Best tool for deep learning on a local GPU workstation

[Heejulee2]

Hi everyone,

I’m new to deep learning and recently built a local workstation for research.
I’d like to fully utilize my GPU instead of relying on Google Colab.

Specs

• CPU: Intel i9-11900K (8 cores, 11th Gen)
• GPU: NVIDIA RTX 5070 (12 GB VRAM)
• RAM: 128 GB DDR5
• SSD: 2 TB NVMe
• OS: Windows 11 / Ubuntu WSL2 (optional)

Goal
To train deep learning models (CNNs, Transformers) using TensorFlow and PyTorch with GPU acceleration.

Tools I’m considering

• JupyterLab
• VS Code
• (others welcome)

Question
Which environment would be best for GPU-based deep learning as a beginner?

(I’m a deep learning beginner, and my professor asked me to check which setup would work best for our upcoming research.)

Thanks a lot for your kind advice!

[Shaikhasna]

Hi @Heejulee2,
Congrats on building such a powerful workstation! 🚀 Here’s a professional perspective on setting up for GPU-accelerated deep learning:

1. Operating System:

Ubuntu (native or WSL2) is highly recommended for TensorFlow/PyTorch GPU support. Linux drivers for NVIDIA GPUs are better maintained and less error-prone than Windows.
2. Environment/IDE:

JupyterLab: Excellent for experimentation, visualization, and iterative model development.

VS Code: Best for production-ready scripts, debugging, and version control integration.
Consider using conda environments to isolate dependencies for TensorFlow, PyTorch, CUDA, and cuDNN.
3. Framework choice:

Both TensorFlow and PyTorch fully support GPU acceleration. For beginners, PyTorch is often easier to debug and more intuitive for research experimentation.
4. Tips for GPU efficiency:

Use mixed precision training to reduce memory usage and speed up training.

Monitor GPU usage with nvidia-smi and optimize batch sizes to avoid memory overflow.

Consider using Docker containers with NVIDIA CUDA images for reproducible environments.
This setup will give you flexibility to train CNNs, Transformers, or custom models locally while fully leveraging your GPU.

Feel free to ping if you want a ready-to-use conda + GPU setup guide — I can share a tested configuration that works with both TensorFlow and PyTorch.