Field types reference#

A field is the location of a piece of metadata in a Neptune object.

When you log metadata, the data type and logging method together determine the resulting field type. The type determines the operations available for the field.

Metadata type	Example	Logging method	Resulting field type
Single value	Parameters, final scores, text, time	`=`/`assign()`	`Float`, `Integer`, `Boolean`, `String`, `Datetime`
Series of values	Metrics, loss, accuracy	`append()`/`extend()`	`FloatSeries`
Series of text	Journal, notes	`append()`/`extend()`	`StringSeries`
Series of files	Image series, predictions	`append()`/`extend()`	`FileSeries`
Single file	Image, plot file, data sample	`upload()`	`File`
Set of files	Large number of files	`upload_files()`	`FileSet`
Tags	Text tags to annotate objects	`add()`	`StringSet`
File(s) to be versioned	Dataset, model file	`track_files()`	`Artifact`

The table in the neptune.types section below displays all the supported field types.

`neptune.types` overview#

Neptune field types can be roughly divided into the following categories.

Type	Description
`Float`, `Integer`, `Boolean`, `String`, `Datetime`, `File`, `GitRef`, and `RunState`	Used for a single value of the given type, or a single file.
`Series`	Used for series of values or files (for example, images). Available types are `FloatSeries`, `StringSeries` and `FileSeries`.
`Artifact`	Field type for versioning datasets, models, and other files.
`FileSet`	Used to hold a larger number of files when access to a single file is rare.
`StringSet`	Used to interact with a Neptune object's tags.
`Handler`	Obtained when you access a field path that doesn't exist yet.
Namespace handler	Obtained when you access a field path that is one or more levels higher than an existing field. Used as a shortcut for accessing fields by specifying only a relative path within the namespace. Example: `params_ns = run["parameters"]`, then `params_ns["lr"] = 0.3`

Simple types#

Field type representing a floating-point number, integer, Boolean value, text string, or datetime value.

You can use the following general recipe to assign values of these types to a field in a Neptune object:

Logging simple type

import neptune

run = neptune.init_run()

run["field_name"] = value_of_simple_type

Similarly, each of these types support fetching with a simple fetch() method:

Fetching simple type

import neptune

run = neptune.init_run(with_id="EXISTING-RUN-ID")

value = run["field_name"].fetch()

Detailed reference for each supported type:

Boolean
Datetime
Float
Integer
String

Series fields#

A series field collects a sequence of values into a single field. You create a series with the append() function. Values are added to a series field iteratively: each append() call adds a new value to the sequence.

You can also append multiple values in a single call with the extend() function.

The following Series types are supported:

Field type	How to create	Where to view
`FileSeries`	`run["field"].append(<file_like_object>)`	Images dashboard and image gallery widget
`FloatSeries`	`run["field"].append(<float>)`	Runs table, Charts dashboard, or Chart/Value list widget
`StringSeries`	`run["field"].append(<str>)`	Runs table or Value list widget

Learn more

Complex types#

Complex field types tend to hold more complicated metadata structures and expose several methods.

See each subsection for details:

Artifact (created with track_files()). Tracks the version of datasets, models, and other files, without uploading contents.
File (created with upload()). Uploads the contents of a single file to Neptune.
FileSet (created with upload_files()). Uploads the contents of a directory or several files to Neptune.
StringSet (created if tags are assigned to the run or model object). Used to interact with the tagset.

Special types#

The following types don't expose any methods or are otherwise not used for manual metadata logging.

GitRef: contains metadata on the local Git repository.
Handler: an interim type that's returned when you access an empty field path.
RunState: contains the information of whether the Neptune object is actively running or not.
Table : an interim type returned by table-fetching methods.

`Artifact`#

Field type for holding datasets, models, and other artifacts.

The artifact can refer to either a single file or a collection of files. Examples:

Dataset in CSV format
Folder with training images as PNG files
Model binaries

Artifacts are useful especially if the files are large and you want to version and compare them between runs, but don't want them taking up storage space in Neptune.

Neptune tracks the following artifact metadata:

Version (MD5 hash)
Location (path)
Size
Folder structure
Last modified time

How the hash is calculated

The following components are used to calculate the hash of each artifact:

Hash of contents
Last modification time
Size
Storage type (local or S3)
Path in artifact
Original location (URI for S3 or path to local file)

When anything in the above changes, the hash changes as well. This includes adding a file to the artifact.

Assignment: `=`#

Convenience alias for assign().

`assign()`#

Assigns the provided artifact object to the field.

Parameters

Name	Type	Default	Description
`value`	`ArtifactVal`	-	Object obtained with `fetch()` from an existing artifact field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

Create an artifact field

import neptune

run = neptune.init_run()

run["datasets/images"].track_files("./images")

Fetch the artifact and assign it to a different field

run["datasets/images-copy"] = run["datasets/images"].fetch()

`download()`#

Downloads all the files that are referenced in the artifact field.

Neptune looks for each file at the path which was logged originally. If the artifact points to an object stored in S3 or GCS, it downloads the object to the local system directly from the remote storage.

Note for Windows

On Windows, this method creates symbolic links to the referenced files.
In order to copy the file references in your local system, you may need to run your terminal as administrator.

