refactor: simplify deploy script, add train-on-this-server.sh

deploy-and-train.sh

@@ -1,159 +1,68 @@
 #!/bin/bash
-# Update a GPU-server clone of agenx-lora-training and run the training.
+# Deploy LoRA training repo (clone + prepare dataset).
+# Training is run manually on the GPU server.
 #
 # Usage:
 #   bash deploy-and-train.sh
 #
-# Optional overrides:
-#   LORA_DEPLOY_REPO_URL=https://gitea.cyaren.com/cmedina/agenx-lora-training.git
-#   LORA_DEPLOY_REPO_DIR=/opt/loras/agenx-lora-training
-#   LORA_DEPLOY_BRANCH=main
+# This will:
+# 1. Clone/update the repo to /opt/loras/agenx-lora-training
+# 2. Print instructions for downloading dataset and training
 #
 
 set -euo pipefail
 
-# Load credentials from env file if it exists (override with env vars).
-if [ -f /home/cyaren/.deploy.env ]; then
-    set -a; source /home/cyaren/.deploy.env; set +a
-fi
-
-DEPLOY_USER="${LORA_DEPLOY_USER:-}"
-DEPLOY_TOKEN="${LORA_DEPLOY_TOKEN:-}"
-
-# Build the repo URL; embed user:token for HTTPS auth when provided.
-# Use remote Gitea (local Gitea instances may not be accessible)
-if [ -n "${DEPLOY_USER}" ] && [ -n "${DEPLOY_TOKEN}" ]; then
-    REPO_URL="${LORA_DEPLOY_REPO_URL:-https://${DEPLOY_USER}:${DEPLOY_TOKEN}@gitea.cyaren.com/cmedina/agenx-lora-training.git}"
-else
-    REPO_URL="${LORA_DEPLOY_REPO_URL:-https://gitea.cyaren.com/cmedina/agenx-lora-training.git}"
-fi
+# Configuration
+REPO_URL="${LORA_DEPLOY_REPO_URL:-https://gitea.cyaren.com/cmedina/agenx-lora-training.git}"
 REPO_DIR="${LORA_DEPLOY_REPO_DIR:-/opt/loras/agenx-lora-training}"
 BRANCH="${LORA_DEPLOY_BRANCH:-main}"
-REMOTE="${LORA_DEPLOY_REMOTE:-origin}"
 
 RUN_USER="${SUDO_USER:-$(id -un)}"
 RUN_GROUP="$(id -gn "${RUN_USER}" 2>/dev/null || id -gn)"
 REPO_PARENT="$(dirname "${REPO_DIR}")"
 
-echo "=== LoRA Training Git Deploy ==="
+echo "=== LoRA Training Deploy ==="
 echo "Repo:   ${REPO_URL}"
 echo "Dir:    ${REPO_DIR}"
 echo "Branch: ${BRANCH}"
+echo ""
 
+# Create parent directory if needed
 if [ ! -d "${REPO_PARENT}" ]; then
+    echo "Creating ${REPO_PARENT}..."
     sudo install -d -m 0755 -o "${RUN_USER}" -g "${RUN_GROUP}" "${REPO_PARENT}"
 fi
 
+# Clone or update repo
 if [ ! -d "${REPO_DIR}/.git" ]; then
+    echo "Cloning repository..."
     git clone --branch "${BRANCH}" "${REPO_URL}" "${REPO_DIR}"
+else
+    echo "Repository exists, pulling latest..."
+    cd "${REPO_DIR}"
+    git fetch origin "${BRANCH}"
+    git checkout "${BRANCH}"
+    git pull --ff-only origin "${BRANCH}"
 fi
 
-cd "${REPO_DIR}"
-
-# Ensure the remote URL matches the authenticated HTTPS URL.
-git remote set-url "${REMOTE}" "${REPO_URL}"
-
-git fetch "${REMOTE}" "${BRANCH}"
-git checkout "${BRANCH}"
-git pull --ff-only "${REMOTE}" "${BRANCH}"
-
-# Setup Python environment and train
 echo ""
-echo "=== Setting up Python environment ==="
-python3 -m venv venv
-source venv/bin/activate
-pip install --upgrade pip
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-pip install transformers datasets trl peft accelerate bitsandbytes
-
-# Prepare dataset
+echo "=== Repository ready ==="
+echo "Location: ${REPO_DIR}"
 echo ""
-echo "=== Preparing dataset ==="
-python3 training/scripts/prepare_dataset.py --input dataset/combined_20k.jsonl --output training/data
-
-# Train
+echo "=== Next Steps ==="
 echo ""
-echo "=== Starting LoRA training ==="
-python3 training/scripts/train.py --config training/configs/llama2-7b-lora.yaml
-
-# Step 3: Setup Python environment
-echo "[3/5] Setting up Python environment..."
-cd "$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 "1. Download dataset (8.77 MB):"
+echo "   cd ${REPO_DIR}"
+echo "   curl -O https://gitea.cyaren.com/cmedina/agenx-lora-training/raw/branch/main/dataset/combined_20k.jsonl"
 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 "2. Setup Python + GPU drivers on your training server"
 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 "3. Prepare dataset:"
+echo "   python3 training/scripts/prepare_dataset.py --input dataset/combined_20k.jsonl --output training/data"
 echo ""
-echo "  Estimated training time: 6-24 hours (depending on GPU)"
+echo "4. Train LoRA (on GPU server):"
+echo "   python3 training/scripts/train.py --config training/configs/llama2-7b-lora.yaml"
 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 "=== To train now (if on GPU server) ==="
+echo "   bash ${REPO_DIR}/train-on-this-server.sh"
 echo ""