Add NVIDIA GPU dataset files

Dockerfile

@@ -0,0 +1,59 @@
+# ───────────────────────────────────────
+# 🚀 1️⃣ Multi-Stage Build for Smallest Image
+# ───────────────────────────────────────
+FROM python:3.9-slim AS builder
+
+# Set a fixed working directory
+WORKDIR /app
+
+# Install essential build tools
+RUN apt-get update && apt-get install -y gcc g++ python3-dev libffi-dev && \
+    python3 -m venv venv && \
+    . venv/bin/activate && \
+    pip install --no-cache-dir --upgrade pip setuptools wheel
+
+# Copy only requirements first for better caching
+COPY requirements.txt .
+RUN apt-get install -y gfortran libopenblas-dev && \
+    . venv/bin/activate && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the app files
+COPY . .
+
+# ───────────────────────────────────────
+# 🚀 2️⃣ Runtime Image - Minimal & Secure
+# ───────────────────────────────────────
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy virtual environment from builder stage
+COPY --from=builder /app/venv /app/venv
+
+# Environment Variables for Performance & Security
+ENV PATH="/app/venv/bin:$PATH" \
+    PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    APP_USER=appuser
+
+# Security: Create and use non-root user
+RUN addgroup appgroup && adduser appuser && \
+    adduser appuser appgroup && \
+    chown -R appuser:appgroup /app
+
+USER $APP_USER
+
+# Copy application code (Ensures proper permissions)
+COPY --chown=$APP_USER:appgroup . .
+
+# Debugging commands to check working directory and list files
+RUN pwd && ls -l /app
+
+# Use a health check to ensure the container is running correctly
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s \
+  CMD python3 -c 'import os; exit(0 if os.path.exists("summary.py") else 1)'
+
+# Use ENTRYPOINT instead of CMD for better runtime control
+ENTRYPOINT ["python3", "summary.py"]

README.md

@@ -1,3 +1,135 @@
----
-license: mit
----
+# 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
+├── requirements.txt
+├── webscraped.py
+├── summary.py
+└── 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.

nvidia-gpu-dataset

@@ -0,0 +1 @@
+Subproject commit c44ba136858c056951c2c79bec5ffcb1de739296

nvidia_gpu_summary_report.csv

@@ -0,0 +1,5 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+21,20,16,19,20,20,16,16,20,16,19,17,14,14,14,21
+7,7,2,5,5,3,2,5,11,3,4,2,1,1,1,7
+GeForce RTX 4090,RTX 4090,N/A,Yes,24 GB,Standard Memory Config,N/A,N/A,8.9,N/A,Maximum Digital Resolution (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+3,3,10,6,6,12,10,10,4,10,9,9,14,14,14,3

nvidia_gpus.arff

@@ -0,0 +1,8 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+GeForce RTX 4090,RTX 4090,N/A,N/A,24 GB,Standard Memory Config,N/A,N/A,4090,N/A,Maximum Resolution & Refresh Rate (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family,RTX 4080,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,320,8.9,4th Generation836 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,Advertising Cookies,N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family,RTX 4070,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,285,8.9,4th Generation 706 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family,RTX 3090,N/A,1.86 GHz,24 GB,Standard Memory Config,N/A,N/A,3090,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family,RTX 3080,N/A,1.67 GHz,12 GB,Standard Memory Config,N/A,N/A,3080,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family,RTX 3070,N/A,1.77 GHz,8 GB,Standard Memory Config,N/A,N/A,3070,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.csv

@@ -0,0 +1,8 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+GeForce RTX 4090,RTX 4090,N/A,N/A,24 GB,Standard Memory Config,N/A,N/A,4090,N/A,Maximum Resolution & Refresh Rate (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family,RTX 4080,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,320,8.9,4th Generation836 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,Advertising Cookies,N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family,RTX 4070,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,285,8.9,4th Generation 706 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family,RTX 3090,N/A,1.86 GHz,24 GB,Standard Memory Config,N/A,N/A,3090,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family,RTX 3080,N/A,1.67 GHz,12 GB,Standard Memory Config,N/A,N/A,3080,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family,RTX 3070,N/A,1.77 GHz,8 GB,Standard Memory Config,N/A,N/A,3070,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.dta

@@ -0,0 +1,8 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+GeForce RTX 4090,RTX 4090,N/A,N/A,24 GB,Standard Memory Config,N/A,N/A,4090,N/A,Maximum Resolution & Refresh Rate (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family,RTX 4080,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,320,8.9,4th Generation836 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,Advertising Cookies,N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family,RTX 4070,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,285,8.9,4th Generation 706 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family,RTX 3090,N/A,1.86 GHz,24 GB,Standard Memory Config,N/A,N/A,3090,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family,RTX 3080,N/A,1.67 GHz,12 GB,Standard Memory Config,N/A,N/A,3080,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family,RTX 3070,N/A,1.77 GHz,8 GB,Standard Memory Config,N/A,N/A,3070,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.json

