neptune-query 1.7.0

Neptune Query is a Python library for retrieving data from Neptune.

Neptune Query API

The neptune_query package is a read-only API for fetching metadata tracked with the Neptune logging client.

With the Query API, you can:

• List experiments, runs, and attributes of a project.
• Fetch experiment or run metadata as a data frame.
• Define filters to fetch experiments, runs, and attributes that meet certain criteria.

Installation

pip install "neptune-query>=1.5.1,<2.0.0"

Set your Neptune API token and project name as environment variables:

export NEPTUNE_API_TOKEN="ApiTokenFromYourNeptuneProfile"

export NEPTUNE_PROJECT="workspace-name/project-name"

For help, see Query metadata: Setup in the Neptune documentation.

Note: You can also pass the project path to the project argument of any querying function.

Usage

import neptune_query as nq

Available functions:

• download_files() – download files from the specified experiments.
• fetch_experiments_table() – runs as rows and attributes as columns.
• fetch_metrics() – series of float or int values, with steps as rows.
• fetch_series() – for series of strings or histograms.
• list_attributes() – all logged attributes of the target project's experiment runs.
• list_experiments() – names of experiments in the target project.
• set_api_token() – set the Neptune API token to use for the session.
• (experimental) fetch_metric_buckets() – get summary values split by X-axis buckets.

For details, see the API reference.

To use the corresponding methods for runs, import the Runs API:

import neptune_query.runs as nq_runs

You can use these methods to target individual runs by ID instead of experiment runs by name.

Example 1: Fetch metric values

To fetch values at each step, use fetch_metrics().

• To filter experiments to return, use the experiments parameter.
• To specify attributes to include as columns, use the attributes parameter.

nq.fetch_metrics(
    experiments=["exp_dczjz"],
    attributes=r"metrics/val_.+_estimated$",
)

                  metrics/val_accuracy_estimated  metrics/val_loss_estimated
experiment  step
exp_dczjz    1.0                        0.432187                    0.823375
             2.0                        0.649685                    0.971732
             3.0                        0.760142                    0.154741
             4.0                        0.719508                    0.504652

Example 2: Fetch metadata as one row per run

To fetch experiment metadata from your project, use the fetch_experiments_table() function:

nq.fetch_experiments_table(
    experiments=r"^exp_",
    attributes=["metrics/train_accuracy", "metrics/train_loss", "learning_rate"],
)

            metrics/train_accuracy   metrics/train_loss   learning_rate
experiment
exp_ergwq                 0.278149             0.336344            0.01
exp_qgguv                 0.160260             0.790268            0.02
exp_hstrj                 0.365521             0.459901            0.01

For series attributes, the value of the last logged step is returned.

Documentation

• How to query metadata
• API reference

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License

This project is licensed under the Apache License Version 2.0. For details, see Apache License Version 2.0.