PyTorch
PyTorch has become the dominant deep learning framework in both research and industry, and for good reason. Its dynamic computation graph, Pythonic API, and first-class ecosystem make it the right tool for developing, debugging, and deploying custom neural networks at any scale.
Why PyTorch
- Dynamic computation graphs, Unlike static graph frameworks, PyTorch builds the computation graph on the fly, making it natural to write models with conditional logic, variable-length inputs, and complex architectures.
- Intuitive debugging, Because PyTorch is eager by default, you can use standard Python debugging tools, breakpoints, print statements, pdb, directly inside model forward passes.
- Rich ecosystem, TorchVision, TorchText, TorchAudio, and the Hugging Face Transformers library (built on PyTorch) provide pre-built architectures, datasets, and pre-trained weights for most common tasks.
- Production-ready, TorchScript, torch.compile, and ONNX export bridge the gap from research prototype to production service. TorchServe provides a scalable model serving infrastructure.
- GPU & distributed training, PyTorch’s CUDA integration and DistributedDataParallel (DDP) make it straightforward to train on multiple GPUs and across multiple nodes.
How We Use PyTorch
We use PyTorch for custom model development, whether that means designing a novel architecture from scratch, fine-tuning a pre-trained transformer on domain-specific data, or optimising an existing model for inference speed. We use PyTorch Lightning to eliminate boilerplate in training loops and ensure reproducible, configurable experiments.
For production deployment, we export trained PyTorch models to ONNX for hardware-agnostic serving, or use TorchScript for deployment environments where Python runtime overhead is a constraint.
PyTorch Use Cases We Have Delivered
- Fine-tuned transformer models for domain-specific text classification and NER
- Custom CNN architectures for industrial defect detection and medical imaging
- Time series models for demand forecasting and predictive maintenance
- RAG pipelines combining sentence transformers with vector databases