Deploy models at REST

Model API hosts don’t read requirements.txt files or execute commands defined in the pre-setup, post-setup, pre-run, or post-run scripts of your environment. If your project uses requirements.txt or any setup scripts to install specific packages or repositories, add them to the Dockerfile instructions of your environment.

Your Model API doesn’t inherit environment variables set at the project level. However, you can set Model API-specific environment variables on the Model settings page. This separation decouples the management of projects and deployed models. See Secure Credential Storage for more details.

Domino adds project files to the image when you build a Model API. Starting or stopping the Model API won’t change the files on the model host. To update the model host files, you need to create a new version.

Domino pulls Git repositories attached to projects when you build a Model API. Starting or stopping the Model API won’t change the files on the model host. To update the model host files, you need to create a new version.

The Model API host mounts project files at /mnt/<username>/<project_name>. This location differs from the default location for Runs or Workspaces, /mnt/. You can use the Domino environment variable DOMINO_WORKING_DIR to reference the directory where your project is mounted.

The Model API image excludes project files listed in the .modelignore file that are located in the project’s root directory. Excluded files are not mounted to the Model API host.

If you use named parameters in your function definition, use a dictionary or parameter array in your JSON request. For example, my_function(x, y, z), use {"data": {"x": 1, "y": 2, "z": 3}} or {"parameters": [1, 2, 3]}.

If you use a dictionary in your function definition, use only a parameter array. For example, my_function(dict) and your function then uses dict["x"], dict["y"]. Send the request: {"parameters": [{"x": 1, "y": 2, "z": 3}]}.

In Python, you can also use kwargs to pass in a variable number of arguments. If you do this: my_function(x, **kwargs) and your function then uses kwargs["y"] and kwargs["z"], you can use a data dictionary to call your model API: {"data": {"x": 1, "y": 2, "z": 3}}.

Request a prediction and retrieve the result: Pass the URL of the Domino Model API, authorization token, and input parameters. The response object contains the status, response headers, and result.

response = requests.post("{DOMINO_URL}/models/{MODEL_ID}/latest/model",
   auth=("{MODEL_ACCESS_TOKEN}"),
   json={"data": {"start": 1, "stop": 100}}
)

print(response.status_code)
print(response.headers)
print(response.json())