MLflow integration API reference

API reference for the Neptune-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 wrapsneptune.init_run(), so you have the same parameters available for customizing your run or limiting what is logged. The exceptions arewith_idandcustom_run_id, which will be ignored if provided (as calling this function always creates a new Neptune run).

Parameters

Set toneptune.ANONYMOUS_API_TOKENto log metadata anonymously.

To keep your token secure, avoid placing it in source code. Instead,save it as an environment variable.

If you leave it out, the value of theNEPTUNE_MODEenvironment variable is used. If that's not set, the defaultasyncis used.

List of source files to be uploaded. Must be list ofstror a singlestr. Uploaded sources are displayed in theSource codesection of the run.

IfNoneis 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 thegloblibrary.

IfNone, Neptune looks for a repository in the path of the script that is executed.

To specify a different location, set toGitRef(repository_path="path/to/repo").

To turn off Git tracking for the run, set toGitRef.DISABLEDorFalse.

Note:Instead of using this argument, you can use Neptune's default callback by setting theNEPTUNE_ENABLE_DEFAULT_ASYNC_LAG_CALLBACKenvironment variable toTRUE.

Note:Instead of using this argument, you can use Neptune's default callback by setting theNEPTUNE_ENABLE_DEFAULT_ASYNC_NO_PROGRESS_CALLBACKenvironment variable toTRUE.

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:
 ...

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 takesapi_tokenandprojectas arguments:

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

1. In the bottom-left corner, expand the user menu and select Get my API token .
2. 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!

Additional options

You can pass the same keyword arguments as forneptune.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]

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

Set your Neptune API token and full project name to theNEPTUNE_API_TOKENandNEPTUNE_PROJECTenvironment variables, respectively.

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 toSettings→Edit the system environment variablesand 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 ( → Details & privacy ).

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 \
 --max-artifact-size 100 \
 /

neptune-notebooks incompatibility

The command doesn't work together with theNeptune-Jupyter extension(neptune-notebooks).

To use the command, you must uninstall neptune-notebooks first.

See also

• neptune-mlflow repo on GitHub

Related Documentation

• Neptune Package
• Run API

This page is originally sourced from the legacy docs.