@@ -0,0 +1,128 @@
+[
+    {
+        "architecture": "Ada Lovelace",
+        "boost_clock": "Yes",
+        "cuda_cores": "16",
+        "gpu_name": "RTX 4090",
+        "memory_interface": "384-bit",
+        "memory_size": "24 GB GDDR6X",
+        "memory_type": "GDDR6X",
+        "model": "GeForce RTX 4090",
+        "price": "N/A",
+        "process_node": "4nm",
+        "release_date": "September 2022",
+        "rt_cores": "3rd Generation",
+        "tdp": "450",
+        "tensor_cores": "4th Generation",
+        "transistor_count": "76.3 million",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/"
+    },
+    {
+        "architecture": "Ada Lovelace",
+        "boost_clock": "2.51 GHz",
+        "cuda_cores": "76",
+        "gpu_name": "RTX 4080",
+        "memory_interface": "256-bit",
+        "memory_size": "16 GB GDDR6X",
+        "memory_type": "GDDR6X",
+        "model": "GeForce RTX 4080",
+        "price": "N/A",
+        "process_node": "4nm",
+        "release_date": "September 2022",
+        "rt_cores": "3rd Generation",
+        "tdp": "320",
+        "tensor_cores": "4th Generation",
+        "transistor_count": "45.9 million",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/"
+    },
+    {
+        "architecture": "N/A",
+        "boost_clock": "N/A",
+        "cuda_cores": "N/A",
+        "gpu_name": "N/A",
+        "memory_interface": "N/A",
+        "memory_size": "N/A",
+        "memory_type": "N/A",
+        "model": "Game Over",
+        "price": "N/A",
+        "process_node": "N/A",
+        "release_date": "N/A",
+        "rt_cores": "N/A",
+        "tdp": "N/A",
+        "tensor_cores": "N/A",
+        "transistor_count": "N/A",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/"
+    },
+    {
+        "architecture": "Ada Lovelace",
+        "boost_clock": "Yes",
+        "cuda_cores": "8.9",
+        "gpu_name": "RTX 4070",
+        "memory_interface": "256-bit",
+        "memory_size": "16 GB GDDR6X",
+        "memory_type": "16 GB GDDR6X",
+        "model": "GeForce RTX 4070 Family",
+        "price": "N/A",
+        "process_node": "Up to 2X performance and power efficiency",
+        "release_date": "N/A",
+        "rt_cores": "",
+        "tdp": "285",
+        "tensor_cores": "4th Generation 706 AI TOPS",
+        "transistor_count": "N/A",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/"
+    },
+    {
+        "architecture": "N/A",
+        "boost_clock": "1.86 GHz",
+        "cuda_cores": "3090",
+        "gpu_name": "RTX 3090",
+        "memory_interface": "N/A",
+        "memory_size": "24 GB",
+        "memory_type": "Standard Memory Config",
+        "model": "GeForce RTX 3090 Family",
+        "price": "N/A",
+        "process_node": "N/A",
+        "release_date": "N/A",
+        "rt_cores": "N/A",
+        "tdp": "N/A",
+        "tensor_cores": "N/A",
+        "transistor_count": "N/A",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/"
+    },
+    {
+        "architecture": "N/A",
+        "boost_clock": "1.67 GHz",
+        "cuda_cores": "3080",
+        "gpu_name": "RTX 3080",
+        "memory_interface": "N/A",
+        "memory_size": "12 GB",
+        "memory_type": "Standard Memory Config",
+        "model": "GeForce RTX 3080 Family",
+        "price": "N/A",
+        "process_node": "N/A",
+        "release_date": "N/A",
+        "rt_cores": "Maximum Digital Resolution (1)",
+        "tdp": "N/A",
+        "tensor_cores": "N/A",
+        "transistor_count": "N/A",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/"
+    },
+    {
+        "architecture": "N/A",
+        "boost_clock": "1.77 GHz",
+        "cuda_cores": "3070",
+        "gpu_name": "RTX 3070",
+        "memory_interface": "N/A",
+        "memory_size": "8 GB",
+        "memory_type": "Standard Memory Config",
+        "model": "GeForce RTX 3070 Family",
+        "price": "N/A",
+        "process_node": "N/A",
+        "release_date": "N/A",
+        "rt_cores": "Maximum Digital Resolution (1)",
+        "tdp": "N/A",
+        "tensor_cores": "N/A",
+        "transistor_count": "N/A",
+        "url": "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/"
+    }
+]

nvidia_gpus.msgpack

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a49947159c09d323389dbd4c51435985fa411c105fb5984c45b69eef1dbe57f0
+size 2781

nvidia_gpus.ndjson

@@ -0,0 +1,7 @@
+{"model":"GeForce RTX 4090","gpu_name":"RTX 4090","architecture":"N\/A","boost_clock":"N\/A","memory_size":"24 GB","memory_type":"Standard Memory Config","memory_interface":"N\/A","tdp":"N\/A","cuda_cores":"4090","tensor_cores":"N\/A","rt_cores":"Maximum Resolution & Refresh Rate (1)","process_node":"Up to 2X performance and power efficiency","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/40-series\/rtx-4090\/"}
+{"model":"GeForce RTX 4080 Family","gpu_name":"RTX 4080","architecture":"Ada Lovelace","boost_clock":"Yes","memory_size":"16 GB GDDR6X","memory_type":"16 GB GDDR6X","memory_interface":"256-bit","tdp":"320","cuda_cores":"8.9","tensor_cores":"4th Generation836 AI TOPS","rt_cores":"","process_node":"Up to 2X performance and power efficiency","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/40-series\/rtx-4080\/"}
+{"model":"Game Over","gpu_name":"N\/A","architecture":"N\/A","boost_clock":"N\/A","memory_size":"N\/A","memory_type":"N\/A","memory_interface":"N\/A","tdp":"N\/A","cuda_cores":"N\/A","tensor_cores":"N\/A","rt_cores":"Advertising Cookies","process_node":"N\/A","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/40-series\/rtx-4070-ti-super\/"}
+{"model":"GeForce RTX 4070 Family","gpu_name":"RTX 4070","architecture":"Ada Lovelace","boost_clock":"Yes","memory_size":"16 GB GDDR6X","memory_type":"16 GB GDDR6X","memory_interface":"256-bit","tdp":"285","cuda_cores":"8.9","tensor_cores":"4th Generation 706 AI TOPS","rt_cores":"","process_node":"Up to 2X performance and power efficiency","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/40-series\/rtx-4070\/"}
+{"model":"GeForce RTX 3090 Family","gpu_name":"RTX 3090","architecture":"N\/A","boost_clock":"1.86 GHz","memory_size":"24 GB","memory_type":"Standard Memory Config","memory_interface":"N\/A","tdp":"N\/A","cuda_cores":"3090","tensor_cores":"N\/A","rt_cores":"Maximum Digital Resolution (1)","process_node":"N\/A","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3090-3090ti\/"}
+{"model":"GeForce RTX 3080 Family","gpu_name":"RTX 3080","architecture":"N\/A","boost_clock":"1.67 GHz","memory_size":"12 GB","memory_type":"Standard Memory Config","memory_interface":"N\/A","tdp":"N\/A","cuda_cores":"3080","tensor_cores":"N\/A","rt_cores":"Maximum Digital Resolution (1)","process_node":"N\/A","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3080-3080ti\/"}
+{"model":"GeForce RTX 3070 Family","gpu_name":"RTX 3070","architecture":"N\/A","boost_clock":"1.77 GHz","memory_size":"8 GB","memory_type":"Standard Memory Config","memory_interface":"N\/A","tdp":"N\/A","cuda_cores":"3070","tensor_cores":"N\/A","rt_cores":"Maximum Digital Resolution (1)","process_node":"N\/A","transistor_count":"N\/A","price":"N\/A","release_date":"N\/A","url":"https:\/\/www.nvidia.com\/en-us\/geforce\/graphics-cards\/30-series\/rtx-3070-3070ti\/"}

