Field types reference#
To support various types of metadata, Neptune fields come in a few different types.
- The metadata type and logging method together determine the resulting field type.
- The field type determines the available operations and methods.
Field types you can create and modify:
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 |
* Previously log()
The table in the neptune.types
section below displays all the supported field types, including those that can be viewed but not manually created.
neptune.types
#
Type | Description |
---|---|
Artifact |
Field type for versioning datasets, models, and other files. |
Float , Integer , Boolean , String , Datetime , File , GitRef , and RunState |
Used for a single value of the given type, or a single file. |
FileSet |
Used to hold a larger number of files when access to a single file is rare. |
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: |
Series |
Used for series of values or files (for example, images). Available types are FloatSeries , StringSeries and FileSeries . |
Set |
Field holding unorganized sets of values. The available type is StringSet , which is used to interact with the object's tags. You can access and modify fields of this type, but not manually create them. |
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
You may want to track files as artifacts especially if they are large (to avoid uploads taking up storage space) but you would still like to version and compare them between runs.
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.
For instructions, see the Tracking artifacts guide.
Assignment: =
#
Convenience alias for assign()
.
assign()
#
Assigns the provided value to the field.
Parameters
Name | Type | Default | Description |
---|---|---|---|
value |
str or Artifact |
- | Value to be stored in the field |
wait |
Boolean , optional |
False |
By default, tracked 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()
run["datasets/images"].track_files("./images")
run["datasets/images-copy"] = run["datasets/images"].fetch()
download()
#
Downloads all the files that are referenced in the field.
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.
|
Examples
import neptune
run = neptune.init_run(
with_id="NER-2", # Neptune ID of a run with artifact
mode="read-only", # read-only mode does not count toward monitoring time
)
run["artifacts/images"].download(destination="datasets/train/images")
fetch()
#
Fetches the field value.
You should use fetch()
only to copy the artifact.
Returns
Artifact
object stored in the field.
Examples
import neptune
run = neptune.init_run()
run["datasets/images"].track_files("./images")
run["datasets/images-copy"] = 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 |
URL of the file in the Neptune app |
"size" |
int |
Size of the file in KB |
"metadata" |
dict |
Dictionary with the following keys:
|
Examples
import neptune
run = neptune.init_run(
with_id="NER-2", # Neptune ID of a run with artifact
mode="read-only", # read-only mode does not count toward monitoring time
)
artifact_list = run["artifacts/images"].fetch_files_list()
artifact_list[0].file_hash
artifact_list[0].file_path
artifact_list[0].metadata["last_modified"]
fetch_hash()
#
Fetches the hash of the artifact.
Returns
Hash of the Neptune artifact as a str
.
Examples
import neptune
run = neptune.init_run(
with_id="NER-2", # Neptune ID of a run with artifact
mode="read-only", # read-only mode does not count toward monitoring time
)
run["artifacts/images"].fetch_hash()
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, tracked 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()
run["artifacts/images"].track_files("datasets/train/images")
Float
, Integer
, Boolean
, String
, and Datetime
#
Field type representing a floating-point number, integer, Boolean value, text string, or datetime value.
Assignment: =
#
Convenience alias for assign()
.
assign()
#
Assigns the provided value to the field.
Parameters
Name | Type | Default | Description |
---|---|---|---|
value |
float |
- | Values to assign to the field. |
wait |
Boolean , optional |
False |
By default, tracked 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
Parameters
Name | Type | Default | Description |
---|---|---|---|
value |
int |
- | Values to assign to the field. |
wait |
Boolean , optional |
False |
By default, tracked 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
Parameters
Name | Type | Default | Description |
---|---|---|---|
value |
Boolean |
- | Values to assign to the field. |
wait |
Boolean , optional |
False |
By default, tracked 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
Parameters
Name | Type | Default | Description |
---|---|---|---|
value |
str |
- | Values to assign to the field. |
wait |
Boolean , optional |
False |
By default, tracked 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()
# 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.
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, tracked 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
import neptune
run = neptune.init_run()
run["train/end"] = datetime.now()
Log when the evaluation started:
Log other time-related metadata:
fetch()
#
Fetches the field value from Neptune servers.
Returns
The float
value stored in the field.
Example
Returns
The int
value stored in the field.
Example
Returns
The bool
value stored in the field.
Example
Returns
The str
value stored in the field.
Example
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 thesource_code/git
field in the Neptune web app.- To inspect the information, in the run view, go to Source code → Git.
- You cannot manually create or modify
GitRef
fields, but you can disable Git tracking (seeDISABLED
).
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 = init_run(git_ref=GitRef(repository_path="/path/to/repo"))
DISABLED
#
Constant that disables Git tracking for your run.
