tracker was used:

from zenml.client import Clientlast_run = client.get_pipeline("<PIPELINE_NAME>").last_run

trainer_step = last_run.get_step("<STEP_NAME>")

tracking_url = trainer_step.run_metadata["experiment_tracker_url"].value

print(tracking_url)

Alternatively, you can see an overview of all experiment runs at https://wandb.ai/{ENTITY_NAME}/{PROJECT_NAME}/runs/.

The naming convention of each Weights & Biases experiment run is {pipeline_run_name}_{step_name} (e.g. wandb_example_pipeline-25_Apr_22-20_06_33_535737_tf_evaluator) and each experiment run will be tagged with both pipeline_name and pipeline_run_name, which you can use to group and filter experiment runs.

Additional configuration

For additional configuration of the Weights & Biases experiment tracker, you can pass WandbExperimentTrackerSettings to overwrite the wandb.Settings or pass additional tags for your runs:

import wandb

from zenml.integrations.wandb.flavors.wandb_experiment_tracker_flavor import WandbExperimentTrackerSettings

wandb_settings = WandbExperimentTrackerSettings(

settings=wandb.Settings(magic=True),

tags=["some_tag"]

@step(

experiment_tracker="<WANDB_TRACKER_STACK_COMPONENT_NAME>",

settings={

"experiment_tracker.wandb": wandb_settings

def my_step(

x_test: np.ndarray,

y_test: np.ndarray,

model: tf.keras.Model,

) -> float:

"""Everything in this step is auto-logged"""

...

Doing the above auto-magically logs all the data, metrics, and results within the step, no further action is required!

Check out the SDK docs for a full list of available attributes and this docs page for more information on how to specify settings.

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