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Update Docker configurations and dependencies for Jetson deployment

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- Added image specifications for services in `docker-compose.demo-jetson.yml` and `docker-compose.jetson.yml` to streamline deployment.
- Updated `Dockerfile.gpu` to use the development version of the CUDA runtime for enhanced compatibility.
- Modified `Dockerfile.jetson` to switch to a newer JetPack base image and adjusted the requirements file to include additional dependencies for improved functionality.
- Removed obsolete deployment scripts and calibration cache generation script to clean up the project structure.

Made-with: Cursor
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-04-06 05:00:08 +03:00
parent 8116b55813
commit 26900d0aee
10 changed files with 103 additions and 154 deletions
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.dockerignore
+18
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@@ -0,0 +1,18 @@
.venv
.venv-e2e
_docs
e2e
tests
data
demo
Logs
build
__pycache__
*.pyc
*.pyd
*.so
*.egg-info
.pytest_cache
.git
*.md
scripts
Dockerfile.gpu
+1 -1
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@@ -1,4 +1,4 @@
FROM nvidia/cuda:12.2.0-runtime-ubuntu22.04
FROM nvidia/cuda:12.2.0-devel-ubuntu22.04
RUN apt-get update && apt-get install -y python3 python3-pip python3-dev gcc libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY requirements.txt requirements-gpu.txt ./
Dockerfile.jetson
+2 -5
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@@ -1,17 +1,14 @@
FROM nvcr.io/nvidia/l4t-base:r36.3.0
FROM nvcr.io/nvidia/l4t-jetpack:r36.2.0
RUN apt-get update && apt-get install -y \
python3 python3-pip python3-dev gcc \
libgl1 libglib2.0-0 \
python3-libnvinfer python3-libnvinfer-dev \
python3-pycuda \
&& rm -rf /var/lib/apt/lists/*
RUN python3 -c "import tensorrt" || \
(echo "TensorRT Python bindings not found; check PYTHONPATH for JetPack installation" && exit 1)
WORKDIR /app
COPY requirements-jetson.txt ./
COPY requirements.txt requirements-jetson.txt ./
RUN pip3 install --no-cache-dir -r requirements-jetson.txt
COPY . .
RUN python3 setup.py build_ext --inplace
docker-compose.demo-jetson.yml
+3
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@@ -2,6 +2,7 @@ name: detections-demo-jetson
services:
loader:
image: ${REGISTRY:-docker.azaion.com}/detections-loader-mock:${TAG:-latest}
build:
context: ./e2e/mocks/loader
ports:
@@ -12,6 +13,7 @@ services:
- demo-net
annotations:
image: ${REGISTRY:-docker.azaion.com}/detections-annotations-mock:${TAG:-latest}
build:
context: ./e2e/mocks/annotations
ports:
@@ -20,6 +22,7 @@ services:
- demo-net
detections:
image: ${REGISTRY:-docker.azaion.com}/detections-jetson:${TAG:-latest}
build:
context: .
dockerfile: Dockerfile.jetson
docker-compose.jetson.yml
+1 -3
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@@ -2,9 +2,7 @@ name: detections-jetson
services:
detections:
build:
context: .
dockerfile: Dockerfile.jetson
image: ${REGISTRY:-docker.azaion.com}/detections-jetson:${TAG:-latest}
ports:
- "8080:8080"
