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→Parallelization Methods
This section details the ways to parallelize your NN. As image recognition is graphical in nature, multiple GPUs are the best way to parallelize dataset training.
=== Data Parallelism ===
The data can be parallelized with multiple GPUs. You can easily put your model on a GPU by writing:
device = torch.device("cuda:0")
model.to(device)
Then, you can copy all your tensors to the GPU:
mytensor = my_tensor.to(device)
However, PyTorch will only use one GPU by default. In order to run on multiple GPUs you need to use <code>DataParallel</code>:
model = nn.DataParallel(model)
==== Imports and Parameters ====
Import the following modules and define your parameters:
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
# Parameters and DataLoaders
input_size = 5
output_size = 2
batch_size = 30
data_size = 100
Device:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
=== Single-Machine Model ===