nvidia_gpus.parquet

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+size 10409

nvidia_gpus.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bac6c9c822cd7ba1f8a18ad0ad0894e858dcb2d69fea682f588007d42acae28
+size 2367

nvidia_gpus.sas

@@ -0,0 +1,8 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+GeForce RTX 4090,RTX 4090,N/A,N/A,24 GB,Standard Memory Config,N/A,N/A,4090,N/A,Maximum Resolution & Refresh Rate (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family,RTX 4080,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,320,8.9,4th Generation836 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,Advertising Cookies,N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family,RTX 4070,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,285,8.9,4th Generation 706 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family,RTX 3090,N/A,1.86 GHz,24 GB,Standard Memory Config,N/A,N/A,3090,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family,RTX 3080,N/A,1.67 GHz,12 GB,Standard Memory Config,N/A,N/A,3080,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family,RTX 3070,N/A,1.77 GHz,8 GB,Standard Memory Config,N/A,N/A,3070,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.spss

@@ -0,0 +1,8 @@
+model,gpu_name,architecture,boost_clock,memory_size,memory_type,memory_interface,tdp,cuda_cores,tensor_cores,rt_cores,process_node,transistor_count,price,release_date,url
+GeForce RTX 4090,RTX 4090,N/A,N/A,24 GB,Standard Memory Config,N/A,N/A,4090,N/A,Maximum Resolution & Refresh Rate (1),Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family,RTX 4080,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,320,8.9,4th Generation836 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,Advertising Cookies,N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family,RTX 4070,Ada Lovelace,Yes,16 GB GDDR6X,16 GB GDDR6X,256-bit,285,8.9,4th Generation 706 AI TOPS,,Up to 2X performance and power efficiency,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family,RTX 3090,N/A,1.86 GHz,24 GB,Standard Memory Config,N/A,N/A,3090,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family,RTX 3080,N/A,1.67 GHz,12 GB,Standard Memory Config,N/A,N/A,3080,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family,RTX 3070,N/A,1.77 GHz,8 GB,Standard Memory Config,N/A,N/A,3070,N/A,Maximum Digital Resolution (1),N/A,N/A,N/A,N/A,https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.tsv

@@ -0,0 +1,8 @@
+model	gpu_name	architecture	boost_clock	memory_size	memory_type	memory_interface	tdp	cuda_cores	tensor_cores	rt_cores	process_node	transistor_count	price	release_date	url
+GeForce RTX 4090	RTX 4090	N/A	N/A	24 GB	Standard Memory Config	N/A	N/A	4090	N/A	Maximum Resolution & Refresh Rate (1)	Up to 2X performance and power efficiency	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+GeForce RTX 4080 Family	RTX 4080	Ada Lovelace	Yes	16 GB GDDR6X	16 GB GDDR6X	256-bit	320	8.9	4th Generation836 AI TOPS		Up to 2X performance and power efficiency	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+Game Over	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	N/A	Advertising Cookies	N/A	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+GeForce RTX 4070 Family	RTX 4070	Ada Lovelace	Yes	16 GB GDDR6X	16 GB GDDR6X	256-bit	285	8.9	4th Generation 706 AI TOPS		Up to 2X performance and power efficiency	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+GeForce RTX 3090 Family	RTX 3090	N/A	1.86 GHz	24 GB	Standard Memory Config	N/A	N/A	3090	N/A	Maximum Digital Resolution (1)	N/A	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+GeForce RTX 3080 Family	RTX 3080	N/A	1.67 GHz	12 GB	Standard Memory Config	N/A	N/A	3080	N/A	Maximum Digital Resolution (1)	N/A	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+GeForce RTX 3070 Family	RTX 3070	N/A	1.77 GHz	8 GB	Standard Memory Config	N/A	N/A	3070	N/A	Maximum Digital Resolution (1)	N/A	N/A	N/A	N/A	https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

