From e37ddd97588cef64b61bd9997fb99e9902a37355 Mon Sep 17 00:00:00 2001 From: Hamza Tahir Date: Fri, 20 Sep 2024 09:51:30 +0200 Subject: [PATCH] Fix Neptune docs (#3026) * Updated neptune docs * Updated neptune docs * Optimised images with calibre/image-actions --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> --- .../.gitbook/assets/zenml_neptune_dag.png | Bin 0 -> 184619 bytes .../.gitbook/assets/zenml_neptune_table.png | Bin 0 -> 75409 bytes .../.gitbook/assets/zenml_neptune_zen2.png | Bin 0 -> 68249 bytes .../experiment-trackers/neptune.md | 247 +++++++++++++----- 4 files changed, 187 insertions(+), 60 deletions(-) create mode 100644 docs/book/.gitbook/assets/zenml_neptune_dag.png create mode 100644 docs/book/.gitbook/assets/zenml_neptune_table.png create mode 100644 docs/book/.gitbook/assets/zenml_neptune_zen2.png diff --git a/docs/book/.gitbook/assets/zenml_neptune_dag.png b/docs/book/.gitbook/assets/zenml_neptune_dag.png new 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a/docs/book/component-guide/experiment-trackers/neptune.md +++ b/docs/book/component-guide/experiment-trackers/neptune.md @@ -28,102 +28,102 @@ zenml integration install neptune -y The Neptune Experiment Tracker needs to be configured with the credentials required to connect to Neptune using an API token. -#### Authentication Methods +### Authentication Methods You need to configure the following credentials for authentication to Neptune: -* `api_token`: [API key token](https://docs.neptune.ai/setup/setting\_api\_token) of your Neptune account. You can create a free Neptune account [here](https://app.neptune.ai/register). If left blank, Neptune will attempt to retrieve the token from your environment variables. +* `api_token`: [API key token](https://docs.neptune.ai/setup/setting_api_token) of your Neptune account. You can create a free Neptune account [here](https://app.neptune.ai/register). If left blank, Neptune will attempt to retrieve the token from your environment variables. * `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. {% tabs %} -{% tab title="Basic Authentication" %} -This option configures the credentials for neptune.ai directly as stack component attributes. - -{% hint style="warning" %} -This is not recommended for production settings as the credentials won't be stored securely and will be clearly visible in the stack configuration. -{% endhint %} - -```shell -# Register the Neptune experiment tracker -zenml experiment-tracker register neptune_experiment_tracker --flavor=neptune \ - --project= --api_token= - -# Register and set a stack with the new experiment tracker -zenml stack register custom_stack -e neptune_experiment_tracker ... --set -``` -{% endtab %} - {% tab title="ZenML Secret (Recommended)" %} This method requires you to [configure a ZenML secret](../../how-to/interact-with-secrets.md) to store the Neptune tracking service credentials securely. You can create the secret using the `zenml secret create` command: ```shell -zenml secret create neptune_secret \ - --project= - --api_token= +zenml secret create neptune_secret --api_token= ``` Once the secret is created, you can use it to configure the `neptune` Experiment Tracker: ```shell # Reference the project and api-token in our experiment tracker component -zenml experiment-tracker register neptune_secret \ +zenml experiment-tracker register neptune_experiment_tracker \ --flavor=neptune \ - --project={{neptune_secret.project}} \ + --project= \ --api_token={{neptune_secret.api_token}} ... + + +# Register and set a stack with the new experiment tracker +zenml stack register neptune_stack -e neptune_experiment_tracker ... --set ``` {% hint style="info" %} Read more about [ZenML Secrets](../../how-to/interact-with-secrets.md) in the ZenML documentation. {% endhint %} + {% endtab %} -{% endtabs %} +{% tab title="Basic Authentication" %} +This option configures the credentials for neptune.ai directly as stack component attributes. -For more, up-to-date information on the Neptune Experiment Tracker implementation and its configuration, you can have a look at [the SDK docs](https://sdkdocs.zenml.io/latest/integration\_code\_docs/integrations-neptune/#zenml.integrations.neptune.experiment\_trackers.neptune\_experiment\_tracker) . +{% hint style="warning" %} +This is not recommended for production settings as the credentials won't be stored securely and will be clearly visible in the stack configuration. +{% endhint %} -### How do you use it? +```shell +# Register the Neptune experiment tracker +zenml experiment-tracker register neptune_experiment_tracker --flavor=neptune \ + --project= --api_token= -To log information from a ZenML pipeline step using the Neptune Experiment Tracker component in the active stack, you need to enable an experiment tracker using the `@step` decorator. Then fetch the Neptune run object and use logging capabilities as you would normally do. For example: +# Register and set a stack with the new experiment tracker +zenml stack register neptune_stack -e neptune_experiment_tracker ... --set +``` +{% endtab %} +{% endtabs %} -```python -import numpy as np +For more, up-to-date information on the Neptune Experiment Tracker implementation and its configuration, you can have a look at [the SDK docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-neptune/#zenml.integrations.neptune.experiment_trackers.neptune_experiment_tracker) . -import tensorflow as tf +### How do you use it? -from neptune_tensorflow_keras import NeptuneCallback +To log information from a ZenML pipeline step using the Neptune Experiment Tracker component in the active stack, you need to enable an experiment tracker using the `@step` decorator. Then fetch the [Neptune run object](https://docs.neptune.ai/api/run/) and use logging capabilities as you would normally do. For example: +```python from zenml.integrations.neptune.experiment_trackers.run_state import ( - get_neptune_run, + get_neptune_run ) -from zenml import step +from neptune.utils import stringify_unsupported +from zenml import get_step_context +from sklearn.model_selection import train_test_split +from sklearn.svm import SVC +from sklearn.datasets import load_iris +from zenml import pipeline, step +from zenml.client import Client +from zenml.integrations.neptune.experiment_trackers import NeptuneExperimentTracker +# Get the experiment tracker from the active stack +experiment_tracker: NeptuneExperimentTracker = Client().active_stack.experiment_tracker -@step(experiment_tracker="") -def tf_trainer( - x_train: np.ndarray, - y_train: np.ndarray, - x_val: np.ndarray, - y_val: np.ndarray, - epochs: int = 5, - lr: float = 0.001 -) -> tf.keras.Model: - ... - neptune_run = get_neptune_run() - model.fit( - x_train, - y_train, - epochs=epochs, - validation_data=(x_val, y_val), - callbacks=[ - NeptuneCallback(run=neptune_run), - ], +@step(experiment_tracker="neptune_experiment_tracker") +def train_model() -> SVC: + iris = load_iris() + X_train, _, y_train, _ = train_test_split( + iris.data, iris.target, test_size=0.2, random_state=42 ) + params = { + "kernel": "rbf", + "C": 1.0, + } + model = SVC(**params) + + model.fit(X_train, y_train) - metric = ... + # Log the model to Neptune + neptune_run = get_neptune_run() + neptune_run["parameters"] = params - neptune_run[""] = metric + return model ``` {% hint style="info" %} @@ -140,7 +140,32 @@ def tf_trainer(...): ``` {% endhint %} -#### Additional configuration +#### 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: + +```python +from zenml import get_step_context +from zenml.integrations.neptune.experiment_trackers.run_state import ( + get_neptune_run +) +from neptune.utils import stringify_unsupported + +@step(experiment_tracker="neptune_tracker") +def my_step(): + neptune_run = get_neptune_run() + context = get_step_context() + + neptune_run["pipeline_metadata"] = stringify_unsupported( + context.pipeline_run.get_metadata().dict() + ) + neptune_run[f"step_metadata/{context.step_name}"] = stringify_unsupported( + context.step_run.get_metadata().dict() + ) + ... +``` + +#### 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: @@ -174,13 +199,115 @@ def my_step( ... ``` -### Neptune UI +Check out the [SDK docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-neptune/#zenml.integrations.neptune.flavors.neptune_experiment_tracker_flavor.NeptuneExperimentTrackerSettings) for a full list of available attributes + +## 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: + +

