Neptune.ai tensorflow-keras integration library
Project description
Neptune + TensorFlow/Keras Integration
Experiment tracking, model registry, data versioning, and live model monitoring for TensorFlow/Keras trained models.
What will you get with this integration?
- Log, display, organize, and compare ML experiments in a single place
- Version, store, manage, and query trained models, and model building metadata
- Record and monitor model training, evaluation, or production runs live
- Collaborate with a team
What will be logged to Neptune?
- hyperparameters for every run,
- learning curves for losses and metrics during training,
- hardware consumption and stdout/stderr output during training,
- TensorFlow tensors as images to see model predictions live,
- training code and git commit information,
- model weights
- other metadata
Example charts in the Neptune UI with logged accuracy and loss
Resources
Example
# On the command line:
pip install tensorflow neptune-client neptune-tensorflow-keras
# In Python:
import neptune.new as neptune
from neptune.new.integrations.tensorflow_keras import NeptuneCallback
# Start a run
run = neptune.init(project="common/tf-keras-integration",
api_token="ANONYMOUS")
# Create a NeptuneCallback instance
neptune_cbk = NeptuneCallback(run=run, base_namespace="metrics")
# Pass the callback to model.fit()
model.fit(x_train, y_train,
epochs=5,
batch_size=64,
callbacks=[neptune_cbk])
# Stop the run
run.stop()
Support
If you got stuck or simply want to talk to us, here are your options:
- Check our FAQ page
- You can submit bug reports, feature requests, or contributions directly to the repository.
- Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP),
- You can just shoot us an email at support@neptune.ai
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