Download model

To download model binaries for models in the model registry, you can programmatically generate signed URLs using the DVC Studio get-download-uris API. This API requires the model to be stored with DVC with a S3, Azure, http or https remote. Note that you need to set up remote cloud credentials for DVC Studio to have rights to the signed URLs. The signed URLs expire after one hour.

Endpoint: api/model-registry/get-download-uris
HTTP Method: GET

Request

param	desc	type	required	example value
repo	Git repo URL	string	true	iterative/demo-bank-customer-churn
name	Model name	string	true	randomforest-model
version	Model version	string	false	v2.0.0
stage	Model stage	string	false	prod

Only one of stage or version is allowed. If no version or stage is specified, the latest version is returned.

When your model is annotated in non-root dvc.yaml file (typical for monorepo case), model name will be constructed from two parts separated by colon: path/to/dvc/yaml:model_name.

header	desc	example value
Authorization	Header for auth	token abc123

Response

Response is a JSON-encoded dict. If the request was successful, keys will be paths to files inside the repo, and values will be signed URLs you can query to actually download the model.

Example

First, you need your DVC Studio client access token with Model Registry scope. For this example, we set it in the DVC_STUDIO_TOKEN environment variable:

$ export DVC_STUDIO_TOKEN=<TOKEN>

Python

import json
import os
import requests


url = "https://studio.iterative.ai/api/model-registry/get-download-uris"
token = os.environ["DVC_STUDIO_TOKEN"]
headers = {"Authorization": f"token {token}"}
params = {
    "repo": "git@github.com:iterative/demo-bank-customer-churn.git",
    "name": "randomforest-model",
    "version": "v2.0.0"
}

response = requests.get(url, headers=headers, params=params)
for rel_path, obj_url in json.loads(response.content).items():
    obj = requests.get(obj_url)
    ...

CLI

$ curl "https://studio.iterative.ai/api/model-registry/get-download-uris?repo=git@github.com:iterative/demo-bank-customer-churn.git&name=randomforest-model&version=v2.0.0" --header "Authorization:token ${DVC_STUDIO_TOKEN}"

{
    ".mlem/model/clf-model": "https://sandbox-datasets-iterative.s3.amazonaws.com/bank-customer-churn/86/bd02376ac675568ba2fac566169ef9?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAU7UXIWDIQFPCO76Q%2F20230706%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230706T134619Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=6807259ddd1f4448ed1e3c5d4503039884f7779381ee556175096b0a884ba1a6"
}

Running this example will fail because it requires that you have set up remote cloud credentials in DVC Studio to where the model is stored.

Download model

Request

Response

Example

Content