> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-docs-1778-mysql-updates.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

> Track a model, the model's dependencies, and other information relevant to that model with the W&B Python SDK.

# Track a model

Track a model, the model's dependencies, and other information relevant to that model with the W\&B Python SDK.

Under the hood, W\&B creates a lineage of [model artifact](./model-management-concepts#model-artifact) that you can view with the W\&B App or programmatically with the W\&B Python SDK. See the [Create model lineage map](./model-lineage) for more information.

## How to log a model

Use the `run.log_model` API to log a model. Provide the path where your model files are saved to the `path` parameter. The path can be a local file, directory, or [reference URI](/models/artifacts/track-external-files/#amazon-s3--gcs--azure-blob-storage-references) to an external bucket such as `s3://bucket/path`.

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

Copy and paste the proceeding code snippet. Ensure to replace values enclosed in `<>` with your own.

```python theme={null}
import wandb

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

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

<details>
  <summary>Example: Log a Keras model to W\&B</summary>

  The proceeding code example shows how to log a convolutional neural network (CNN) model to W\&B.

  ```python theme={null}
  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-project", config=config)

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

  model = keras.Sequential(
      [
          layers.Input(shape=input_shape),
          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.Flatten(),
          layers.Dropout(0.5),
          layers.Dense(num_classes, activation="softmax"),
      ]
  )

  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)
  model.save(filepath=full_path)

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

  # Explicitly tell W&B to end the run.
  run.finish()
  ```
</details>