neptune-scale 0.7.2

A minimal client library

Neptune Scale client

[!NOTE] This package only works with the 3.0 version of neptune.ai called Neptune Scale, which is in beta.

You can't use the Scale client with the stable Neptune 2.x versions currently available to SaaS and self-hosting customers. For the Python client corresponding to Neptune 2.x, see https://github.com/neptune-ai/neptune-client.

What is Neptune?

Neptune is an experiment tracker. It enables researchers to monitor their model training, visualize and compare model metadata, and collaborate on AI/ML projects within a team.

What's different about Neptune Scale?

Neptune Scale is the next major version of Neptune. It's built on an entirely new architecture for ingesting and rendering data, with a focus on responsiveness and accuracy at scale.

Neptune Scale supports forked experiments, with built-in mechanics for retaining run ancestry. This way, you can focus on analyzing the latest runs, but also visualize the full history of your experiments.

Installation

pip install neptune-scale

Configure API token and project

1. Log in to your Neptune Scale workspace.

2. Create a project, or find an existing project you want to send the run metadata to.

3. Get your API token from your user menu in the bottom left corner.

   If you're a workspace admin, you can also set up a service account. This way, multiple people or machines can share the same API token. To get started, go to the workspace settings in the top right corner.

4. In the environment where neptune-scale is installed, set the following environment variables to the API token and project name:

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

   export NEPTUNE_PROJECT="team-alpha/project-x"
   

You're ready to start using Neptune Scale.

For more help with setup, see Get started in the Neptune documentation.

Example usage

Create an experiment:

from neptune_scale import Run

run = Run(
    experiment_name="ExperimentName",
    run_id="SomeUniqueRunIdentifier",
)

Then, call logging methods on the run and pass the metadata as a dictionary.

Log configuration or other simple values with log_configs():

run.log_configs(
    {
        "learning_rate": 0.001,
        "batch_size": 64,
    }
)

Inside a training loop or other iteration, use log_metrics() to append metric values:

# inside a loop
for step in range(100):
    run.log_metrics(
        data={"acc": 0.89, "loss": 0.17},
        step=step,
    )

To help identify and group runs, you can apply tags:

run.add_tags(tags=["tag1", "tag2"])

The run is stopped when exiting the context or the script finishes execution, but you can use close() to stop it once logging is no longer needed:

run.close()

To explore your experiment, open the project in Neptune and navigate to Runs. For an example, see the demo project →

For more instructions, see the Neptune documentation:

• Quickstart
• Create an experiment
• Log metadata

API reference

Run

Representation of experiment tracking metadata logged with Neptune Scale.

Initialization

Initialize with the class constructor:

from neptune_scale import Run

run = Run(...)

or using a context manager:

from neptune_scale import Run

with Run(...) as run:
    ...

Parameters

Name	Type	Default	Description
run_id	str	-	Identifier of the run. Must be unique within the project. Max length: 128 bytes.
project	str, optional	None	Name of a project in the form workspace-name/project-name. If None, the value of the NEPTUNE_PROJECT environment variable is used.
api_token	str, optional	None	Your Neptune API token or a service account's API token. If None, the value of the NEPTUNE_API_TOKEN environment variable is used. To keep your token secure, don't place it in source code. Instead, save it as an environment variable.
resume	bool, optional	False	If False (default), creates a new run. To continue an existing run, set to True and pass the ID of an existing run to the run_id argument. To fork a run, use fork_run_id and fork_step instead.
mode	"async" or "disabled"	"async"	Mode of operation. If set to "disabled", the run doesn't log any metadata.
experiment_name	str, optional	None	Name of the experiment to associate the run with. Learn more about experiments in the Neptune documentation.
creation_time	datetime, optional	None	Custom creation time of the run.
fork_run_id	str, optional	None	The ID of the run to fork from.
fork_step	int, optional	None	The step number to fork from.
max_queue_size	int, optional	1M	Maximum number of operations queued for processing. 1 000 000 by default. You should raise this value if you see the on_queue_full_callback function being called.
on_queue_full_callback	Callable[[BaseException, Optional[float]], None], optional	None	Callback function triggered when the queue is full. The function must take as an argument the exception that made the queue full and, as an optional argument, a timestamp of when the exception was last raised.
on_network_error_callback	Callable[[BaseException, Optional[float]], None], optional	None	Callback function triggered when a network error occurs.
on_error_callback	Callable[[BaseException, Optional[float]], None], optional	None	The default callback function triggered when an unrecoverable error occurs. Applies if an error wasn't caught by other callbacks. In this callback you can choose to perform your cleanup operations and close the training script. For how to end the run in this case, use terminate().
on_warning_callback	Callable[[BaseException, Optional[float]], None], optional	None	Callback function triggered when a warning occurs.

Examples

Create a new run:

from neptune_scale import Run

with Run(
    project="team-alpha/project-x",
    api_token="h0dHBzOi8aHR0cHM6...Y2MifQ==",
    run_id="likable-barracuda",
) as run:
    ...

For help, see Create an experiment in the Neptune docs.

[!TIP] Find your API token in your user menu, in the bottom-left corner of the Neptune app.

