API reference: Detectron2 integration#

You can use a Neptune hook to capture model training metadata during training and validation of detectron2 models.

`NeptuneHook`#

Captures model training metadata and logs them to Neptune.

Parameters

Name	Type	Default	Description
`run`	`Run` or `Handler`	-	(required) An existing run reference, as returned by `neptune.init_run()`, or a namespace handler.
`base_namespace`	`str`, optional	`"training"`	Namespace under which all metadata logged by the Neptune hook will be stored.
`metrics_update_freq`	`int`, optional	20	How often NeptuneHook should log metrics (every nth epoch). The value must be greater than zero. Example: Setting it to `10` will log metrics every 10th epoch. Also applies to checkpoints, if `log_checkpoints` is set to `True`
`log_model`	`bool`, optional	`False`	Whether to upload the final model checkpoint, whenever it is saved by the Trainer. Expects `CheckpointHook` to be present.
`log_checkpoints`	`bool`, optional	`False`	Whether to upload checkpoints whenever they are saved by the Trainer. Expects `CheckpointHook` to be present.

Examples#

Creating a hook that sends the logs to an existing Neptune run object#

import neptune

neptune_run = neptune.init_run()

neptune_hook = NeptuneHook(run=neptune_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
)

API token: In the bottom-left corner, expand the user menu and select Get my API token.
Project name: in the top-right menu: → 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",
)

Creating a Neptune run and hook with more options#

In the following example, we set the Trainer to save model checkpoints every 10th epoch. Neptune will upload those checkpoints and metrics at the same interval.

neptune_run = neptune.init_run(
    project="workspace-name/project-name",  # (1)!
    name="My detectron2 run",
    tags = ["validation"],
    capture_stdout=False,
)

neptune_hook = NeptuneHook(
    run=neptune_run,
    log_checkpoints=True,
    metrics_update_freq=10,
)

The full project name. For example, "ml-team/classification".

To copy it, navigate to the project settings in the top-right () and select Properties.

Additionally, we pass an extra option not to capture the stdout during the run (capture_stdout=False). For all the keyword arguments you can use, see neptune ≫ init_run().

Passing a handler to the hook#

To log the metadata to a specific namespace of the run, you can create a namespace handler object and pass that to the hook.

import neptune

neptune_run = neptune.init_run()

handler = neptune_run["validation"]

neptune_hook = NeptuneHook(run=handler)

In this case, all the metadata will be logged under the validation namespace (folder) inside the run.