Field types

neptune.types
Types in Neptune can be separated into few categories sharing similar traits and logging/fetching methods:
​
​
Atom fields
Fields that you use to store a single value of the given type or a single file. It's possible to upload and fetchFloat, Integer, Boolean, String, Datetime and File fields. GitRef, RunState fields are possible to access, but manual creation or modification of the fields of these types is not possible.
Series fields
Fields that you use to store a series of values or a series of files (e.g. images). Available types are FloatSeries, StringSeries and FileSeries.
FileSet
Field type for holding a larger number of files, when access to a single file is rare. FileSet can be used for a folder with source files, a folder with image examples, etc. Things that can be easily browsed through the web interface, and are typically accessed as a whole.
Sets fields
Fields holding unorganized sets of values. Only one set type currenlty exists - StringSet that is used to interact with the tags of the experiment. It's possible to access it and modify it, but manual creation of the fields of this type is not possible.
Handler
When you access a run's path that doesn't exist yet you obtain a Handler object. Think of it as a wildcard that can become any of the types once you invoke on it specific logging method. If you invoke .upload() it becomes a File type, if you invoke .log(2)it will become a FloatSeries etc. Note that it can also become a Namespace handler if you create a field at a lower level of organization.
Namespace handler
If you access a run's path that is one or more levels higher than an existing field you obtain a Namespace handler object, e.g. when you access params when you have a field params/max_epochs. Namespace handler can be used as a shortcut for accessing fields by only specifying a relative path within the namespace.
Float
Field type representing a floating-point number.
=
Convenience alias for .assign(). 
.assign()
Assigns the provided value to the field.
Parameters
​
value
(float) - Value to be stored in a field.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
run = neptune.init()
​
# You can both use the Python assign operator (=)
run["params/lr"] = 0.8
# as well as directly use the .assign method
run["params/lr"].assign(0.8)
.fetch()
Fetches field value from Neptune servers.
Returns
float value stored in the field.
Examples
import neptune.new as neptune
​
# Continue run with ID "SAN-23"
run = neptune.init(run="SAN-23")
​
# Fetch best accuracy so far
best_acc = run["train/acc/best"].fetch()
Integer
Field type representing an integer number.
=
Convenience alias for .assign(). 
.assign()
Assigns the provided value to the field.
Parameters
​
value
(int) - Value to be stored in a field.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
run = neptune.init()
​
# You can both use the Python assign operator (=)
run["params/max_epochs"] = 10
# as well as directly use the .assign method
run["params/max_epochs"].assign(10)
.fetch()
Fetches field value from Neptune servers.
Returns
int value stored in the field.
Examples
import neptune.new as neptune
​
# Continue run with ID "SAN-23"
run = neptune.init(run="SAN-23")
​
# Fetch epoch count
epoch = run["train/epoch"].fetch()
Boolean
Field type representing a boolean value.
=
Convenience alias for .assign(). 
.assign()
Assigns the provided value to the field.
Parameters
​
value
(Boolean ) - Value to be stored in a field.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
run = neptune.init()
​
# You can both use the Python assign operator (=)
run["params/use_preprocessing"] = True
# as well as directly use the .assign method
run["params/use_preprocessing"].assign(True)
.fetch()
Fetches field value from Neptune servers.
Returns
bool value stored in the field.
Examples
import neptune.new as neptune
​
# Continue run with ID "SAN-23"
run = neptune.init(run="SAN-23")
​
# Fetch use_proprocessing parameter
use_preprocessing = run['params/use_preprocessing'].fetch()
String
Field type representing a small piece of text.
=
Convenience alias for .assign(). 
.assign()
Assigns the provided value to the field.
Parameters
​
value
(str or String ) - Value to be stored in a field.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
run = neptune.init()
​
# You can both use the Python assign operator (=)
run["params/optimizer"] = "Adam"
# as well as directly use the .assign method
run["params/optimizer"].assign("Adam")
.fetch()
Fetches field value from Neptune servers.
Returns
str value stored
File
Field type for holding a single file of any type.
.upload()
Uploads provided file under specified field path
Parameters
​
value
(stror File) - Path to the file to be uploaded or File value object
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
run = neptune.init()
​
# Upload example data
run["dataset/data_sample"].upload("sample_data.csv")
​
# Both the content and the extension is stored
# When downloaded the filename is a combination of path and the extension
run["dataset/data_sample"].download() # data_sample.csv
​
# Explicitely create File value object
from neptune.new.types import File
run["dataset/data_sample"].upload(File("sample_data.csv"))
​
# Lot's of types will be implicitly converted to Files on the fly
# E.g. image-like objects such as Matplotlip figures etc.
import matplotlib.pyplot as plt
plt.plot(data)
run["dataset/distribution"].log(plt.gcf()) # .gcf() gets the figure object
=
Convenience alias for .assign(). 
.assign()
You can also upload a file by assigning the File value object to the specified field path.
Parameters
​
value
(File) - File value object
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init()
​
run["dataset/data_sample"] = File("sample_data.csv")
.download()
Downloads the stored file to the working directory or specified destination. 
Parameters
​
destination
(str, optional, default is None) - Path to where the file should be downloaded. If None file will be downloaded to the working directory.