runtime: nvidia
requirements-jetson.txt
+14 -1
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@@ -1 +1,14 @@
-r requirements.txt
fastapi==0.135.2
uvicorn[standard]==0.42.0
PyJWT==2.12.1
h11==0.16.0
python-multipart==0.0.22
Cython==3.2.4
opencv-python==4.10.0.84
numpy==2.2.6
pynvml==12.0.0
requests==2.32.4
loguru==0.7.3
av==14.2.0
xxhash==3.5.0
pycuda
requirements.txt
+1 -1
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@@ -5,7 +5,7 @@ h11==0.16.0
python-multipart==0.0.22
Cython==3.2.4
opencv-python==4.10.0.84
numpy==2.3.0
numpy==2.2.6
onnxruntime==1.22.0
pynvml==12.0.0
requests==2.32.4
scripts/deploy_demo_jetson.sh
-143
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@@ -1,143 +0,0 @@
#!/bin/bash
# Deploy Azaion detections demo stack to a Jetson over SSH.
#
# Usage:
# JETSON_HOST=192.168.x.x bash scripts/deploy_demo_jetson.sh \
# --onnx /local/path/azaion.onnx \
# --classes /local/path/classes.json \
# [--int8-cache /local/path/azaion.int8_calib.cache] \
# [--calibration-images /local/path/images/]
#
# Optional env vars:
# JETSON_HOST (required) IP or hostname of the Jetson
# JETSON_USER SSH user (default: jetson)
# REMOTE_DIR Path on Jetson to deploy into (default: ~/detections)
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PROJECT_ROOT="$(dirname "$SCRIPT_DIR")"
JETSON_HOST="${JETSON_HOST:-}"
JETSON_USER="${JETSON_USER:-jetson}"
REMOTE_DIR="${REMOTE_DIR:-~/detections}"
ONNX_PATH=""
CLASSES_PATH=""
INT8_CACHE_PATH=""
CALIBRATION_IMAGES=""
usage() {
echo "Usage: JETSON_HOST=<ip> bash $0 --onnx <path> --classes <path> [options]"
echo ""
echo "Required:"
echo " --onnx <path> Local path to azaion.onnx"
echo " --classes <path> Local path to classes.json"
echo ""
echo "Optional:"
echo " --int8-cache <path> Local path to azaion.int8_calib.cache (skips calibration)"
echo " --calibration-images <dir> Local image directory; rsync to Jetson and run INT8 calibration"
echo " --help Show this message"
echo ""
echo "Env vars:"
echo " JETSON_HOST (required) Jetson IP or hostname"
echo " JETSON_USER SSH user (default: jetson)"
echo " REMOTE_DIR Deploy directory on Jetson (default: ~/detections)"
exit 0
}
while [[ $# -gt 0 ]]; do
case "$1" in
--onnx) ONNX_PATH="$2"; shift 2 ;;
--classes) CLASSES_PATH="$2"; shift 2 ;;
--int8-cache) INT8_CACHE_PATH="$2"; shift 2 ;;
--calibration-images) CALIBRATION_IMAGES="$2"; shift 2 ;;
--help) usage ;;
*) echo "Unknown argument: $1"; usage ;;
esac
done
[[ -z "$JETSON_HOST" ]] && { echo "ERROR: JETSON_HOST is required"; exit 1; }
[[ -z "$ONNX_PATH" ]] && { echo "ERROR: --onnx is required"; exit 1; }
[[ -z "$CLASSES_PATH" ]] && { echo "ERROR: --classes is required"; exit 1; }
[[ -f "$ONNX_PATH" ]] || { echo "ERROR: ONNX file not found: $ONNX_PATH"; exit 1; }
[[ -f "$CLASSES_PATH" ]] || { echo "ERROR: classes.json not found: $CLASSES_PATH"; exit 1; }
SSH="ssh ${JETSON_USER}@${JETSON_HOST}"
SCP="scp"
echo "=== Azaion Demo — Jetson Deployment ==="
echo " Host: ${JETSON_USER}@${JETSON_HOST}"
echo " Remote dir: ${REMOTE_DIR}"
echo ""
# ── 1. Sync project ─────────────────────────────────────────────────────────
echo "--- Syncing project files ---"
$SSH "mkdir -p ${REMOTE_DIR}/demo/models"
rsync -az --exclude='.git' --exclude='__pycache__' --exclude='*.pyc' \
--exclude='*.egg-info' --exclude='.venv' --exclude='demo/models' \
"${PROJECT_ROOT}/" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/"
# ── 2. Upload model artifacts ────────────────────────────────────────────────
echo "--- Uploading model artifacts ---"
$SCP "$ONNX_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/azaion.onnx"
$SCP "$CLASSES_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/classes.json"
if [[ -n "$INT8_CACHE_PATH" ]]; then
[[ -f "$INT8_CACHE_PATH" ]] || { echo "ERROR: INT8 cache not found: $INT8_CACHE_PATH"; exit 1; }
echo "--- Uploading INT8 calibration cache ---"
$SCP "$INT8_CACHE_PATH" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/models/azaion.int8_calib.cache"
fi
# ── 3. Optional: run INT8 calibration on Jetson ──────────────────────────────
if [[ -n "$CALIBRATION_IMAGES" ]] && [[ -z "$INT8_CACHE_PATH" ]]; then
