GPU Setup

1. Choosing My Primary IDE: VSCode with CUDA and Remote Development Support

• Why I Chose It: VSCode is versatile, lightweight, and I can easily customize it for heavy GPU development. It handles both Python and C++ well, so it’s perfect for my projects that use GPU-accelerated libraries.
• Key Benefits:
  • Remote Development Support: VSCode makes it simple to work on remote GPU machines, which helps if I want to scale my projects or work with cloud GPUs.
  • CUDA Extensions: Installing the C++ and NVIDIA CUDA extensions allows me to dive into low-level GPU programming.
  • Python and Jupyter Extensions: For Python, VSCode has extensions for Python, Jupyter, and TensorFlow that ensure full GPU support for deep learning tasks.
• Best For: This setup is ideal for Python-based deep learning (e.g., TensorFlow, PyTorch) and low-level GPU programming in CUDA and C++.

2. Setting Up Deep Learning Frameworks

• For deep learning, I rely on frameworks that can fully leverage the GPU. My top choices:
  • TensorFlow and PyTorch: Both offer excellent GPU support. PyTorch is great for flexibility and debugging, while TensorFlow shines in production-level deployment.
  • Installation Steps: I install CUDA and cuDNN (NVIDIA’s libraries for GPU support), and then set up my Python environment for deep learning.
  • VSCode Setup:
    • I use the Python extension.
    • Then, I set up a Python environment (like Miniforge or Conda) with TensorFlow or PyTorch.
    • Adding Jupyter provides interactive notebook support.

3. Using JupyterLab or Jupyter Notebooks for Interactive Work

• Why I Use It: JupyterLab is perfect for interactive, exploratory work, common in data science and machine learning. It lets me work with code in cells, visualize data, and handle large datasets directly on the GPU.
• Installation:
  • I install JupyterLab in my Python environment.
  • Then, I confirm GPU support by testing torch.cuda.is_available() (for PyTorch) or tf.config.list_physical_devices('GPU') (for TensorFlow).
• Best For: Experimenting with data, training models, quick tests, and visualization.

4. NVIDIA RAPIDS for GPU-Accelerated Data Science

• Why: RAPIDS, a suite of libraries from NVIDIA, powers end-to-end data science pipelines on the GPU. Libraries like cuDF (Pandas-like DataFrame on GPU), cuML (GPU-accelerated machine learning), and cuGraph enable a fully GPU-accelerated workflow.
• Installation: I use Conda or Docker to install RAPIDS, with NVIDIA recommending Conda for ease of setup.
• Best For: This is ideal for data preprocessing, analysis, and tasks traditionally CPU-bound (like using Pandas or scikit-learn).

5. NVIDIA Nsight for Profiling and Debugging

• Why: NVIDIA Nsight is invaluable for profiling and debugging GPU-accelerated applications, especially with CUDA programming.
• Installation: I download NVIDIA Nsight from the developer tools site. It’s compatible with both C++/CUDA applications on NVIDIA GPUs.
• Best For: Profiling performance, monitoring memory usage, and optimizing CUDA code to maximize GPU utilization.

6. Docker with NVIDIA Docker for Containerized GPU Workloads

• Why: Docker lets me containerize applications, making reproducibility and dependency management straightforward.
• Installation: I set up Docker along with the NVIDIA Docker toolkit, allowing me to pull pre-configured Docker images with GPU support (like TensorFlow, PyTorch, and RAPIDS).
• Best For: This setup is useful for managing environments, reproducibility, and deploying projects with specific dependencies in deep learning or data science.

My Suggested Workflow

1. Install CUDA and cuDNN:
   • I ensure CUDA and cuDNN are installed and configured correctly. These libraries are essential for running deep learning frameworks on NVIDIA GPUs.
2. Select My Primary IDE:
   • I go with VSCode for a flexible, high-performance setup across Python and C++ with full GPU support.
   • I set up Python environments with TensorFlow or PyTorch in VSCode for deep learning.
   • For C++ and CUDA, I install the C++ and CUDA extensions to handle low-level GPU programming.
3. Use JupyterLab for Interactive Work:
   • I keep JupyterLab alongside VSCode to prototype or visualize data, accessing the same Python environment with GPU capabilities.
4. Install RAPIDS for Accelerated Data Science:
   • I add the RAPIDS libraries when working with large datasets or tasks that benefit from GPU acceleration.
5. Use NVIDIA Nsight for Profiling (Optional):
   • When I need to work directly with CUDA or optimize code, NVIDIA Nsight is my go-to tool for profiling and debugging at the GPU level.

Final Recommendation

For a powerful, GPU-focused workflow, VSCode with TensorFlow/PyTorch, RAPIDS, and JupyterLab gives me flexibility and GPU acceleration for machine learning and data science. These tools also provide support for remote development if I decide to connect to additional GPU resources down the road.