Parameters

Name	Type	Default	Description
`destination`	`str`, optional	`None`	Path to where the files should be downloaded. If `None`, the files are downloaded to the current working directory. If `destination` is a directory, the files are downloaded to the specified directory, with filenames composed of the field name and extension (if present). If `destination` is a path to a file, the file is downloaded under the specified name.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

Examples

import neptune

run = neptune.init_run(
    with_id="NER-2",  # (1)!
    mode="read-only",
)

run["artifacts/images"].download(destination="datasets/train/images")

Neptune ID of a run that has an artifact stored at the field path artifacts/images

`fetch()`#

Fetches the artifact at the accessed field path.

You should use fetch() only to copy the artifact object.

Returns

ArtifactVal object stored in the field.

Examples

Previously logged artifact

import neptune

run = neptune.init_run()  # Neptune ID becomes NER-2

run["datasets/images"].track_files("...")

Fetch an artifact and copy it to a new field in a new run

old_run = neptune.init_run(
    with_id="NER-2",  # ID of the previous Neptune run which has the artifact
    mode="read-only",
)
new_run = neptune.init_run()
new_run["datasets/images-copy"] = old_run["datasets/images"].fetch()

`fetch_files_list()`#

Fetches a list of artifact files.

Returns

List of ArtifactFileData objects for all the files referenced in the artifacts.

You can use the following fields of the ArtifactFileData object:

Name	Type	Description
`"file_hash"`	`str`	Hash of the file
`"file_path"`	`str`	Path of the file, relative to the root of the virtual artifact directory
`"size"`	`int`	Size of the file (kB)
`"metadata"`	`dict`	Dictionary with the following keys: `"file_path"`: location (path) of the file either on local storage or S3-compatible storage `"last_modified"`: when the artifact content was last changed

Examples

>>> import neptune
>>> run = neptune.init_run(with_id="NER-2", mode="read-only") # (1)!
>>> artifact_list = run["artifacts/images"].fetch_files_list()
>>> artifact_list[0].file_hash
'b34affafe1ce65908c9b34631aa2986fa8d0a6a0'
>>> artifact_list[0].file_path
'val.csv'
>>> artifact_list[0].metadata["last_modified"]
'2022-04-22 09:53:53'
>>> artifact_list[0].metadata["file_path"]     
'file://C:/Users/Jackie/repos/llm-project/datasets/val.csv'

Neptune ID of a run that has an artifact stored at the field path artifacts/images

`fetch_hash()`#

Fetches the hash of the artifact.

Returns

str: Hash of the Neptune artifact.

Examples

>>> import neptune
>>> run = neptune.init_run(
>>>     with_id="NER-2", # (1)!
>>>     mode="read-only",
>>> )
>>> run["artifacts/images"].fetch_hash()
'9a113b799082e5fd628be178bedd52837ba12eb9fdec24e9175babd0f6f9d28s'

Neptune ID of a run that has an artifact stored at the field path artifacts/images

`track_files()`#

Saves the following artifact metadata to Neptune:

Version (MD5 hash)
Location (path)
Size
Folder structure
Contents

Works for files, folders, or S3-compatible storage.

Parameters

Name	Type	Default	Description
`path`	`str`	-	File path or S3-compatible path to the file or folder that you want to track.
`destination`	`str`	`None`	Location in the Neptune artifact namespace where you want to log the metadata.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

import neptune

run = neptune.init_run()

Single file

run["train/dataset"].track_files("./datasets/train.csv")

Folder

run["train/images"].track_files("./datasets/images")

For more detailed examples, see Tracking artifacts.

`Boolean`#

Assignment: `=` or `assign()`#

Assigns the provided integer to the field.

Parameters

Name	Type	Default	Description
`value`	`Boolean`	-	Value to assign to the field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

run = neptune.init_run()

# You can use the Python assign operator (=)
run["params/use_preprocessing"] = True
# as well as the assign() method
run["params/use_preprocessing"].assign(True)

`fetch()`#

Returns the field value from the Neptune servers.

Example

# Initialize existing run with ID "NER-12"
run = neptune.init_run(with_id="NER-12")

# Fetch use_proprocessing parameter
use_preprocessing = run["params/use_preprocessing"].fetch()

`Datetime`#

Assignment: `=` or `assign()`#

Assigns the provided integer to the field.

You can use the Python datetime library to express and assign dates and times.

Parameters

Name	Type	Default	Description
`value`	`datetime` object	-	Values to assign to the field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

Log the exact end of training:

from datetime import datetime

run = neptune.init_run()

run["train/end"] = datetime.now()

Log when the evaluation started

run["eval/start"] = datetime.now()

Log other time-related metadata

run["dataset/creation_date"] = datetime.fromisoformat("1998-11-01")

`fetch()`#

Returns the field value from the Neptune servers.

Example

# Initialize existing run with ID "NER-12"
run = neptune.init_run(with_id="NER-12")

# Fetch the time when the training ended
eval_start = run["train/end"].fetch()

`File`#

Holds a single file of any type.

`upload()`#

Uploads the provided file under the specified field path.

Parameters

Name	Type	Default	Description
`value`	`str` or `File`	-	Path of the file to upload, or `File` value object
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

import neptune

run = neptune.init_run()

# Upload example data
run["dataset/data_sample"].upload("sample_data.csv")

Both the content and the extension is stored. When downloaded, by default, the filename is a combination of the path and extension:

run["dataset/data_sample"].download()  # data_sample.csv

Many types are implicitly converted to File on the fly. For example, image-like objects such as Matplotlib figures:

import matplotlib.pyplot as plt

plt.plot(data)
run["dataset/distribution"].upload(plt.gcf()) # (1)!

