`Run`#

Representation of all metadata of a tracked experiment.

Initialization#

Initialize with the init_run() function or the class constructor.

init_run()Run()

import neptune

run = neptune.init_run()

from neptune import Run

run = Run()

If Neptune can't find your project name or API token

As a best practice, you should save your Neptune API token and project name as environment variables:

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8"

export NEPTUNE_PROJECT="ml-team/classification"

Alternatively, you can pass the information when using a function that takes api_token and project as arguments:

run = neptune.init_run(
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8", # (1)!
    project="ml-team/classification", # (2)!
)

In the bottom-left corner, expand the user menu and select Get my API token.
You can copy the path from the project details ( → Details & privacy).

If you haven't registered, you can log anonymously to a public project:

api_token=neptune.ANONYMOUS_API_TOKEN
project="common/quickstarts"

Make sure not to publish sensitive data through your code!

With the run object, you can:

Log metadata in a structure of your choosing.
Download run metadata to your local machine.
Delete metadata from the run.

See also: Defining a custom init_run() function, for keeping the logging consistent within your team.

Parameters

Name	Type	Default	Description
`project`	`str`, optional	`None`	Name of a project in the form `workspace-name/project-name`. If `None`, the value of the `NEPTUNE_PROJECT` environment variable is used.
`api_token`	`str`, optional	`None`	Your Neptune API token (or a service account's API token). If `None`, the value of the `NEPTUNE_API_TOKEN` environment variable is used. To keep your token secure, avoid placing it in source code. Instead, save it as an environment variable.
`with_id`	`str`, optional	`None`	The Neptune identifier of an existing run to resume, such as "CLS-11". The identifier is stored in the object's `sys/id` field. If omitted or `None` is passed, a new tracked run is created.
`custom_run_id`	`str`, optional	`None`	A unique identifier that can be used to log metadata to a single run from multiple locations. Max length: 36 characters. If `None` and the `NEPTUNE_CUSTOM_RUN_ID` environment variable is set, Neptune will use that as the `custom_run_id` value. For details, see Set custom run ID.
`mode`	`str`, optional	`async`	Connection mode in which the logging will work. Possible values are `async`, `sync`, `offline`, `read-only`, and `debug`. If you leave it out, the value of the `NEPTUNE_MODE` environment variable is used. If that's not set, the default `async` is used.
`name`	`str`, optional	Neptune ID	Custom name for the run. You can use it as a human-readable ID and add it as a column in the experiments table (`sys/name`). If left empty, once the run is synchronized with the server, Neptune sets the auto-generated identifier (`sys/id`) as the name.
`description`	`str`, optional	`""`	Editable description of the run. You can add it as a column in the experiments table (`sys/description`).
`tags`	`list`, optional	`[]`	Must be a list of `str` which represent the tags for the run. You can edit them after run is created, either in the run information or experiments table.
`source_files`	`list` or `str`, optional	`None`	List of source files to be uploaded. Must be list of `str` or a single `str`. Uploaded sources are displayed in the Source code section of the run. If `None` is passed, the Python file from which the run was created will be uploaded. When resuming a run, no file will be uploaded by default. Pass an empty list (`[]`) to upload no files. Unix style pathname pattern expansion is supported. For example, you can pass `".py"` to upload all Python source files from the current directory. Paths of uploaded files are resolved relative to the calculated common root of all uploaded source files. For recursion lookup, use `"**/.py"` (for Python `3.5` and later). For details, see the glob library.
`capture_stdout`	`Boolean`, optional	`True`	Whether to log the standard output stream. Is logged in the `monitoring` namespace.
`capture_stderr`	`Boolean`, optional	`True`	Whether to log the standard error stream. Is logged in the `monitoring` namespace.
`capture_hardware_metrics`	`Boolean`, optional	`True`	Whether to track hardware consumption (CPU, GPU, memory utilization). Logged in the `monitoring` namespace.
`fail_on_exception`	`Boolean`, optional	`True`	If an uncaught exception occurs, whether to set run's `Failed` state to `True`.
`monitoring_namespace`	`str`, optional	`"monitoring"`	Namespace inside which all monitoring logs will be stored.
`flush_period`	`float`, optional	`5` (seconds)	In asynchronous (default) connection mode, how often Neptune should trigger disk flushing.
`proxies`	`dict`, optional	`None`	Argument passed to HTTP calls made via the Requests library. For details on proxies, see the Requests documentation.
`capture_traceback`	`Boolean`, optional	`True`	In case of an exception, whether to log the traceback of the run.
`git_ref`	`GitRef` or `Boolean`	`None`	`GitRef` object containing information about the Git repository path. If `None`, Neptune looks for a repository in the path of the script that is executed. To specify a different location, set to `GitRef(repository_path="path/to/repo")`. To turn off Git tracking for the run, set to `GitRef.DISABLED` or `False`. For examples, see Logging Git info.
`dependencies`	`str`, optional	`None`	Tracks environment requirements. If you pass `"infer"` to this argument, Neptune logs dependencies installed in the current environment. You can also pass a path to your dependency file directly. If left empty, no dependency file is uploaded.
`async_lag_callback`	`NeptuneObjectCallback`, optional	`None`	Custom callback function which is called if the lag between a queued operation and its synchronization with the server exceeds the duration defined by `async_lag_threshold`. The callback should take a `Run` object as the argument and can contain any custom code, such as calling `stop()` on the object. Note: Instead of using this argument, you can use Neptune's default callback by setting the `NEPTUNE_ENABLE_DEFAULT_ASYNC_LAG_CALLBACK` environment variable to `TRUE`.
`async_lag_threshold`	`float`, optional	`1800.0` (seconds)	Duration between the queueing and synchronization of an operation. If a lag callback (default callback enabled via environment variable or custom callback passed to the `async_lag_callback` argument) is enabled, the callback is called when this duration is exceeded.
`async_no_progress_callback`	`NeptuneObjectCallback`, optional	`None`	Custom callback function which is called if there has been no synchronization progress whatsoever for the duration defined by `async_no_progress_threshold`. The callback should take a `Run` object as the argument and can contain any custom code, such as calling `stop()` on the object. Note: Instead of using this argument, you can use Neptune's default callback by setting the `NEPTUNE_ENABLE_DEFAULT_ASYNC_NO_PROGRESS_CALLBACK` environment variable to `TRUE`.
`async_no_progress_threshold`	`float`, optional	`300.0` (seconds)	For how long there has been no synchronization progress. If a no-progress callback (default callback enabled via environment variable or custom callback passed to the `async_no_progress_callback` argument) is enabled, the callback is called when this duration is exceeded.

