fetch_runs_table()

Python package: neptune-fetcher

Fetches a project's run metadata as a table.

Returns a data frame similar to the runs table in the Neptune app.

Parameters

Name	Type	Default	Description
runs	Union[str, Filter], optional	None	A filter specifying which runs to include: a regex that the run ID must match, or a Filter object. If no filter is specified, all runs are returned.
attributes	Union[str, AttributeFilter], optional	"sys/custom_run_id" and "sys/name"	A filter specifying which attributes to include in the table: a regex that the attribute name must match, or an AttributeFilter object. If no filter is specified, the custom run ID and associated experiment name are included.
sort_by	Union[str, Attribute], optional	"sys/creation_time"	An attribute name or an Attribute object specifying type and, optionally, aggregation.
sort_direction	Literal["asc", "desc"], optional	"desc"	"asc" or "desc"
limit	int, optional	None	Maximum number of experiments to return. By default all experiments are returned.
type_suffix_in_column_names	bool, optional	False	If True, columns of the returned DataFrame are suffixed with :<type>. For example, "attribute1:float_series", "attribute1:string". If set to False, the method throws an exception if there are multiple types under one path.
context	Context, optional	None	Which project and API token to use for the fetching operation. Useful for switching projects.

Specifying aggregations

If the fetched attributes include metric attributes of type FloatSeries, only the last logged value is returned by default.

To get different metric aggregations, pass an attribute filter to the attributes argument. In this case, metric aggregates are returned as sub-columns of a metric column. In pandas terms, the columns have a MultiIndex with two levels: "attribute name" as the first level and "aggregation" as the second level.

For a demonstration, see the Examples section.

The available aggregation functions are:

• average
• last
• max
• min
• variance

For details, see AttributeFilter.

Examples

from neptune_fetcher.alpha import runs
from neptune_fetcher.alpha.filters import Filter


filtered_runs = (
    Filter.matches_none("sys/description", r"test")
    & Filter.le("params/lr", 0.002)
    & Filter.matches_all("sys/custom_run_id", r"trout")
)

runs.fetch_runs_table(
    runs=filtered_runs,
    attributes=r"params/",
    sort_by="params/lr",
)

Output

attribute       params/batch_size    params/lr    params/optimizer
aggregation
           run
trout-nk7la147                 64        0.002                Adam
trout-45hxc318                 64        0.001                Adam
trout-z6th1103                 64        0.001                Adam

Including metric aggregates

runs.fetch_runs_table(
    runs=r"kittiwake_02.*25",
    attributes=r".*metric.*/val_.+",
)

Output

                   metrics/val_accuracy metrics/val_loss
                                   last             last
run
kittiwake_0287625              0.278149         0.336344
kittiwake_025c425              0.160260         0.790268
kittiwake_02kj725              0.702490         0.366390
kittiwake_024vcx125            0.301545         0.917683
kittiwake_02gt11x25            0.999489         0.069839

Fetch the average and variance of two accuracy metrics:

from neptune_fetcher.alpha.filters import AttributeFilter


acc_avg_and_var = AttributeFilter(
    name_eq=["train/accuracy", "validation/accuracy"]
    aggregations=["average", "variance"],
)

runs.fetch_runs_table(
    runs=r"kittiwake_02.*25",
    attributes=acc_avg_and_var,
)

Output

attribute              train/accuracy             validation/accuracy
aggregation                   average  variance               average  variance
run
kittiwake_0287625            0.689133  0.000273               0.56911  0.000269
kittiwake_025c425            0.756778  0.594614               0.45659  0.595119
...