https://github.com/tensorchord/modelz-visual-chatgpt
https://github.com/tensorchord/modelz-visual-chatgpt
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/tensorchord/modelz-visual-chatgpt
- Owner: tensorchord
- Created: 2023-03-15T09:53:16.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-08T10:22:52.000Z (about 3 years ago)
- Last Synced: 2024-02-25T12:34:22.013Z (over 2 years ago)
- Language: Python
- Size: 36.1 KB
- Stars: 6
- Watchers: 3
- Forks: 1
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Modelz Visual ChatGPT
[visual-chatgpt](https://github.com/microsoft/visual-chatgpt) with Dockerfile and build.envd.
## 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 -f Dockerfile.cpu .
docker push docker.io/USER/IMAGE
# GPU
docker build -t docker.io/USER/IMAGE -f Dockerfile .
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.