Fetch metadata via API

Python package: neptune-fetcher

To fetch a data frame similar to the table view in the web app, use the fetch_*() functions of the Fetcher API.

Fetching metadata as table

To fetch experiment metadata from your project, use fetch_experiments_table().

To limit the scope, use the filtering parameters:

• experiments: Only fetch attributes from experiments that pass the filter.
• attributes: Only include attributes that pass the filter as columns.

For both arguments, you can specify a simple string to match experiment or attribute names against. To construct more complex filters, use the Filter constructors.

import neptune_fetcher.alpha as npt


npt.fetch_experiments_table(
    experiments=r"exp.*",
    attributes=r".*metric.*/val_.+",
)

Output

           metrics/val_accuracy metrics/val_loss
                           last             last
experiment
exp_ergwq              0.278149         0.336344
exp_qgguv              0.160260         0.790268
exp_cnuwh              0.702490         0.366390
exp_kokxd              0.301545         0.917683
exp_gyvpp              0.999489         0.069839

note

Fetching metrics this way returns an aggregated value for each attribute. The default aggregation is the last logged value.

To fetch actual metric values at each step, see Fetching metric values.

The following example fetches metadata from experiments that meet two criteria:

• The validation/loss attribute value is less than 0.1
• The experiment name matches the regex exp.*

Additionally, only attributes matching validation are included as columns.

from neptune_fetcher.alpha.filters import Filter


loss_filter = Filter.lt("validation/loss", 0.1)
name_filter = r"exp.*"

combined_filter = loss_filter & name_filter

npt.fetch_experiments_table(
    experiments=combined_filter,
    attributes=r"validation",
)

Output

            validation/accuracy  validation/loss
                           last             last
experiment
exp_ergwq              0.278149         0.336344
exp_qgguv              0.160260         0.790268
exp_cnuwh              0.702490         0.366390
exp_kokxd              0.301545         0.917683
exp_gyvpp              0.999489         0.069839

Fetching metric values

To fetch individual values from one or more FloatSeries attributes, use fetch_metrics():

npt.fetch_metrics(
    experiments=r"exp.*",
    attributes=r"metrics/.*",
)

Output

  experiment  step  metrics/accuracy  metrics/loss  metrics/val_accuracy  metrics/val_loss
0  exp_dczjz     0          0.819754      0.588655              0.432187          0.823375
1  exp_dczjz     1          0.347907      0.297161              0.649685          0.971732
2  exp_dczjz     2          0.858863      0.988583              0.760142          0.154741
3  exp_dczjz     3          0.217097          None              0.719508          0.504652
4  exp_dczjz     4          0.089981      0.463146              0.180321          0.503800
5  exp_hgctc     0          0.318828      0.386347              0.012390          0.171790
6  exp_hgctc     1          0.035026      0.087053              0.392041          0.768675
7  exp_hgctc     2          0.004711      0.061848                  None          0.847072
8  exp_hgctc     3          0.359770      0.130022              0.479945          0.323537
9  exp_hgctc     4          0.007815      0.746344              0.102646          0.055511

Fetch metric previews

To fetch point previews, set the include_point_previews argument to True:

npt.fetch_metrics(
    experiments=r"exp.*",
    attributes=r"metrics/.*",
    include_point_previews=True,
)

When previews are included, the returned data frame includes additional sub-columns with preview information: is_preview and preview_completion.