The software stack: from tensors to a live process

Picture it

Picture your AI model resting atop a towering, multi-tiered cake. The frosting is your Python code, sitting on thick layers of tensor frameworks and GPU kernels, all resting on the dense foundations of the operating system and hardware. A software container wraps up the upper layers like a tidy box, but it still heavily relies on the physical foundation below. When the cake wobbles, finding the failure means identifying exactly which layer gave way.

What to understand

• Python environments, tensors, shapes, dtype, device, and autograd
• Linux processes, host/GPU memory, containers, ports, logs, and driver/runtime compatibility

Follow the references

1. 1 · Tutorials

   PyTorch · Learn the basics ↗

   Use tensors, autograd, and save/load tutorials. Identify what inference can skip compared with training.

2. 2 · Documentation

   Docker · What is a container? ↗

   Follow the process-isolation explanation. List which dependencies live in the image and which remain on the host.

Make it concrete

Trace a tiny tensor program from CPU to an available accelerator. Record shapes, dtype, device, and memory. Write a minimal environment manifest and identify where to inspect process, import, allocation, and network errors.