I saw there is a val_check_interval, but it seems it's not for that purpose. For instance, in the example above, the learning rate would be multiplied by 0.1 at every batch. Training takes place after you define a model and set its parameters, and requires labeled data. How to speed up your PyTorch training - megaserg blog Neural Regression Using PyTorch: Training - Visual Studio Magazine 3 wandering007, krebin, and lucasthim reacted with thumbs up emoji Train PyTorch Model - Azure Machine Learning | Microsoft Docs PyTorch Lightning - Production But when I tried to t GitHub - PiotrNawrot/hourglass: Hourglass Custom Object Detection using PyTorch Faster RCNN you want to validate the model after every n steps in the same epoch. From here, you can easily access the saved items by simply querying the dictionary as you would expect. Part(1/3): Brief introduction and Installation Part(2/3): Data Preparation Part(3/3): Fine-tuning of the model In the last articles, we saw a brief . Saving and Loading Models - PyTorch We then call torch.save to save our PyTorch model weights to disk so that we can load them from disk and make predictions from a separate Python script. Epoch number and .pt extension (for pytorch) . Also, I find this code to be good reference: def calc_accuracy(mdl, X, Y): # reduce/collapse the classification dimension according to max op # resulting in most likely label max_vals, max_indices = mdl(X).max(1) # assumes the first dimension is batch size n = max_indices.size(0) # index 0 for extracting the # of elements # calulate acc (note .item() to do float division) acc = (max_indices . To accomplish this task, we'll need to implement a training script which: Creates an instance of our neural network architecture. . The section below illustrates the steps to save and restore the model. ModelCheckpoint — PyTorch Lightning 1.7.0dev documentation