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INSTALLATION.md

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Installation Guide

Prerequisites

  • Python 3.9 or higher
  • CUDA 12.x (for GPU support)
  • At least 8GB of RAM
  • 10GB of free disk space

Quick Installation

1. Clone the Repository

git clone https://github.com/or4k2l/robust-vision.git
cd robust-vision

2. Create Virtual Environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies

pip install --upgrade pip
pip install -r requirements.txt

4. Install Package

pip install -e .

GPU Setup

CUDA 12.x

The default installation includes JAX with CUDA 12 support:

pip install "jax[cuda12]>=0.4.20"

CUDA 11.x

If you need CUDA 11 support:

pip install "jax[cuda11_cudnn82]>=0.4.20"

CPU Only

For CPU-only installation:

pip install jax>=0.4.20

Docker Installation

Build Docker Image

docker build -t robust-vision:latest .

Run Training in Docker

docker run --gpus all \
  -v $(pwd)/data:/app/data \
  -v $(pwd)/checkpoints:/app/checkpoints \
  -v $(pwd)/logs:/app/logs \
  robust-vision:latest

Interactive Docker Session

docker run --gpus all -it \
  -v $(pwd):/app \
  robust-vision:latest bash

Verification

Verify your installation:

import jax
import flax
import tensorflow as tf

print(f"JAX version: {jax.__version__}")
print(f"Flax version: {flax.__version__}")
print(f"TensorFlow version: {tf.__version__}")
print(f"JAX devices: {jax.devices()}")

Troubleshooting

CUDA Not Found

If JAX doesn't detect your GPU:

  1. Check CUDA installation: nvcc --version
  2. Reinstall JAX with CUDA support
  3. Check CUDA driver compatibility

Out of Memory

If you encounter OOM errors:

  1. Reduce batch size in config
  2. Use gradient accumulation
  3. Enable mixed precision training

TensorFlow Warnings

TensorFlow warnings about GPU can be ignored if you're only using it for data loading.

Cloud Setup

Google Colab

!pip install git+https://github.com/or4k2l/robust-vision.git

AWS

Use Deep Learning AMI with CUDA 12:

aws ec2 run-instances \
  --image-id ami-xxxxxxxxx \
  --instance-type p3.2xlarge \
  --key-name your-key

GCP

Use Deep Learning VM Image:

gcloud compute instances create robust-vision \
  --zone=us-central1-a \
  --machine-type=n1-standard-8 \
  --accelerator=type=nvidia-tesla-v100,count=1 \
  --image-family=common-cu121 \
  --image-project=deeplearning-platform-release

Next Steps

  • Read TRAINING.md for training instructions
  • Check DEPLOYMENT.md for deployment options
  • Explore example notebooks in notebooks/