If destination is a directory, the file will be downloaded to the specified directory with a filename composed from field name and extension (if present).
​
If destination is a path to a file, the file will be downloaded under the specified name.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
.fetch_extension()
A programmatic way to discover an extension of the stored file.
Returns
Str with the extensions of the stored File.
Examples
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init()
​
# 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 Matplotlib figures, PIL images, NumPy arrays, as static images.
Parameters
​
image
Image-like object to be converted. Supported are PyTorch tensors, TensorFlow/Keras tensors, NumPy arrays, PIL images, Matplotlib figures.
Returns
File value object with converted image
Examples
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init()
​
# 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 upload 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 Altair, Bokeh, Plotly, Matplotlib interactive charts or upload directly Pandas DataFrame objects to explore them in Neptune UI.
Parameters
​
value
An object to be converted. Supported are Altair, Bokeh, Plotly, Matplotlib interactive charts, and Pandas DataFrame objects.
Returns
File value object with converted object
Examples
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init()
​
# Convert Pandas DataFrame to File value object and upload it
run["train/results"].upload(File.as_html(df_predictions))
​
# Convert Altair interactive chart to File value object and upload it
altair_file = File.as_html(altair_chart)
run["dataset/data_sample/img1"].upload(altair_file)
​
# You can upload Altair interactive chart without explicit conversion
run["dataset/data_sample/img2"].upload(altair_chart)
.as_pickle()
Pickles a Python object and stores it in File value object. This way you can upload any Python object for future use.
Parameters
​
value
An object to be converted. Supported are Altair, Bokeh, Plotly, Matplotlib interactive charts, and Pandas DataFrame objects.
Returns
File value object with pickled object
Examples
import neptune.new as neptune
from neptune.new.types import File
run = neptune.init()
​
# Pickle model object and upload it
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. In the case of text content, UTF-8 encoding will be used.
Parameters
​
content
(strorbytes) - Text or binary content to stored in the File value object.
extension
(str, optional, default is None) - Extension of the file created 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
.from_stream()
Factory method for creating File value objects directly from binary and text streams. In the case of text stream, UTF-8 encoding will be used.
Parameters
​
stream
(IOBase) - Stream to be converted.
seek
(int, optional, default is 0) - See IOBase documentation​
extension
(str, optional, default is None) - Extension of the file created that will be used for interpreting the type of content for visualization. If None it will be bin for binary stream and txt for text stream.
Returns
File value object created from the stream.
Datetime
A field representing a time. Datetime fields are possible to access, but manual creation or modification of the fields of these types is not possible.
.fetch()
Fetches field value from Neptune servers.
Returns
datetime value stored
RunState
A field containing the state of the tracked run. RunState fields are possible to access, but manual creation or modification of the fields of these types is not possible. RunState does not expose any methods.
GitRef
A field containing the information about the git repository at the time of starting the tracked run. GitRef fields are possible to access, but manual creation or modification of the fields of these types is not possible. GitRef does not expose any methods.
FloatSeries
A field containing a series of numbers for example training metrics or change of performance of the model on production. The series can be indexed both by index and by time.
.log()
Logs the provided number or a collection of numbers.
Parameters
​
value
(float or int, or collection of float or int) - The value to be logged or collections of values to be logged. If a collection is provided step and timestamp parameters must be None.
