From 0d2277f92111765f99c668b1d2efa73cc4366a07 Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Mon, 17 Dec 2018 15:36:15 +0100 Subject: [PATCH 1/6] initial version --- examples/models/openvino_R4/Makefile | 12 +++ examples/models/openvino_R4/Prediction.py | 100 ++++++++++++++++++ examples/models/openvino_R4/README.md | 8 ++ examples/models/openvino_R4/client.py | 103 +++++++++++++++++++ examples/models/openvino_R4/dog.jpeg | Bin 0 -> 7694 bytes examples/models/openvino_R4/environment_grpc | 8 ++ examples/models/openvino_R4/requirements.txt | 2 + wrappers/s2i/python/Makefile | 4 + wrappers/s2i/python/README.md | 2 + 9 files changed, 239 insertions(+) create mode 100644 examples/models/openvino_R4/Makefile create mode 100644 examples/models/openvino_R4/Prediction.py create mode 100644 examples/models/openvino_R4/README.md create mode 100644 examples/models/openvino_R4/client.py create mode 100644 examples/models/openvino_R4/dog.jpeg create mode 100644 examples/models/openvino_R4/environment_grpc create mode 100644 examples/models/openvino_R4/requirements.txt diff --git a/examples/models/openvino_R4/Makefile b/examples/models/openvino_R4/Makefile new file mode 100644 index 0000000000..22c5045cb1 --- /dev/null +++ b/examples/models/openvino_R4/Makefile @@ -0,0 +1,12 @@ + + +build_image: + s2i build -E environment_grpc . seldon_python_openvino seldon-openvino-prediction + + +clean: + rm -rf model + rm -rf proto/__pycache__ + rm -f proto/*.py + rm -r proto/*.proto + rm -rf tensorflow diff --git a/examples/models/openvino_R4/Prediction.py b/examples/models/openvino_R4/Prediction.py new file mode 100644 index 0000000000..6997961a29 --- /dev/null +++ b/examples/models/openvino_R4/Prediction.py @@ -0,0 +1,100 @@ +import numpy as np +import logging +import datetime +import os +import sys +from urllib.parse import urlparse +from google.cloud import storage +from openvino.inference_engine import IENetwork, IEPlugin + + +def get_logger(name): + logger = logging.getLogger(name) + log_formatter = logging.Formatter("%(asctime)s - %(name)s - " + "%(levelname)s - %(message)s") + logger.setLevel('DEBUG') + + console_handler = logging.StreamHandler() + console_handler.setFormatter(log_formatter) + logger.addHandler(console_handler) + + return logger + +logger = get_logger(__name__) + + +def gs_download_file(path): + if path is None: + return None + parsed_path = urlparse(path) + bucket_name = parsed_path.netloc + file_path = parsed_path.path[1:] + gs_client = storage.Client() + bucket = gs_client.get_bucket(bucket_name) + blob = bucket.blob(file_path) + tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) + blob.download_to_filename(tmp_path) + return tmp_path + + +def s3_download_file(path): + if path is None: + return None + s3_endpoint = os.getenv('S3_ENDPOINT') + s3_client = boto3.client('s3', endpoint_url=s3_endpoint) + parsed_path = urlparse(path) + bucket_name = parsed_path.netloc + file_path = parsed_path.path[1:] + tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) + s3_transfer = boto3.s3.transfer.S3Transfer(s3_client) + s3_transfer.download_file(bucket_name, file_path, tmp_path) + return tmp_path + + +def GetLocalPath(requested_path): + parsed_path = urlparse(requested_path) + if parsed_path.scheme == '': + return requested_path + elif parsed_path.scheme == 'gs': + return gs_download_file(path=requested_path) + elif parsed_path.scheme == 's3': + return s3_download_file(path=requested_path) + + +class Prediction(object): + def __init__(self): + try: + xml_path = os.environ["XML_PATH"] + bin_path = os.environ["BIN_PATH"] + + except KeyError: + print("Please set the environment variables XML_PATH, BIN, PATH, INPUT and OUTPUT") + sys.exit(1) + + xml_local_path = GetLocalPath(xml_path) + bin_local_path = GetLocalPath(bin_path) + print('path object', xml_local_path) + + CPU_EXTENSION = os.getenv('CPU_EXTENSION', '/usr/local/lib/libcpu_extension.so') + + plugin = IEPlugin(device='CPU', plugin_dirs=None) + if CPU_EXTENSION: + plugin.add_cpu_extension(CPU_EXTENSION) + net = IENetwork.from_ir(model=xml_local_path, weights=bin_local_path) + self.input_blob = next(iter(net.inputs)) + self.out_blob = next(iter(net.outputs)) + self.batch_size = net.inputs[self.input_blob].shape[0] + self.inputs = net.inputs + self.outputs = net.outputs + self.exec_net = plugin.load(network=net, num_requests=self.batch_size) + + + def predict(self,X,feature_names): + start_time = datetime.datetime.now() + results = self.exec_net.infer(inputs={self.input_blob: X}) + predictions = results[self.out_blob] + end_time = datetime.datetime.now() + duration = (end_time - start_time).total_seconds() * 1000 + logger.debug("Processing time: {:.2f} ms".format(duration)) + return predictions.astype(np.float64) + diff --git a/examples/models/openvino_R4/README.md b/examples/models/openvino_R4/README.md new file mode 100644 index 0000000000..b1f5d759dc --- /dev/null +++ b/examples/models/openvino_R4/README.md @@ -0,0 +1,8 @@ +# OpenVINO prediction component + +Model configuration is implemented using environment variables: + +XML_PATH - s3, gs or local path to xml file in OpenVINO model server + +BIN_PATH - s3, gs or local path to bin file in OpenVINO model server + diff --git a/examples/models/openvino_R4/client.py b/examples/models/openvino_R4/client.py new file mode 100644 index 0000000000..fabc04a8d7 --- /dev/null +++ b/examples/models/openvino_R4/client.py @@ -0,0 +1,103 @@ +import json +import requests +import base64 +from proto import prediction_pb2 +from proto import prediction_pb2_grpc +import grpc +import numpy as np +import pickle +import tensorflow as tf +import cv2 +import datetime +import tensorflow as tf + +def image_2_vector(input_file): + nparr = np.fromfile(input_file, dtype=np.float32) + print("nparr",nparr.dtype,nparr.shape) + img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR) + print("img",img.dtype,img.shape) + print("Initial size",img.shape) + image = cv2.resize(img, (w, h)) + print("image",image.dtype) + print("Converted size",image.shape) + + vector = image.reshape((w * h * 3)) + print("vector shape",vector.shape, "vector type", vector.dtype ) + return vector + +def image_2_bytes(input_file): + with open(input_file, "rb") as binary_file: + # Read the whole file at once + data = binary_file.read() + + #data = data.tobytes() + #print(data) + print("binary data size:", len(data), type(data)) + return data + +def run(function,image_path,iterations=1): + w = 224 + h = 224 + + # NOTE(gRPC Python Team): .close() is possible on a channel and should be + # used in circumstances in which the with statement does not fit the needs + # of the code. + with grpc.insecure_channel('localhost:5000') as channel: + stub = prediction_pb2_grpc.ModelStub(channel) + print("seldon stub", stub) + start_time = datetime.datetime.now() + processing_times = np.zeros((0),int) + + img = cv2.imread(image_path) + print("img type", type(img)) + print("img",img.shape) + print("Initial size",img.shape) + image = cv2.resize(img, (w, h)) + image = image.reshape(1, w, h, 