Datasets:

bniladridas
/

nvidia-gpu-dataset

@@ -9,71 +9,21 @@ tags:
 datasets:
 - nvidia-gpu-dataset
 ---
-# NVIDIA Project
-This project processes scraped data related to NVIDIA GPUs using a Dockerized Python environment. It runs scripts to scrape data (`webscraped.py`) and generate a summary report (`summary.py`).
-## Pushing to Hugging Face
-To push the dataset to Hugging Face, follow these steps:
-1. **Install the Hugging Face Datasets library**:
-   ```bash
-   pip install datasets
-   ```
-2. **Prepare your dataset**: Ensure your dataset is in a compatible format (e.g., JSON, CSV).
-3. **Upload the dataset**:
-   Use the following command to upload your dataset:
-   ```bash
-   huggingface-cli dataset create --dataset_name bniladridas/nvidia-gpu-dataset --path <path_to_your_dataset>
-   ```
-4. **Add metadata**: Optionally, you can add a dataset card to describe your dataset.
-5. **Validate your dataset**: After uploading, check the Hugging Face website to ensure your dataset is correctly formatted and accessible.
-For more detailed instructions, refer to the [Hugging Face documentation](https://huggingface.co/docs).
-To push the dataset to Hugging Face, follow these steps:
-1. **Install the Hugging Face Datasets library**:
-   ```bash
-   pip install datasets
-   ```
-2. **Prepare your dataset**: Ensure your dataset is in a compatible format (e.g., JSON, CSV).
-3. **Upload the dataset**:
-   Use the following command to upload your dataset:
-   ```bash
-   huggingface-cli dataset create --dataset_name <your_dataset_name> --path <path_to_your_dataset>
-   ```
-4. **Add metadata**: Optionally, you can add a dataset card to describe your dataset.
-5. **Validate your dataset**: After uploading, check the Hugging Face website to ensure your dataset is correctly formatted and accessible.
-For more detailed instructions, refer to the [Hugging Face documentation](https://huggingface.co/docs).
-This project processes scraped data related to NVIDIA GPUs using a Dockerized Python environment. It runs scripts to scrape data (`webscraped.py`) and generate a summary report (`summary.py`), outputting results like `nvidia_gpu_summary_report.csv`.
-## Prerequisites
-- [Docker](https://docs.docker.com/get-docker/) installed on your system.
-- Basic familiarity with command-line tools.
-## Project Setup and Usage
-### Building the Docker Image
-The entire environment, including dependencies, is managed by Docker—no need for virtual environments like `virtualenv` inside the container. Everything is defined in the `Dockerfile` and `requirements.txt`.
-### Directory Structure
 ```
 nvidia_project/
 ├── Dockerfile
@@ -83,65 +33,71 @@ nvidia_project/
 └── README.md
 ```
-## Workflow
-### 1. Build the Docker Image
-Build the container image with all dependencies:
 ```bash
 docker build -t nvidia_project .
 ```
-### 2. Run the Container
-Launch the container to execute the scripts (`webscraped.py` followed by `summary.py`):
 ```bash
 docker run --rm -it nvidia_project
 ```
-- `--rm`: Automatically removes the container after it stops, keeping your system clean.
-- `-it`: Runs interactively with a terminal.
-### 3. Access the Output
-Copy the generated report from the container to your local machine (adjust the path as needed):
 ```bash
-docker cp $(docker ps -l -q):/app/nvidia_gpu_summary_report.csv /path/to/your/local/folder
 ```
 Example:
 ```bash
 docker cp $(docker ps -l -q):/app/nvidia_gpu_summary_report.csv /Users/niladridas/Desktop/nvidia_doc
 ```
-## Debugging
-If something goes wrong, inspect the container:
-1. **Enter the container**:
    ```bash
    docker run -it nvidia_project /bin/sh
    ```
-   Check files with:
    ```bash
    ls -l /app
    ```
-2. **View logs**:
    ```bash
    docker logs $(docker ps -l -q)
    ```
-## Key Lessons
-- **Trust Docker**: No need to activate a virtual environment (e.g., `source .venv/bin/activate`) inside the container—Docker handles isolation.
-- Keep workflows simple and let the container manage dependencies.
-## Notes
-- Ensure your `Dockerfile` is configured to copy `webscraped.py` and `summary.py` into `/app` and set the entrypoint to run them.
-- Update the local path in the `docker cp` command to match your system.

 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](https://docs.docker.com/get-docker/) on your machine.
+- Basic command-line chops.
+## Quick Start
+### Project Layout
 ```
 nvidia_project/
 ├── Dockerfile
 └── README.md
 ```
+### 1. Build It
+Fire up the Docker image—all dependencies baked in, no virtualenv nonsense:
 ```bash
 docker build -t nvidia_project .
 ```
+### 2. Run It
+Spin the container and let the scripts rip:
 ```bash
 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:
 ```bash
+docker cp $(docker ps -l -q):/app/nvidia_gpu_summary_report.csv /your/local/path
 ```
 Example:
 ```bash
 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:
+1. **Get the Tools**:
+   ```bash
+   pip install datasets
+   ```
+2. **Prep the Data**: Make sure it’s in JSON or CSV shape.
+3. **Upload It**:
+   ```bash
+   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.)
+4. **Spice It Up**: Add a dataset card with the juicy details.
+5. **Check It**: Hit Hugging Face to confirm it’s live and legit.
+More deets? Peek at the [Hugging Face docs](https://huggingface.co/docs).
+## Debugging
+Stuff breaking? Dive in:
+- **Peek Inside**:
    ```bash
    docker run -it nvidia_project /bin/sh
    ```
+   Scope out `/app` with:
    ```bash
    ls -l /app
    ```
+- **Read the Tea Leaves**:
    ```bash
    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.