nvidia_gpus.xml

@@ -0,0 +1,136 @@
+<?xml version='1.0' encoding='utf-8'?>
+<data>
+  <row>
+    <index>0</index>
+    <model>GeForce RTX 4090</model>
+    <gpu_name>RTX 4090</gpu_name>
+    <architecture>N/A</architecture>
+    <boost_clock>N/A</boost_clock>
+    <memory_size>24 GB</memory_size>
+    <memory_type>Standard Memory Config</memory_type>
+    <memory_interface>N/A</memory_interface>
+    <tdp>N/A</tdp>
+    <cuda_cores>4090</cuda_cores>
+    <tensor_cores>N/A</tensor_cores>
+    <rt_cores>Maximum Resolution &amp; Refresh Rate (1)</rt_cores>
+    <process_node>Up to 2X performance and power efficiency</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/</url>
+  </row>
+  <row>
+    <index>1</index>
+    <model>GeForce RTX 4080 Family</model>
+    <gpu_name>RTX 4080</gpu_name>
+    <architecture>Ada Lovelace</architecture>
+    <boost_clock>Yes</boost_clock>
+    <memory_size>16 GB GDDR6X</memory_size>
+    <memory_type>16 GB GDDR6X</memory_type>
+    <memory_interface>256-bit</memory_interface>
+    <tdp>320</tdp>
+    <cuda_cores>8.9</cuda_cores>
+    <tensor_cores>4th Generation836 AI TOPS</tensor_cores>
+    <rt_cores/>
+    <process_node>Up to 2X performance and power efficiency</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/</url>
+  </row>
+  <row>
+    <index>2</index>
+    <model>Game Over</model>
+    <gpu_name>N/A</gpu_name>
+    <architecture>N/A</architecture>
+    <boost_clock>N/A</boost_clock>
+    <memory_size>N/A</memory_size>
+    <memory_type>N/A</memory_type>
+    <memory_interface>N/A</memory_interface>
+    <tdp>N/A</tdp>
+    <cuda_cores>N/A</cuda_cores>
+    <tensor_cores>N/A</tensor_cores>
+    <rt_cores>Advertising Cookies</rt_cores>
+    <process_node>N/A</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/</url>
+  </row>
+  <row>
+    <index>3</index>
+    <model>GeForce RTX 4070 Family</model>
+    <gpu_name>RTX 4070</gpu_name>
+    <architecture>Ada Lovelace</architecture>
+    <boost_clock>Yes</boost_clock>
+    <memory_size>16 GB GDDR6X</memory_size>
+    <memory_type>16 GB GDDR6X</memory_type>
+    <memory_interface>256-bit</memory_interface>
+    <tdp>285</tdp>
+    <cuda_cores>8.9</cuda_cores>
+    <tensor_cores>4th Generation 706 AI TOPS</tensor_cores>
+    <rt_cores/>
+    <process_node>Up to 2X performance and power efficiency</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/</url>
+  </row>
+  <row>
+    <index>4</index>
+    <model>GeForce RTX 3090 Family</model>
+    <gpu_name>RTX 3090</gpu_name>
+    <architecture>N/A</architecture>
+    <boost_clock>1.86 GHz</boost_clock>
+    <memory_size>24 GB</memory_size>
+    <memory_type>Standard Memory Config</memory_type>
+    <memory_interface>N/A</memory_interface>
+    <tdp>N/A</tdp>
+    <cuda_cores>3090</cuda_cores>
+    <tensor_cores>N/A</tensor_cores>
+    <rt_cores>Maximum Digital Resolution (1)</rt_cores>
+    <process_node>N/A</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/</url>
+  </row>
+  <row>
+    <index>5</index>
+    <model>GeForce RTX 3080 Family</model>
+    <gpu_name>RTX 3080</gpu_name>
+    <architecture>N/A</architecture>
+    <boost_clock>1.67 GHz</boost_clock>
+    <memory_size>12 GB</memory_size>
+    <memory_type>Standard Memory Config</memory_type>
+    <memory_interface>N/A</memory_interface>
+    <tdp>N/A</tdp>
+    <cuda_cores>3080</cuda_cores>
+    <tensor_cores>N/A</tensor_cores>
+    <rt_cores>Maximum Digital Resolution (1)</rt_cores>
+    <process_node>N/A</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/</url>
+  </row>
+  <row>
+    <index>6</index>
+    <model>GeForce RTX 3070 Family</model>
+    <gpu_name>RTX 3070</gpu_name>
+    <architecture>N/A</architecture>
+    <boost_clock>1.77 GHz</boost_clock>
+    <memory_size>8 GB</memory_size>
+    <memory_type>Standard Memory Config</memory_type>
+    <memory_interface>N/A</memory_interface>
+    <tdp>N/A</tdp>
+    <cuda_cores>3070</cuda_cores>
+    <tensor_cores>N/A</tensor_cores>
+    <rt_cores>Maximum Digital Resolution (1)</rt_cores>
+    <process_node>N/A</process_node>
+    <transistor_count>N/A</transistor_count>
+    <price>N/A</price>
+    <release_date>N/A</release_date>
+    <url>https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/</url>
+  </row>
+</data>

nvidia_gpus.yaml

@@ -0,0 +1,112 @@
+- architecture: Ada Lovelace
+  boost_clock: 'Yes'
+  cuda_cores: '16'
+  gpu_name: RTX 4090
+  memory_interface: 384-bit
+  memory_size: 24 GB GDDR6X
+  memory_type: GDDR6X
+  model: GeForce RTX 4090
+  price: N/A
+  process_node: 4nm
+  release_date: September 2022
+  rt_cores: 3rd Generation
+  tdp: '450'
+  tensor_cores: 4th Generation
+  transistor_count: 76.3 million
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/
+- architecture: Ada Lovelace
+  boost_clock: '2.51 GHz'
+  cuda_cores: '76'
+  gpu_name: RTX 4080
+  memory_interface: 256-bit
+  memory_size: 16 GB GDDR6X
+  memory_type: GDDR6X
+  model: GeForce RTX 4080
+  price: N/A
+  process_node: 4nm
+  release_date: September 2022
+  rt_cores: 3rd Generation
+  tdp: '320'
+  tensor_cores: 4th Generation
+  transistor_count: 45.9 million
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/
+- architecture: N/A
+  boost_clock: N/A
+  cuda_cores: N/A
+  gpu_name: N/A
+  memory_interface: N/A
+  memory_size: N/A
+  memory_type: N/A
+  model: Game Over
+  price: N/A
+  process_node: N/A
+  release_date: N/A
+  rt_cores: Advertising Cookies
+  tdp: N/A
+  tensor_cores: N/A
+  transistor_count: N/A
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/
+- architecture: Ada Lovelace
+  boost_clock: 'Yes'
+  cuda_cores: '8.9'
+  gpu_name: RTX 4070
+  memory_interface: 256-bit
+  memory_size: 16 GB GDDR6X
+  memory_type: 16 GB GDDR6X
+  model: GeForce RTX 4070 Family
+  price: N/A
+  process_node: Up to 2X performance and power efficiency
+  release_date: N/A
+  rt_cores: ''
+  tdp: '285'
+  tensor_cores: 4th Generation 706 AI TOPS
+  transistor_count: N/A
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/
+- architecture: N/A
+  boost_clock: 1.86 GHz
+  cuda_cores: '3090'
+  gpu_name: RTX 3090
+  memory_interface: N/A
+  memory_size: 24 GB
+  memory_type: Standard Memory Config
+  model: GeForce RTX 3090 Family
+  price: N/A
+  process_node: N/A
+  release_date: N/A
+  rt_cores: Maximum Digital Resolution (1)
+  tdp: N/A
+  tensor_cores: N/A
+  transistor_count: N/A
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/
+- architecture: N/A
+  boost_clock: 1.67 GHz
+  cuda_cores: '3080'
+  gpu_name: RTX 3080
+  memory_interface: N/A
+  memory_size: 12 GB
+  memory_type: Standard Memory Config
+  model: GeForce RTX 3080 Family
+  price: N/A
+  process_node: N/A
+  release_date: N/A
+  rt_cores: Maximum Digital Resolution (1)
+  tdp: N/A
+  tensor_cores: N/A
+  transistor_count: N/A
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/
+- architecture: N/A
+  boost_clock: 1.77 GHz
+  cuda_cores: '3070'
+  gpu_name: RTX 3070
+  memory_interface: N/A
+  memory_size: 8 GB
+  memory_type: Standard Memory Config
+  model: GeForce RTX 3070 Family
+  price: N/A
+  process_node: N/A
+  release_date: N/A
+  rt_cores: Maximum Digital Resolution (1)
+  tdp: N/A
+  tensor_cores: N/A
+  transistor_count: N/A
+  url: https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/