A pipeline with a Neptine run linked as metadata

-Neptune comes with a web-based UI that you can use to find further details about your tracked experiments. Each pipeline run will be logged as a separate experiment run in Neptune, which you can inspect in the Neptune UI. +Each pipeline run will be logged as a separate experiment run in Neptune, which you can inspect in the Neptune UI. + +

A list of Neptune runs from ZenML pipelines

+ +Clicking on one run will reveal further metadata logged within the step: + +

Details of a Neptune run via a ZenML pipeline

+ +## Full Code Example + +This section shows an end to end run with the ZenML Neptune integration. + +
+ +Code Example of this Section + +```python +from zenml.integrations.neptune.experiment_trackers.run_state import ( + get_neptune_run +) +from neptune.utils import stringify_unsupported +from zenml import get_step_context +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_iris +from sklearn.svm import SVC +from sklearn.metrics import accuracy_score +from zenml import pipeline, step +from zenml.client import Client +from zenml.integrations.neptune.experiment_trackers import NeptuneExperimentTracker +import neptune.integrations.sklearn as npt_utils + +# Get the experiment tracker from the active stack +experiment_tracker: NeptuneExperimentTracker = Client().active_stack.experiment_tracker + + +@step(experiment_tracker=experiment_tracker.name) +def train_model() -> SVC: + iris = load_iris() + X_train, _, y_train, _ = train_test_split( + iris.data, iris.target, test_size=0.2, random_state=42 + ) + params = { + "kernel": "rbf", + "C": 1.0, + } + model = SVC(**params) + + model.fit(X_train, y_train) + + # Log parameters and model to Neptune + neptune_run = get_neptune_run() + neptune_run["parameters"] = params + neptune_run["estimator/pickled-model"] = npt_utils.get_pickled_model(model) + return model + + +@step(experiment_tracker=experiment_tracker.name) +def evaluate_model(model: SVC): + iris = load_iris() + _, X_test, _, y_test = train_test_split( + iris.data, iris.target, test_size=0.2, random_state=42 + ) + y_pred = model.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + + neptune_run = get_neptune_run() + context = get_step_context() + + # Log metadata using Neptune + neptune_run["zenml_metadata/pipeline_metadata"] = stringify_unsupported( + context.pipeline_run.get_metadata().model_dump() + ) + neptune_run[f"zenml_metadata/{context.step_name}"] = stringify_unsupported( + context.step_run.get_metadata().model_dump() + ) + + # Log accuracy metric to Neptune + neptune_run["metrics/accuracy"] = accuracy + + return accuracy + + +@pipeline +def ml_pipeline(): + model = train_model() + accuracy = evaluate_model(model) + + +if __name__ == "__main__": + from zenml.integrations.neptune.flavors import NeptuneExperimentTrackerSettings + + neptune_settings = NeptuneExperimentTrackerSettings( + tags={"regression", "sklearn"} + ) + + ml_pipeline.with_options(settings={"experiment_tracker": neptune_settings})() +``` -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. +
-### Further reading +## Further reading Check [Neptune's docs](https://docs.neptune.ai/integrations/zenml/) for further information on how to use this integration and Neptune in general.