Taeda

A lightweight Python library for GPU utilities and performance estimation.

Taeda is a Python package that adds more functions to Pytorch.

Features

• Detect available NVIDIA CUDA GPUs.
• Retrieve GPU clock speeds in real time.
• Calculate CUDA core counts.
• Estimate theoretical FP32 TFLOPS.
• Support multiple NVIDIA GPUs.
• Simple Python API.

Installation

pip install taeda

Requirements

• Python 3.9+
• PyTorch
• NVIDIA GPU
• NVIDIA drivers
• nvidia-ml-py

Usage

import taeda

# Get theoretical GPU performance
tflops = taeda.get_tflops(0)

print(f"GPU Performance: {tflops:.3f} TFLOPS")

# Get GPU information and statistics
stats = taeda.get_gpu_stats(0)

print(stats)

Example output:

GPU Performance: 0.622 TFLOPS

CUDA is available
Device name: NVIDIA GeForce GTX 1070 Ti
Device index: 0
CUDA version: 12.1
Total VRAM: 8.00 GB
Used VRAM: 1.24 GB
Free VRAM: 6.76 GB

API

get_tflops(device_num=0)

Estimates the theoretical FP32 performance of an NVIDIA GPU.

Returns: Estimated FP32 performance in TFLOPS (float), or None if CUDA is unavailable.

Raises: ValueError if the GPU index is invalid.

The clock speed is retrieved dynamically through NVML.

get_gpu_stats(device_num=0)

Retrieves GPU information and memory statistics using PyTorch CUDA.

Returns: A formatted string containing:

• CUDA availability
• GPU device name
• GPU device index
• CUDA version
• Total VRAM
• Used VRAM
• Free VRAM

Raises: Returns an error message if CUDA is unavailable or the GPU index is invalid.

Limitations

• TFLOPS values are theoretical estimates, not measured benchmark results.
• Clock speeds fluctuate depending on GPU load, temperature and power limits.
• CUDA core counts depend on GPU architecture.
• Currently intended for supported NVIDIA CUDA GPUs.
• Actual application performance depends on memory bandwidth, workload and other hardware limitations.
• GPU memory statistics depend on PyTorch CUDA availability.

License

MIT License.