ovuruska

services:
  iot_1:
    build:
      context: .
      dockerfile: iot.Dockerfile
    networks:
      closed_network:
        ipv4_address: 10.5.0.2

  iot_2:

ovuruska

version: "3.5"
#$ docker run -d -p 6379:6379 redis
services:
  redis:
    image : redis
    ports:
      - "6379:6379"
  api_server:
    depends_on:
      - redis

ovuruska

version: "3.5"
#$ docker run -d -p 6379:6379 redis
services:
  redis:
    image : redis
    ports:
      - "6379:6379"

  api_server:
    depends_on:

ovuruska

version: "3.5"
#$ docker run -d -p 6379:6379 redis
services:
  redis:
    image : redis
    ports:
      - "6379:6379"

  api_server:
    depends_on:

ovuruska

version: "3.5"
#$ docker run -d -p 6379:6379 redis
services:
  redis:
    image : redis
    ports:
      - "6379:6379"

  api_server:
    depends_on:

ovuruska

version: "3"

networks:
  cctv:
    # IP Address Management
    ipam:
      config:
        - subnet: 192.168.0.0/16

services:

ovuruska

version: "3.5"

services:


  camera_1: &camera
    restart: unless-stopped
    container_name: camera_1
    build: ./camera
    environment:

ovuruska

FROM nvcr.io/nvidia/pytorch:20.03-py3

RUN git clone https://github.com/NVIDIA-AI-IOT/torch2trt
RUN cd torch2trt && python setup.py install --plugins

VOLUME ./jetson/ ./jetson/


WORKDIR /workspace

ovuruska

pip3 install -r requirements.txt


# Install protobuf
python3 -c "import google.protobuf as pb;from packaging.version import parse;assert(parse(pb.__version__) >= parse('3.0.0'))"
STATUS_CODE=$?


if [[ $STATUS_CODE != 0 ]]; then

ovuruska

from torch.utils.data import Subset,DataLoader
from sklearn.model_selection import train_test_split


def train_val_dataset(dataset, val_split=0.2,batch_size=16):
    train_idx, val_idx = train_test_split(list(range(len(dataset))), test_size=val_split)
    datasets = {}
    datasets['train'] = Subset(dataset, train_idx)
    datasets['val'] = Subset(dataset, val_idx)