Pytorch parallelization

content/pytorch/concepts/parallelizing-models/parallelizing-models.md

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+---
+Title: 'parallelizing-models'
+Description: 'PyTorch parallelizing models distribute computations across multiple processors or GPUs, improving performance and enabling faster training of large models.'
+Subjects:
+  - 'Computer Science'
+  - 'Machine Learning'
+  - 'Data Science'
+Tags:
+  - 'Algorithms'
+  - 'PyTorch'
+  - 'Machine Learning'
+CatalogContent:
+  - 'intro-to-py-torch-and-neural-networks'
+  - 'paths/build-a-machine-learning-model'
+---
+
+**Model parallelization** in PyTorch allows the training of deep learning models that require more memory than a single GPU can provide. The model is divided into different parts (e.g., layers or modules), with each part assigned to a separate GPU. These GPUs perform computations simultaneously, speeding up the processing of large models. They communicate with each other and share data to ensure that the output from one GPU can be used by another when necessary.
+
+## Syntax
+
+To utilize model parallelization the model should be wrapped using the following syntax:
+
+```shell
+class ModelParallel(nn.Module)
+```
+
+## Example
+
+The layers or modules should then be assigned to a specified GPU. The following example demonstrates how this can be accomplished.
+
+```py
+import torch.nn as nn
+
+# Define a model split across two GPUs
+class ModelParallel(nn.Module):
+    def __init__(self):
+        super(ModelParallel, self).__init__()
+        self.layer1 = nn.Linear(1000, 500).to('cuda:0')  # First GPU
+        self.layer2 = nn.Linear(500, 100).to('cuda:1')   # Second GPU
+
+    def forward(self, x):
+        x = x.to('cuda:0')  # Input to first GPU
+        x = self.layer1(x)
+        x = x.to('cuda:1')  # Output of first layer to second GPU
+        x = self.layer2(x)
+        return x
+
+model = ModelParallel()
+x = torch.randn(64, 1000)
+output = model(x)
+```
+
+The output of the above code would result in a tensor. The exact values would depend on the initialization of the model weights and the input data, but could be expected to look similar to the following output:
+
+```shell
+tensor([[ 0.1324, -0.2847,  ...,  0.5921],  # First sample in the batch
+        [-0.0412,  0.4891,  ..., -0.2345],  # Second sample in the batch
+        ...
+        [ 0.2347, -0.1011,  ...,  0.4567]]) # 64 rows, each with 100 values
+```