mirror of
https://github.com/azaion/detections.git
synced 2026-04-22 07:06:32 +00:00
[AZ-180] Update Jetson deployment documentation and remove obsolete task file
- Added Jetson-specific deployment instructions to `deploy_scripts.md`, detailing prerequisites and service management. - Updated `deploy_status_report.md` to reflect the completion of the AZ-180 cycle and the readiness of Jetson support. - Removed outdated task documentation for Jetson Orin Nano support from the todo list. Made-with: Cursor
This commit is contained in:
Oleksandr Bezdieniezhnykh
Executable
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#!/bin/bash
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# Deploy Azaion detections demo stack to a Jetson over SSH.
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#
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# Usage:
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# JETSON_HOST=192.168.x.x bash scripts/deploy_demo_jetson.sh \
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# --onnx /local/path/azaion.onnx \
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# --classes /local/path/classes.json \
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# [--int8-cache /local/path/azaion.int8_calib.cache] \
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# [--calibration-images /local/path/images/]
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#
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# Optional env vars:
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# JETSON_HOST (required) IP or hostname of the Jetson
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# JETSON_USER SSH user (default: jetson)
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# REMOTE_DIR Path on Jetson to deploy into (default: ~/detections)
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
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JETSON_HOST="${JETSON_HOST:-}"
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JETSON_USER="${JETSON_USER:-jetson}"
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REMOTE_DIR="${REMOTE_DIR:-~/detections}"
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ONNX_PATH=""
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CLASSES_PATH=""
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INT8_CACHE_PATH=""
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CALIBRATION_IMAGES=""
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usage() {
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echo "Usage: JETSON_HOST=<ip> bash $0 --onnx <path> --classes <path> [options]"
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echo ""
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echo "Required:"
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echo " --onnx <path> Local path to azaion.onnx"
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echo " --classes <path> Local path to classes.json"
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echo ""
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echo "Optional:"
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echo " --int8-cache <path> Local path to azaion.int8_calib.cache (skips calibration)"
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echo " --calibration-images <dir> Local image directory; rsync to Jetson and run INT8 calibration"
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echo " --help Show this message"
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echo ""
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echo "Env vars:"
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echo " JETSON_HOST (required) Jetson IP or hostname"
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echo " JETSON_USER SSH user (default: jetson)"
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echo " REMOTE_DIR Deploy directory on Jetson (default: ~/detections)"
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exit 0
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}
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--onnx) ONNX_PATH="$2"; shift 2 ;;
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--classes) CLASSES_PATH="$2"; shift 2 ;;
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--int8-cache) INT8_CACHE_PATH="$2"; shift 2 ;;
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--calibration-images) CALIBRATION_IMAGES="$2"; shift 2 ;;
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--help) usage ;;
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*) echo "Unknown argument: $1"; usage ;;
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esac
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done
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[[ -z "$JETSON_HOST" ]] && { echo "ERROR: JETSON_HOST is required"; exit 1; }
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[[ -z "$ONNX_PATH" ]] && { echo "ERROR: --onnx is required"; exit 1; }
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[[ -z "$CLASSES_PATH" ]] && { echo "ERROR: --classes is required"; exit 1; }
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[[ -f "$ONNX_PATH" ]] || { echo "ERROR: ONNX file not found: $ONNX_PATH"; exit 1; }
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[[ -f "$CLASSES_PATH" ]] || { echo "ERROR: classes.json not found: $CLASSES_PATH"; exit 1; }
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SSH="ssh ${JETSON_USER}@${JETSON_HOST}"
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SCP="scp"
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echo "=== Azaion Demo — Jetson Deployment ==="
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echo " Host: ${JETSON_USER}@${JETSON_HOST}"
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echo " Remote dir: ${REMOTE_DIR}"
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echo ""
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# ── 1. Sync project ─────────────────────────────────────────────────────────
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echo "--- Syncing project files ---"
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$SSH "mkdir -p ${REMOTE_DIR}/demo/models"
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rsync -az --exclude='.git' --exclude='__pycache__' --exclude='*.pyc' \
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--exclude='*.egg-info' --exclude='.venv' --exclude='demo/models' \
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"${PROJECT_ROOT}/" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/"
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# ── 2. Upload model artifacts ────────────────────────────────────────────────
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echo "--- Uploading model artifacts ---"
