Introduction
PyTorch is the de facto deep learning library used for building, training, and deploying neural networks, offering an intuitive interface, dynamic computation graph, and seamless integration with Python tooling. Whether prototyping an LLM-powered agent, training an embedding model, or performing custom evaluations on GPU-backed infrastructure, PyTorch empowers teams to move from experimentation to production with confidence and speed.
Key benefits of using PyTorch include:
Pythonic and Dynamic: Designed to feel native to Python, with a dynamic computation graph that makes debugging, iteration, and research workflows highly intuitive.
GPU Acceleration and Distributed Training: Native CUDA support for fast GPU training, and robust tools like torch.distributed, torchrun, and PyTorch Lightning for scalable multi-GPU and multi-node workloads.
Rich Ecosystem: Seamlessly integrates with Hugging Face Transformers, TorchVision, TorchAudio, and Cake’s own infrastructure for LLMs, embeddings, and multimodal workflows.Interoperability with Cake Tooling: Plays well with Ray (for distributed execution), MLflow and ClearML (for tracking), Feast (for feature stores), and DeepEval (for model evaluation).
TorchScript and ONNX Export: Supports model serialization and export for deployment on various backends including TorchServe, Triton, vLLM, and custom runtime layers.
PyTorch is the core engine behind:
Fine-tuning LLMs and encoder models for domain-specific tasks
Training custom neural networks for text, image, and structured data
Performing embedding generation and contrastive learning
Building retrieval, classification, summarization, and generative models
Running inference workflows via Ray Serve, KServe, or TorchServe
By adopting PyTorch, you can empower its ML teams to prototype quickly, train at scale, and deploy state-of-the-art models efficiently—forming the deep learning backbone of the platform's most advanced capabilities.