step
(float or int, optional, default is None) - Index of the log entry being appended. Must be strictly increasing. 
timestamp
(float or int, optional, default is None) - Time index of the log entry being appended in form of Unix time. If None current time (time.time()) will be used as a timestamp.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.
.fetch_last()
Fetches last value stored in the series from Neptune servers.
Returns
Last float value logged.
.fetch_values()
Fetches all values stored in the series from Neptune servers.
Parameters
​
include_timestamp
(Boolean, optional, default is True) - Whether to include the fetched data should include the timestamp field.
Returns
Pandas.DataFrame containing all the values and their indexes stored in the series field.
StringSeries
A field containing a series of text values. 
Single text log is limited to 1000 characters, but there is no limit to the number of logs in the series. If the logged text exceeds this limit it will be truncated to match it.
.log()
Logs the provided text or a collection of texts.
Parameters
​
value
(str or collection of str) - The value to be logged or collections of values to be logged. If a collection is provided step and timestamp parameters must be None.
step
(float or int, optional, default is None) - Index of the log entry being appended. Must be strictly increasing. 
timestamp
(float or int, optional, default is None) - Time index of the log entry being appended in form of Unix time. If None current time (time.time()) will be used as a timestamp.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.
.fetch_last()
Fetches last value stored in the series from Neptune servers.
Returns
Last str value logged.
.fetch_values()
Fetches all values stored in the series from Neptune servers.
Parameters
​
include_timestamp
(Boolean, optional, default is True) - Whether to include the fetched data should include the timestamp field.
Returns
Pandas.DataFrame containing all the values and their indexes stored in the series field.
FileSeries
A field containing a series of files of any type. 
We are still working on support for files of any type. In the meantime, FileSeries fields accept only image-like files.
.log()
Logs the provided file.
Parameters
​
value
(File value object or collection of File) - The file to be logged or collections of files to be logged. If a collection is provided step and timestamp parameters must be None.
step
(float or int, optional, default is None) - Index of the log entry being appended. Must be strictly increasing. 
timestamp
(float or int, optional, default is None) - Time index of the log entry being appended in form of Unix time. If None current time (time.time()) will be used as a timestamp.
name
(str, optional, default is None) - name of the logged file.
description
(str, optional, default is None) - short description of the logged file.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.
Examples
import neptune.new as neptune
from neptune.new.types import File
​
run = neptune.init()
​
# Log image file
run['train/prediction_example'].log(File(path_to_file))
​
# Log Matplotlib figure with implicit casting
run["train/distribution"].log(plt_histogram, step=epoch)
​
# This is equivalent to following explicit casting
run["train/distribution"].log(File.as_image(plt_histogram), step=epoch)
# Read more about supported conversion in File.as_image
​
# Convert NumPy array to File value object and log it
run["train/prediction_example"].log(File.as_image(numpy_array))
​
# You can also log name and description for the image
​
# Log data sample
for plt_image, class_name in data_sample:
    run['data/sample'].log(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(
        ["class {}: {}".format(i, pred) for i, pred in enumerate(y_pred)]
    )
    run['train/predictions'].log(image, description=description)
.download_last()
Downloads the last File stored in the series from Neptune servers and save it locally.
Parameters
​
destination
(str , optional, default is None) - The directory where the file will be downloaded. If None is passed, the file will be downloaded to the current working directory.