Returns

Run object that is used to manage the tracked run and log metadata to it.

Examples

Initialize a new run with optional arguments

from neptune import Run

run = Run(
    name="serious-cherry",
    description="Write a longer description here.",
    tags=["pytorch", "test"],
    source_files=["training_with_pytorch.py", "net.py"],
    dependencies="infer",
    monitoring_namespace="monitoring",
)

Initialize existing run with idenfitier CLS-16, to resume logging

from neptune import Run

run = Run(with_id="CLS-16")

Initialize existing run in read-only mode, to avoid modifying or adding data

from neptune import Run

run = Run(with_id="CLS-16", mode="read-only")

Resume a run in order to delete data:

run = neptune.init_run(
    with_id="CLAS-134",
    capture_hardware_metrics=False,
    capture_stderr=False,
    capture_stdout=False,
    capture_traceback=False,
    git_ref=False,
    source_code=[],
)

del run["namespace"]

Field lookup: `[]`#

Accesses the field of a run through a dict-like lookup: run[field_path].

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

When you assign some metadata to a new path in the run, it creates a field of a certain type.

Create new Float field

run["parameters/momentum"] = 0.9

You can assign a new value of the same type to the field. Unless the logging method is iterative, this will overwrite the previous value.

Update the value of the field

run["parameters/momentum"] = 0.8

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.

Parameters

Name	Type	Default	Description
`value`	`dict`	`None`	A dictionary with values to assign, where keys (`str`) become the paths of the fields. The dictionary can be nested, in which case the path will be a combination of all keys.
`wait`	`Boolean`, optional	`False`	By default, logging calls and other Neptune operations are periodically synchronized with the server in the background. If `True`, Neptune first waits to complete any queued operations, then executes the call and continues script execution. See Connection modes.

Examples

run["parameters/max_epochs"].assign(10)
run["parameters/optimizer"].assign("Adam") # (1)!

# Assign multiple fields from a dictionary
run.assign({"parameters": {"max_epochs": 10, "optimizer": "Adam"}}) # (2)!

Equivalent to:

run["parameters/max_epochs"] = 10
run["parameters/optimizer"] = "Adam"

Equivalent to:

params = {"max_epochs": 10, "optimizer": "Adam"}
run["parameters"] = params

`del`#

Completely removes the field or namespace and all associated metadata stored under the path.

If you need to free up storage or reduce the number of fields, use del to remove unneeded metadata that takes up space, such as notebook training checkpoints.

`exists()`#

Checks if there is a field or namespace under the specified path.

Info

This method checks the local representation of the run. If the field was created by another process or the metadata has not reached the Neptune servers, it may not be possible to fetch. In this case you can:

Call sync() on the run to synchronize the local representation with the server.
Call wait() on the run to wait for all logging calls to finish.

Parameters

Name	Type	Default	Description
`path`	`str`	-	Path to check for the existence of a field or namespace

Examples

# Resume run with identifier CLS-34
run = neptune.init_run(with_id="CLS-34")

# If the training is complete, remove training checkpoints if present
if run.exists("model/checkpoints") and run["train/finished"].fetch() == True:
    del run["model/checkpoints"]

Info

When working in asynchronous (default) mode, the metadata you track may not be immediately available to fetch from the server, even if it appears in the local representation.

