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This guide helps you resolve common issues with GPU Memory Profiler.

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Common issues

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Import errors

Solution:
# Install the package
pip install -e .

# Or install from PyPI
pip install gpu-memory-profiler
Solution:
# Install PyTorch
pip install torch

# Or install with CUDA support
pip install torch --index-url https://download.pytorch.org/whl/cu118
Solution:
# Install TensorFlow (GPU support is included automatically)
pip install tensorflow

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CUDA issues

Symptoms:
  • Error: CUDA not available
  • Profiler falls back to CPU mode
Solutions:
1

Check CUDA installation

nvidia-smi
nvcc --version
2

Verify PyTorch CUDA

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA version: {torch.version.cuda}")
3

Verify TensorFlow CUDA

import tensorflow as tf
print(f"GPU devices: {tf.config.list_physical_devices('GPU')}")
4

Install CUDA-compatible versions

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

# TensorFlow (GPU support is included automatically)
pip install tensorflow
Symptoms:
  • Error: CUDA out of memory
  • Training crashes
Solutions:
# Reduce batch size
dataloader = DataLoader(dataset, batch_size=16)  # Instead of 64

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Memory leak issues

Symptoms:
  • Memory usage grows over time
  • Profiler detects memory leaks
Solutions:
# Ensure tensors are properly deleted
del tensor
torch.cuda.empty_cache()

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CLI issues

Solution:
# Reinstall the package
pip install -e .

# Check if entry points are installed
pip show gpu-memory-profiler
Solutions:
1

Check Python path

which python
which gpumemprof
2

Reinstall with entry points

pip uninstall gpu-memory-profiler
pip install -e .
3

Use Python module directly

python -m gpumemprof.cli info
python -m tfmemprof.cli info

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Visualization issues

Symptoms:
  • No plots appear
  • Error: No display name and no $DISPLAY environment variable
Solutions:
import matplotlib
matplotlib.use('Agg')  # Use non-interactive backend
Symptoms:
  • Error: ImportError: No module named 'dash'
Solution:
pip install dash

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Performance issues

Symptoms:
  • Training is significantly slower
  • High CPU usage
Solutions:

Increase sampling interval

profiler = GPUMemoryProfiler()
profiler.start_monitoring(interval=2.0)  # Sample every 2 seconds

Disable visualization

profiler = GPUMemoryProfiler(track_tensors=False)

Selective profiling

# Only profile specific functions
from gpumemprof import profile_function

@profile_function
def critical_function():
    pass

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Dependency conflicts

Symptoms:
  • Error with TensorFlow CLI
  • Version conflicts between packages
Solutions:
1

Check versions

pip list | grep typing
2

Install compatible version

pip install typing-extensions==4.5.0
3

Use virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e .

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Platform-specific issues

Solution:
CUDA is not available on macOS. Use CPU mode or MPS (Metal Performance Shaders) instead.
# Install PyTorch with MPS support
pip install torch torchvision
Solution:
# Install TensorFlow (Apple Silicon is supported natively since TF 2.13)
pip install tensorflow

# For Metal GPU acceleration, also install:
pip install tensorflow-metal
Solution:
# Use forward slashes or raw strings
python -m gpumemprof.cli info
Solution:
# Run as administrator or use --user flag
pip install --user -e .

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Debug mode

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Enable debug logging

import logging
logging.basicConfig(level=logging.DEBUG)

from gpumemprof import GPUMemoryProfiler
profiler = GPUMemoryProfiler()

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Verbose CLI output

# Use detailed/system output commands
gpumemprof info --detailed
gpumemprof monitor --duration 10

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Check system information

# Quickest way to check environment health
gpumemprof info --detailed
tfmemprof info

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Getting help

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Before asking for help

1

Check the documentation

2

Run diagnostics

gpumemprof info --detailed
gpumemprof diagnose --duration 0 --output ./diag_bundle
tfmemprof info
tfmemprof diagnose --duration 0 --output ./tf_diag_bundle
3

Test with minimal example

from gpumemprof import GPUMemoryProfiler
import torch

profiler = GPUMemoryProfiler()

def test():
    return torch.randn(100, 100).cuda()

profile = profiler.profile_function(test)
summary = profiler.get_summary()
print(profile.to_dict())
print(summary)

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Reporting issues

When reporting issues, include:

System information

  • OS and version
  • Python version
  • PyTorch/TensorFlow versions
  • CUDA version (if applicable)

Error messages

  • Full error traceback
  • Any warning messages

Reproduction steps

  • Minimal code example
  • Expected vs actual behavior

Environment

  • Virtual environment details
  • Package versions (pip freeze)

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Community support

GitHub Issues

Report bugs and request features

Documentation

Browse the complete documentation

Examples

Check out example code

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See also

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