The gcf() function returns the Matplotlib figure object.

Assignment: `=`#

Convenience alias for assign().

`assign()`#

You can upload a file by assigning the File value object to the specified field path.

Parameters

Name	Type	Default	Description
`value`	`File`	-	`File` value object
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Example

import neptune
from neptune.types import File

run = neptune.init_run()

run["dataset/data_sample"] = File("sample_data.csv")

`download()`#

Downloads the stored file to the working directory or specified destination.

Parameters

Name	Type	Default	Description
`destination`	`str`, optional	`None`	Path to where the file should be downloaded. If `None`, the file is downloaded to the working directory. If `destination` is a directory, the file is downloaded to the specified directory with a filename composed of the field name and extension (if present). If `destination` is a path to a file, the file is downloaded under the specified name.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

Examples

import neptune

run = neptune.init_run(with_id="NLI-8", mode="read-only")

Download a file to the current working directory

run["data/sample"].download()

Download a file to the specified directory

run["trained_model"].download(destination="path/to/destination")

Unless disabled, you can fetch uncommitted changes from the "source_code/diff" field.

run["source_code/diff"].download()

This downloads the diff.patch file into the working directory, which you can then apply as needed.

`fetch_extension()`#

A programmatic way to find out the extension of a stored file.

Returns

str with the extension of the stored file.

Examples

import neptune
from neptune.types import File

run = neptune.init_run()

# Upload model as a File field
run["model/last"].upload("model.pt")

# Check extension of the uploaded File
ext = run["model/last"].fetch_extension()
ext == "pt"  # True

`as_image()`#

Static method for converting image objects or image-like objects to an image File value object.

This way you can upload figures, arrays, and tensors as static images.

Name	Type	Default	Description
`image`	-	-	Image-like object to be converted. Supported: PyTorch tensors, TensorFlow/Keras tensors, NumPy arrays, PIL images, Matplotlib, Seaborn figures.
`autoscale`	`bool`	`True`	Whether Neptune should try to scale image pixel values to better render them in the web app. Scaling can distort images if their pixels lie outside the [0.0, 1.0] or [0, 255] range. To disable auto-scaling, set the argument to `False`.

Returns

File value object with converted image.

Examples

import neptune
from neptune.types import File

run = neptune.init_run()

Convert NumPy array to File value object and upload it

run["train/prediction_example"].upload(File.as_image(numpy_array))

Convert PIL image to File value object and upload it

pil_file = File.as_image(pil_image)
run["dataset/data_sample/img1"].upload(pil_file)

You can also upload a PIL image without explicit conversion:

run["dataset/data_sample/img2"].upload(pil_image)

`as_html()`#

Converts an object to an HTML File value object.

This way you can upload interactive charts or data frames to explore them in the Neptune app.

Name	Description
`chart`	Object to be converted. Supported: Altair, Bokeh, Plotly, Seaborn, Matplotlib interactive charts pandas DataFrame objects

Generate ROC plot and upload it as HTML

from scikitplot.metrics import plot_roc
import matplotlib.pyplot as plt

import neptune
from neptune.types import File

fig, ax = plt.subplots(figsize=(16, 12))
plot_roc(y, y_pred, ax=ax)

run = neptune.init_run()
run["roc_curve"].upload(File.as_html(fig))

`as_pickle()`#

Pickles a Python object and stores it in a File value object.

This way you can upload any Python object for future use.

Name	Description
`obj`	Object to be converted (any Python object that supports pickling).

Returns

File value object with the pickled object.

Examples

Pickle model object and upload it

import neptune
from neptune.types import File

run = neptune.init_run()
run["results/pickled_model"].upload(File.as_pickle(trained_model))

`from_content()`#

Factory method for creating File value objects directly from binary and text content.

UTF-8 encoding is used for text content.

Parameters

Name	Type	Default	Description
`content`	`str` or `bytes`	-	Text or binary content to stored in the `File` value object.
`extension`	`str`, optional	`None`	Extension of the created file that will be used for interpreting the type of content for visualization. If `None`, it will be `bin` for binary content and `txt` for text content.

Returns

File value object created from the content.

Example

import neptune
from neptune.types import File

run = neptune.init_run()

run["large_text_as_file"].upload(File.from_content(variable_with_my_text))

HTML example

html_str = (
    "<button type='button', style='background-color:#005879; width:400px; "
    "height:400px; font-size:30px'> <a style='color: #ccc', "
    "href='https://docs.neptune.ai'>Take me back to the docs!<a> </button>"
)

with open("sample.html", "w") as f:
    f.write(html_str)
    html_obj = File.from_content(html_str, extension="html")
    run["html_content"].upload(html_obj)

`from_path()`#

Creates a File value object from a given path.

Equivalent to File(path), but you can specify the extension separately.

Parameters

Name	Type	Default	Description
`path`	`str` or `bytes`	-	Path of the file to be stored in the `File` value object.
`extension`	`str`, optional	`None`	Extension of the file, if not included in the path argument.

Returns

File value object created based on the path.

Example

import neptune
from neptune.types import File

run = neptune.init_run()

run["sample_text"].upload(File.from_path(path="data/test/sample", extension="txt"))

`from_stream()`#

Factory method for creating File value objects directly from binary and text streams.

UTF-8 encoding is used for text content.

