gangplank 0.3.3

Prometheus metrics collectors for Keras

Gangplank

Exposing Keras Metrics to Prometheus

Keras is a library for creating artificial neural networks. Prometheus is a monitoring system that pulls metrics from applications and infrastructure. Gangplank is a bridge from Keras to Prometheus that exports Keras training, evaluation and inference metrics to Prometheus.

Keras metrics are exposed in two ways:

• Training and testing metrics use Keras callbacks to push metrics to a Prometheus Pushgateway.
• Inference metrics are exposed by instrumenting a proxy of a Keras model.

The examples demonstrate both techniques to export metrics to Prometheus.

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.

Acknowledgement

The example code uses a model from "Deep Learning with Python, Second Edition" by François Chollet. Gangplank was inspired by the same book's coverage of callbacks and TensorBoard.