push_nvidia_gpu_dataset.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# Check if Git LFS is installed
+if ! command -v git-lfs &> /dev/null
+then
+    echo "Git LFS is not installed. Installing..."
+    brew install git-lfs
+    git lfs install
+fi
+
+# Check if the repository already exists
+if [ -d "nvidia-gpu-dataset" ]; then
+    echo "Removing existing nvidia-gpu-dataset directory..."
+    rm -rf nvidia-gpu-dataset
+fi
+
+# Clone the Hugging Face dataset repository
+git clone https://huggingface.co/datasets/bniladridas/nvidia-gpu-dataset
+
+# Check if the clone was successful
+if [ ! -d "nvidia-gpu-dataset" ]; then
+    echo "Failed to clone the repository."
+    exit 1
+fi
+
+# Copy dataset files into the cloned repository
+cp -r /Users/niladridas/Desktop/nvidia_doc/* nvidia-gpu-dataset/
+
+# Change directory to the cloned repository
+cd nvidia-gpu-dataset/
+
+# Log in to Hugging Face
+huggingface-cli login
+
+# Add files to git
+git add .
+
+# Commit the changes
+git commit -m "Add NVIDIA GPU dataset files"
+
+# Push the changes to Hugging Face
+git push

requirements.txt

@@ -0,0 +1,20 @@
+requests
+beautifulsoup4
+pandas
+selenium
+webdriver-manager
+pyarrow
+fastavro
+h5py
+lxml
+pyyaml
+scipy
+msgpack
+openpyxl
+fastavro
+msgpack
+pyarrow
+h5py
+scipy
+openpyxl
+pandas

summary.py

@@ -0,0 +1,46 @@
+import pandas as pd
+import json
+import os
+from datasets import Dataset, DatasetDict
+
+# Function to read CSV file
+def read_csv(file_path):
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"The file {file_path} does not exist.")
+    return pd.read_csv(file_path)
+
+# Function to read JSON file
+def read_json(file_path):
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"The file {file_path} does not exist.")
+    with open(file_path, 'r') as file:
+        return [json.loads(line) for line in file]
+
+# Function to read NDJSON file
+def read_ndjson(file_path):
+    if not os.path.exists(file_path):
+        raise FileNotFoundError(f"The file {file_path} does not exist.")
+    with open(file_path, 'r') as file:
+        return [json.loads(line) for line in file]
+
+# Consolidate data from different formats
+def consolidate_data():
+    csv_data = read_csv('nvidia_gpus.csv')
+    json_data = read_json('nvidia_gpus.json')
+    ndjson_data = read_ndjson('nvidia_gpus.ndjson')
+
+    # Combine all data into a single DataFrame
+    combined_data = pd.concat([csv_data, pd.DataFrame(json_data), pd.DataFrame(ndjson_data)], ignore_index=True)
+    return combined_data
+
+# Generate summary report
+def generate_summary():
+    output_file = 'nvidia_gpu_summary_report.csv'
+    data = consolidate_data()
+    summary = data.describe(include='all')
+    summary.to_csv(output_file, index=False)
+    print(f"Summary report generated: {output_file}")
+    print("Summary report generated: nvidia_gpu_summary_report.csv")
+
+if __name__ == "__main__":
+    generate_summary()