Parameters

Name	Type	Default	Description
`stream`	`IOBase`	-	Stream to be converted.
`seek`	`int`, optional	`0`	Change the stream position to the given byte offset. For details, see the IOBase documentation .
`extension`	`str`, optional	`None`	Extension of the created file that will be used for interpreting the type of content for visualization. If `None`, it will be `bin` for binary content and `txt` for text content.

Returns

File value object created from the stream.

Example

import neptune
from neptune.types import File

run = neptune.init_run()

with open("image.jpg", "rb") as f:
    image = File.from_stream(f, extension="jpg")
    run["upload_image_from_stream"].upload(image)

`FileSeries`#

A field containing a series of image files.

`append()`#

Logs the provided file to the end of the series.

append() replaces log()

As of neptune-client 0.16.14, append() and extend() are the preferred methods for logging series of values.

You can upgrade your installation with pip install -U neptune-client or continue using log().

Parameters

Name	Type	Default	Description
`value`	`File` value object	-	The file to be appended.
`step`	`float`, `int`, optional	`None`	Index of the log entry being appended. Must be strictly increasing.
`timestamp`	`float`, `int`, optional	`None`	Time index of the log entry being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`name`	`str`, optional	`None`	Name of the logged file.
`description`	`str`, optional	`None`	Short description of the logged file.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

Append an image file to a FileSeries field:

import neptune
from neptune.types import File

run = neptune.init_run()
run["train/prediction_example"].append(File(path_to_file))

Log a Matplotlib figure object:

run["train/distribution"].append(plt_histogram, step=epoch)

Convert a NumPy array to File value object and log it:

run["train/prediction_example"].append(File.as_image(numpy_array))

You can also log a name and description for the image:

Log data sample

for plt_image, class_name in data_sample:
    run["data/sample"].append(plt_image, name=class_name)

Log predictions with class probabilities

for image, y_pred in zip(x_test_sample, y_test_sample_pred):
    description = "\n".join(
        [f"class {i}: {pred}" for i, pred in enumerate(y_pred)]
    )
    run["train/predictions"].append(image, description=description)

Logging with custom step:

import matplotlib.pyplot as plt

run = neptune.init_run()

for epoch in range(100):
    plt_fig = get_histogram()

    run["train/distribution"].append(
        plt_fig,
        step=epoch,
    )

`extend()`#

Appends the provided collection of File value objects to the series.

Parameters

Name	Type	Default	Description
`values`	collection of `File` value objects	-	The collection or dictionary of files to be appended to the series field.
`steps`	collection of `float` or collection of `int` (optional)	`None`	Indices of the values being appended. Must be strictly increasing.
`timestamps`	collection of `float` or collection of `int` (optional)	`None`	Time indices of the values being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

`log()`#

See append() (append one value at a time) or extend() (append a collection of values).

`download()`#

Downloads all the files stored in the series and saves them locally.

Parameters

Name	Type	Default	Description
`destination`	`str`, optional	`None`	Path to where the files should be downloaded. If `None`, the files are downloaded to the working directory. If `destination` is a directory, the file is downloaded to the specified directory with a filename composed of the field name and extension. If `destination` is a path to a file, the file is downloaded under the specified name.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

`download_last()`#

Downloads the last file stored in the series and saves it locally.

Parameters

Name	Type	Default	Description
`destination`	`str`, optional	`None`	Path to where the file should be downloaded. If `None`, the file is downloaded to the working directory. If `destination` is a directory, the file is downloaded to the specified directory with a filename composed of the field name and extension. If `destination` is a path to a file, the file is downloaded under the specified name.

`FileSet`#

Field type used to hold a larger number of files when access to a single file is rare.

Best used for items that can be easily browsed through the web interface and are typically accessed as a whole, such as a folder of source files or image examples.

`upload_files()`#

Uploads the provided file or files and stores them under the FileSet field.

Useful when you don't require advanced display options for individual files.

Parameters

Name	Type	Default	Description
`globs`	`str` or collection of `str`	-	Path or paths to the files to be uploaded.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Example

import neptune

run = neptune.init_run()
run["datasets_folder"].upload_files("datasets")

`delete_files()`#

Deletes the specified files from the FileSet field.

Parameters

Name	Type	Default	Description
`paths`	`str` or collection of `str`	-	Path or paths to files or folders to be deleted. Note that these are paths relative to the FileSet itself: If the `FileSet` contains file `sample.txt`, `ner/notes.txt`, and `ner/data.csv`, to delete whole subfolder you would pass `ner` as the argument.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Example

Deleting a file from a FileSet field of an existing run:

>>> import neptune
>>> run = neptune.init_run(with_id="CLAS-14")
>>> run["datasets_folder"].delete_files("datasets/data_sample_v1.csv")

`download()`#

Downloads all the files stored in the FileSet field in the form of a ZIP archive.

Parameters

Name	Type	Default	Description
`destination`	`str`, optional	`None`	Path to where the files should be downloaded. If `None`, the files are downloaded to the working directory. If `destination` is a directory, the file is downloaded to the specified directory with a filename composed of the field name and extension. If `destination` is a path to a file, the file is downloaded under the specified name.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

`list_fileset_files()`#

Fetches metadata about the file set.

If the top-level artifact of the field is a directory, only metadata about this directory is returned.

You can use the path argument to list metadata about files contained inside the directory or subdirectories.

Parameters

Name	Type	Default	Description
`path`	`str`, optional	`None`	Path to a nested directory, to get metadata about files contained within the directory.

