/ML-python-tensorflow-mnist-gpu-training

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ML-python-tensorflow-mnist-gpu-training

Quickstart project for training a MNIST classifier using TensorFlow on a GPU.

  • NOTE: tensorflow-gpu==1.15.0 must be installed via anaconda for training to be carried out on the GPU. This is handled in the run-training.sh script. This may change with tensorflow==2.x .

  • 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.

  • 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.