webscraped.py

@@ -0,0 +1,580 @@
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import time
+import json
+import re
+import logging
+from urllib.parse import urlparse
+import fastavro
+import msgpack
+import os
+from datasets import Dataset
+
+# Function to scrape data and return as a Hugging Face Dataset
+def scrape_data(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    
+    # Example scraping logic (to be customized)
+    data = []
+    for item in soup.find_all('div', class_='gpu-item'):
+        gpu_info = {
+            'gpu_name': item.find('h2').text,
+            'architecture': item.find('span', class_='architecture').text,
+            'memory_size': item.find('span', class_='memory-size').text,
+            # Add more fields as necessary
+        }
+        data.append(gpu_info)
+    
+    # Convert to Hugging Face Dataset
+    return Dataset.from_list(data)
+
+# Import additional libraries for new formats
+import pyarrow as pa
+import pyarrow.parquet as pq
+import fastavro
+import h5py
+import sqlite3
+import xml.etree.ElementTree as ET
+import yaml
+import pickle
+from scipy.io import savemat
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Try to import Selenium components - they'll be used if available
+try:
+    from selenium import webdriver
+    from selenium.webdriver.chrome.options import Options
+    from selenium.webdriver.chrome.service import Service
+    from selenium.webdriver.common.by import By
+    from selenium.webdriver.support.ui import WebDriverWait
+    from selenium.webdriver.support import expected_conditions as EC
+    from webdriver_manager.chrome import ChromeDriverManager
+    SELENIUM_AVAILABLE = True
+    logger.info("Selenium is available and will be used for JavaScript-heavy sites")
+except ImportError:
+    SELENIUM_AVAILABLE = False
+    logger.warning("Selenium not available. Install with: pip install selenium webdriver-manager")
+
+class NvidiaGpuScraper:
+    def __init__(self, use_selenium=True):
+        self.use_selenium = use_selenium and SELENIUM_AVAILABLE
+        self.driver = self._setup_driver() if self.use_selenium else None
+        
+    def _setup_driver(self):
+        """Set up and return a Selenium WebDriver if available"""
+        if not SELENIUM_AVAILABLE:
+            return None
+            
+        try:
+            options = Options()
+            options.add_argument('--headless')
+            options.add_argument('--no-sandbox')
+            options.add_argument('--disable-dev-shm-usage')
+            options.add_argument('--disable-gpu')
+            options.add_argument("--window-size=1920,1080")
+            options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36")
+            
+            service = Service(ChromeDriverManager().install())
+            driver = webdriver.Chrome(service=service, options=options)
+            return driver
+        except Exception as e:
+            logger.error(f"Failed to initialize Selenium: {e}")
+            return None
+    
+    def _fetch_with_selenium(self, url):
+        """Fetch page content using Selenium for JavaScript-heavy sites"""
+        if self.driver is None:
+            return None
+            
+        try:
+            logger.info(f"Fetching with Selenium: {url}")
+            self.driver.get(url)
+            # Wait for the page to load completely
+            WebDriverWait(self.driver, 20).until(
+                EC.presence_of_element_located((By.TAG_NAME, "body"))
+            )
+            
+            # Scroll down to load lazy content
+            self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight/2);")
+            time.sleep(1)
+            self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
+            time.sleep(2)  # Additional wait for dynamic content
+            
+            # Expand any "See more specifications" buttons if they exist
+            try:
+                see_more_buttons = self.driver.find_elements(By.XPATH, 
+                    "//button[contains(text(), 'See more') or contains(text(), 'specifications') or contains(text(), 'specs')]")
+                for button in see_more_buttons:
+                    self.driver.execute_script("arguments[0].click();", button)
+                    time.sleep(1)
+            except Exception as e:
+                logger.warning(f"Could not expand specification sections: {e}")
+            
+            # Get the page source after JavaScript execution
+            page_source = self.driver.page_source
+            return BeautifulSoup(page_source, 'html.parser')
+        except Exception as e:
+            logger.error(f"Selenium error for {url}: {e}")
+            return None
+    
+    def _fetch_with_requests(self, url):
+        """Fetch page content using requests library"""
+        headers = {
+            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/98.0.4758.102 Safari/537.36',
+            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
+            'Accept-Language': 'en-US,en;q=0.5',
+            'Referer': 'https://www.google.com/',
+            'DNT': '1',
+            'Connection': 'keep-alive',
+            'Upgrade-Insecure-Requests': '1',
+            'Cache-Control': 'max-age=0',
+        }
+        
+        for attempt in range(3):
+            try:
+                logger.info(f"Fetching with requests: {url}")
+                response = requests.get(url, timeout=30, headers=headers)
+                response.raise_for_status()
+                return BeautifulSoup(response.content, 'html.parser')
+            except requests.exceptions.RequestException as e:
+                wait_time = 2 ** attempt
+                logger.warning(f"Request error for {url}: {e}. Retrying in {wait_time} seconds...")
+                time.sleep(wait_time)
+        
+        return None
+    
+    def fetch_page(self, url):
+        if not url:
+            raise ValueError("The URL provided is empty.")
+        """Fetch page content, trying Selenium first if available"""
+        if self.use_selenium:
+            soup = self._fetch_with_selenium(url)
+            if soup:
+                return soup
+                
+        # Fall back to requests if Selenium failed or isn't available
+        return self._fetch_with_requests(url)
+    
+    def extract_gpu_specs(self, soup, url):
+        """Extract GPU specifications from NVIDIA product pages"""
+        specs = {
+            'model': 'N/A',
+            'gpu_name': 'N/A',
+            'architecture': 'N/A',
+            'boost_clock': 'N/A',
+            'memory_size': 'N/A',
+            'memory_type': 'N/A',
+            'memory_interface': 'N/A',
+            'tdp': 'N/A',
+            'cuda_cores': 'N/A',
+            'tensor_cores': 'N/A',
+            'rt_cores': 'N/A',
+            'process_node': 'N/A',
+            'transistor_count': 'N/A',
+            'price': 'N/A',
+            'release_date': 'N/A',
+            'url': url,
+        }
+        
+        try:
+            # Extract model name
+            for selector in ['h1', '.product-title', '.product-name', '.prod-title']:
+                title_element = soup.select_one(selector)
+                if title_element and title_element.text.strip():
+                    specs['model'] = title_element.text.strip()
+                    # Try to extract GPU name (e.g., RTX 4090)
+                    gpu_match = re.search(r'(GTX|RTX|RTX\s+SUPER|GTX\s+SUPER)\s+(\d{4}\s*(?:Ti|SUPER)?)', 
+                                         specs['model'], re.IGNORECASE)
+                    if gpu_match:
+                        specs['gpu_name'] = f"{gpu_match.group(1)} {gpu_match.group(2)}".strip()
+                    break
+            
+            # Field mapping dictionary - different ways NVIDIA might label each spec
+            field_mappings = {
+                'architecture': ['gpu architecture', 'architecture', 'nvidia architecture'],
+                'boost_clock': ['boost clock', 'gpu boost clock', 'clock speed', 'boost'],
+                'memory_size': ['memory size', 'standard memory config', 'memory configuration', 'video memory'],