If destination is a directory, the file will be downloaded to the specified directory with a filename composed from field name and extension (if present).
​
If destination is a path to a file, the file will be downloaded under the specified name.
.download()
Downloads all files stored in the series from Neptune servers.
Parameters
​
destination
(str , optional, default is None) - The directory where the file will be downloaded. If None is passed, the file will be downloaded to the current working directory.

If destination is a directory, the file will be downloaded to the specified directory with a filename composed from field name and extension (if present).
​
If destination is a path to a file, the file will be downloaded under the specified name.
StringSet
A field containing unorganized set of strings used for interacting with your run's tags. It's possible to access it and modify it, but manual creation of new fields of this type is not possible.
.add()
Adds the provided tag or tags to the run's tags.
Parameters
​
values
(str or collection of str) - Tag or tags to be added. 

Tip: If you want you can use emojis in your tags eg. Exploration 🧪
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server first. This makes the call synchronous, see Connection modes guide.
.remove()
Removes the provided tag or tags from the set.
Parameters
​
values
(stror collection of str) - Tag or tags to be removed. 
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server first. This makes the call synchronous, see Connection modes guide.
.clear()
Removes all tags from the StringSet.
Parameters
​
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server first. This makes the call synchronous, see Connection modes guide.
.fetch()
Fetches all tags from Neptune servers.
Parameters
​
value
(File) - File value object
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
Returns
Run's tags in a form of set of str.
FileSet
Field type for holding a larger number of files, when access to a single file is rare. FileSet can be used for a folder with source files, a folder with image examples, etc. Things that can be easily browsed through the web interface, and are typically accessed as a whole.
.upload_files()
Uploads the provided file or files and stores them inside the FileSet.
Parameters
​
globs
(stror collection of str) - Path or paths to the files to be uploaded. 
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
.delete_files()
Delete the file or files specified by paths from the FileSet stored on the Neptune servers.
Parameters
​
paths
(stror collection of str) - Path or paths to files or folders to be deleted. Note that these are paths relative to the FileSet itself e.g. if the FileSet contains file example.txt, varia/notes.txt, varia/data.csv to delete whole subfolder you would pass varia as the argument.
wait
(Boolean, optional, default is False) - If True the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.
.download()
Downloads all files stored in the field from Neptune servers.
Parameters
​
destination
(str , optional, default is None) - The directory where the files will be downloaded. If None is passed, the files will be downloaded to the current working directory.

If destination is a directory, the file will be downloaded to the specified directory with a filename composed from field name and extension (if present).
​
If destination is a path to a file, the file will be downloaded under the specified name.
Namespace handler
An object representing a namespace in your run's metadata structure. You can think of namespaces as folders for organizing your metadata. When you have a fields params/max_epochs and params/lr they will be grouped under a params namespace.
Namespace handler exposes similar methods as the Run object, however, all field paths are relative to the namespace:
import neptune.new as neptune
​
# You can upload the value through an absolute path
run["params/max_epochs"] = 20
​
# Or with a relative path and a namepsace handler
params_ns = run["params"]
params_ns["max_epochs"] = 20
[ ] (field lookup)
See Run's [] field lookup. Remember that the resulting paths will be a combination of the namespace path and the provided relative path.
=
Convenience alias for .assign(). Can be used only in combination with [] (field lookup)​
.assign()
See Run's .assign(). Remember that the resulting paths will be a combination of the namespace path and the provided relative path.
Examples
import neptune.new as neptune
run = neptune.init()
​
# Assign a single parameter
run["model/params/batch_size"] = 64
​
# 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 [] field lookup
params_ns = PARAMS # Does not work
run["model/params"] = PARAMS # Works
​
# This also works with relative pahts
model_ns = run["model"]
model_ns["params"] = PARAMS
.fetch()
Fetch values of all non-File Atom fields as a dictionary. 
The result will preserve the hierarchical structure of the run's metadata but will contain only non-File Atom fields. You can use this method e.g. to quickly retrieve run parameters.
Returns
dict containing all non-File Atom fields values.
Examples
import neptune.new as neptune
​
# Resume run with SUN-123 identifier
run = neptune.init(project="my_workspace/my_project", run="SUN-123")
​
# Fetch the runs parameters
params = run["model/params"].fetch()
Handler
When you access a run's path that doesn't exist yet you obtain a Handler object. Think of it as a wildcard that can become any of the types once you invoke on it specific logging method. If you invoke .upload() it becomes a File type, if you invoke .log(2)it will become a FloatSeries etc. Note that it can also become a Namespace handler if you create a field at a lower level of organization.
Handler object exposes all tracking methods: .assign(), .log(), .upload(), .upload_files(), etc. of all other field types as well as Namespace handler methods.
Examples
import neptune.new as neptune
run = neptune.init()
​
# Accessing "train/batch/acc" returns a Handler as the field does not exist yet
handler_object = run["train/batch/acc"]
​
# You can use it as if you would use any other field
handler_object.log(0.7)
​
# Once the field is created under the path, only the fields methods are accepted
handler_object.log(0.6) # Works
handler_object.upload("image.png") # Error, handler_object is a FloatSeries now

