Programming Framework

Explore popular ML frameworks like PyTorch and TensorFlow

Overview

Modern machine learning relies heavily on high-level programming frameworks that abstract away the complexity of low-level GPU programming while providing powerful tools for building and training models.

PyTorch

PyTorch is an open-source machine learning library developed by Meta AI. It provides:

  • Dynamic computation graphs
  • Pythonic interface
  • Strong GPU acceleration support
  • Extensive ecosystem (TorchVision, TorchText, etc.)

TensorFlow

TensorFlow is Google’s open-source machine learning platform:

  • Static and dynamic computation graphs (TF 2.x)
  • Production-ready deployment tools
  • TensorFlow Serving and TensorFlow Lite
  • Keras high-level API

Framework Internals

Understanding how these frameworks work internally is crucial for MLSys research:

  • Automatic Differentiation: How gradients are computed
  • Operator Fusion: Optimizing computation graphs
  • Memory Management: Efficient GPU memory allocation
  • Distributed Training: Multi-GPU and multi-node training

Hands-on Example

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)