From the course: Full-Stack Deep Learning with Python

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Making predictions using MLflow artifacts

Making predictions using MLflow artifacts - Python Tutorial

From the course: Full-Stack Deep Learning with Python

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Making predictions using MLflow artifacts

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- [Instructor] Let's visualize some of the training metrics for our model within our notebook, there is a metrics.csv file under the log directory of our PyTorch Lightning model trainer. I'm going to read the contents of this metrics.csv file, and this is a data frame that gives you all of the training accuracy, the various steps, and other details of training. I'm going to use Seaborn to visualize some of these training and validation metrics as a line plot. If you take a look at this line plot, you see the validation accuracy, validation loss, and training accuracy and loss per epoch. Observe the two lines for accuracy, the straight blue and the dotted green line. The accuracy improves during training on the training data and the validation data while the loss falls. Every MLflow run has a unique run ID, and you can access this programmatically by calling mlflow.last_active_run().info.run_id, this is the run ID of the run…

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