PyTorch integration API reference

API reference for the NeptuneLogger class of the Neptune-PyTorch integration.

You can use theNeptuneLoggerto capture model training metadata when working with PyTorch.

NeptuneLogger

Captures model training metadata and logs them to Neptune.

Parameters

Examples

Creating a Neptune run and callback

Create a run:

import neptune

run = neptune.init_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!

Instantiate the Neptune callback:

from neptune_pytorch import NeptuneLogger

neptune_logger = NeptuneLogger(run=run, model=model)

Train your model:

for epoch in range(1, 4):
 model.train()
 for batch_idx, (data, target) in enumerate(train_loader):
 data, target = data.to(device), target.to(device)
 optimizer.zero_grad()
 output = model(data)
 loss = F.nll_loss(output, target)
 loss.backward()
 optimizer.step()

Additional options

import neptune
from neptune_pytorch import NeptuneLogger

run = neptune.init_run(
 name="My PyTorch run",
 tags=["test", "pytorch"],
 dependencies="infer",
)

neptune_logger = NeptuneLogger(
 run=run,
 model=model,
 base_namespace="test",
 log_model_diagram=True,
 log_gradients=True,
 log_parameters=True,
 log_freq=50,
)

log_checkpoint()

Uploads a model checkpoint to Neptune, into a namespace calledmodel/checkpointsnested under the base namespace of the run.

The filename is set tocheckpoint_<checkpoint number>.ptby default, but can be customized.

Parameters

Example

from neptune_pytorch import NeptuneLogger

neptune_logger = NeptuneLogger(...)
...
for epoch in range(parameters["epochs"]):
 ...
 neptune_logger.log_checkpoint()

log_model()

Uploads the model to Neptune, into a namespace calledmodelnested under the base namespace of the run.

The filename is set tomodel.ptby default, but can be customized.

Parameters

Example

from neptune_pytorch import NeptuneLogger

neptune_logger = NeptuneLogger(...)
...
neptune_logger.log_model()

save_checkpoint

Seelog_checkpoint.

save_model

Seelog_model.

See also

neptune-pytorch repo onGitHub

Related Documentation

• Neptune Package
• Run API

This page is originally sourced from the legacy docs.