Installation
pip install mainboard # CPU and Apple probing, pure Python
pip install mainboard[cuda] # adds NVIDIA telemetry via the CUDA Python bindings
Working in a chefe project? Add it to your manifest:
chefe add mainboard -l python
The base install is light and pure Python, so GPU-less Linux hosts like a Raspberry Pi pull nothing CUDA-related. The cuda extra installs the NVIDIA bindings on Linux for full GPU detection and telemetry, and provider detection degrades gracefully to no NVIDIA devices whenever the bindings or the hardware are absent.
The root API uses the PEP 810 __lazy_modules__ compatibility bridge. Python 3.14
keeps the normal eager import behavior. Python 3.15 defers only the audited Mainboard
modules until their exported names are first used. Mainboard does not enable global
lazy imports. GPU.all() and NPU.all() explicitly load provider registration before
probing, so lazy imports cannot hide hardware backends.
What it is
mainboard tells Python what compute is on the current machine, without assuming the world is only CUDA. It models CPUs, GPUs, and NPUs as Units, keeps vendor-specific probing behind providers (Apple and NVIDIA today), and gives you the whole board in one call.
from mainboard import Machine
print(Machine().model_dump_json(indent=2)) # cpu, memory, gpus, npus, and the host environment
Usage
machine = Machine()
machine.cpu.snapshot() # CPU identity and capacity
machine.gpus[0].snapshot() # per-GPU telemetry
machine.environment # user, group(s), and job scheduler on the host
machine.model_dump_json() # one-call JSON probe of the whole machine
The CLI renders a Rich schematic of the board:
mainboard
Timing spans
span is a dormant annotation that is safe to leave in application code. It contains no collection policy and performs no clock, memory, marker, device, or context variable work until a Profiler is active. The profiler decides what to collect through Profiler.Feature flags. The resulting Profile shows only evidence that was observed.
from mainboard.profiling import Profiler, span
with Profiler(features=Profiler.Feature.SPANS) as profiler:
with span("pipeline"):
with span("extract"):
...
with span("embed"):
...
profiler.show()
Profiler.run("package.module") profiles a module or script once. Python 3.15 sampling, span timing, process GPU telemetry, native markers, and GPU activity all feed the same result. A detected but unused GPU creates no GPU section. See docs/profiling.md for feature costs, CLI usage, Tachyon requirements, and native activity behavior.
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