Returns

List of FileEntry items with the following metadata:

name: Name of the file or directory
size: Size of the file in bytes
mtime: Last modification time
file_type: Whether it's a file or directory

Examples

In this example, we're using a glob pattern to upload a set of Python script files under one field.

Log all .py scripts as FileSet

>>> import neptune
>>> run = neptune.init_run()
>>> run["scripts"].upload_files("*.py")

Access the FileSet metadata

>>> run["scripts"].list_fileset_files()
[FileEntry(name="script.py", size=13935, mtime=datetime.datetime(2023, 8, 8, 10,
53, 7, 387000, tzinfo=tzutc()), file_type="file"), FileEntry(name=
"another_script.py", size=13935, mtime=datetime.datetime(2023, 8, 8, 10, 53, 16,
387000, tzinfo=tzutc()), file_type="file"), ...]

In the next example, we're uploading a directory called "data" which has the following structure:

data/
|-- sample.csv
|-- datasets/
    |-- dataset_v2.csv
    |-- dataset_v3.csv
    |-- ...

We'd log the folder with the following:

>>> run["data"].upload_files("data")

Then we can access the metadata of the FileSet and its nested directories as follows:

>>> run["data"].list_fileset_files()
[FileEntry(name='data', size=None, mtime=datetime.datetime(2023, 8, 17, 10, 31, 54,
278601, tzinfo=tzutc()), file_type='directory')]
>>> run["data"].list_fileset_files(path="data")
[FileEntry(name='datasets', size=None, mtime=datetime.datetime(2023, 8, 17, 10, 34,
6, 777017, tzinfo=tzutc()), file_type='directory'), FileEntry(name='sample.csv',
size=215, mtime=datetime.datetime(2023, 8, 17, 10, 31, 26, 402000, tzinfo=tzutc()),
file_type='file')]
>>> run["data"].list_fileset_files(path="data/datasets")
[FileEntry(name='dataset_v2.csv', size=215, mtime=datetime.datetime(2023, 8, 17, 10,
31, 26, 491000, tzinfo=tzutc()), file_type='file'), FileEntry(name='dataset_v3.csv',
size=215, mtime=datetime.datetime(2023, 8, 17, 10, 31, 26, 338000, tzinfo=tzutc()),
file_type='file'), ...]

`Float`#

Assignment: `=` or `assign()`#

Assigns the provided floating point number to the field.

Parameters

Name	Type	Default	Description
`value`	`float`	-	Value to assign to the field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

run = neptune.init_run()

# You can use the Python assign operator (=)
run["params/lr"] = 0.8
# as well as the assign() method
run["params/lr"].assign(0.8)

`fetch()`#

Returns the field value from the Neptune servers.

Example

# Initialize existing run with ID "NER-12"
run = neptune.init_run(with_id="NER-12")

# Fetch highest accuracy so far
top_acc = run["train/acc/highest"].fetch()

`FloatSeries`#

Field containing a series of numbers, for example:

Training metrics
Change of performance of the model in production

You can index the series by step or by time.

`append()`#

Appends the provided value to the series.

append() replaces log()

As of neptune-client 0.16.14, append() and extend() are the preferred methods for logging series of values.

You can upgrade your installation with pip install -U neptune-client or continue using log().

Parameters

Name	Type	Default	Description
`value`	`float` or `int`	-	The value to be added to the series field.
`step`	`float`, `int`, optional	`None`	Index of the log entry being appended. Must be strictly increasing. Note: This is effectively how you set custom x values for a chart.
`timestamp`	`float`, `int`, optional	`None`	Time index of the log entry being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

run = neptune.init_run()

for epoch in range(parameters["n_epochs"]):
    ...  # My training loop

    run["train/epoch/loss"].append(loss)
    run["train/epoch/accuracy"].append(acc)

Setting custom step values:

run["metric"].append(
    value=acc,
    step=i,
)

You can also append values to multiple series at once by passing a dictionary of values. Pass the field name as the key.

run["train"].append({"acc": acc, "loss": loss})

`extend()`#

Appends the provided collection of values to the series.

Parameters

Name	Type	Default	Description
`values`	collection of `float` or collection of `int`	-	The collection or dictionary of values to be appended to the series field.
`steps`	collection of `float` or collection of `int` (optional)	`None`	Indices of the values being appended. Must be strictly increasing. Note: This is effectively how you set custom x values for a chart.
`timestamps`	collection of `float` or collection of `int` (optional)	`None`	Time indices of the values being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Example

The following example reads a CSV file into a pandas DataFrame and extracts the values to create a Neptune series field.

Create a DataFrame and extract values

df = pandas.read_csv("time_series.csv")
ys = df["value"]
ts = df["timestamp"]

Create a Neptune run and log values as series

run = neptune.init_run()
run["data/example_series"].extend(ys, timestamps=ts)

`log()`#

See append() (append one value at a time) or extend() (append a collection of values).

`fetch_last()`#

Fetches the last value stored in the series.

Returns

Last logged float value.

Example

>>> import neptune
>>> run = neptune.init_run(with_id="CLS-15")
>>> run["train/loss"].fetch_last()
0.15250000000000002

`fetch_values()`#

Fetches all values stored in the series.

Parameters

Name	Type	Default	Description
`include_timestamp`	`Boolean`, optional	`True`	Whether the fetched data should include the timestamp field.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

Returns

pandas.DataFrame containing all the values and their indexes stored in the series field.

