From the course: Full-Stack Deep Learning with Python

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Training a model within an MLflow run

Training a model within an MLflow run - Python Tutorial

From the course: Full-Stack Deep Learning with Python

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Training a model within an MLflow run

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- [Instructor] With our model configured, now it's time for us to train the model and track the metrics, parameters, and artifacts of this model using ML Flow. Notice the imports on lines one, two, and three. These are imports that have to do with logging all of the details of the model, including the model signature, the schema, and tensor specifications for the tensors that we feed into the model. I instantiate the model on line five. That is our emnist_model. And on line seven, I instantiate the CSVLogger. The CSVLogger is what we use to log our details of the training process to our local machine. The actual training process will be run using the pytorch lightning trainer, that I instantiate on line nine. We'll train for a maximum of 10 epochs. And notice, I pass in the CSV logger as an input argument, so that the trainer can use that logger to log out details during training. Now, ML Flow can track parameters and…

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