ML-python-tensorflow-mnist-cpu-training

Quickstart project for training a MNIST classifier using TensorFlow on a CPU. Includes TensorBoard logging of training loss and training accuracy.

• In accordance with MLOps principles, running requirements.txt then python app.py will train a model and, if threshold metrics are passed, will convert the model to .onnx format, saving it as .model.onnx.

• Additionally, metrics will be saved to a .metrics/ folder.

• Tensorboard logs will also be saved in the .metrics/ folder.

• Upon successful training, a Pull Request will automatically be made on the corresponding service project with the model and metrics folder being copied across.

• Jenkins X requires the metrics and model to be saved in this format and the defined locations in order to promote the model to the service stage.