PyTorch
The Research-to-Production Framework That Powers Modern AI
PyTorch is the framework behind GPT, Llama, and Stable Diffusion. Its dynamic computation graph, Pythonic API, and rich research ecosystem make it the default for building, fine-tuning, and deploying deep learning models. We build and operationalize PyTorch models for teams moving from research to production.
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
class TextClassifier(nn.Module):
def __init__(self, vocab_size, n_classes):
super().__init__()
self.embed = nn.Embedding(vocab_size, 128)
self.lstm = nn.LSTM(128, 256, batch_first=True)
self.fc = nn.Linear(256, n_classes)
def forward(self, x):
x = self.embed(x)
_, (h, _) = self.lstm(x)
return self.fc(h[-1])
model = TextClassifier(50000, 5).cuda()Key Strengths
Where PyTorch wins for production model work: LLM fine-tuning, computer vision, and custom AI products.
Dynamic Computation Graphs
PyTorch's eager execution lets you debug models like normal Python code with no graph compilation step, making research, prototyping, and debugging dramatically faster.
Research-to-Production
TorchScript, ONNX export, and TorchServe bridge the gap between research models and production deployments without requiring a rewrite in a different framework.
Hugging Face Native
The entire Hugging Face Transformers library is built on PyTorch. Access 500K+ pre-trained models for NLP, vision, and audio with a few lines of code.
GPU Acceleration
PyTorch's CUDA integration abstracts GPU programming. Move tensors and models to GPU with .cuda() and get 10 to 100 times faster training speeds.
Distributed Training
torch.distributed and FSDP enable training large models across multiple GPUs and nodes, which is essential for fine-tuning LLMs and large vision models.
TorchServe Production Serving
Package PyTorch models as production REST APIs with TorchServe, handling model versioning, batching, A/B testing, and scaling.
Your PyTorch vs TensorFlow Questions, Answered.
Direct answers on when PyTorch is the right framework for an ML project and where TensorFlow still earns its keep in production.
When does PyTorch fit better than TensorFlow for an ML project?
Get an ML framework recommendation for your project.
Talk to an ML engineerWhat We Build With PyTorch
Real fine-tuning, vision, and recommendation models our engineers have shipped to production in PyTorch.
Medical Imaging AI
Build FDA-compliant computer vision models for radiology, pathology, and dermatology, detecting anomalies in X-rays, MRIs, and tissue slides with PyTorch and MONAI.
Custom LLM Fine-Tuning
Fine-tune Llama, Mistral, or Qwen on your proprietary dataset using PyTorch and PEFT/LoRA, building domain-specific language models for legal, medical, or technical use cases.
Visual Inspection and Quality Control
Deploy real-time PyTorch vision models for manufacturing defect detection, product classification, and automated quality control on production lines.
Deep Learning Recommendation Systems
Build personalized recommendation engines with PyTorch embedding models, powering product discovery, content feeds, and user engagement systems.
PyTorch at a Glance
How PyTorch performs on training, fine-tuning, and serving for the workloads we run for clients.
PyTorch is the right choice when:
Great fit for
Consider alternatives when
PyTorch Stack & Integrations
The Hugging Face, PEFT, vLLM, and Triton stack we pair with PyTorch in shipped AI systems.
Software Pro's PyTorch Track Record
Headquartered in NYC, Software Pro ships PyTorch in production across FinTech, Healthcare, SaaS, and Enterprise clients, with real benchmarks, clean architectures, and zero shortcuts.