[[ -d "$CALIBRATION_IMAGES" ]] || { echo "ERROR: calibration images dir not found: $CALIBRATION_IMAGES"; exit 1; }
echo "--- Syncing calibration images to Jetson ---"
$SSH "mkdir -p ${REMOTE_DIR}/demo/calibration"
rsync -az "${CALIBRATION_IMAGES}/" "${JETSON_USER}@${JETSON_HOST}:${REMOTE_DIR}/demo/calibration/"
echo "--- Building detections image for calibration ---"
$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml build detections"
echo "--- Running INT8 calibration (this takes several minutes) ---"
$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml run --rm \
-v ${REMOTE_DIR}/demo/calibration:/calibration \
detections \
python3 scripts/generate_int8_cache.py \
--images-dir /calibration \
--onnx /models/azaion.onnx \
--output /models/azaion.int8_calib.cache"
echo "--- Calibration cache written to ${REMOTE_DIR}/demo/models/azaion.int8_calib.cache ---"
fi
# ── 4. Start services ────────────────────────────────────────────────────────
echo "--- Building and starting services ---"
$SSH "cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml up -d --build"
# ── 5. Health check ───────────────────────────────────────────────────────────
echo "--- Health check ---"
HEALTH_URL="http://${JETSON_HOST}:8080/health"
MAX_RETRIES=15
RETRY_INTERVAL=5
for i in $(seq 1 $MAX_RETRIES); do
STATUS=$(curl -s -o /dev/null -w "%{http_code}" "$HEALTH_URL" 2>/dev/null || true)
if [[ "$STATUS" == "200" ]]; then
echo ""
echo "=== Demo is live at http://${JETSON_HOST}:8080 ==="
echo ""
echo "Endpoints:"
echo " POST http://${JETSON_HOST}:8080/detect/image"
echo " POST http://${JETSON_HOST}:8080/detect/video"
echo " GET http://${JETSON_HOST}:8080/health"
echo ""
echo "Note: On first start the service converts azaion.onnx to a TRT engine."
echo " Check /health until AI status shows 'enabled'."
exit 0
fi
echo " Waiting for service… (${i}/${MAX_RETRIES}, HTTP ${STATUS})"
sleep "$RETRY_INTERVAL"
done
echo "ERROR: Health check failed after $((MAX_RETRIES * RETRY_INTERVAL))s"
echo "Check logs with: ssh ${JETSON_USER}@${JETSON_HOST} \"cd ${REMOTE_DIR} && docker compose -f docker-compose.demo-jetson.yml logs detections\""
exit 1
scripts/generate_int8_cache.py → scripts/jetson/generate_int8_cache.py
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scripts/jetson/sample_calibration_images.py
+63
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@@ -0,0 +1,63 @@
#!/usr/bin/env python3
"""
Sample a random subset of images from a YOLO dataset for INT8 calibration.
Run locally (on your dev machine) before deploying to Jetson:
python3 scripts/jetson/sample_calibration_images.py \
--dataset /path/to/dataset-2025-05-22 \
--output /tmp/calibration \
--num-samples 500
The output directory can then be passed directly to deploy_demo_jetson.sh
via --calibration-images, or to generate_int8_cache.py via --images-dir.
"""
import argparse
import random
import shutil
import sys
from pathlib import Path
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", required=True, help="Root of the YOLO dataset (must contain images/)")
parser.add_argument("--output", required=True, help="Destination directory for sampled images")
parser.add_argument("--num-samples", type=int, default=500)
parser.add_argument("--seed", type=int, default=42)
return parser.parse_args()
def collect_images(dataset_root: Path) -> list[Path]:
images_dir = dataset_root / "images"
if not images_dir.is_dir():
print(f"ERROR: {images_dir} not found", file=sys.stderr)
sys.exit(1)
images: list[Path] = []
for pattern in ("**/*.jpg", "**/*.jpeg", "**/*.png"):
images += sorted(images_dir.glob(pattern))
return images
def main():
args = parse_args()
dataset_root = Path(args.dataset)
output_dir = Path(args.output)
images = collect_images(dataset_root)
if not images:
print(f"ERROR: no images found in {dataset_root / 'images'}", file=sys.stderr)
sys.exit(1)
rng = random.Random(args.seed)
sample = rng.sample(images, min(args.num_samples, len(images)))
output_dir.mkdir(parents=True, exist_ok=True)
for src in sample:
shutil.copy2(src, output_dir / src.name)
print(f"Sampled {len(sample)} images → {output_dir}")
if __name__ == "__main__":
main()