API reference: Sacred integration#

The Neptune-Sacred integration provides a Sacred observer that logs experiment metadata to Neptune.

`NeptuneObserver`#

Logs Sacred experiment data to Neptune.

Parameters

Name	Type	Default	Description
`run`	`Run` or `Handler`	-	An existing run reference, as returned by `neptune.init_run()`, or a namespace handler.
`base_namespace`	`str`, optional	`"experiment"`	Namespace under which all metadata logged by the observer will be stored.

Examples

Start a Neptune run:

import neptune

run = neptune.init_run()

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:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8"

export NEPTUNE_PROJECT="ml-team/classification"

Alternatively, you can pass the information when using a function that takes api_token and project as arguments:

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

In the bottom-left corner, expand the user menu and select Get my API token.
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!

Create an experiment:

from sacred import Experiment

ex = Experiment("image_classification", interactive=True)

Add a Neptune observer:

from neptune.integrations.sacred import NeptuneObserver

ex.observers.append(NeptuneObserver(run=run))

Define the model and run the experiment:

class BaseModel(torch.nn.Module):
    ...

model = BaseModel()

@ex.config
def cfg():
    ...

@ex.main
def _run(...):
    ...

ex.run()