3) + image = image.transpose((0,3,1,2)) + print("image",image.dtype) + print("Converted size",image.shape) + + if function == "tensor": + datadef = prediction_pb2.DefaultData( + names = 'x', + tensor = prediction_pb2.Tensor( + shape = image.shape, + values = image.ravel().tolist() + ) + ) + elif function == "tftensor": + print("Create tftensor") + datadef = prediction_pb2.DefaultData( + names = 'x', + tftensor = tf.make_tensor_proto(image) + ) + + GRPC_request = prediction_pb2.SeldonMessage( + data = datadef + ) + + for I in range(iterations): + start_time = datetime.datetime.now() + response = stub.Predict(request=GRPC_request) + end_time = datetime.datetime.now() + duration = (end_time - start_time).total_seconds() * 1000 + processing_times = np.append(processing_times,np.array([int(duration)])) + + print('processing time for all iterations') + for x in processing_times: + print(x,"ms") + print('processing_statistics') + print('average time:',round(np.average(processing_times),1), 'ms; average speed:', round(1000/np.average(processing_times),1),'fps') + print('median time:',round(np.median(processing_times),1), 'ms; median speed:',round(1000/np.median(processing_times),1),'fps') + print('max time:',round(np.max(processing_times),1), 'ms; max speed:',round(1000/np.max(processing_times),1),'fps') + print('min time:',round(np.min(processing_times),1),'ms; min speed:',round(1000/np.min(processing_times),1),'fps') + print('time percentile 90:',round(np.percentile(processing_times,90),1),'ms; speed percentile 90:',round(1000/np.percentile(processing_times,90),1),'fps') + print('time percentile 50:',round(np.percentile(processing_times,50),1),'ms; speed percentile 50:',round(1000/np.percentile(processing_times,50),1),'fps') + print('time standard deviation:',round(np.std(processing_times))) + print('time variance:',round(np.var(processing_times))) + + +#run("tensor","./dog.jpeg",iterations=100) + +run("tftensor","./dog.jpeg",iterations=100) diff --git a/examples/models/openvino_R4/dog.jpeg b/examples/models/openvino_R4/dog.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..8d83ed006c277b2ae03eb9727c7d5617019a6892 GIT binary patch literal 7694 zcmbW*S5y@D0)F3sW^ePAlp(r3- zI#Q%4(jp+B^ro!;@7uF`&OYt#oipT;S+FQ*OT?z8r4uqyA9H~u&5|8TMVb5T%If+)fNaRDiU|8)=xB^57%`l^~K*xBzIpJWsbt9r_-x=vcS zl-Vzc3uc_|I=}Syo4@}<`!Cu59W46)OZLBD|JyYOU;qLCT^@)9paR%c>M%Bllu(s+ z(m{nfP859Mt23iHDy58QJx0G1oviCs(s{BYmDVflywB3_L~%Z836ouSoFI}hc;F+A zxk!GIH$&Pyfuq%$!D3mWxk2T85d}ifGxN6L4xg1t83mkk#BT~1j@>m$gq-(j*HmB5 zJ|M*%+La}lP8G@FiLrNP-_TA@B>d+c1WO7JoJE~fJ(O$jgKa_s>#dS?;n=e?ilO|g6@msiRqDv*;G9+*mX5lIn3afQ;fpr9Zj7fD2A4T)Vi%YVx{^m-plo80whRb{eV9oG3qt= z;qwGvibra=)xF}fQ;c3`)WO-b`1V&*hxhJE&BG}TyYYsWJuV=*6>Xo zrN9~{P91Ft!y*5<7E$`Q#$WqtWnjY)*FZl5t?s9b;L_?q7Q~)KlliXwHG73y2mYi8 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IMAGE_NAME=$(IMAGE_NAME)-candidate test/run +.PHONY: build_python_openvino +build_python_openvino: + docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t openvino_base:latest . + docker build -f Dockerfile_model_openvino --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t seldon_model_openvino . .PHONY: clean clean: diff --git a/wrappers/s2i/python/README.md b/wrappers/s2i/python/README.md index 41bc321df7..adec4d5f7a 100644 --- a/wrappers/s2i/python/README.md +++ b/wrappers/s2i/python/README.md @@ -13,6 +13,8 @@ e.g. from 0.3-SNAPSHOT to release 0.3 and create 0.4-SNAPSHOT ``` ./update_python_version.sh X X+1```, e.g ```./update_python_version.sh 0.2 0.3``` + * building