![]() ![]() Each training process uses network for data downloading and GPU communication. In cloud, you can choose how many GPUs your machine has. In synchronized distributed training, GPUs communicate on each step to share gradients. Each training process uses GPU for most of the forward pass, backward pass, and weight update. With DistributedDataParallel ( DDP, current SotA for distributed training), we run one training PyTorch process per one GPU. In cloud, you can choose how many CPU cores your machine has. Each training process uses CPUs 1) to receive, parse, preprocess, and batch data examples 2) to execute some NN operations not supported by GPU and 3) to postprocess metrics, save checkpoints, and write summaries. The training speed is fundamentally limited by three factors: CPU, GPU, and network. This performance optimization guide is written with cloud-based, multi-machine multi-GPU training in mind, but can be useful even if all you have is a desktop with a single GPU under your bed. Let’s learn how to speedup your training! This means you can try more things faster and get better results. If you’re training PyTorch models, you want to train as fast as possible.
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