Key Features
Dynamic Computation Graphs
Build models with flexible, runtime-defined graphs that adapt to your data and logic.
Production Ready
Deploy models using TorchScript and PyTorch Serve for scalable inference in production.
Cross-platform Support
Run PyTorch on Linux, Windows, macOS, and mobile platforms with GPU acceleration.
Active Research Community
Used by top researchers and institutions for cutting-edge AI development and publications.
How It Works
Install PyTorch
Use pip or conda to install PyTorch with CPU or GPU support based on your setup.
Import Libraries
Load PyTorch and supporting libraries like torchvision, NumPy, and Matplotlib.
Define Your Model
Use `nn.Module` to create custom neural networks with full control over architecture.
Train & Evaluate
Use autograd and optimizers to train your model and validate performance on test data.
Deploy & Monitor
Export models with TorchScript and deploy using PyTorch Serve or ONNX for production.
Code Example
import torch
import torch.nn as nn
import torch.optim as optim
# Define a simple model
class SimpleModel(nn.Module):
def __init__(self):
super(SimpleModel, self).__init__()
self.fc1 = nn.Linear(10, 64)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(64, 1)
def forward(self, x):
return self.fc2(self.relu(self.fc1(x)))
model = SimpleModel()
# Loss and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Dummy data
x_train = torch.rand(100, 10)
y_train = torch.rand(100, 1)
# Training loop
for epoch in range(5):
outputs = model(x_train)
loss = criterion(outputs, y_train)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(model)Use Cases
Computer Vision
Train models for image classification, object detection, and segmentation using torchvision.
Natural Language Processing
Build transformers, RNNs, and BERT-based models for text generation and understanding.
Reinforcement Learning
Implement RL agents with dynamic graphs and policy gradients using PyTorch’s flexibility.
Scientific Research
Prototype and publish novel architectures with ease—used widely in academia and papers.
Integrations & Resources
Explore PyTorch’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- TorchVision for image datasets and models
- TorchText for NLP pipelines
- TorchServe for scalable deployment
- ONNX export for cross-framework compatibility
- Lightning & HuggingFace integration for rapid prototyping
Helpful Resources
FAQ
Common questions about PyTorch’s capabilities, usage, and ecosystem.