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Atom fields	Fields that you use to store a single value of the given type or a single file. It's possible to upload and fetch`Float`, `Integer`, `Boolean`, `String`, `Datetime` and `File` fields. `GitRef`, `RunState` fields are possible to access, but manual creation or modification of the fields of these types is not possible.
Series fields	Fields that you use to store a series of values or a series of files (e.g. images). Available types are `FloatSeries`, `StringSeries` and `FileSeries`.
FileSet	Field type for holding a larger number of files, when access to a single file is rare. `FileSet` can be used for a folder with source files, a folder with image examples, etc. Things that can be easily browsed through the web interface, and are typically accessed as a whole.
Sets fields	Fields holding unorganized sets of values. Only one set type currenlty exists - `StringSet` that is used to interact with the tags of the experiment. It's possible to access it and modify it, but manual creation of the fields of this type is not possible.
Handler	When you access a run's path that doesn't exist yet you obtain a `Handler` object. Think of it as a wildcard that can become any of the types once you invoke on it specific logging method. If you invoke `.upload()` it becomes a File type, if you invoke `.log(2)`it will become a FloatSeries etc. Note that it can also become a Namespace handler if you create a field at a lower level of organization.
Namespace handler	If you access a run's path that is one or more levels higher than an existing field you obtain a Namespace handler object, e.g. when you access `params` when you have a field `params/max_epochs`. Namespace handler can be used as a shortcut for accessing fields by only specifying a relative path within the namespace.

Parameters
value	(`float`) - Value to be stored in a field.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`int`) - Value to be stored in a field.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`Boolean` ) - Value to be stored in a field.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`str` or `String` ) - Value to be stored in a field.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`str`or `File`) - Path to the file to be uploaded or File value object
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`File`) - File value object
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
image	Image-like object to be converted. Supported are PyTorch tensors, TensorFlow/Keras tensors, NumPy arrays, PIL images, Matplotlib figures.

Parameters
value	An object to be converted. Supported are Altair, Bokeh, Plotly, Matplotlib interactive charts, and Pandas DataFrame objects.

Parameters
content	(`str`or`bytes`) - Text or binary content to stored in the File value object.
extension	(`str`, optional, default is `None`) - Extension of the file created 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.

Parameters
stream	(`IOBase`) - Stream to be converted.
seek	(`int`, optional, default is `0`) - See IOBase documentation
extension	(`str`, optional, default is `None`) - Extension of the file created that will be used for interpreting the type of content for visualization. If `None` it will be `bin` for binary stream and `txt` for text stream.

Parameters
value	(`float` or `int`, or `collection` of `float` or `int`) - The value to be logged or collections of values to be logged. If a collection is provided `step` and `timestamp` parameters must be `None`.
step	(`float` or `int`, optional, default is `None`) - Index of the log entry being appended. Must be strictly increasing.
timestamp	(`float` or `int`, optional, default is `None`) - Time index of the log entry being appended in form of Unix time. If `None` current time (`time.time()`) will be used as a timestamp.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.

Parameters
include_timestamp	(`Boolean`, optional, default is `True`) - Whether to include the fetched data should include the `timestamp` field.

Parameters
value	(`str` or `collection` of `str`) - The value to be logged or collections of values to be logged. If a collection is provided `step` and `timestamp` parameters must be `None`.
step	(`float` or `int`, optional, default is `None`) - Index of the log entry being appended. Must be strictly increasing.
timestamp	(`float` or `int`, optional, default is `None`) - Time index of the log entry being appended in form of Unix time. If `None` current time (`time.time()`) will be used as a timestamp.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.

Parameters
value	(`File` value object or `collection` of `File`) - The file to be logged or collections of files to be logged. If a collection is provided `step` and `timestamp` parameters must be `None`.
step	(`float` or `int`, optional, default is `None`) - Index of the log entry being appended. Must be strictly increasing.
timestamp	(`float` or `int`, optional, default is `None`) - Time index of the log entry being appended in form of Unix time. If `None` current time (`time.time()`) will be used as a timestamp.
name	(`str`, optional, default is `None`) - name of the logged file.
description	(`str`, optional, default is `None`) - short description of the logged file.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server. This makes the call synchronous, see Connection modes guide.

Parameters
values	(`str` or collection of `str`) - Tag or tags to be added. Tip: If you want you can use emojis in your tags eg. `Exploration 🧪`
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server first. This makes the call synchronous, see Connection modes guide.

Parameters
values	(`str`or collection of `str`) - Tag or tags to be removed.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server first. This makes the call synchronous, see Connection modes guide.

Parameters
globs	(`str`or collection of `str`) - Path or paths to the files to be uploaded.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.

Parameters
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 e.g. if the FileSet contains file `example.txt,` `varia/notes.txt`, `varia/data.csv` to delete whole subfolder you would pass `varia` as the argument.
wait	(`Boolean`, optional, default is `False`) - If `True` the client will wait to send all tracked metadata to the server before making the assignment. This makes the call synchronous, see Connection modes guide.