To work around this, you can call wait() on the run.

run["learning_rate"] = 0.1

# The path exists in the local representation
if run.exists("learning_rate"):
    # However, the tracking call may have not reached Neptune servers yet
    run["learning_rate"].fetch()  # Error: the field does not exist

run.wait()

`fetch()`#

Fetches the values of all single-value fields (that are not of type File) as a dictionary.

The result preserves the hierarchical structure of the model metadata. You can use this method, for example, to quickly retrieve run parameters.

Returns

dict containing the values of all non-File single-value fields.

Example

# Resume run with identifier CLS-123
run = neptune.init_run(with_id="CLS-123", mode="read-only")

# Fetch the run parameters
params = run["model/params"].fetch()

`get_structure()`#

Returns the metadata structure of a run in the form of a dictionary.

This method can be used to traverse the metadata structure programmatically when using Neptune in automated workflows.

`get_url()`#

Returns a direct link to the run in Neptune. The same link is printed in the console once the run has been initialized.

Returns

str with the URL of the run in Neptune.

Example

>>> import neptune
>>> run = neptune.init_run(with_id="NER-35", mode="read-only")
>>> run.get_url()
https://app.neptune.ai/jackie/named-entity-recognition/e/NER-35

`pop()`#

Completely removes the field or namespace and all associated metadata stored under the path.

`print_structure()`#

Pretty-prints the structure of the run metadata. Paths are ordered lexicographically and the structure is colored.

`stop()`#

Stops the connection to Neptune and synchronizes all data.

Info

Every active run keeps a connection open with Neptune, monitors hardware usage, etc. If you are performing multiple training jobs from one script, one after the other, stop each run once tracking is no longer needed.

You can also use context managers. Neptune will automatically call stop() when exiting the run context.

Warning

Always call stop() in interactive environments, such as a Python interpreter or Jupyter notebook. The connection to Neptune is not stopped when the cell has finished executing, but rather when the entire notebook stops.

If you're running a script, the connection is stopped automatically when the script finishes executing. However, it's a best practice to call stop() when the connection is no longer needed.

Parameters

Name	Type	Default	Description
`seconds`	`int` or `float`, optional	`None`	Wait for the specified time for all logging calls to finish before stopping the connection. If `None`, wait for all logging calls to finish.

Examples

If you are creating tracked runs from a Python script, Neptune stops the run automatically when the script finishes executing.

import neptune

run = neptune.init_run()

[...]  # Your training or monitoring code

# stop() is automatically called at the end for every Neptune object

Stopping multiple tracked runs manually at the end of a training job:

for config in configs:
    run = neptune.init_run()
    [...]  # Your training or monitoring code
    run.stop()

Using with statement and context manager:

for config in configs:
    with neptune.init_run() as run:
        [...]  # Your training or monitoring code
        # stop() is automatically called when code execution exits the with statement

`sync()`#

Synchronizes the local representation of the run with Neptune servers.

Parameters

Name	Type	Default	Description
`wait`	`Boolean`, optional	`False`	By default, logging calls and other Neptune operations are periodically synchronized with the server in the background. If `True`, Neptune first waits to complete any queued operations, then executes the call and continues script execution. See Connection modes.

Example

# Connect to a run from Worker #3
worker_id = 3
run = neptune.init_run(
    with_id="DIST-43",
    monitoring_namespace=f"monitoring/{worker_id}",
)

# Try to access logs that were created in the meantime by Worker #2
worker_2_status = run["status/2"].fetch()
# Error if this field was created after this script starts

run.sync() # Synchronize local representation with Neptune servers
worker_2_status = run["status/2"].fetch() # No error

`wait()`#

Wait for all the logging calls to finish.

Parameters

Name	Type	Default	Description
`disk_only`	`Boolean`, optional	`False`	If `True`, the process will wait only for the data to be saved locally from memory, but will not wait for it to reach Neptune servers.

Deprecated#

`get_run_url()`#

Deprecated

This method is deprecated. As of neptune 1.0 it's no longer supported.

Use get_url() instead.

Name	Type	Default	Description
`path`	`str`	-	Path of the field or namespace to be removed.
`wait`	`Boolean`, optional	`False`	By default, logging calls and other Neptune operations are periodically synchronized with the server in the background. If `True`, Neptune first waits to complete any queued operations, then executes the call and continues script execution. See Connection modes.

Run#

Initialization#

Field lookup: []#

Assignment: =#

assign()#

del#

exists()#

fetch()#

get_structure()#

get_url()#

pop()#

print_structure()#

stop()#

sync()#

wait()#

Deprecated#

get_run_url()#

`Run`#

Field lookup: `[]`#

Assignment: `=`#

`assign()`#

`del`#

`exists()`#

`fetch()`#

`get_structure()`#

`get_url()`#

`pop()`#

`print_structure()`#

`stop()`#

`sync()`#

`wait()`#

`get_run_url()`#