Example

>>> import neptune
>>> run = neptune.init_run(with_id="CLS-15")
>>> run["train/loss"].fetch_values()
   step  value               timestamp
0   0.0   0.00 2022-07-08 12:30:30.087
1   1.0   0.43 2022-07-08 12:30:30.087
2   2.0   0.86 2022-07-08 12:30:30.087
3   3.0   1.29 2022-07-08 12:30:30.087
...

`GitRef`#

Contains information about the Git repository at the time of starting a tracked run.

The GitRef type does not expose any methods, but you can view the source_code/git field in the Neptune web app (Run details → Source code → Git).

Parameters

Name Description

repository_path

Custom path where Neptune should look for a Git repository. Neptune looks for a repository in the supplied path as well as its parent directories.

If not provided, the path to the script that is currently executed is used.

Returns

GitRef value object with the Git information. For details, see Logging Git info.

Example

import neptune
from neptune.types import GitRef

run = neptune.init_run(git_ref=GitRef(repository_path="/path/to/repo"))

`DISABLED`#

Constant that disables Git tracking for your run.

import neptune
from neptune.types import GitRef

run = neptune.init_run(git_ref=GitRef.DISABLED)

Tip

You can also disable Git tracking by setting the gitref argument to false when initializing the run:

run = neptune.init_run(gitref=false)

`Integer`#

Assignment: `=` or `assign()`#

Assigns the provided integer to the field.

Parameters

Name	Type	Default	Description
`value`	`int`	-	Value to assign to the field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

run = neptune.init_run()

# You can use the Python assign operator (=)
run["params/max_epochs"] = 10
# as well as the assign() method
run["params/max_epochs"].assign(10)

`fetch()`#

Returns the field value from the Neptune servers.

Example

# Initialize existing run with ID "NER-12"
run = neptune.init_run(with_id="NER-12")

# Fetch epoch count
epoch = run["train/epoch"].fetch()

`RunState`#

Contains the state (Active/Inactive) of a Neptune run.

You cannot manually create or modify RunState fields.
The RunState type does not expose any methods, but you can:
- view the sys/state field in the Neptune web app
- query runs based on state with project.fetch_runs_table(state="<state>")

`String`#

Assignment: `=` or `assign()`#

Assigns the provided string to the field.

Parameters

Name	Type	Default	Description
`value`	`str`	-	Value to assign to the field.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

run = neptune.init_run()

# You can use the Python assign operator (=)
run["params/optimizer"] = "Adam"
# as well as the assign() method
run["params/optimizer"].assign("Adam")

Note

Due to a technical limitation, we limit the number of indexed characters in String fields to 1000 characters. This means that when searching the runs table, only the first 1000 characters will be considered.

`fetch()`#

Returns the field value from the Neptune servers.

Example

# Initialize existing run with ID "NER-12"
run = neptune.init_run(with_id="NER-12")

# Fetch optimizer parameter
optimizer = run["params/optimizer"].fetch()

`StringSeries`#

Field containing series of text values.

Info

A single text log entry is limited to 1000 characters, but there is no limit to the number of entries in the series.

If the logged text exceeds this character limit, the entry will be truncated to match the limit.

`append()`#

Appends the provided value to the end of the series.

append() replaces log()

As of neptune-client 0.16.14, append() and extend() are the preferred methods for logging series of values.

You can upgrade your installation with pip install -U neptune-client or continue using log().

Parameters

Name	Type	Default	Description
`value`	`str`	-	The value to be logged.
`step`	`float`, `int`, optional	`None`	Index of the log entry being appended. Must be strictly increasing. Note: This is effectively how you set custom x values for a chart.
`timestamp`	`float`, `int`, optional	`None`	Time index of the log entry being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Example

Iteratively log a series of short text entries (max 1000 characters):

for epoch in range(epochs_nr):
    token = str(...)
    run["train/tokens"].append(token)

`extend()`#

Appends the provided collection of values to the series.

Parameters

Name	Type	Default	Description
`values`	collection of `str`	-	The collection or dictionary of strings to be appended to the series field.
`steps`	collection of `float` or collection of `int` (optional)	`None`	Indices of the values being appended. Must be strictly increasing. Note: This is effectively how you set custom x values for a chart.
`timestamps`	collection of `float` or collection of `int` (optional)	`None`	Time indices of the values being appended, in Unix time format. If `None`, the current time (obtained with `time.time()`) is used.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

`log()`#

See append() (append one value at a time) or extend() (append a collection of values).

`fetch_last()`#

Fetches the last value stored in the series.

Returns

Last logged str value.

Example

import neptune

run = neptune.init_run(with_id="CLS-15")

last_token = run["train/tokens"].fetch_last()

`fetch_values()`#

Fetches all values stored in the series.

Parameters

Name	Type	Default	Description
`include_timestamp`	`Boolean`, optional	`True`	Whether the fetched data should include the timestamp field.
`progress_bar`	`bool` or `Type[ProgressBarCallback]`, optional	`None`	Set to `False` to disable the download progress bar, or pass a type of `ProgressBarCallback` to use your own progress bar. If set to `None` or `True`, the default tqdm-based progress bar will be used.

Returns

pandas.DataFrame containing all the values and their indexes stored in the series field.

Example

import neptune

run = neptune.init_run(with_id="CLS-15")

tokens = run["train/tokens"].fetch_values()

`StringSet`#

A field containing an unorganized set of strings.

Currently, the only supported StringSet field is sys/tags. You can use it to manage tags for runs and other Neptune objects, either by passing tags=["tag1", "tag2", ...] at initialization (runs only) or later with the add() method.

