API reference: MLflow integration#

`create_neptune_tracking_uri()`#

Sets up a Neptune tracking URI for logging MLflow experiment metadata.

This lets you direct your metadata to Neptune instead of MLflow, without needing to change your MLflow logging code.

The function wraps neptune.init_run(), so you have the same parameters available for customizing your run or limiting what is logged. The exceptions are with_id and custom_run_id, which will be ignored if provided (as calling this function always creates a new Neptune run).

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`	User's API token. If `None`, the value of the `NEPTUNE_API_TOKEN` environment variable is used. Set to `neptune.ANONYMOUS_API_TOKEN` to log metadata anonymously. To keep your token secure, avoid placing it in source code. Instead, save it as an environment variable.
`mode`	`str`, optional	`async`	Connection mode in which the tracking 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	`"Untitled"`	Custom name of the run. You can edit it in the Run information and add it as a column in the runs table (`sys/name`).
`description`	`str`, optional	`""`	Description of the run. You can edit it in the Run information and add it as a column in the runs table (`sys/description`).
`tags`	`list`, optional	`None`	Must be a list of `str` which represents the tags for the run. You can edit them after run is created, either in the run information or runs 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`	In asynchronous (default) connection mode, how often Neptune should trigger disk flushing (in seconds).
`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 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`	In 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 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`	In seconds, for how long there has been no synchronization progress whatsoever. 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.

Example#

from neptune_mlflow_plugin import create_neptune_tracking_uri

uri = create_neptune_tracking_uri()  # (1)!
mlflow.set_tracking_uri(uri)

with mlflow.start_run() as mlflow_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( # (1)!
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jv...Yh3Kb8",  # your token here
    project="ml-team/classification",  # your full project name here
)

Also works for init_model(), init_model_version(), init_project(), and integrations that create Neptune runs underneath the hood, such as NeptuneLogger or NeptuneCallback.
API token: In the bottom-left corner, expand the user menu and select Get my API token.
Project name: You can copy the path from the project details ( → Edit project details).

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!

Additional options#

You can pass the same keyword arguments as for neptune.init_run(). This way, you can enable more options for the run or limit what is logged.

uri = create_neptune_tracking_uri(
    name="my-custom-run-name",
    description="Description of this run",
    tags=["test", "mlflow"],
    source_files="**/.py",
    dependencies="infer",
)

`neptune mlflow`#

Exports existing MLflow metadata to Neptune.

Command syntax: neptune mlflow [--api-token] [--project] [--mlflow-tracking-uri] [--exclude-artifacts] [--max-artifact-size]

Options	Type	Default	Description
`-p`, `--project`	`str`	-	Neptune project name. If `None`, the value of the `NEPTUNE_PROJECT` environment variable is used.
`-a`, `--api-token`	`str`	-	Neptune API token. If `None`, the value of the `NEPTUNE_API_TOKEN` environment variable is used.
`-u`, `--mlflow-tracking-uri`	`str`	-	Your MLflow tracking URI. If not provided, it is left to the MLflow client to resolve it. For more, see the MLflow documentation .
`-e`, `--exclude-artifacts`	`bool`	`False`	Whether to skip uploading artifacts to Neptune.
`-m`, `--max-artifact-size`	`int`	`50`	Maximum size of the artifact to be uploaded to Neptune (in MB). For directories, this will be treated as the max size of the entire directory.
`--help`	-	-	Show help message and exit.

Examples

If you've set your Neptune credentials as environment variables, you can use the following command to export data with default settings:

neptune mlflow

How do I save my credentials as environment variables?

Set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.

Linux macOS Windows Jupyter Notebook

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

export NEPTUNE_PROJECT="ml-team/classification"

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

export NEPTUNE_PROJECT="ml-team/classification"

setx NEPTUNE_API_TOKEN "h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

setx NEPTUNE_PROJECT "ml-team/classification"

You can also navigate to Settings → Edit the system environment variables and add the variables there.

%env NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...ifQ=="

%env NEPTUNE_PROJECT="ml-team/classification"

To find your credentials:

API token: In the bottom-left corner of the Neptune app, expand your user menu and select Get your API token. If you need the token of a service account, go to the workspace or project settings and enter the Service accounts settings.
Project name: Your full project name has the form workspace-name/project-name. You can copy it from the project menu ( → Edit project details).

If you're working in Google Colab, you can set your credentials with the os and getpass libraries:

import os
from getpass import getpass
os.environ["NEPTUNE_API_TOKEN"] = getpass("Enter your Neptune API token: ")
os.environ["NEPTUNE_PROJECT"] = "workspace-name/project-name"

Otherwise, you can pass the credentials as options:

neptune mlflow \
    --api-token I2YzgMz1h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvh0dHBzOi8aHR0Yh3Kb8MifQ== \
    --project ml-team/llm-project \

Using the command with more options:

neptune mlflow \
    --project ml-team/llm-project-2 \
    --mlflow-tracking-uri file:///tmp/my_tracking \
    --max-artifact-size 100 \
    /

neptune-notebooks incompatibility

Currently, the CLI component of the integration does not work together with the Neptune-Jupyter extension (neptune-notebooks).

Until a fix is released, if you have neptune-notebooks installed, you must uninstall it to be able to use the neptune mlflow command.

API reference: MLflow integration#

create_neptune_tracking_uri()#

Example#

Additional options#

neptune mlflow#

`create_neptune_tracking_uri()`#

`neptune mlflow`#