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, tracked 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, the filename is a combination of the path and extension:
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()) # gcf() gets the figure object
Assignment: =
#
Convenience alias for assign()
.
assign()
#
You can also 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, tracked 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
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.
|
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 | Description |
---|---|
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
Convert NumPy array to File value object and upload it:
import neptune
from neptune.types import File
run = neptune.init_run()
run["train/prediction_example"].upload(File.as_image(numpy_array))
Convert PIL image to File value object and upload it:
You can also upload a PIL image without explicit conversion:
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 |
---|---|
value |
Object to be converted. Supported:
|
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
import neptune
from neptune.types import File
run = neptune.init_run()
# 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.
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, tracked 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:
Convert a NumPy array to File
value object and log it:
You can also log a name and description for the image:
for plt_image, class_name in data_sample:
run["data/sample"].append(plt_image, name=class_name)
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, tracked 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.
|
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.
|
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.
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, tracked 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
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 |
wait |
Boolean , optional |
False |
By default, tracked 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.
|
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, tracked 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:
You can also append values to multiple series at once by passing a dictionary of values. Pass the field name as the key.
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, tracked 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.
run = neptune.init_run()
df = pandas.read_csv("time_series.csv")
ys = df["value"]
ts = df["timestamp"]
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")
>>> final_loss = run["train/loss"].fetch_last()
>>> print(final_loss)
0.15250000000000002
fetch_values()
#
Fetches all values stored in the series.
Parameters
Name | Type | Default | Description |
---|---|---|---|
include_timestamp |
Boolean , optional |
False |
Whether the fetched data should include the timestamp field. |
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")
>>> losses = run["train/loss"].fetch_values()
>>> print(losses)
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
4 4.0 1.72 2022-07-08 12:30:30.087
5 5.0 2.15 2022-07-08 12:30:30.087
6 6.0 2.58 2022-07-08 12:30:30.087
7 7.0 3.01 2022-07-08 12:30:30.087
8 8.0 3.44 2022-07-08 12:30:30.088
9 9.0 3.87 2022-07-08 12:30:30.088
Handler
#
When you access the path of a run that doesn't exist yet, you obtain a Handler
object.
Think of it as a wildcard that can become any type once you invoke a specific logging method on it – if you invoke upload()
, it becomes a File
field; if you invoke append()
, it becomes a FloatSeries
.
Note
A Handler
object can also become a namespace handler if you create a field at a lower level of organization.
The Handler
object exposes:
- All tracking methods – such as
assign()
,append()
,upload()
, andupload_files()
– of all other field types - 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 report_accuracy()
function:
Examples
import neptune
run = neptune.init_run()
# 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.append(0.7)
# Once the field is created under the path, only field methods are accepted
handler_object.append(0.6) # Works
handler_object.upload("image.png") # Error: handler_object became a FloatSeries
Namespace handler#
An object representing a namespace in the metadata structure of a Neptune object. Example: Accessing run["params"]
when you have a field params/max_epochs
.
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"
.
The namespace handler exposes similar methods as other Neptune objects – however, all field paths are relative to the namespace:
import neptune
# You can upload the value through an absolute path
run["params/max_epochs"] = 20
# Or with a namespace handler and a relative path
params_ns = run["params"]
params_ns["max_epochs"] = 20
Field lookup: []
#
You can access the field of an object through a dict-like field lookup: run[field_path]
.
This way, you can
-
track metadata:
-
fetch already tracked metadata, such as retrieving parameters when resuming a run or downloading metadata for a custom analysis:
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: Field |
Namespace handler object |
Example
import neptune
run = neptune.init_run()
params_ns = run["parameters"]
params_ns["lr"] = 0.3 # Stores 0.3 under the path "parameters/lr"
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()
# 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 "[]"
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/steps/pretraining/params"] = {...}
...
pretraining = run["workflow/steps/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/steps" namespace inside the run object. As such, the
pretraining
object is a namespace handler object.
RunState
#
Contains the state (Active
/Inactive
) of a tracked 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>")
- view the
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, tracked 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):
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, tracked 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_stderr_line = run["monitoring/stderr"].fetch_last()
fetch_values()
#
Fetches all values stored in the series.
Parameters
Name | Type | Default | Description |
---|---|---|---|
include_timestamp |
Boolean , optional |
False |
Whether the fetched data should include the timestamp field. |
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")
stderr = run["monitoring/6519428b/stderr"].fetch_values()
StringSet
#
A field containing an unorganized set of strings. You can access and modify fields of this type, but not manually create them.
Currently, the supported StringSet
field is "sys/tags"
. You can use it to manage tags for runs and other Neptune objects.
add()
#
Adds the provided text string or strings to the set.
Parameters
Name | Type | Default | Description |
---|---|---|---|
values |
str or collection of str |
- | Tag or tags to be applied. |
wait |
Boolean , optional |
False |
By default, tracked 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
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, tracked 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
clear()
#
Removes all tags from the set.
Parameters
Name | Type | Default | Description |
---|---|---|---|
wait |
Boolean , optional |
False |
By default, tracked 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
fetch()
#
Fetches all tags from the set.
Returns
set
of str
with an object's tags.
Example