This is a template for creating a Gradio app on Modelz.

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: This file contains instructions for building a Docker image that encapsulates the server code and its dependencies.

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.

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 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.

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

Please refer to the Modelz documentation for more details.