main.py` file: This file contains the code for making predictions.
- A `requirements.txt` file: This file lists all the dependencies required for the server code to run.
- A `Dockerfile` or a simpler [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html): This file contains instructions for building a Docker image that encapsulates the server code and its dependencies.
## Build
In the `Dockerfile`, you need to define the instructions for building a Docker image that encapsulates the server code and its dependencies.
In most cases, you could use the template in the repository.
```bash
docker build -t docker.io/USER/IMAGE .
docker push docker.io/USER/IMAGE
# GPU
docker build -t docker.io/USER/IMAGE -f Dockerfile.gpu .
docker push docker.io/USER/IMAGE
```
On the other hand, a [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html) is a simplified alternative to a Dockerfile. It provides python-based interfaces that contains configuration settings for building a image.
It is easier to use than a Dockerfile as it involves specifying only the dependencies of your machine learning model, not the instructions for CUDA, conda, and other system-level dependencies.
```bash
envd build --output type=image,name=docker.io/USER/IMAGE,push=true
# GPU
envd build --output type=image,name=docker.io/USER/IMAGE,push=true -f :build_gpu
```
## Deploy
Please refer to the [Modelz documentation](https://docs.modelz.ai/gettingstarted/deploy) for more details.