CPU Compatibility Guide

Stormlog is still useful on machines without CUDA. The main difference is that GPU-specific PyTorch profiling is replaced by CPU-backed monitoring and profiling helpers.

What still works on CPU-only machines

gpumemprof info
gpumemprof monitor
gpumemprof track
gpumemprof analyze
gpumemprof diagnose
CPUMemoryProfiler
CPUMemoryTracker
the TUI overview, monitoring, diagnostics, and CLI tabs
TensorFlow flows when you have a CPU-backed TensorFlow install

What does not work without CUDA

GPUMemoryProfiler
CUDA memory allocator statistics
CUDA-specific PyTorch sample workloads

If you need the CUDA path, see the GPU Setup Guide.

Fast local validation

Use the CLI first. Do not use examples.cli.quickstart for pip installs, because the examples/ package is not included in the PyPI distribution.

export CUDA_VISIBLE_DEVICES=
gpumemprof info
gpumemprof track --duration 10 --interval 0.5 --output cpu_track.json --format json
gpumemprof analyze cpu_track.json --format txt --output cpu_analysis.txt
gpumemprof diagnose --duration 0 --output ./cpu_diag

On Windows, clear CUDA_VISIBLE_DEVICES with the shell-appropriate syntax before running the same steps.

On Apple Silicon, clearing CUDA_VISIBLE_DEVICES disables CUDA but gpumemprof info may still report the mps backend. Treat this as a non-CUDA smoke test rather than a strict CPU-only force.

CPU profiling in Python

from stormlog import CPUMemoryProfiler

profiler = CPUMemoryProfiler()

with profiler.profile_context("cpu_task"):
    values = [i * i for i in range(250_000)]
    values.reverse()

summary = profiler.get_summary()
print(summary["mode"])
print(summary["peak_memory_usage"])

CPU tracking over time

from stormlog import CPUMemoryTracker

tracker = CPUMemoryTracker(sampling_interval=0.5)
tracker.start_tracking()

# run workload here

tracker.stop_tracking()
stats = tracker.get_statistics()
print(stats["total_events"])

CLI workflows on CPU-only hosts

Bounded monitoring

gpumemprof monitor --duration 30 --interval 0.5 --output cpu_monitor.json --format json

Event tracking

gpumemprof track --duration 30 --interval 0.5 --output cpu_track.json --format json

Analysis and artifact capture

gpumemprof analyze cpu_track.json --format txt --output cpu_analysis.txt
gpumemprof diagnose --duration 0 --output ./cpu_diag

TUI workflows on CPU-only hosts

The TUI remains useful even when CUDA is not available:

Overview still shows system information
Monitoring still runs the CPU tracker
Visualizations can export plots once tracking has collected samples
Diagnostics can load saved telemetry or diagnose bundles
CLI & Actions remains available for quick commands

Launch it with:

pip install "stormlog[tui,torch]"
stormlog

The current TUI startup path still imports PyTorch immediately, even if you only plan to use CPU monitoring and diagnostics flows once the app is open.

Recommended CPU-only checklist

verify the install with gpumemprof info
run gpumemprof track --duration 10 --interval 0.5 --output cpu_track.json --format json
run gpumemprof analyze cpu_track.json --format txt --output cpu_analysis.txt
run gpumemprof diagnose --duration 0 --output ./cpu_diag
if you have a source checkout, optionally run python -m examples.scenarios.cpu_telemetry_scenario
load the result into the TUI if you need an interactive review

Common confusion points

“Why does the PyTorch demo skip itself?”

examples.basic.pytorch_demo intentionally requires CUDA because it demonstrates GPUMemoryProfiler. It is also source-checkout only. On CPU-only hosts, use the CLI sequence above or CPUMemoryProfiler instead.

“Why do I still see `gpumemprof` on a CPU machine?”

The CLI is broader than CUDA-only profiling. It still supports CPU-backed monitoring, tracking, analysis, and diagnose flows.

“Can I still use TensorFlow?”

Yes, as long as TensorFlow itself is installed. TFMemoryProfiler works on CPU-backed TensorFlow environments too.

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