Here we will train our model with multiple GPU's or CPU's device.


  • Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples.
  • Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. Then the model spits out a prediction.
  • Deep Learning Studio supports multiple GPU's devices which accelerates the training.
  • In the training tab, you can change the default “Run Name”.
  • Also, you can “Save Weights” in three ways :
    • “End of epoch”
    • “Best Accuracy”
    • “Lowest Loss"
  • Select CPU or GPU whichever you have by clicking on the drop-down button of “Select Devices”.
    • You can choose multiple devices (“CPU’s” or “GPUs”) at the same time.
  • You can also start new training by “Loading the previous weight”.
  • While training is going on you will see the graph of Accuracy and Loss.
    • After completion of every batch, you will see the changes in Accuracy and Loss graph.
  • While training is in progress, you can stop the training in between if you wish.
    • Note : Early stopping will stop the model from training before the number of epochs is reached if the model stops improving.
    • suppose you are stop the training after 3 epoch. This means that after 3 epochs in a row in which the model doesn’t improve, training will stop.
  • On the training screen you can able to see the :
    • Processing Speed
    • Epoch Speed
    • "Loss"
    • "Accuracy"
    • Load Memory” for CPU and GPU
    • Free Memory” for CPU and GPU
    • Elapsed Time” for training
    • Epochs” .