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skorch integration guide#

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Charts displaying validation metrics logged with skorch

With the Neptune-skorch integration, you can log relevant metadata from your net history to Neptune. These include:

  • Model summary information
  • Model configuration (such as learning rate and optimizer)
  • Epoch durations
  • Metrics per train and validation
  • event_lr
  • Change points (when available)

See example in Neptune  Code examples 

Before you start#

Installing the integration#

The integration is implemented as part of the logging module of the skorch framework, so you don't need to install anything else.

To install Neptune:

pip install -U neptune
conda install -c conda-forge neptune
Passing your Neptune credentials

Once you've registered and created a project, set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...6Lc"

To find your API token: In the bottom-left corner of the Neptune app, expand the user menu and select Get my API token.

export NEPTUNE_PROJECT="ml-team/classification"

Your full project name has the form workspace-name/project-name. You can copy it from the project settings: Click the menu in the top-right → Edit project details.

On Windows, navigate to SettingsEdit the system environment variables, or enter the following in Command Prompt: setx SOME_NEPTUNE_VARIABLE 'some-value'

While it's not recommended especially for the API token, you can also pass your credentials in the code when initializing Neptune.

run = neptune.init_run(
    project="ml-team/classification",  # your full project name here
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh...3Kb8",  # your API token here

For more help, see Set Neptune credentials.


To log skorch metrics with Neptune:

  1. Create a Neptune run:

    import neptune
    run = neptune.init_run() # (1)!
    1. If you haven't set up your credentials, you can log anonymously:

  2. Create a NeptuneLogger callback.

    Pass the run object as the first argument:

    neptune_logger = NeptuneLogger(run, close_after_train=False)
  3. Pass the callback to the classifier and fit the estimator to your data:

    net = NeuralNetClassifier(
    ), y_train)
  4. If you set close_after_train to False, stop the run once you're finished:

More options#

For ways to customize the NeptuneLogger callback, see the API reference.

Logging additional metrics after training#

You can log metrics after the training has finished.

from sklearn.metrics import roc_auc_score

y_pred = net.predict_proba(X)
auc = roc_auc_score(y, y_pred[:, 1])["roc_auc_score"].append(auc)


Log metrics

Logging performance charts#

You can additionally log charts, such as an ROC curve.

from scikitplot.metrics import plot_roc
import matplotlib.pyplot as plt

from neptune.types import File

fig, ax = plt.subplots(figsize=(16, 12))
plot_roc(y, y_pred, ax=ax)["roc_curve"].upload(File.as_html(fig))

Logging trained model#

You can log the net object after training.



If you set close_after_train to False, stop the run once you're finished logging:

Uploading checkpoints#

You can upload checkpoint files to Neptune by passing the checkpoints directory to the callback arguments and manually uploading it to the Neptune run.

  1. Pass the directory where the checkpoint files are saved to the callback arguments.

    checkpoint_dirname = "path/to/checkpoints/directory"  # replace with your own
    checkpoint = Checkpoint(dirname=checkpoint_dirname)
    net = NeuralNetClassifier(
        callbacks=[neptune_logger, checkpoint],
  2. Upload that same directory to the Neptune run, in a namespace of your choice.["training/model/checkpoints"].upload_files(checkpoint_dirname)

    In this case, the checkpoints are uploaded to a namespace called "checkpoints", which is nested under "model" and "training". You can replace it with a structure of your own choosing.

  3. If you set close_after_train to False, stop the run once you're finished logging: