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Documentation Index

Fetch the complete documentation index at: https://mintlify.com/Silas-Asamoah/stormlog/llms.txt

Use this file to discover all available pages before exploring further.

This matrix reflects the current behavior of the repository and is versioned for the v0.2 documentation refresh.
Matrix version: v0.2
Last verified: 2026-02-16

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Source of truth

  • pyproject.toml (requires-python >=3.10, framework dependency floors)
  • CLI entry points in pyproject.toml
  • Runtime backend detection in gpumemprof/device_collectors.py and tfmemprof/utils.py
  • CI smoke checks in .github/workflows/ci.yml

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Runtime and version support

SurfaceSupportedNotes
Python3.10, 3.11, 3.12Python 3.8/3.9 are no longer supported for v0.2
PyTorch package floortorch>=1.8.0Runtime backend can be CUDA, ROCm, MPS, or CPU
TensorFlow package floortensorflow>=2.4.0Runtime backend can be CUDA, ROCm, Metal, or CPU
OSLinux, macOS, WindowsBackend availability depends on installed framework/runtime
The minimum PyTorch version is 1.8.0, but newer versions are recommended for full feature support.

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CLI feature availability by environment

CUDA (NVIDIA)

All CLI commands fully supported:
  • gpumemprof info/monitor/track/analyze/diagnose
  • tfmemprof info/monitor/track/analyze/diagnose

ROCm (AMD Linux)

All CLI commands fully supported:
  • gpumemprof info/monitor/track/analyze/diagnose
  • tfmemprof info/monitor/track/analyze/diagnose

Apple Metal / MPS

All CLI commands fully supported:
  • gpumemprof info/monitor/track/analyze/diagnose
  • tfmemprof info/monitor/track/analyze/diagnose

CPU-only host

All CLI commands with CPU fallback:
  • gpumemprof info/monitor/track/analyze/diagnose
  • tfmemprof info/monitor/track/analyze/diagnose

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Complete CLI availability table

Environmentgpumemprof infogpumemprof monitorgpumemprof trackgpumemprof analyzegpumemprof diagnosetfmemprof infotfmemprof monitortfmemprof tracktfmemprof analyzetfmemprof diagnose
CUDA (NVIDIA)✅✅✅✅✅✅✅✅✅✅
ROCm (AMD Linux)✅✅✅✅✅✅✅✅✅✅
Apple Metal / MPS✅✅✅✅✅✅✅✅✅✅
CPU-only host✅✅ (CPU fallback)✅ (CPU fallback)✅✅✅✅✅✅✅

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Backend capability details

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PyTorch (gpumemprof)

Runtime backendTypical platformTelemetry collectordevice_total/free supportNotes
cudaNVIDIA + CUDAgpumemprof.cuda_tracker✅Uses torch.cuda.memory_*
rocmAMD + ROCm (Linux)gpumemprof.rocm_tracker✅Uses HIP-backed torch.cuda.memory_*
mpsApple Silicon (macOS)gpumemprof.mps_trackerPartialDepends on torch.mps.recommended_max_memory() availability
cpuAny hostgpumemprof.cpu_trackerN/ACPUMemoryProfiler / CPUMemoryTracker fallback
Full support for device memory queries including:
  • Total device memory
  • Free device memory
  • Allocated memory
  • Reserved memory
  • Active/inactive segments
Implementation: Uses torch.cuda.memory_* APIs
Full support through HIP-backed PyTorch CUDA APIs:
  • Same API surface as CUDA backend
  • Automatic detection via detect_torch_runtime_backend()
  • AMD GPU memory management through ROCm
Implementation: Uses HIP-backed torch.cuda.memory_* APIs
Partial support for Apple Silicon:
  • Memory allocation tracking
  • Limited device total/free memory
  • Depends on torch.mps.recommended_max_memory() availability
Implementation: Uses torch.mps APIs when available
CPU-only mode when no GPU is available:
  • Tracks system memory usage
  • No device memory queries
  • Full profiling and analysis features still available
Implementation: Uses psutil for memory monitoring

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TensorFlow (tfmemprof)

Runtime backendTypical platformTelemetry collectorNotes
cudaNVIDIA + CUDAtfmemprof.memory_trackerBuild/runtime diagnostics shown in tfmemprof info
rocmAMD + ROCm (Linux)tfmemprof.memory_trackerBuild/runtime diagnostics shown in tfmemprof info
metalApple Silicontfmemprof.memory_trackerCounters can be runtime-dependent on Metal stack
cpuAny hosttfmemprof.memory_trackerFull CLI surface remains available
Full support for CUDA devices:
  • GPU memory profiling
  • Session-based tracking
  • Keras model profiling
Check build info: Run tfmemprof info to see CUDA build configuration
Full support for AMD GPUs:
  • ROCm-enabled TensorFlow builds
  • Same profiling capabilities as CUDA
Check build info: Run tfmemprof info to see ROCm build configuration
Apple Silicon support:
  • Native Metal GPU acceleration
  • Memory tracking dependent on Metal stack
  • Use tensorflow-metal for GPU acceleration
Installation:
pip install tensorflow tensorflow-metal
CPU-only mode:
  • Full CLI surface available
  • Memory profiling for CPU operations
  • Session and graph execution monitoring

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Backend capability metadata

Tracker exports may include backend capability hints under metadata: 
{
  "backend": "cuda",
  "supports_device_total": true,
  "supports_device_free": true,
  "sampling_source": "torch.cuda.memory_stats"
}

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Metadata fields

FieldTypeDescription
backendstringBackend type: "cuda", "rocm", "mps", "cpu", "metal"
supports_device_totalbooleanWhether total device memory query is supported
supports_device_freebooleanWhether free device memory query is supported
sampling_sourcestringSource of memory samples (e.g., torch.cuda.memory_stats)

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Validation notes

1

Documentation linkage

The compatibility matrix is linked from README.md and docs/index.md
2

CLI examples validation

CLI examples smoke validation is part of CI (examples.cli.quickstart) in .github/workflows/ci.yml
3

Backend metadata

Backend capability metadata is emitted in tracker exports for analysis and debugging

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Platform-specific considerations

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Linux

Best support for CUDA and ROCm backends
Full feature availability
Recommended for production use

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macOS

MPS support on Apple Silicon
Metal support for TensorFlow
CUDA not available - use MPS or CPU fallback

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Windows

CUDA support available
Full PyTorch and TensorFlow support
ROCm support limited on Windows

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Installation examples

# Install PyTorch with CUDA
pip install torch --index-url https://download.pytorch.org/whl/cu118

# Install TensorFlow (includes GPU support)
pip install tensorflow

# Install GPU Memory Profiler
pip install gpu-memory-profiler

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See also

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