Or, to use shared API tokens for multiple users or non-human accounts, create a service account in your workspace settings.

To restart an experiment, create a forked run:

with Run(
    run_id="adventurous-barracuda",
    experiment_name="swim-further",
    fork_run_id="likable-barracuda",
    fork_step=102,
) as run:
    ...

Continue a run:

with Run(
    run_id="likable-barracuda",  # a Neptune run with this ID already exists
    resume=True,
) as run:
    ...

close()

The regular way to end a run. Waits for all locally queued data to be processed by Neptune (see wait_for_processing()) and closes the run.

This is a blocking operation. Call the function at the end of your script, after your model training is completed.

Examples

from neptune_scale import Run

run = Run(...)

# logging and training code

run.close()

If using a context manager, Neptune automatically closes the run upon exiting the context:

with Run(...) as run:
    ...

# run is closed at the end of the context

log_configs()

Logs the specified metadata to a Neptune run.

You can log configurations or other single values. Pass the metadata as a dictionary {key: value} with

• key: path to where the metadata should be stored in the run.
• value: the piece of metadata to log.

For example, {"parameters/learning_rate": 0.001}. In the field path, each forward slash / nests the field under a namespace. Use namespaces to structure the metadata into meaningful categories.

Any datetime values that don't have the tzinfo attribute set are assumed to be in the local timezone.

Parameters

Name	Type	Default	Description
data	Dict[str, Union[float, bool, int, str, datetime, list, set, tuple]], optional	None	Dictionary of configs or other values to log. Available types: float, integer, Boolean, string, and datetime.

Examples

Create a run and log metadata:

from neptune_scale import Run

with Run(...) as run:
    run.log_configs(
        data={
            "parameters/learning_rate": 0.001,
            "parameters/batch_size": 64,
        },
    )

log_metrics()

Logs the specified metrics to a Neptune run.

You can log metrics representing a series of numeric values. Pass the metadata as a dictionary {key: value} with

• key: path to where the metadata should be stored in the run.
• value: the piece of metadata to log.

For example, {"metrics/accuracy": 0.89}. In the field path, each forward slash / nests the field under a namespace. Use namespaces to structure the metadata into meaningful categories.

Parameters

Name	Type	Default	Description
data	Dict[str, Union[float, int]]	None	Dictionary of metrics to log. Each metric value is associated with a step. To log multiple metrics at once, pass multiple key-value pairs.
step	Union[float, int]	None	Index of the log entry. Must be increasing. Tip: Using float rather than int values can be useful, for example, when logging substeps in a batch.
timestamp	datetime, optional	None	Time of logging the metadata. If not provided, the current time is used. If provided, and timestamp.tzinfo is not set, the time is assumed to be in the local timezone.

Examples

Create a run and log metrics:

from neptune_scale import Run

with Run(...) as run:
    run.log_metrics(
        data={"loss": 0.14, "acc": 0.78},
        step=1.2,
    )

Note: To correlate logged values, make sure to send all metadata related to a step in a single log_metrics() call, or specify the step explicitly.

When the run is forked off an existing one, the step can't be smaller than the step value of the fork point.

add_tags()

Adds the list of tags to the run.

Parameters

Name	Type	Default	Description
tags	Union[List[str], Set[str], Tuple[str]]	-	List or set of tags to add to the run.
group_tags	bool, optional	False	Add group tags instead of regular tags.

Example

with Run(...) as run:
    run.add_tags(tags=["tag1", "tag2", "tag3"])

remove_tags()

Removes the specified tags from the run.

Parameters

Name	Type	Default	Description
tags	Union[List[str], Set[str], Tuple[str]]	-	List or set of tags to remove from the run.
group_tags	bool, optional	False	Remove group tags instead of regular tags.

Example

with Run(...) as run:
    run.remove_tags(tags=["tag2", "tag3"])

wait_for_submission()

Waits until all metadata is submitted to Neptune for processing.

When submitted, the data is not yet saved in Neptune (see wait_for_processing()).

Parameters

Name	Type	Default	Description
timeout	float, optional	None	In seconds, the maximum time to wait for submission.
verbose	bool, optional	True	If True (default), prints messages about the waiting process.

Example

from neptune_scale import Run

with Run(...) as run:
    run.log_configs(...)
    ...
    run.wait_for_submission()
    run.log_metrics(...)  # called once queued Neptune operations have been submitted

wait_for_processing()

Waits until all metadata is processed by Neptune.

Once the call is complete, the data is saved in Neptune.

Parameters

Name	Type	Default	Description
timeout	float, optional	None	In seconds, the maximum time to wait for processing.
verbose	bool, optional	True	If True (default), prints messages about the waiting process.

Example

from neptune_scale import Run

with Run(...) as run:
    run.log_configs(...)
    ...
    run.wait_for_processing()
    run.log_metrics(...)  # called once submitted data has been processed

terminate()

In case an unrecoverable error is encountered, you can terminate the failed run in your error callback.

Note: This effectively disables processing in-flight operations as well as logging new data. However, the training process isn't interrupted.

Example

from neptune_scale import Run

def my_error_callback(exc):
    run.terminate()


run = Run(..., on_error_callback=my_error_callback)

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Getting help

For help, contact [e-mail].