You can't manually create or modify other StringSet fields.

`add()`#

Adds the provided text string or strings to the set field.

Parameters

Name	Type	Default	Description
`values`	`str` or collection of `str`	-	Tag or tags to be applied.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

import neptune

model_version = neptune.init_model_version(model="CLS-PRETRAINED")

model_version["sys/tags"].add(["maskRCNN", "finetune"])

`remove()`#

Removes the provided tag or tags from the set.

Parameters

Name	Type	Default	Description
`values`	`str` or collection of `str`	-	Tag or tags to be removed.
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

import neptune

model_version = neptune.init_model_version(with_id="CLS-PRETRAINED-8") # (1)!

model_version["sys/tags"].remove(["finetune"])

Connect to an existing model version

`clear()`#

Removes all tags from the set.

Parameters

Name	Type	Default	Description
`wait`	`Boolean`, optional	`False`	By default, logged metadata is sent to the server in the background. With this option set to `True`, Neptune first sends all data before executing the call. See Connection modes.

Examples

import neptune

model_version = neptune.init_model_version(with_id="CLS-PRETRAINED-8") # (1)!

model_version["sys/tags"].clear()

Connect to an existing model version

`fetch()`#

Fetches all tags from the set.

Returns

set of str with an object's tags.

Example

import neptune

run = neptune.init_run(with_id="NLI-8") # (1)!
run_tags = run["sys/tags"].fetch()
if "maskRCNN" in run_tags:
    print_analysis()

Connect to an existing run

`Table`#

An interim object containing the metadata of fetched objects. It's returned by the table-fetching methods: fetch_runs_table(), fetch_models_table(), and fetch_model_versions_table().

To access the data, you need to convert it to a pandas DataFrame by invoking to_pandas().

Example

Fetch a runs table and convert it to a DataFrame:

import neptune

project = neptune.init_project(project="ml-team/nli", mode="read-only")
runs_table = project.fetch_runs_table()  # a Table is returned
runs_table_df = runs_table.to_pandas()

Now you can operate on the table like a DataFrame.

runs_table_df = runs_table_df.sort_values(by="sys/creation_time", ascending=False)

`to_pandas()`#

Converts a Table object to a pandas DataFrame.

Returns

pandas.DataFrame with the metadata of the objects contained in the table.

Example

Fetch list of all version of the model as pandas DataFrame

>>> import neptune
>>> model = neptune.init_model(with_id="CLS-FOREST", mode="read-only")
[neptune] [info   ] Neptune initialized...
>>> models_df = model.fetch_model_versions_table().to_pandas()
>>> print(models_df)
                  sys/creation_time         sys/id  ... val/acc ...
0  2022-08-26 05:19:54.712000+00:00  CLS-FOREST-12  ...    0.98 ...
1  2022-08-26 05:19:17.197000+00:00  CLS-FOREST-11  ...    0.53 ...
2  2022-08-26 05:19:01.999000+00:00  CLS-FOREST-10  ...    0.19 ...
3  2022-08-26 05:18:42.380000+00:00  CLS-FOREST-9   ...    0.35 ...

`Handler`#

When you access a path that doesn't exist yet, you obtain a Handler object.

import neptune

run = neptune.init_run()
handler = run["train/batch/acc"]
# no such field exists in the run yet, so a Handler object is returned

Think of it as a wildcard that can become any type once you invoke a specific logging method on it. If you invoke track_files(), it becomes an Artifact field; if you invoke append(float), it becomes a FloatSeries.

Note

A Handler object can also become a namespace handler if you create a field at a lower hierarchy level.

The Handler object exposes:

All logging methods – such as assign(), append(), upload(), and upload_files()
Namespace handler methods

Kedro note

The Handler class is located in neptune.handler.Handler. When setting up nodes.py, pass this value to the neptune_run option of the evaluate_model() function:

nodes.py

def evaluate_model(
    regressor: LinearRegression,
    X_test: pd.DataFrame,
    y_test: pd.Series,
    neptune_run: neptune.handler.Handler,
):
...

Examples

import neptune

run = neptune.init_run()

handler_object = run["train/batch/acc"]
# Returns a Handler, as no such field exists in the run yet

You can use the handler like any other field:

handler_object.append(0.7)
# "train/batch/acc" is now a FloatSeries field

Namespace handler#

An object representing a namespace in the metadata structure of a Neptune object.

You can think of namespaces as folders for organizing your metadata. If you log the fields "params/max_epochs" and "params/lr", they will be grouped under a namespace called "params". In this case, accessing run["params"] would return a namespace handler object:

import neptune

run = neptune.init_run()
run["params/max_epochs"] = 100
namespace_handler = run["params"]

The namespace handler exposes similar methods as other Neptune objects – however, all field paths are relative to the namespace. This helps organize metadata from different steps into separate namespaces, yet under the same run. For a full guide, see Setting a base namespace.

You can also start by creating a generic Handler object and turn it into a namespace by organizing metadata inside it:

Example

params_ns = run["params"]  # Create a namespace handler

params_ns["max_epochs"] = 20  # Log directly to the namespace
params_ns["batch_size"] = 32 # (1)!

The result is the same as if the following had been called:

run["params/max_epochs"] = 20
run["params/batch_size"] = 32

Field lookup: `[]`#

You can access the field of a namespace handler through a dict-like field lookup: namespace_handler[field_path].

