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LICENSE

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+Proprietary License
+
+This project and the accompanying materials are made available under the terms of this Proprietary License which accompanies this distribution.
+
+NO WARRANTY
+
+ANY USE OF THE PROVIDED SOFTWARE IS AT YOUR OWN RISK. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT ANY WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, AND NON-INFRINGEMENT.
+
+LIMITATION OF LIABILITY
+
+IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE, UNLESS REQUIRED BY APPLICABLE LAW (SUCH AS DELIBERATE AND GROSSLY NEGLIGENT ACTS) OR AGREED TO IN WRITING, SHALL THE AUTHORS BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY CHARACTER ARISING AS A RESULT OF THIS LICENSE OR THE USE OF THE SOFTWARE INCLUDING, WITHOUT LIMITATION, DAMAGES FOR LOSS OF GOODWILL, WORK STOPPAGE, COMPUTER FAILURE OR MALFUNCTION, OR ANY AND ALL OTHER COMMERCIAL DAMAGES OR LOSSES, EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.

README.md

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+# CudaStresser
+
+**CudaStresser** is a Python-based tool designed to stress test CUDA-enabled GPUs. It performs various operations to measure GPU memory bandwidth, stress the device, and log GPU utilization metrics. This tool is useful for developers, researchers, and system administrators who want to benchmark or stress test their GPU hardware.
+
+## Features
+
+- **CUDA Core Stress Testing**: Create and manipulate tensors on the GPU to stress test the CUDA cores.
+- **VRAM Load and Unload**: Simulate heavy memory operations to test GPU memory bandwidth and consistency.
+- **Real-time GPU Monitoring**: Log GPU utilization, memory usage, and temperature during the stress test.
+- **Multiprocessing**: Efficiently utilize multiple CPU cores for concurrent GPU stress testing.
+
+## Requirements
+
+- Python 3.7+
+- PyTorch 1.7+
+- CUDA-enabled GPU(s)
+- NVIDIA drivers with `nvidia-smi` available
+
+## Installation
+
+1. **Clone the repository:**
+
+   ```bash
+   git clone https://github.com/your-username/cuda-stresser.git
+   cd cuda-stresser
+   ```
+
+2. **Create and activate a virtual environment (optional but recommended):**
+
+   ```bash
+   python3 -m venv venv
+   source venv/bin/activate  # On Windows: venv\Scripts\activate
+   ```
+
+3. **Install the required Python packages:**
+
+   ```bash
+   pip install torch
+   ```
+
+4. **Ensure `nvidia-smi` is available:**
+
+   Make sure `nvidia-smi` is accessible in your system's PATH. This is usually installed with the NVIDIA drivers.
+
+## Usage
+
+### Basic Stress Test
+
+To perform a basic CUDA stress test:
+
+```bash
+python cuda_stresser.py
+```
+
+This command will:
+
+- Detect available CUDA devices.
+- Stress test the CUDA cores by creating and manipulating tensors for 60 seconds.
+- Log GPU utilization, memory usage, and temperature every 5 seconds.
+- Display the progress and results in the console.
+
+### Custom Stress Test
+
+You can customize the test parameters using the `CudaStresser` class:
+
+```python
+from cuda_stresser import CudaStresser
+
+# Initialize the stresser with 99% VRAM load
+stresser = CudaStresser(load_perc=0.99)
+
+# Perform a stress test for 60 seconds with 15 tensors
+results = stresser.cuda_stress(timing=60, tensor_num=15)
+print(results)
+
+# Perform a VRAM load/unload test for 60 seconds
+vram_results = stresser.cuda_load_unload(timing=60)
+print(vram_results)
+```
+
+### Logging GPU Information
+
+During the stress test, the script logs GPU utilization, memory usage, and temperature data. The logs can be accessed from the console or modified to save to a file.
+
+## Customization
+
+### Parameters
+
+- **`load_perc`**: Percentage of VRAM to load (default: `0.99`).
+- **`timing`**: Duration of the stress test in seconds (default: `60`).
+- **`tensor_num`**: Number of tensors to create for the stress test (default: `1000`).
+- **`poll_time`**: Interval for logging GPU data in seconds (default: `5`).
+
+### Example with Custom Parameters
+
+```python
+stresser = CudaStresser(load_perc=0.90)
+gpu_log = stresser.cuda_stress(timing=120, tensor_num=20, poll_time=10)
+print("GPU Log:", gpu_log)
+```
+
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## Acknowledgements
+
+Special thanks to the developers and contributors of [PyTorch](https://pytorch.org) for providing an excellent framework for machine learning and GPU computing.
+
+---
+
+**Note**: This tool is intended for testing and benchmarking purposes. Use it responsibly, especially on production systems, as it can put significant load on your hardware.