PyTorch is a popular open-source machine learning library developed by Facebook's AI Research lab. It is widely used for deep learning applications due to its dynamic computation graph and ease of use. PyTorch provides a flexible platform for building and training neural networks, supporting both CPU and GPU computations.
When working with recurrent neural networks (RNNs) in PyTorch, you might encounter the following error message: RuntimeError: cudnn RNN backward can only be called in training mode. This error typically arises during the backpropagation step of training an RNN model.
The error message appears when you attempt to perform backpropagation on an RNN model. This usually happens when you mistakenly try to compute gradients while the model is in evaluation mode.
The error message indicates that the cuDNN library, which PyTorch uses for efficient computation on NVIDIA GPUs, requires the RNN to be in training mode to perform backpropagation. In PyTorch, models can be toggled between training and evaluation modes using model.train() and model.eval() respectively. The error occurs because the model is in evaluation mode when backpropagation is attempted, which is not supported by cuDNN for RNNs.
In evaluation mode, certain layers like dropout and batch normalization behave differently compared to training mode. This mode is intended for inference, where gradient computation is not required. Attempting to compute gradients in this mode leads to the observed error.
To resolve this error, you need to ensure that your RNN model is in training mode before performing backpropagation. Follow these steps:
Before starting the training loop, set your model to training mode by calling:
model.train()
This command ensures that the model is in the correct mode for training, allowing backpropagation to proceed without errors.
Double-check that the model is in training mode right before the backpropagation step. You can add a simple assertion to confirm:
assert model.training, "Model is not in training mode!"
Ensure that your training loop consistently sets the model to training mode at the start of each epoch or batch iteration. This practice helps prevent accidental mode mismatches.
For more information on PyTorch's training and evaluation modes, you can refer to the official PyTorch documentation. Additionally, the PyTorch CIFAR-10 tutorial provides a practical example of managing training and evaluation modes.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