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$SCP "$ONNX_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/azaion.onnx"
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$SCP "$CLASSES_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/classes.json"
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if [[ -n "$INT8_CACHE_PATH" ]]; then
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[[ -f "$INT8_CACHE_PATH" ]] || { echo "ERROR: INT8 cache not found: $INT8_CACHE_PATH"; exit 1; }
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echo "--- Uploading INT8 calibration cache ---"
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$SCP "$INT8_CACHE_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/azaion.int8_calib.cache"
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fi
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# ── 3. Optional: run INT8 calibration on Jetson ──────────────────────────────
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if [[ -n "$CALIBRATION_IMAGES" ]] && [[ -z "$INT8_CACHE_PATH" ]]; then
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[[ -d "$CALIBRATION_IMAGES" ]] || { echo "ERROR: calibration images dir not found: $CALIBRATION_IMAGES"; exit 1; }
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echo "--- Syncing calibration images to Jetson ---"
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$SSH "mkdir -p ${REMOTE_DIR}/demo/calibration"
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rsync -az "${CALIBRATION_IMAGES}/" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/calibration/"
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echo "--- Building detections image for calibration ---"
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$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml build detections"
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echo "--- Running INT8 calibration (this takes several minutes) ---"
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$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml run --rm \
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-v ${REMOTE_DIR}/demo/calibration:/calibration \
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detections \
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python3 scripts/generate_int8_cache.py \
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--images-dir /calibration \
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--onnx /models/azaion.onnx \
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--output /models/azaion.int8_calib.cache"
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echo "--- Calibration cache written to ${REMOTE_DIR}/demo/models/azaion.int8_calib.cache ---"
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fi
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# ── 4. Start services ────────────────────────────────────────────────────────
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echo "--- Building and starting services ---"
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$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml up -d --build"
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# ── 5. Health check ───────────────────────────────────────────────────────────
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echo "--- Health check ---"
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HEALTH_URL="http://${JETSON_HOST}:8080/health"
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MAX_RETRIES=15
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RETRY_INTERVAL=5
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for i in $(seq 1 $MAX_RETRIES); do
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STATUS=$(curl -s -o /dev/null -w "%{http_code}" "$HEALTH_URL" 2>/dev/null || true)
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if [[ "$STATUS" == "200" ]]; then
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echo ""
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echo "=== Demo is live at http://${JETSON_HOST}:8080 ==="
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echo ""
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echo "Endpoints:"
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echo " POST http://${JETSON_HOST}:8080/detect/image"
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echo " POST http://${JETSON_HOST}:8080/detect/video"
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echo " GET http://${JETSON_HOST}:8080/health"
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echo ""
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echo "Note: On first start the service converts azaion.onnx to a TRT engine."
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echo " Check /health until AI status shows 'enabled'."
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exit 0
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fi
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echo " Waiting for service… (${i}/${MAX_RETRIES}, HTTP ${STATUS})"
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sleep "$RETRY_INTERVAL"
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done
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echo "ERROR: Health check failed after $((MAX_RETRIES * RETRY_INTERVAL))s"
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echo "Check logs with: ssh ${JETSON_USER}@${JETSON_HOST} \"cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml logs detections\""
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exit 1
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#!/usr/bin/env python3
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"""
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Generate an INT8 calibration cache for TensorRT on Jetson.
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Run this INSIDE the Jetson Docker container:
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docker compose -f docker-compose.demo-jetson.yml run --rm \
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-v /path/to/images:/calibration \
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detections \
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python3 scripts/generate_int8_cache.py \
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--images-dir /calibration \
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--onnx /models/azaion.onnx \
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--output /models/azaion.int8_calib.cache
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The cache file must be in the loader's models volume so the detections
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service can download it on startup via the Loader API.
