gangplank 0.3.0

Prometheus metrics collectors for Keras

Gangplank

Export Keras metrics to Prometheus

Prometheus is a monitoring system that pulls metrics from applications and infrastructure. Gangplank is a Python package for exposing Keras model metrics to Prometheus. Metrics can be exported from training, evaluation and inference tasks. Training and testing metrics are exported using the Prometheus Pushgateway. Inference metrics are exposed by instrumenting a proxy of a Keras model.

What metrics are exported?

Training Metrics

During training, the following metrics are exported:

• The number of completed training epochs
• The time spent training
• The number of model weights (both trainable and non-trainable)
• The model's loss
• All metrics configured for the model (e.g. accuracy for a classification model or mean absolute error for a regression model)
• (Optionally) A histogram of the model's trainable weights at the end of the training run

Testing (Evaluation) Metrics

For testing (i.e. evaluation), the following metrics are exported:

• The time spent testing
• The model's loss
• All metrics configured for the model (accuracy, mean absolute error, etc.)
• (Optionally) A histogram of the model's trainable weights

Prediction (Inference) Metrics

A deployed model can expose the following metrics:

• The total number of model predictions
• The time spent doing inference

Installing Gangplank

Gangplank can be installed from PyPI

pip install gangplank

The installation will also install Keras. Keras needs a tensor arithmetic backend like TensorFlow, JAX or PyTorch. You can install a backend at the same time as installing Gangplank by running one of the following

pip install gangplank[tensorflow]
pip install gangplank[jax]
pip install gangplank[torch]

Note: Running, e.g., pip install gangplank[jax] will install a CPU-only version of JAX. If you want, say, CUDA support you should install JAX separately

pip install gangplank
pip install jax[cuda12]

Similar comments apply to TensorFlow and PyTorch.

Examples

Examples of using Gangplank can be found here.