This way, you can

log metadata and collect everything in a single base namespace (without needing to spell out the full path each time):
Organize logged metadata under "eval" namespace
```
eval_ns = run["eval"]

eval_ns["max_epochs"] = 10

for epoch in range(20):
    eval_ns["acc"].append(eval_acc)
...
```
fetch already logged metadata from a particular namespace:
Create namespace handler and perform fetching operations on it
```
eval_ns = run["eval"]

eval_acc_df = eval_ns["acc"].fetch_values()
eval_ns["diagnostic_plots"].download()
```

Returns

The returned type depends on the field type and whether a field is stored under the given path.

Path Example Returns

Field exists - The returned type matches the type of the field

Field does not exist - Handler object

Path is namespace and has field

Path: "train"

Field "train/acc" exists

Namespace handler object

Examples

import neptune

run = neptune.init_run()

train_ns = run["train"]
train_ns["params/learning_rate"] = 0.3 # (1)!

Stores 0.3 under the path "train/params/learning_rate" inside the run.

If there exists a value at a nested field, you can also obtain a namespace handler by accessing any of the containing namespaces.

>>> run.exists("parameters/learning_rate")
True
>>> params_ns = run["parameters"]
>>> params_ns
<Namespace field at "parameters">

Assignment: `=`#

Convenience alias for assign().

`assign()`#

Assign values to multiple fields from a dictionary. For example, you can use this method to quickly log all the parameters of a run.

Remember that the resulting paths will be a combination of the namespace path and the provided relative path.

Example

import neptune

run = neptune.init_run()

# Access params namespace handler
params_ns = run["model/params"]

# Assign additional parameters in batch
PARAMS = {"max_epochs": 20, "optimizer": "Adam"}
params_ns.assign(PARAMS)

# "=" needs to be used in combination with "[]"
params_ns = PARAMS  # Does not work
run["model/params"] = PARAMS  # Works

# This also works with relative paths
model_ns = run["model"]
model_ns["params"] = PARAMS

`get_root_object()`#

Returns the root level object of a namespace handler.

For example, if you call the method on the namespace of a run, the Run object is returned.

Example

import neptune

run = neptune.init_run()
run["workflow/pretraining/params"] = {...}
...

pretraining = run["workflow/pretraining"] # (1)!
pretraining.stop()  # Error: pretraining is a namespace, not a run

pretraining_run = pretraining.get_root_object()
pretraining_run.stop()  # The root run is stopped

The namespace "pretraining" is nested under the "workflow" namespace inside the run object. As such, the pretraining object is a namespace handler object.

Field types reference#

neptune.types overview#

Simple types#

Series fields#

Complex types#

Special types#

Artifact#

Assignment: =#

assign()#

download()#

fetch()#

fetch_files_list()#

fetch_hash()#

track_files()#

Boolean#

Assignment: = or assign()#

fetch()#

Datetime#

Assignment: = or assign()#

fetch()#

File#

upload()#

Assignment: =#

assign()#

download()#

fetch_extension()#

as_image()#

as_html()#

as_pickle()#

from_content()#

from_path()#

from_stream()#

FileSeries#

append()#

extend()#

log()#

download()#

download_last()#

FileSet#

upload_files()#

delete_files()#

download()#

list_fileset_files()#

Float#

Assignment: = or assign()#

fetch()#

FloatSeries#

append()#

extend()#

log()#

fetch_last()#

fetch_values()#

GitRef#

DISABLED#

Integer#

Assignment: = or assign()#

fetch()#

RunState#

String#

Assignment: = or assign()#

fetch()#

StringSeries#

append()#

extend()#

log()#

fetch_last()#

fetch_values()#

StringSet#

add()#

remove()#

clear()#

fetch()#

Table#

to_pandas()#

Handler#

Namespace handler#

Field lookup: []#

Assignment: =#

assign()#

get_root_object()#

`neptune.types` overview#

`Artifact`#

Assignment: `=`#

`assign()`#

`download()`#

`fetch()`#

`fetch_files_list()`#

`fetch_hash()`#

`track_files()`#

`Boolean`#

Assignment: `=` or `assign()`#

`fetch()`#

`Datetime`#

Assignment: `=` or `assign()`#

`fetch()`#

`File`#

`upload()`#

Assignment: `=`#

`assign()`#

`download()`#

`fetch_extension()`#

`as_image()`#

`as_html()`#

`as_pickle()`#

`from_content()`#

`from_path()`#

`from_stream()`#

`FileSeries`#

`append()`#

`extend()`#

`log()`#

`download()`#

`download_last()`#

`FileSet`#

`upload_files()`#

`delete_files()`#

`download()`#

`list_fileset_files()`#

`Float`#

Assignment: `=` or `assign()`#

`fetch()`#

`FloatSeries`#

`append()`#

`extend()`#

`log()`#

`fetch_last()`#

`fetch_values()`#

`GitRef`#

`DISABLED`#

`Integer`#

Assignment: `=` or `assign()`#

`fetch()`#

`RunState`#

`String`#

Assignment: `=` or `assign()`#

`fetch()`#

`StringSeries`#

`append()`#

`extend()`#

`log()`#

`fetch_last()`#

`fetch_values()`#

`StringSet`#

`add()`#

`remove()`#

`clear()`#

`fetch()`#

`Table`#

`to_pandas()`#

`Handler`#

Field lookup: `[]`#

Assignment: `=`#

`assign()`#

`get_root_object()`#