Taeda

A lightweight Python library for GPU utilities and performance estimation.

Taeda is a Python package built with PyTorch and NVIDIA Management Library (NVML) to retrieve GPU information and estimate theoretical floating-point performance (TFLOPS).

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

tflops = taeda.get_tflops(0)

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

Example output:

GPU Performance: 0.622 TFLOPS

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.

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.

License

MIT License.