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Log Models

The following guide describes how to log models to a W&B run and interact with them.


The following APIs are useful for tracking models as a part of your experiment tracking workflow. Use the APIs listed on this page to quickly log models to a run, in addition to metrics, tables, media and other objects.

W&B suggests that you use W&B Artifacts if you want to:

  • Create and keep track of different versions of serialized data besides models, such as datasets, prompts, and more.
  • Explore lineage graphs of a model or any other objects tracked in W&B.
  • Interact with the model artifacts these methods created, such as updating properties (metadata, aliases, and descriptions)

For more information on W&B Artifacts and advanced versioning use cases, see the Artifacts documentation.


See this Colab notebook for an end-to-end example of how to use the APIs described on this page.

Log a model to a W&B runโ€‹

Use the log_model to log a model artifact that contains content within a directory you specify. The log_model method also marks the resulting model artifact as an output of the W&B run.

You can track a model's dependencies and the model's associations if you mark the model as the input or output of a W&B run. View the lineage of the model within the W&B App UI. See the Explore and traverse artifact graphs page within the Artifacts chapter for more information.

Provide the path where your model file(s) are saved to the path parameter. The path can be a local file, directory, or reference URI to an external bucket such as s3://bucket/path.

Ensure to replace values enclosed in <> with your own.

import wandb

# Initialize a W&B run
run = wandb.init(project="<your-project>", entity="<your-entity>")

# Log the model
run.log_model(path="<path-to-model>", name="<name>")

Optionally provide a name for the model artifact for the name parameter. If name is not specified, W&B will use the basename of the input path prepended with the run ID as the name.


Keep track of the name that you, or W&B assigns, to the model. You will need the name of the model to retrieve the model path with the use_model method.

See log_model in the API Reference guide for more information on possible parameters.

Example: Log a model to a run
import os
import wandb
from tensorflow import keras
from tensorflow.keras import layers

config = {"optimizer": "adam", "loss": "categorical_crossentropy"}

# Initialize a W&B run
run = wandb.init(entity="charlie", project="mnist-experiments", config=config)

# Hyperparameters
loss = run.config["loss"]
optimizer = run.config["optimizer"]
metrics = ["accuracy"]
num_classes = 10
input_shape = (28, 28, 1)

# Training algorithm
model = keras.Sequential(
layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
layers.MaxPooling2D(pool_size=(2, 2)),
layers.Dense(num_classes, activation="softmax"),

# Configure the model for training
model.compile(loss=loss, optimizer=optimizer, metrics=metrics)

# Save model
model_filename = "model.h5"
local_filepath = "./"
full_path = os.path.join(local_filepath, model_filename)

# Log the model to the W&B run
run.log_model(path=full_path, name="MNIST")

When the user called log_model, a model artifact named MNIST was created and the file model.h5 was added to the model artifact. Your terminal or notebook will print information of where to find information about the run the model was logged to.

View run different-surf-5 at:
Synced 5 W&B file(s), 0 media file(s), 1 artifact file(s) and 0 other file(s)
Find logs at: ./wandb/run-20231206_103511-wlby6fuw/logs

Download and use a logged modelโ€‹

Use the use_model function to access and download models files previously logged to a W&B run.

Provide the name of the model artifact where the model file(s) you are want to retrieve are stored. The name you provide must match the name of an existing logged model artifact.

If you did not define name when originally logged the file(s) with log_model, the default name assigned is the basename of the input path, prepended with the run ID.

Ensure to replace other the values enclosed in <> with your own:

import wandb

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

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

The use_model function returns the path of downloaded model file(s). Keep track of this path if you want to link this model later. In the preceding code snippet, the returned path is stored in a variable called downloaded_model_path.

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 is 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(project=project, entity=entity)
# Access and download model. Returns path to downloaded artifact
downloaded_model_path = run.use_model(name = f"{model_artifact_name}:{alias}")

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

Use the link_model method to log model file(s) to a W&B run and link it to the W&B Model Registry. If no registered model exists, W&B will create a new one for you with the name you provide for the registered_model_name parameter.


You can think of linking a model similar to 'bookmarking' or 'publishing' a model to a centralized team repository of models that others members of your team can view and consume.

Note that when you link a model, that model is not duplicated in the Model Registry. That model is also not moved out of the project and intro the registry. A linked model is a pointer to the original model in your project.

Use the Model Registry to organize your best models by task, manage model lifecycle, facilitate easy tracking and auditing throughout the ML lifecyle, and automate downstream actions with webhooks or jobs.

A Registered Model is a collection or folder of linked model versions in the Model Registry. Registered models typically represent candidate models for a single modeling use case or task.

The proceeding code snippet shows how to link a model with the link_model API. Ensure to replace other the values enclosed in <> with your own:

import wandb

run = wandb.init(entity="<your-entity>", project="<your-project>")
run.link_model(path="<path-to-model>", registered_model_name="<registered-model-name>")

See link_model in the API Reference guide for more information on optional parameters.

If the registered-model-name matches the name of a registered model that already exists within the Model Registry, the model will be linked to that registered model. If no such registered model exists, a new one will be created and the model will be the first one linked.

For example, suppose you have an existing registered model named "Fine-Tuned-Review-Autocompletion" in your Model Registry (see example here). And suppose that a few model versions are already linked to it: v0, v1, v2. If you call link_model with registered-model-name="Fine-Tuned-Review-Autocompletion", the new model will be linked to this existing registered model as v3. If no registered model with this name exists, a new one will be created and the new model will be linked as v0.

Example: Log and link a model to the W&B Model Registry

For example, the proceeding code snippet logs model files and links the model model to a registered model name "Fine-Tuned-Review-Autocompletion".

To do this, a user calls the link_model API. When they call the API, they provide a local filepath that points the content of the model (path) and they provide a name for the registered model to link it to (registered_model_name).

import wandb

path = "/local/dir/"
registered_model_name = "Fine-Tuned-Review-Autocompletion"

run = wandb.init(project="llm-evaluation", entity="noa")
run.link_model(path=path, registered_model_name=registered_model_name)

Reminder: A registered model houses a collection of bookmarked model versions.

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