GPU training (FAQ)¶
How should I adjust the learning rate when using multiple devices?¶
When using distributed training make sure to modify your learning rate according to your effective batch size.
Let’s say you have a batch size of 7 in your dataloader.
class LitModel(LightningModule): def train_dataloader(self): return Dataset(..., batch_size=7)
Whenever you use multiple devices and/or nodes, your effective batch size will be 7 * devices * num_nodes.
# effective batch size = 7 * 8 Trainer(accelerator="gpu", devices=8, strategy=...) # effective batch size = 7 * 8 * 10 Trainer(accelerator="gpu", devices=8, num_nodes=10, strategy=...)
Huge batch sizes are actually really bad for convergence. Check out: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
How do I use multiple GPUs on Jupyter or Colab notebooks?¶
To use multiple GPUs on notebooks, use the DDP_NOTEBOOK mode.
Trainer(accelerator="gpu", devices=4, strategy="ddp_notebook")
If you want to use other strategies, please launch your training via the command-shell. See also: Interactive Notebooks (Jupyter, Colab, Kaggle)