PyTorch

PyTorch is an open-source machine learning framework used to build and train deep learning models. The framework provides a flexible Python interface for working with tensors, neural networks, and automatic differentiation, which are core components of modern artificial intelligence systems. PyTorch is widely used in research and production to develop applications such as computer vision models, natural language processing systems, and large language models.

One of the main advantages of PyTorch is ease of use and flexibility. The framework uses a dynamic computation graph, which means models are built and executed step by step during runtime. This approach makes experimentation, debugging, and model development easier than static graph frameworks. PyTorch also integrates naturally with Python, allowing developers to use familiar tools and libraries while building machine learning pipelines.

PyTorch is designed to take advantage of GPU acceleration. Tensors and neural network operations can run directly on GPUs using NVIDIA CUDA, enabling highly parallel computation for training and inference workloads. GPU execution significantly reduces training time for large models and allows complex neural networks to process large datasets efficiently.

SwissGPU workstations provide dedicated NVIDIA GPUs running directly on bare-metal hardware, which makes the infrastructure well suited for PyTorch workloads. Users can install PyTorch on the workstation and train machine learning models directly on the dedicated GPU. Because each machine provides exclusive access to the GPU and system resources, users can run large training jobs, experiment with deep learning models, and process large datasets efficiently.

Practice with TorchCode

TorchCode is an open-source project focused on practical PyTorch workflows. The project can help the user start faster with examples and implementation patterns that are useful when building and training models on a dedicated GPU workstation.

Resources

SwissGPU