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API reference: Sacred integration#

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

Related

NeptuneObserver#

Logs Sacred experiment data to Neptune.

Parameters

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

Examples

Start a Neptune run:

import neptune.new as neptune

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="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8"
export NEPTUNE_PROJECT="ml-team/classification"

You can, however, also pass them as arguments when initializing Neptune:

run = neptune.init_run(
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh3Kb8",  # your token here
    project="ml-team/classification",  # your full project name here
)
  • Find and copy your API token by clicking your avatar and selecting Get my API token.
  • Find and copy your project name in the project Settings → Properties.

If you haven't registered, you can also log anonymously to a public project (make sure not to publish sensitive data through your code!):

run = neptune.init_run(
    api_token=neptune.ANONYMOUS_API_TOKEN,
    project="common/quickstarts",
)

Create an experiment:

from sacred import Experiment

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

Add a Neptune observer:

from neptune.new.integrations.sacred import NeptuneObserver

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