+                'memory_type': ['memory type', 'memory spec', 'standard memory'],
+                'memory_interface': ['memory interface', 'memory bus', 'interface width', 'bit width'],
+                'tdp': ['graphics card power', 'tdp', 'total graphics power', 'power consumption', 'tgp', 'maximum power'],
+                'cuda_cores': ['cuda cores', 'cuda', 'nvidia cuda cores'],
+                'tensor_cores': ['tensor cores', 'tensor', 'ai cores'],
+                'rt_cores': ['rt cores', 'ray tracing cores', 'rt'],
+                'process_node': ['process', 'fabrication process', 'manufacturing process', 'fab'],
+                'transistor_count': ['transistor', 'transistor count', 'number of transistors'],
+                'price': ['price', 'msrp', 'suggested price', 'starting at'],
+                'release_date': ['release date', 'availability', 'launch date', 'available']
+            }
+            
+            # Look for various specs sections
+            spec_sections = soup.select('.specs-section, .tech-specs, .product-specs, .specs, .spec-table, .spec, [class*="spec"]')
+            
+            # If no dedicated sections found, look through the entire page
+            if not spec_sections:
+                spec_sections = [soup]
+            
+            for section in spec_sections:
+                # Method 1: Look for labeled pairs or tables
+                self._extract_from_tables_and_pairs(section, specs, field_mappings)
+                
+                # Method 2: Look for text patterns throughout the page
+                self._extract_from_text_patterns(section, specs)
+            
+            # Extract from specification headings and adjacent elements
+            self._extract_from_spec_headings(soup, specs, field_mappings)
+            
+            # Try to find any JSON-LD or structured data with specs
+            self._extract_from_json_ld(soup, specs)
+            
+            # Clean and standardize specs
+            self._clean_specs(specs)
+            
+            logger.info(f"Extracted NVIDIA GPU specs: {specs}")
+            return specs
+            
+        except Exception as e:
+            logger.error(f"Error extracting GPU specs: {e}")
+            return specs
+    
+    def _extract_from_tables_and_pairs(self, section, specs, field_mappings):
+        """Extract specs from table-like structures or label-value pairs"""
+        # Check for table rows
+        rows = section.select('tr, .spec-row, .specs-row, [class*="row"]')
+        for row in rows:
+            cells = row.select('th, td, .spec-label, .spec-value, .specs-label, .specs-value')
+            if len(cells) >= 2:
+                header = cells[0].text.strip().lower()
+                value = cells[1].text.strip()
+                
+                # Match header to our fields
+                for field, possible_headers in field_mappings.items():
+                    if any(h in header for h in possible_headers):
+                        specs[field] = value
+        
+        # Check for definition lists
+        terms = section.select('dt, .term, .specs-term')
+        for term in terms:
+            header = term.text.strip().lower()
+            value_el = term.find_next_sibling(['dd', '.definition', '.specs-definition'])
+            if value_el:
+                value = value_el.text.strip()
+                
+                # Match header to our fields
+                for field, possible_headers in field_mappings.items():
+                    if any(h in header for h in possible_headers):
+                        specs[field] = value
+        
+        # Check for labeled pairs (common in NVIDIA's newer layout)
+        labels = section.select('.specs-label, .spec-label, .specs-name, .label, [class*="label"]')
+        for label in labels:
+            header = label.text.strip().lower()
+            # Try to find the adjacent value element
+            value_el = label.find_next_sibling('.specs-value, .spec-value, .specs-data, .value, [class*="value"]')
+            if value_el:
+                value = value_el.text.strip()
+                
+                # Match header to our fields
+                for field, possible_headers in field_mappings.items():
+                    if any(h in header for h in possible_headers):
+                        specs[field] = value
+    
+    def _extract_from_text_patterns(self, section, specs):
+        """Extract specs using regex patterns in the page text"""
+        text = section.get_text(' ', strip=True)
+        
+        # Extract CUDA cores
+        cuda_matches = re.search(r'(\d[\d,]+)\s*(?:nvidia)?\s*cuda\s*cores', text, re.IGNORECASE)
+        if cuda_matches and specs['cuda_cores'] == 'N/A':
+            specs['cuda_cores'] = cuda_matches.group(1)
+        
+        # Extract Tensor cores
+        tensor_matches = re.search(r'(\d+)\s*(?:nvidia)?\s*tensor\s*cores', text, re.IGNORECASE)
+        if tensor_matches and specs['tensor_cores'] == 'N/A':
+            specs['tensor_cores'] = tensor_matches.group(1)
+        
+        # Extract RT cores
+        rt_matches = re.search(r'(\d+)\s*(?:nvidia)?\s*rt\s*cores', text, re.IGNORECASE)
+        if rt_matches and specs['rt_cores'] == 'N/A':
+            specs['rt_cores'] = rt_matches.group(1)
+        
+        # Extract memory size
+        mem_matches = re.search(r'(\d+)\s*GB\s*(?:G?DDR\d+[X]?)', text, re.IGNORECASE)
+        if mem_matches and specs['memory_size'] == 'N/A':
+            specs['memory_size'] = f"{mem_matches.group(1)} GB"
+            if specs['memory_type'] == 'N/A':
+                specs['memory_type'] = mem_matches.group(2)
+        
+        # Extract boost clock
+        clock_matches = re.search(r'boost\s*clock\s*(?:up\s*to)?\s*:?\s*([\d.]+)\s*(?:MHz|GHz)', text, re.IGNORECASE)
+        if clock_matches and specs['boost_clock'] == 'N/A':
+            value = clock_matches.group(1)
+            unit = 'GHz' if float(value) < 100 else 'MHz'  # Infer unit if not in match
+            specs['boost_clock'] = f"{value} {unit}"
+        
+        # Extract memory interface
+        interface_matches = re.search(r'(\d+)[\s-]*bit(?:\s*memory)?\s*(?:interface|bus)', text, re.IGNORECASE)
+        if interface_matches and specs['memory_interface'] == 'N/A':
+            specs['memory_interface'] = f"{interface_matches.group(1)}-bit"
+    
+    def _extract_from_spec_headings(self, soup, specs, field_mappings):
+        """Extract specs from headings and their adjacent content"""
+        for field, terms in field_mappings.items():
+            if specs[field] != 'N/A':  # Skip if already found
+                continue
+                
+            for term in terms:
+                # Look for headings containing the term
+                headers = soup.select(f'h1:contains("{term}"), h2:contains("{term}"), h3:contains("{term}"), h4:contains("{term}"), h5:contains("{term}")')
+                
+                for header in headers:
+                    # Look at next sibling or child for the value
+                    value_el = header.find_next()
+                    if value_el:
+                        specs[field] = value_el.text.strip()
+                        break
+    
+    def _extract_from_json_ld(self, soup, specs):
+        """Extract specs from JSON-LD structured data if available"""
+        for script in soup.select('script[type="application/ld+json"]'):
+            try:
+                data = json.loads(script.string)
+                
+                # Look for product data
+                if 'name' in data and specs['model'] == 'N/A':
+                    specs['model'] = data['name']
+                
+                # Check for specs in properties
+                if 'additionalProperty' in data:
+                    for prop in data['additionalProperty']:
+                        name = prop.get('name', '').lower()
+                        value = prop.get('value', '')
+                        
+                        if 'cuda' in name and specs['cuda_cores'] == 'N/A':
+                            specs['cuda_cores'] = value
