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README.md
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README.md<br>Outline<br>VertexByteStream (VBS-NN) — Docker Execution Guide
This repository contains the official Docker deployment setups for evaluating the VertexByteStream (VBS-NN) architecture. With these isolated environments, you can reproduce our extreme 512k context length needle-in-a-haystack stress tests on local hardware using either AMD (ROCm) or NVIDIA (CUDA) graphics cards.
⚠️ Disclaimer: This is the very first trial release of the architecture, distributed strictly for demo, evaluation, and non-commercial research purposes.
🖥️ System Requirements
Before running the stress tests, ensure your local environment meets the following criteria:
OS: Linux (Ubuntu 22.04+ or Debian Trixie recommended).
Docker: Installed and running.
VRAM: At least 12 GB of VRAM is recommended to comfortably handle extreme context scaling due to our integrated dynamic gradient checkpointing (GC:True).
GPU Drivers:
For AMD: Properly installed ROCm kernel drivers on the host machine.
For NVIDIA: Installed NVIDIA Container Toolkit on the host to enable the --gpus flag inside Docker.
🚀 Quick Start (Docker Installation)
Follow these steps to clone, build, and deploy the architecture evaluation container based on your GPU hardware vendor.
1. Clone the Repository
Open your terminal, clone the repository, and navigate into the root directory:
git clone [https://github.com/ega4l/VBS-NN.git](https://github.com/ega4l/VBS-NN.git) && cd VBS-NN/code
2. Deployment on AMD GPUs (ROCm)
The AMD setup utilizes direct device sharing to communicate with Radeon cards.
a. Build the ROCm Image:
docker build -f docker/Dockerfile.rocm -t local-vbs.nn:rocm .
b. Run the 512k Context Needle Stress-Test:
docker run -it --rm \<br>--device=/dev/kfd \<br>--device=/dev/dri \<br>--group-add video \<br>--ipc=host \<br>--name needle-test \<br>local-vbs.nn:rocm
💡 Note: --ipc=host is critical for PyTorch to allow high-throughput shared memory allocation during deep hierarchical gradient aggregation.
3. Deployment on NVIDIA GPUs (CUDA)
The NVIDIA setup relies on the standard unified container runtime for Pascal, Ampere, Ada Lovelace, or Hopper architectures.
a. Build the CUDA Image:
docker build -f docker/Dockerfile.cuda -t local-vbs.nn:cuda .
b. Run the 512k Context Needle Stress-Test:
docker run -it --rm \<br>--gpus all \<br>--ipc=host \<br>--name needle-test \<br>local-vbs.nn:cuda
📄 License
This project uses a dual-licensing model to protect core software assets while allowing public validation of academic data:
Source Code & Model Weights: Licensed under a custom Evaluation and Non-Commercial Research License . Permitted strictly for academic research, educational use, and private benchmarks. Commercial deployment or derivation is strictly prohibited. Please see the LICENSE file in the main repository for full terms.
Documentation & Logs: Licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are welcome to share and cite our benchmark data as long as proper attribution is provided.
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