TensorBoard integration API reference

API reference for the Neptune-TensorBoard integration.

The Neptune-TensorBoard integration has the following components:

• The enable_tensorboard_logging() function, for logging metadata to TensorBoard and Neptune simultaneously.
• The neptune tensorboard CLI command, for exporting existing TensorBoard logs to Neptune.

enable_tensorboard_logging()

Logs the tracked metadata to both thetensorboarddirectory and the Neptune run.

Parameters

Examples

import neptune
from neptune_tensorboard import enable_tensorboard_logging

run = neptune.init_run()
enable_tensorboard_logging(run)

As a best practice, you should save your Neptune API token and project name as environment variables:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8"

export NEPTUNE_PROJECT="ml-team/classification"

Alternatively, you can pass the information when using a function that takesapi_tokenandprojectas arguments:

run = neptune.init_run(
 api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8", # (1)!
 project="ml-team/classification", # (2)!
)

1. In the bottom-left corner, expand the user menu and select Get my API token .
2. You can copy the path from the project details ( → Details & privacy ).

If you haven't registered, you can log anonymously to a public project:

api_token=neptune.ANONYMOUS_API_TOKEN
project="common/quickstarts"

Make sure not to publish sensitive data through your code!

You can also customize the Neptune run with more options at initialization:

run = neptune.init_run(
 name="My TensorBoard run",
 tags=["test", "tensorboard", "fail_on_exception"],
 dependencies="infer",
 fail_on_exception=True,
)

For more, seeneptune.init_run().

neptune tensorboard

Exports TensorBoard logs from thelogsdirectory to Neptune.

• Linux: Command line
• macOS: Terminal app
• Windows: PowerShell or Command Prompt
• Jupyter Notebook: In a cell, prefixed with an exclamation mark: !your-command-here

Command syntax:neptune tensorboard [--api_token] [--project] logs

Examples

If you've set your Neptune credentials as environment variables, you can use the following command:

neptune tensorboard logs

Set your Neptune API token and full project name to theNEPTUNE_API_TOKENandNEPTUNE_PROJECTenvironment variables, respectively.

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

export NEPTUNE_PROJECT="ml-team/classification"

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

export NEPTUNE_PROJECT="ml-team/classification"

setx NEPTUNE_API_TOKEN "h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

setx NEPTUNE_PROJECT "ml-team/classification"

You can also navigate toSettings→Edit the system environment variablesand add the variables there.

%env NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

%env NEPTUNE_PROJECT="ml-team/classification"

To find your credentials:

• API token : In the bottom-left corner of the Neptune app, expand your user menu and select Get your API token . If you need the token of a service account, go to the workspace or project settings and enter the Service accounts settings.
• Project name : Your full project name has the form workspace-name/project-name . You can copy it from the project menu ( → Details & privacy ).

If you're working in Google 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"

Otherwise, you can pass the credentials as options:

neptune tensorboard --api_token I2YzgMz1...MifQ== --project ml-team/llm-project logs

neptune-notebooks incompatibility

The command doesn't work together with theNeptune-Jupyter extension(neptune-notebooks).

To use the command, you must uninstall neptune-notebooks first.

See also

neptune-tensorboard repo onGitHub

Related Documentation

• Neptune Package
• Run API

This page is originally sourced from the legacy docs.