feat: add deploy-and-train.sh script for server deployment

deploy-and-train.sh

@@ -0,0 +1,122 @@
+#!/bin/bash
+#
+# LoRA Training Deployment Script
+# Clones the repo and trains the LoRA adapter on the server
+#
+# Usage:
+#   bash deploy-and-train.sh
+#
+# This will:
+# 1. Clone the repo to /opt/loras/agenx-lora-training
+# 2. Setup Python environment with GPU support
+# 3. Prepare the dataset
+# 4. Train the LoRA adapter
+#
+
+set -e
+
+# Configuration
+REPO_URL="https://gitea.cyaren.com/cmedina/agenx-lora-training.git"
+INSTALL_DIR="/opt/loras/agenx-lora-training"
+PYTHON_VERSION="3.10"
+
+echo "=============================================="
+echo "LoRA Training Deployment"
+echo "=============================================="
+echo ""
+
+# Step 1: Create installation directory
+echo "[1/5] Creating installation directory..."
+mkdir -p /opt/loras
+if [ -d "$INSTALL_DIR" ]; then
+    echo "  Directory already exists: $INSTALL_DIR"
+    echo "  Removing old clone..."
+    rm -rf "$INSTALL_DIR"
+fi
+
+# Step 2: Clone the repository
+echo "[2/5] Cloning repository..."
+git clone "$REPO_URL" "$INSTALL_DIR"
+echo "  Repository cloned to: $INSTALL_DIR"
+
+# Step 3: Setup Python environment
+echo "[3/5] Setting up Python environment..."
+cd "$INSTALL_DIR"
+
+# Check if Python is available
+if ! command -v python3 &> /dev/null; then
+    echo "ERROR: Python3 not found. Please install Python $PYTHON_VERSION or later."
+    exit 1
+fi
+
+# Create virtual environment
+python3 -m venv venv
+source venv/bin/activate
+
+# Upgrade pip
+pip install --upgrade pip
+
+# Install PyTorch with CUDA support
+echo "  Installing PyTorch with CUDA support..."
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+
+# Install other dependencies
+echo "  Installing training dependencies..."
+pip install transformers datasets trl peft accelerate bitsandbytes
+
+# Verify GPU availability
+echo ""
+echo "  Checking GPU availability..."
+python3 -c "
+import torch
+if torch.cuda.is_available():
+    print(f'  ✓ CUDA available: {torch.cuda.get_device_name(0)}')
+    print(f'  ✓ VRAM: {torch.cuda.get_device_properties(0).total_mem / 1e9:.1f} GB')
+else:
+    print('  ✗ CUDA not available. GPU training will not work.')
+    print('  Please ensure CUDA drivers are installed.')
+    exit(1)
+"
+
+# Step 4: Prepare dataset
+echo "[4/5] Preparing dataset..."
+python3 training/scripts/prepare_dataset.py \
+    --input dataset/combined_20k.jsonl \
+    --output training/data
+
+echo "  Dataset prepared:"
+echo "    - training/data/train.jsonl"
+echo "    - training/data/test.jsonl"
+
+# Step 5: Train the model
+echo "[5/5] Starting training..."
+echo ""
+echo "  Training configuration:"
+echo "    - Model: meta-llama/Llama-2-7b-hf"
+echo "    - Method: QLoRA (4-bit quantization)"
+echo "    - Epochs: 3"
+echo "    - Batch size: 4"
+echo "    - Learning rate: 2e-4"
+echo ""
+echo "  Estimated training time: 6-24 hours (depending on GPU)"
+echo ""
+
+python3 training/scripts/train.py \
+    --config training/configs/llama2-7b-lora.yaml
+
+echo ""
+echo "=============================================="
+echo "Training complete!"
+echo "=============================================="
+echo ""
+echo "Trained model saved to: $INSTALL_DIR/training/output/llama2-7b-lora/"
+echo ""
+echo "To generate summaries:"
+echo "  cd $INSTALL_DIR"
+echo "  source venv/bin/activate"
+echo "  python3 training/scripts/inference.py \\"
+echo "      --model training/output/llama2-7b-lora \\"
+echo "      --task \"Your task here\" \\"
+echo "      --files src/file.py \\"
+echo "      --tests-run --test-count 100"
+echo ""

training/README.md

@@ -23,25 +23,24 @@ training/
 - GPU with 40GB+ VRAM (A100 recommended)
 - 64GB+ system RAM
 - 100GB+ free disk space
+- CUDA drivers installed
 
-## Installation
+## Server Deployment (Recommended)
+
+Use the deployment script to clone and train on your GPU server:
 
 ```bash
-cd scripts/lora_training/training
+# Deploy and train in one command
-
+bash deploy-and-train.sh
-# Create virtual environment
-python -m venv venv
-source venv/bin/activate
-
-# Install dependencies
-pip install transformers datasets trl peft accelerate bitsandbytes
-
-# Or use conda
-conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
-pip install transformers datasets trl peft accelerate bitsandbytes
 ```
 
-## Usage
+This will:
+1. Clone the repo to `/opt/loras/agenx-lora-training`
+2. Setup Python environment with CUDA support
+3. Prepare the dataset
+4. Train the LoRA adapter
+
+## Manual Setup
 
 ### 1. Prepare Dataset
 
@@ -123,7 +122,3 @@ Trained model is saved to `output/llama2-7b-lora/`:
 **Generation too long/short:**
 - Adjust `max_new_tokens` in inference script
 - Tune `temperature` and `top_p`
-
-## License
-
-This training infrastructure is part of the AgenX project.