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TensorBoard integration guide#

Open in Colab

Custom dashboard displaying metadata logged with TensorBoard

With the Neptune-TensorBoard integration, you can track metrics and log metadata to both TensorBoard and Neptune at the same time. You can also use the CLI utility to export existing TensorBoard logs to Neptune.

See example in Neptune  Code examples 

Before you start#

Installing the integration#

To use your preinstalled version of Neptune together with the integration:

pip install -U neptune-tensorboard

To install both Neptune and the integration:

pip install -U "neptune[tensorboard]"
How do I save my credentials as environment variables?

Set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.

For example:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...6Lc" # (1)!
  1. On Windows, the command is set instead of export.
export NEPTUNE_PROJECT="ml-team/classification" # (1)!
  1. On Windows, the command is set instead of export.

Finding your credentials:

  • API token: In the bottom-left corner of the Neptune app, expand your user menu and select Get your API token.
  • Project: Your full project name has the form workspace-name/project-name. To copy the name, click the menu in the top-right corner and select Edit project details.

If you're working in Colab, you can set your credentials with the os and getpass libraries:

import os
from getpass import getpass
os.environ["NEPTUNE_API_TOKEN"] = getpass("Enter your Neptune API token: ")
os.environ["NEPTUNE_PROJECT"] = "workspace-name/project-name"

If you'd rather follow the guide without any setup, you can run the example in Colab .

Basic logging example#

In this guide, we'll show you how to use the enable_tensorboard_logging() function to log metadata to both TensorBoard and Neptune at the same time.

  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:

      neptune.init_run(
        api_token=neptune.ANONYMOUS_API_TOKEN,
        project="common/tensorboard-integration",
)

    For more run customization options, see neptune.init_run().

  2. Import the logging function:

    from neptune_tensorboard import enable_tensorboard_logging

  3. Call the integration function, passing the Neptune run object as the argument.

    enable_tensorboard_logging(run)

    This will log the metadata to both the tensorboard directory and the Neptune run.

    Tip

    The function also works with:

    • SummaryWriter from PyTorch and tensorboardX
    • TensorBoard callback for Keras

  4. Proceed with your model training script. For a full example, see the TensorBoard code samples on GitHub .

  5. To stop the connection to Neptune and sync all data, call the stop() method:

    run.stop()

  6. Run your script as you normally would.

    To open the run, click the Neptune link that appears in the console output.

    Sample output

    https://app.neptune.ai/workspace-name/project-name/e/RUN-100/metadata

    The general format is https://app.neptune.ai/<workspace>/<project> followed by the Neptune ID of the initialized object.

    If Neptune can't find your project name or API token

    As a best practice, you should save your Neptune API token and project name as environment variables. However, you can also pass them as arguments when you're using a function that takes api_token and project as parameters:

    • api_token="Your Neptune API token here"
      • Find and copy your API token by expanding your user menu and selecting Get my API token.
    • project="workspace-name/project-name""
      • Find and copy your project name in the top-right menu: → Edit project details.

    For example:

    Neptune client library
    model_version = neptune.init_model_version(
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8",
    project="ml-team/named-entity-recognition",
    model= ...,
)
    Neptune integration
    neptune_logger = dl.NeptuneLogger(
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8",
    project="ml-team/named-entity-recognition",
)

Analyzing the results#

You'll find the logged metadata in All metadata → tensorboard.

You can also see the metadata visualized in the other dashboards, as well as create your own dashboard. See the Neptune example for inspiration.

See example in Neptune 

Exporting TensorBoard logs to Neptune#

You can export TensorBoard logs from the logs directory to Neptune with the neptune tensorboard CLI command:

neptune tensorboard logs

If you haven't set your credentials as environment variables, you can also pass them as arguments:

neptune tensorboard --api_token YourNeptuneApiToken --project YourProjectName logs
How do I save my credentials as environment variables?

Set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.

For example:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...6Lc" # (1)!
  1. On Windows, the command is set instead of export.
export NEPTUNE_PROJECT="ml-team/classification" # (1)!
  1. On Windows, the command is set instead of export.

Finding your credentials:

  • API token: In the bottom-left corner of the Neptune app, expand your user menu and select Get your API token.
  • Project: Your full project name has the form workspace-name/project-name. To copy the name, click the menu in the top-right corner and select Edit project details.

If you're working in Colab, you can set your credentials with the os and getpass libraries:

import os
from getpass import getpass
os.environ["NEPTUNE_API_TOKEN"] = getpass("Enter your Neptune API token: ")
os.environ["NEPTUNE_PROJECT"] = "workspace-name/project-name"

neptune-notebooks incompatibility

Currently, the CLI component of the integration does not work together with the Neptune-Jupyter extension (neptune-notebooks).

Until a fix is released, if you have neptune-notebooks installed, you must uninstall it to be able to use the neptune tensorboard command.

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