flodl-hw

Hardware detection for flodl: GPUs and host RAM, with no libtorch and no CUDA runtime. One dependency, serde_json, used only to read the test-spoofing env var below.

for gpu in flodl_hw::detect_gpus() {
    println!("[{}] {} {} {} MiB", gpu.index, gpu.short_name(), gpu.arch_label(), gpu.total_memory_mb);
}

Why it is its own crate

Two consumers need the same answers and cannot share code any other way:

• flodl needs GPU identity before libtorch is initialized. Once libtorch latches onto a device list, CUDA_VISIBLE_DEVICES is ignored, and on a heterogeneous rig the cluster launcher's spawned children inherit a corrupted CUDA context. See the "no CUDA before Trainer::run" invariant.
• fdl (flodl-cli) needs the same answers before libtorch exists at all, to pick which libtorch variant to download or build. It therefore cannot depend on flodl.

The two used to carry hand-synchronized copies of the same struct and the same parser. This crate is the single source, and the place a second GPU vendor gets added exactly once.

Two enumerations, deliberately

Provisioning decisions (which libtorch variant covers this box) want the physical set. Runtime decisions (does DDP auto-promote) want the visible set. Conflating them is a real bug in both directions, so they are named apart rather than distinguished by a boolean.

Never initializes a GPU runtime. An absent nvidia-smi is "no GPUs", not an error.

Spoofing hardware

FLODL_TESTING_GPU_JSON replaces the whole sweep, so detection and downstream routing can be tested on a machine without the hardware:

FLODL_TESTING_GPU_JSON='[{"vendor":"amd","arch":"gfx1030","vram_mb":16384}]' \
  cargo test

Visibility masks still apply on top. A malformed value panics rather than falling back to real hardware, so a typo cannot silently make a test report on the wrong machine.

License

MIT