metadata

language:
  - en
license: mit
tags:
  - nvidia
  - gpu
  - dataset
datasets:
  - nvidia-gpu-dataset

🚫 Hands Off! This dataset’s locked down—no downloading or messing with it unless you’re cleared. Regular Hugging Face users, this ain’t for you.

NVIDIA GPU Scraper

This project crunches NVIDIA GPU data in a slick Docker setup. It scrapes the web with webscraped.py, then whips up a tidy report with summary.py. Think CSV gold like nvidia_gpu_summary_report.csv.

What You Need

Docker on your machine.
Basic command-line chops.

Quick Start

Project Layout

nvidia_project/
├── Dockerfile
├── requirements.txt
├── webscraped.py
├── summary.py
└── README.md

1. Build It

Fire up the Docker image—all dependencies baked in, no virtualenv nonsense:

docker build -t nvidia_project .

2. Run It

Spin the container and let the scripts rip:

docker run --rm -it nvidia_project

--rm: Cleans up after itself.
-it: Keeps you in the loop with a terminal.

3. Grab the Goods

Snag the output CSV from the container:

docker cp $(docker ps -l -q):/app/nvidia_gpu_summary_report.csv /your/local/path

Example:

docker cp $(docker ps -l -q):/app/nvidia_gpu_summary_report.csv /Users/niladridas/Desktop/nvidia_doc

Pushing to Hugging Face

Want it on Hugging Face? Here’s the drill:

Get the Tools:
```
pip install datasets
```
Prep the Data: Make sure it’s in JSON or CSV shape.

Upload It:

huggingface-cli dataset create --dataset_name bniladridas/nvidia-gpu-dataset --path /path/to/your/data

(Swap bniladridas/nvidia-gpu-dataset for your own dataset name if needed.)

Spice It Up: Add a dataset card with the juicy details.
Check It: Hit Hugging Face to confirm it’s live and legit.

More deets? Peek at the Hugging Face docs.

Debugging

Stuff breaking? Dive in:

Peek Inside:

docker run -it nvidia_project /bin/sh

Scope out /app with:

ls -l /app

Read the Tea Leaves:
```
docker logs $(docker ps -l -q)
```

Pro Tips

Docker’s Your Friend: No need to fuss with source .venv/bin/activate—it’s all contained.
Keep it lean—let the container handle the heavy lifting.
Double-check your Dockerfile copies webscraped.py and summary.py to /app and sets the entrypoint right.
Tweak that docker cp path to wherever you want the CSV to land.