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"""
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import argparse
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--images-dir", required=True, help="Directory with calibration images (JPG/PNG)")
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parser.add_argument("--onnx", required=True, help="Path to azaion.onnx")
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parser.add_argument("--output", default="azaion.int8_calib.cache")
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parser.add_argument("--input-size", type=int, default=1280, help="Model input H=W (default 1280)")
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parser.add_argument("--num-samples", type=int, default=500)
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return parser.parse_args()
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def collect_images(images_dir: str, num_samples: int) -> list[Path]:
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root = Path(images_dir)
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images: list[Path] = []
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for pattern in ("**/*.jpg", "**/*.jpeg", "**/*.png"):
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images += sorted(root.glob(pattern))
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return images[:num_samples]
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def preprocess(path: Path, h: int, w: int) -> np.ndarray | None:
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img = cv2.imread(str(path))
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if img is None:
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return None
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = cv2.resize(img, (w, h))
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img = img.astype(np.float32) / 255.0
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return np.ascontiguousarray(img.transpose(2, 0, 1)[np.newaxis]) # NCHW
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def main():
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args = parse_args()
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try:
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import pycuda.autoinit # noqa: F401
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import pycuda.driver as cuda
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import tensorrt as trt
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except ImportError as e:
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print(f"ERROR: {e}\nRun this script inside the Jetson Docker container.", file=sys.stderr)
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sys.exit(1)
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images = collect_images(args.images_dir, args.num_samples)
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if not images:
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print(f"No images found in {args.images_dir}", file=sys.stderr)
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sys.exit(1)
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print(f"Using {len(images)} calibration images")
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H = W = args.input_size
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class _ImageCalibrator(trt.IInt8EntropyCalibrator2):
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def __init__(self):
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super().__init__()
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self._idx = 0
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self._buf = cuda.mem_alloc(3 * H * W * 4)
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def get_batch_size(self):
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return 1
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def get_batch(self, names):
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while self._idx < len(images):
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arr = preprocess(images[self._idx], H, W)
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self._idx += 1
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if arr is None:
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continue
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cuda.memcpy_htod(self._buf, arr)
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return [int(self._buf)]
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return None
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def read_calibration_cache(self):
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return None
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def write_calibration_cache(self, cache):
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with open(args.output, "wb") as f:
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f.write(cache)
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print(f"Cache written → {args.output}")
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onnx_data = Path(args.onnx).read_bytes()
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logger = trt.Logger(trt.Logger.INFO)
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explicit_batch = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
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with (
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trt.Builder(logger) as builder,
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builder.create_network(explicit_batch) as network,
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trt.OnnxParser(network, logger) as parser,
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builder.create_builder_config() as config,
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):
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config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 4 * 1024 ** 3)
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config.set_flag(trt.BuilderFlag.INT8)
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if builder.platform_has_fast_fp16:
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config.set_flag(trt.BuilderFlag.FP16)
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calibrator = _ImageCalibrator()
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config.int8_calibrator = calibrator
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if not parser.parse(onnx_data):
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for i in range(parser.num_errors):
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print(parser.get_error(i), file=sys.stderr)
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sys.exit(1)
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inp = network.get_input(0)
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shape = inp.shape
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C = shape[1]
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if shape[0] == -1:
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profile = builder.create_optimization_profile()
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profile.set_shape(inp.name, (1, C, H, W), (1, C, H, W), (1, C, H, W))
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config.add_optimization_profile(profile)
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print("Building TensorRT engine with INT8 calibration (several minutes on Jetson)…")
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plan = builder.build_serialized_network(network, config)
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if plan is None:
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print("Engine build failed", file=sys.stderr)
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sys.exit(1)
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print("Done. Upload the cache to the Loader before (re)starting the detections service.")
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if __name__ == "__main__":
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main()
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