Neptune
Logging and visualizing experiments with neptune.ai
When would you want to use it?
You should use the Neptune Experiment Tracker:
if you have already been using neptune.ai to track experiment results for your project and would like to continue doing so as you are incorporating MLOps workflows and best practices in your project through ZenML.
if you are looking for a more visually interactive way of navigating the results produced from your ZenML pipeline runs (e.g. models, metrics, datasets)
if you would like to connect ZenML to neptune.ai to share the artifacts and metrics logged by your pipelines with your team, organization, or external stakeholders
How do you deploy it?
The Neptune Experiment Tracker flavor is provided by the Neptune-ZenML integration. You need to install it on your local machine to be able to register the Neptune Experiment Tracker and add it to your stack:
The Neptune Experiment Tracker needs to be configured with the credentials required to connect to Neptune using an API token.
Authentication Methods
You need to configure the following credentials for authentication to Neptune:
project
: The name of the project where you're sending the new run, in the form "workspace-name/project-name". If the project is not specified, Neptune will attempt to retrieve it from your environment variables.
You can create the secret using the zenml secret create
command:
Once the secret is created, you can use it to configure the neptune
Experiment Tracker:
How do you use it?
Logging ZenML pipeline and step metadata to the Neptune run
You can use the get_step_context
method to log some ZenML metadata in your Neptune run:
Adding tags to your Neptune run
You can pass a set of tags to the Neptune run by using the NeptuneExperimentTrackerSettings
class, like in the example below:
Neptune UI
Neptune comes with a web-based UI that you can use to find further details about your tracked experiments. You can find the URL of the Neptune run linked to a specific ZenML run printed on the console whenever a Neptune run is initialized. You can also find it in the dashboard in the metadata tab of any step that has used the tracker:
Each pipeline run will be logged as a separate experiment run in Neptune, which you can inspect in the Neptune UI.
Clicking on one run will reveal further metadata logged within the step:
Full Code Example
This section shows an end to end run with the ZenML Neptune integration.
Further reading
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