Supercharge your ZenML pipelines with Neptune's powerful experiment tracking capabilities
Seamlessly integrate Neptune's advanced experiment tracking features into your ZenML workflows to optimize your machine learning experimentation process. Leverage Neptune's intuitive UI to log, visualize, and compare pipeline runs, making it easier to identify the best performing models and iterate faster.
import numpy as np
from zenml import step
from zenml.integrations.neptune.experiment_trackers.run_state import (
get_neptune_run,
)
from zenml.integrations.neptune.flavors import NeptuneExperimentTrackerSettings
neptune_settings = NeptuneExperimentTrackerSettings(tags={"classifier", "mnist"})
@step(
experiment_tracker="<NEPTUNE_TRACKER_STACK_COMPONENT_NAME>",
settings={
"experiment_tracker": neptune_settings
}
)
def training_step(
x_test: np.ndarray,
y_test: np.ndarray,
model,
) -> float:
"""Log metadata to Neptune run"""
neptune_run = get_neptune_run()
neptune_run["metrics"] = ...
...Expand your ML pipelines with more than 50 ZenML Integrations
