Dhruv1238/Parallelism_In_Neural_Networks

Step 1: Download the ResNet-50 Model

You can download the pre-trained ResNet-50 model using PyTorch. This model will serve as the base for your image classification task.

Step 2: Create a Dataset of Around 300 Images per Class

Organize your dataset into folders by class, with each folder containing around 300 images. For example:

dataset/
    train/
        class1/
            img1.jpg
            img2.jpg
            ...
        class2/
            img1.jpg
            img2.jpg
            ...
        ...
    val/
        class1/
            img1.jpg
            img2.jpg
            ...
        class2/
            img1.jpg
            img2.jpg
            ...
        ...

Step 3: Perform Image Classification Using ResNet-50 Model on Your Custom Dataset

Use your custom dataset to train the ResNet-50 model. This involves setting up data loaders, defining a loss function and optimizer, and training the model.

import torch
from torchvision import datasets, transforms

# Define transformations
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load your dataset
train_dataset = datasets.ImageFolder('path/to/train', transform=transform)
val_dataset = datasets.ImageFolder('path/to/val', transform=transform)

# Create data loaders
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False)

# Define loss function and optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Train the model
for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

Step 4: Implement GPipe for Parallelism

from gpipe import gpipe

# Assuming `model` is your ResNet-50 model partitioned into shards
model = gpipe(model, num_microbatches=4, devices=["cuda:0", "cuda:1"])

Step 5: Train Your Model

With GPipe set up, you can now train your model using your custom dataset. This involves setting up data loaders, defining a loss function and optimizer, and training the model in a loop.

Step 6: Compare

After training both the non-parallelized and parallelized versions of your model, compare their performance. This can include metrics such as accuracy, loss, and training time.

Additional Considerations

1. Microbatches: GPipe splits the input data into smaller microbatches to keep multiple GPUs busy at any given time. This is crucial for achieving efficient parallelism, especially on models that do not fit into a single GPU's memory 5.

2. Performance Profiling: The partitioning of an arbitrary model among GPUs to balance computation and minimize communication requires performance profiling. This is particularly relevant for models like Transformers, which consist of blocks with the same operations and dimensions 5.

By following these steps, you can implement GPipe for parallelism in your project, leveraging the power of multiple GPUs to train your ResNet-50 model more efficiently.