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Download a model version

Use the W&B Python SDK to download a model artifact that you linked to the Model Registry. Downloading a model is particularly useful if you want to load or consume a model in a future to evaluate a model's performance, make predictions with a dataset, or use ship the model to production.


You are responsible for providing additional Python functions, API calls to reconstruct, deserialize your model into a form that you can work with.

W&B suggests that you document information on how to load models into memory with model cards. For more information, see the Document machine learning models page.

Replace values within <> with your own:

import wandb

# Initialize a run
run = wandb.init(project="<project>", entity="<entity>")

# Access and download model. Returns path to downloaded artifact
downloaded_model_path = run.use_model(name="<your-model-name>")

Reference a model version with one of following formats listed:

  • latest - Use latest alias to specify the model version that is most recently linked.
  • v# - Use v0, v1, v2, and so on to fetch a specific version in the Registered Model
  • alias - Specify the custom alias that you and your team assigned to your model version

See use_model in the API Reference guide for more information on possible parameters and return type.

Example: Download and use a logged model

For example, in the proceeding code snippet a user called the use_model API. They specified the name of the model artifact they want to fetch and they also provided a version/alias. They then stored the path that returned from the API to the downloaded_model_path variable.

import wandb

entity = "luka"
project = "NLP_Experiments"
alias = "latest" # semantic nickname or identifier for the model version
model_artifact_name = "fine-tuned-model"

# Initialize a run
run = wandb.init()
# Access and download model. Returns path to downloaded artifact

downloaded_model_path = run.use_model(name=f"{entity/project/model_artifact_name}:{alias}")
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