kernel-lab/tasks/00_env_sanity/spec.md

Task 00: Environment Sanity

1. Problem Statement

Confirm that your machine can see the GPU software stack needed for the rest of the lab.

2. Expected Input/Output Shapes

This task is informational rather than tensor-shaped. The outputs are environment facts:

• PyTorch version
• CUDA availability
• Triton import status
• GPU name
• device capability
• toolkit and driver hints when available

3. Performance Intuition

Do not benchmark anything yet. First confirm that the environment is what you think it is.

4. Memory Access Discussion

Not applicable yet. The point is to avoid debugging kernels when the real problem is a mismatched driver or toolkit.

5. What Triton Is Abstracting

Even importing Triton depends on a compatible Python, PyTorch, driver, and GPU stack.

6. What CUDA Makes Explicit

CUDA makes the toolkit and architecture targeting explicit. Keep that explicit throughout this repo.

7. Reflection Questions

• What exact GPU name does the system report?
• What device capability does PyTorch report?
• Does Triton import cleanly?
• Which part of the stack would you inspect first if CUDA is unavailable?

8. Implementation Checklist

• Run python tools/check_env.py
• Run python tools/print_device_info.py
• Write down the reported capability
• Set KERNEL_LAB_CUDA_ARCH explicitly if you need to change architecture targeting