# 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