+                        elif 'clock' in name and 'boost' in name and specs['boost_clock'] == 'N/A':
+                            specs['boost_clock'] = value
+                        elif 'memory' in name and 'size' in name and specs['memory_size'] == 'N/A':
+                            specs['memory_size'] = value
+                        # Add other mappings as needed
+                
+                # Check for offer data
+                if 'offers' in data and specs['price'] == 'N/A':
+                    if isinstance(data['offers'], list) and len(data['offers']) > 0:
+                        specs['price'] = data['offers'][0].get('price', 'N/A')
+                    elif isinstance(data['offers'], dict):
+                        specs['price'] = data['offers'].get('price', 'N/A')
+            except:
+                pass
+    
+    def _clean_specs(self, specs):
+        """Clean and standardize the extracted specs"""
+        # Clean CUDA cores (remove commas)
+        if specs['cuda_cores'] != 'N/A':
+            specs['cuda_cores'] = specs['cuda_cores'].replace(',', '')
+        
+        # Standardize memory size format
+        if specs['memory_size'] != 'N/A' and 'GB' not in specs['memory_size']:
+            if specs['memory_size'].isdigit():
+                specs['memory_size'] = f"{specs['memory_size']} GB"
+        
+        # Standardize boost clock format
+        if specs['boost_clock'] != 'N/A':
+            # If it's just a number, add units
+            if re.match(r'^\d+(\.\d+)?$', specs['boost_clock']):
+                value = float(specs['boost_clock'])
+                if value > 100:  # Likely MHz
+                    specs['boost_clock'] = f"{value} MHz"
+                else:  # Likely GHz
+                    specs['boost_clock'] = f"{value} GHz"
+    
+    def scrape_gpu(self, url):
+        if not url:
+            raise ValueError("The URL provided is empty.")
+        """Scrape a single GPU product page"""
+        soup = self.fetch_page(url)
+        if not soup:
+            return {
+                'model': 'Failed to fetch',
+                'url': url
+            }
+        
+        return self.extract_gpu_specs(soup, url)
+    
+    def scrape_multiple_gpus(self, urls):
+        if not urls:
+            raise ValueError("The list of URLs is empty.")
+        """Scrape multiple GPU product pages"""
+        results = []
+        
+        for url in urls:
+            try:
+                specs = self.scrape_gpu(url)
+                results.append(specs)
+                # Be polite with a delay between requests
+                time.sleep(2)
+            except Exception as e:
+                logger.error(f"Error processing {url}: {e}")
+                results.append({
+                    'model': f"Error: {str(e)[:50]}",
+                    'url': url
+                })
+        
+        return results
+    
+    def cleanup(self):
+        """Clean up resources"""
+        if self.driver:
+            self.driver.quit()
+
+# Main execution function
+def main():
+    # NVIDIA GPU product URLs - focused on specific product pages
+    nvidia_urls = [
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4090/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4080/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070-ti-super/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/40-series/rtx-4070/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3090-3090ti/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3080-3080ti/",
+        "https://www.nvidia.com/en-us/geforce/graphics-cards/30-series/rtx-3070-3070ti/",
+    ]
+    
+    # Create the scraper and run
+    scraper = NvidiaGpuScraper(use_selenium=SELENIUM_AVAILABLE)
+    
+    try:
+        # Scrape the GPUs
+        results = scraper.scrape_multiple_gpus(nvidia_urls)
+        
+        # Create and save DataFrame
+        df = pd.DataFrame(results)
+        df.to_csv('nvidia_gpus.csv', index=False)
+        df.to_json('nvidia_gpus.json', orient='records', lines=True)
+        df.to_excel('nvidia_gpus.xlsx', index=False)
+        
+        # Save DataFrame in various formats
+        try:
+            df.to_parquet('nvidia_gpus.parquet')
+        except Exception as e:
+            logger.warning(f"Failed to save as Parquet: {e}")
+
+        try:
+            # Convert DataFrame to list of dictionaries
+            records = df.to_dict(orient='records')
+            # Define Avro schema
+            schema = {
+                'type': 'record',
+                'name': 'GPU',
+                'fields': [
+                    {'name': col, 'type': ['string', 'null']} for col in df.columns
+                ]
+            }
+            # Write to Avro file
+            with open('nvidia_gpus.avro', 'wb') as avro_file:
+                fastavro.writer(avro_file, schema, records)
+        except Exception as e:
+            logger.warning(f"Failed to save as Avro: {e}")
+
+        try:
+            df.to_orc('nvidia_gpus.orc')
+        except Exception as e:
+            logger.warning(f"Failed to save as ORC: {e}")
+
+        try:
+            df.to_hdf('nvidia_gpus.h5', key='df', mode='w')
+        except Exception as e:
+            logger.warning(f"Failed to save as HDF5: {e}")
+
+        try:
+            with sqlite3.connect('nvidia_gpus.db') as conn:
+                df.to_sql('gpus', conn, if_exists='replace', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as SQLite: {e}")
+
+        try:
+            df.to_xml('nvidia_gpus.xml')
+        except Exception as e:
+            logger.warning(f"Failed to save as XML: {e}")
+
+        try:
+            with open('nvidia_gpus.yaml', 'w') as yaml_file:
+                yaml.dump(df.to_dict(orient='records'), yaml_file)
+        except Exception as e:
+            logger.warning(f"Failed to save as YAML: {e}")
+
+        try:
+            with open('nvidia_gpus.pkl', 'wb') as pickle_file:
+                pickle.dump(df, pickle_file)
+        except Exception as e:
+            logger.warning(f"Failed to save as Pickle: {e}")
+
+        try:
+            savemat('nvidia_gpus.mat', {'gpus': df.to_dict(orient='records')})
+        except Exception as e:
+            logger.warning(f"Failed to save as MAT: {e}")
+
+        try:
+            df.to_csv('nvidia_gpus.tsv', sep='\t', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as TSV: {e}")
+
+        try:
+            df.to_json('nvidia_gpus.ndjson', orient='records', lines=True)
+        except Exception as e:
+            logger.warning(f"Failed to save as NDJSON: {e}")
+
+        try:
+            df.to_csv('nvidia_gpus.arff', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as ARFF: {e}")
+
+        try:
+            # Convert DataFrame to dictionary
+            data = df.to_dict(orient='records')
+            # Write to MessagePack file
+            with open('nvidia_gpus.msgpack', 'wb') as msgpack_file:
+                msgpack.pack(data, msgpack_file)
+        except Exception as e:
+            logger.warning(f"Failed to save as MessagePack: {e}")
+
+        try:
+            df.to_pickle('nvidia_gpus.protobuf')
+        except Exception as e:
+            logger.warning(f"Failed to save as ProtoBuf: {e}")
+
+        try:
+            df.to_csv('nvidia_gpus.dta', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as DTA: {e}")
+
+        try:
+            df.to_csv('nvidia_gpus.sas', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as SAS: {e}")
+
+        try:
+            df.to_csv('nvidia_gpus.spss', index=False)
+        except Exception as e:
+            logger.warning(f"Failed to save as SPSS: {e}")
+
+        print("\nResults:")
+        print("\nResults:")
+        print(df)
+        
+        # Print summary
+        successful = sum(1 for spec in results if spec.get('model') not in ['N/A', 'Failed to fetch'])
+        print(f"\nSummary: Successfully scraped {successful} out of {len(results)} NVIDIA GPUs")
+        
+        return df
+        
+    finally:
+        # Always clean up resources
+        scraper.cleanup()
+
+if __name__ == "__main__":
+    main()