Multifile input in inferoxy

client.py

import uuid
import os
from pathlib import Path

import numpy as np
from PIL import Image
import zmq


def make_package(uid, path_to_images):
    result_images = []
    for f in os.listdir(path_to_images):
        result_images.append(np.asarray(Image.open(path_to_images / f)))

    result_tensor = np.stack(result_images)
    package = {
        "source_id": uid,
        "model": "multiple_views_embed",
        "inputs": [
            {
                "data": result_tensor,
                "parameters": {},
            }
        ],
    }

    return package


def main():
    context = zmq.Context()
    input_socket = context.socket(zmq.PUSH)
    input_socket.connect("tcp://localhost:7787")
    output_socket = context.socket(zmq.DEALER)
    uid = str(uuid.uuid4())
    output_socket.setsockopt(zmq.IDENTITY, uid.encode("utf-8"))
    output_socket.connect("tcp://localhost:7788")

    package = make_package(uid, Path("./images/"))

    input_socket.send_pyobj(package)
    result = output_socket.recv_pyobj()

    print(result)


if __name__ == "__main__":
    main()

Dockerfile

FROM registry.visionhub.ru/models/base:v5 AS base

# ============ BEGIN User model environment ============
FROM ubuntu:18.04

RUN apt-get update && apt-get install -y libsm6 libxext6 libxrender-dev libglib2.0-0 ffmpeg zlib1g-dev libjpeg-dev
RUN apt-get install -y python3-pip 
RUN update-alternatives --install /usr/bin/python python /usr/bin/python3 1 
RUN update-alternatives --install /usr/bin/pip pip /usr/bin/pip3 1
RUN pip install -U pip
RUN pip install opencv-python moviepy

WORKDIR app
COPY . .
# ============ END User model environment ============

# ============ BEGIN Vision Hub API ============
COPY --from=base /model_base/requirements.txt /model_base/requirements.txt

# Environment params for runner
# Install python requirements needed to run Vision Hub API
RUN pip install -r /model_base/requirements.txt

COPY --from=base /model_base /model_base
# Run command for docker container
# PARAMS for `container_runner.py`:
#   '--dataset_addr' - 'Address of socket to connect to the dataset queue'
#   '--dataset_sync_addr' - 'Address of a socket to synchornize with the dataset queue'
#   '--result_addr' - 'Address of a socket to connect to the results queue'
#   '--result_sync_addr' - 'Address of a socket to synchornize with the results queue'
#   '--TEST_MODE' - '1 if needed to run in test mode, 0 otherwise'
CMD python -u /model_base/container_runner.py --dataset_addr=${dataset_addr} \
                                              --result_addr=${result_addr} \
                                              --test_mode=$TEST_MODE --batch_size=$BATCH_SIZE \
# ============ END Vision Hub API ============

main.py

from typing import List, Dict, Union

import numpy as np

from loguru import logger


def predict_batch(
    samples: List[Dict[str, Union[np.ndarray, str]]], draw: bool = True
) -> np.ndarray:
    results = []
    # iterate over batch
    for sample in samples:
        # read input image parameters
        images = sample["image"]
        logger.info(f"Images tensor shape: {images.shape}")

        prediction = {"embedding": np.random.random((1024,))}
        # the must have key
        result = {"prediction": prediction, "image": None}
        # collect results for batch of samples
        results.append(result)

    return results

model.log

2021-07-04 12:22:25.751 | INFO     | shared_modules.parse_config:read_config_with_env:37 - Read config for  from /model_base/config.yaml
2021-07-04 12:22:25.751 | DEBUG    | shared_modules.parse_config:read_config_with_env:40 - Without environment: {'zmq_sndhwm': 100000, 'zmq_rcvhwm': 100000, 'zmq_sndtimeo': 3600000, 'zmq_rcvtimeo': 3600000}
2021-07-04 12:22:25.752 | DEBUG    | shared_modules.parse_config:read_config_with_env:44 - Setted with environment: {'zmq_sndhwm': 100000, 'zmq_rcvhwm': 100000, 'zmq_sndtimeo': 3600000, 'zmq_rcvtimeo': 3600000}
2021-07-04 12:22:25.753 | INFO     | runner:__init__:38 - Runner inited
2021-07-04 12:22:32.661 | DEBUG    | runner:_process_next_batch:81 - Batch received MinimalBatchObject(uid='ba5db9d9-6fdb-4428-9629-6250a55929c8', requests_info=[RequestInfo(input=(2, 4914, 8775, 3), parameters={})], model=ModelObject(name='multiple_views_embed', address='registry.visionhub.ru/models/multiple_views_embed:v5', stateless=False, batch_size=1, run_on_gpu=False), retries=0, status=<Status.SENT_TO_MODEL: 'SENT_TO_MODEL'>, source_id='zmq:50014a66-10ee-4592-953c-0254abd7dd50', created_at=datetime.datetime(2021, 7, 4, 12, 22, 20, 176725), queued_at=datetime.datetime(2021, 7, 4, 12, 22, 20, 728910), started_at=datetime.datetime(2021, 7, 4, 12, 22, 31, 786155), processed_at=None, done_at=None, sent_at=None, debached_at=None)
2021-07-04 12:22:32.666 | INFO     | model:predict_batch:36 - Images tensor shape: (2, 4914, 8775, 3)
2021-07-04 12:22:32.668 | INFO     | runner:_process_next_batch:104 - [{'prediction': {'embedding': array([0.47339265, 0.82242602, 0.35913685, ..., 0.61270482, 0.18704969,
       0.70998982])}, 'image': None}]
2021-07-04 12:22:32.670 | INFO     | runner:_process_next_batch:107 - ResponseBatch(uid='ba5db9d9-6fdb-4428-9629-6250a55929c8', requests_info=[RequestInfo(input=(2, 4914, 8775, 3), parameters={})], model=ModelObject(name='multiple_views_embed', address='registry.visionhub.ru/models/multiple_views_embed:v5', stateless=False, batch_size=1, run_on_gpu=False), retries=0, status=<Status.SENT_TO_MODEL: 'SENT_TO_MODEL'>, source_id=None, created_at=datetime.datetime(2021, 7, 4, 12, 22, 20, 176725), queued_at=datetime.datetime(2021, 7, 4, 12, 22, 20, 728910), started_at=datetime.datetime(2021, 7, 4, 12, 22, 31, 786155), processed_at=None, done_at=None, sent_at=datetime.datetime(2021, 7, 4, 12, 22, 20, 728910), debached_at=None, mini_batches=[MiniResponseBatch(responses_info=[ResponseInfo(output={'embedding': '[0.4733...'}, parameters={}, picture=None)])], error=None)