openvino python base image +```docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t openvino_base:latest . ``` From 4c862ce7142efc4872d760aac89d41ccd48a65a7 Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Mon, 17 Dec 2018 15:40:10 +0100 Subject: [PATCH 2/6] example docker files --- wrappers/s2i/python/Dockerfile_model_openvino | 25 ++++++++ wrappers/s2i/python/Dockerfile_openvino_base | 63 +++++++++++++++++++ 2 files changed, 88 insertions(+) create mode 100644 wrappers/s2i/python/Dockerfile_model_openvino create mode 100644 wrappers/s2i/python/Dockerfile_openvino_base diff --git a/wrappers/s2i/python/Dockerfile_model_openvino b/wrappers/s2i/python/Dockerfile_model_openvino new file mode 100644 index 0000000000..c43f6ab23b --- /dev/null +++ b/wrappers/s2i/python/Dockerfile_model_openvino @@ -0,0 +1,25 @@ +FROM openvino_base:latest +LABEL io.openshift.s2i.scripts-url="image:///s2i/bin" + +#RUN apt-get update -y +RUN apt-get install -y build-essential + +RUN mkdir microservice +WORKDIR /microservice + +ENV LD_LIBRARY_PATH=/usr/local/lib +ENV PYTHONPATH=/usr/local/lib + +RUN conda create --name myenv -y +ENV PATH /opt/conda/envs/myenv/bin:$PATH + +COPY _python /microservice + +RUN cd /microservice/python && \ + pip install . + +#RUN pip install seldon-core + +COPY ./s2i/bin/ /s2i/bin + +EXPOSE 5000 diff --git a/wrappers/s2i/python/Dockerfile_openvino_base b/wrappers/s2i/python/Dockerfile_openvino_base new file mode 100644 index 0000000000..312f98129f --- /dev/null +++ b/wrappers/s2i/python/Dockerfile_openvino_base @@ -0,0 +1,63 @@ +FROM intelpython/intelpython3_core as DEV +RUN apt-get update && apt-get install -y \ + curl \ + ca-certificates \ + libgfortran3 \ + vim \ + build-essential \ + cmake \ + curl \ + wget \ + libssl-dev \ + ca-certificates \ + git \ + libboost-regex-dev \ + gcc-multilib \ + g++-multilib \ + libgtk2.0-dev \ + pkg-config \ + unzip \ + automake \ + libtool \ + autoconf \ + libpng-dev \ + libcairo2-dev \ + libpango1.0-dev \ + libglib2.0-dev \ + libgtk2.0-dev \ + libswscale-dev \ + libavcodec-dev \ + libavformat-dev \ + libgstreamer1.0-0 \ + gstreamer1.0-plugins-base \ + libusb-1.0-0-dev \ + libopenblas-dev + +ARG DLDT_DIR=/dldt-2018_R4 +RUN git clone --depth=1 -b 2018_R4 https://github.com/opencv/dldt.git ${DLDT_DIR} && \ + cd ${DLDT_DIR} && git submodule init && git submodule update --recursive && \ + rm -Rf .git && rm -Rf model-optimizer + +WORKDIR ${DLDT_DIR} +RUN curl -L -o ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_lnx_2019.0.1.20180928.tgz && \ + tar -xzf ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz && rm ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz +WORKDIR ${DLDT_DIR}/inference-engine +RUN mkdir build && cd build && cmake -DGEMM=MKL -DMKLROOT=${DLDT_DIR}/mklml_lnx_2019.0.1.20180928 -DENABLE_MKL_DNN=ON -DCMAKE_BUILD_TYPE=Release .. +RUN cd build && make -j4 +RUN pip install cython numpy && mkdir ie_bridges/python/build && cd ie_bridges/python/build && \ + cmake -DInferenceEngine_DIR=${DLDT_DIR}/inference-engine/build -DPYTHON_EXECUTABLE=`which python` -DPYTHON_LIBRARY=/opt/conda/lib/libpython3.6m.so -DPYTHON_INCLUDE_DIR=/opt/conda/include/python3.6m .. && \ + make -j4 + +FROM intelpython/intelpython3_core as PROD + +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl \ + ca-certificates \ + vim + +COPY --from=DEV /dldt-2018_R4/inference-engine/bin/intel64/Release/lib/*.so /usr/local/lib/ +COPY --from=DEV /dldt-2018_R4/inference-engine/ie_bridges/python/build/ /usr/local/lib/openvino/ +COPY --from=DEV /dldt-2018_R4/mklml_lnx_2019.0.1.20180928/lib/lib*.so /usr/local/lib/ +ENV LD_LIBRARY_PATH=/usr/local/lib +ENV PYTHONPATH=/usr/local/lib + From fde3aa7c5fd210f04dd6fce45c15bf6c2ee666a3 Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Tue, 5 Feb 2019 22:40:21 +0100 Subject: [PATCH 3/6] updated openvino prediction pipeline --- .../models/openvino_imagenet_ensemble/car.png | Bin 310725 -> 0 bytes .../input_images.txt | 3 + .../openvino_imagenet_ensemble.ipynb | 559 ++---------------- .../openvino_imagenet_ensemble/pelican.jpeg | Bin 0 -> 10901 bytes .../openvino_imagenet_ensemble/pvc.json | 43 -- .../resources/combiner/ImageNetCombiner.py | 5 +- .../resources/combiner/Makefile | 2 +- .../resources/combiner/README.md | 8 + .../resources/model/Prediction.py | 100 ++++ .../resources/model/README.md | 41 ++ .../resources/model/environment_grpc | 5 + .../resources/model/requirements.txt | 2 + .../transformer/ImageNetTransformer.py | 49 +- .../resources/transformer/Makefile | 2 +- .../resources/transformer/README.md | 24 + .../resources/transformer/environment_grpc | 1 + .../resources/transformer/requirements.txt | 3 - .../seldon_grpc_client.py | 60 ++ .../seldon_ov_predict_ensemble.json | 235 ++++++++ ...nsemble.json => seldon_ovms_ensemble.json} | 4 +- .../openvino_imagenet_ensemble/zebra.jpeg | Bin 0 -> 188550 bytes 21 files changed, 569 insertions(+), 577 deletions(-) delete mode 100644 examples/models/openvino_imagenet_ensemble/car.png create mode 100644 examples/models/openvino_imagenet_ensemble/input_images.txt create mode 100644 examples/models/openvino_imagenet_ensemble/pelican.jpeg delete mode 100644 examples/models/openvino_imagenet_ensemble/pvc.json create mode 100644 examples/models/openvino_imagenet_ensemble/resources/combiner/README.md create mode 100644 examples/models/openvino_imagenet_ensemble/resources/model/Prediction.py create mode 100644 examples/models/openvino_imagenet_ensemble/resources/model/README.md create mode 100644 examples/models/openvino_imagenet_ensemble/resources/model/environment_grpc create mode 100644 examples/models/openvino_imagenet_ensemble/resources/model/requirements.txt create mode 100644 examples/models/openvino_imagenet_ensemble/resources/transformer/README.md create mode 100644 examples/models/openvino_imagenet_ensemble/seldon_grpc_client.py create mode 100644 examples/models/openvino_imagenet_ensemble/seldon_ov_predict_ensemble.json rename examples/models/openvino_imagenet_ensemble/{seldon_openvino_ensemble.json => seldon_ovms_ensemble.json} (98%) create mode 100644 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zi&rU=lTAopqqG+FmhCSm>rXQ3tQ{4zPZXeMf;u?hOW4{-@bNEgCSn1@+gy2k2U{m=Y)H7N1Zp+PJLjrNdDjP@Cu zF!7=6#>jX*wnej!nKD~5$896kX@gbIaajp~jtYjVl!tco=2rIOi|)DEHsuel683!} zwYf{uJec*}-~QMLNq*`|9eS~#JpnBZ#lTGRaqR3|$*~c*Q0h zOgu0$iyBM(*tX6*u=doRxPFk&-d%N~muq_&(pZvEHtEcf4?fHpl34H9cdSZcyf)-p zQ)59}cfXlnT_@7#a11~Aoqo-+5(g_)?CaWBeue3xAzw4f>_yj0OqEh#=p8el zRoahE_pZC0I{yIwQ~i+T5;alBTN`xzy5HxsuP-KJw!d=7C+#_O57NHzy@(f1{~+b- zwf%7@YdVfpox{qFeOUWl-&e4)T}lDSHD*)iI*x-m" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dependencies\n", - "\n", - " * Seldon-core (```pip install seldon-core```)\n", - " * Numpy\n", - " * Keras\n", - " * Matplotlib\n", - " * Tensorflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download Squeezenet Model\n", - "\n", - "We will download a pre-trained and optimized model for OpenVINO CPU into a local folder." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2019-01-07 12:40:12-- https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.xml\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.48.60\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.48.60|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 37345 (36K) [text/xml]\n", - "Saving to: ‘models/squeezenet/1/squeezenet1.1.xml’\n", - "\n", - "models/squeezenet/1 100%[===================>] 36.47K --.-KB/s in 0.02s \n", - "\n", - "2019-01-07 12:40:12 (1.47 MB/s) - ‘models/squeezenet/1/squeezenet1.1.xml’ saved [37345/37345]\n", - "\n", - "--2019-01-07 12:40:12-- https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.mapping\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.48.60\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.48.60|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 9318 (9.1K) [binary/octet-stream]\n", - "Saving to: ‘models/squeezenet/1/squeezenet1.1.mapping’\n", - "\n", - "models/squeezenet/1 100%[===================>] 9.10K --.-KB/s in 0s \n", - "\n", - "2019-01-07 12:40:13 (109 MB/s) - ‘models/squeezenet/1/squeezenet1.1.mapping’ saved [9318/9318]\n", - "\n", - "--2019-01-07 12:40:13-- https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.bin\n", - "Resolving s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)... 52.218.48.60\n", - "Connecting to s3-eu-west-1.amazonaws.com (s3-eu-west-1.amazonaws.com)|52.218.48.60|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 4941984 (4.7M) [application/octet-stream]\n", - "Saving to: ‘models/squeezenet/1/squeezenet1.1.bin’\n", - "\n", - "models/squeezenet/1 100%[===================>] 4.71M 11.4MB/s in 0.4s \n", - "\n", - "2019-01-07 12:40:13 (11.4 MB/s) - ‘models/squeezenet/1/squeezenet1.1.bin’ saved [4941984/4941984]\n", - "\n" - ] - } - ], - "source": [ - "!mkdir -p models/squeezenet/1 && \\\n", - " wget -O models/squeezenet/1/squeezenet1.1.xml https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.xml && \\\n", - " wget -O models/squeezenet/1/squeezenet1.1.mapping https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.mapping && \\\n", - " wget -O models/squeezenet/1/squeezenet1.1.bin https://s3-eu-west-1.amazonaws.com/seldon-public/openvino-squeeznet-model/squeezenet1.1.bin " - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -221,7 +147,9 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [ { "name": "stdout", @@ -367,20 +295,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Mount local folder onto minikube for HostPath\n", - "Run in the current folder:\n", - "```\n", - "minikube mount ./models:/opt/ml\n", - "```\n", - "\n", - "This will allow the model folder containing the Squeezenet model to be accessed. For production deployments you would use a NFS volume." + "## Build Seldon base images with OpenVINO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!cd ../../../wrappers/s2i/python_openvino && cp ../python/s2i . && \\\n", + "docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t seldon_openvino_base:latest ." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Build Combiner and Transformer Images" + "## Build Model, Combiner and Transformer Images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!cd resources/model && s2i build -E environment_grpc . seldon_openvino_base:latest seldon-openvino-prediction:0.1" ] }, { @@ -398,13 +339,15 @@ } ], "source": [ - "!eval $(minikube docker-env) && cd resources/combiner && s2i build -E environment_grpc . seldonio/seldon-core-s2i-python36:0.5-SNAPSHOT seldonio/imagenet_combiner:0.1" + "!cd resources/combiner && s2i build -E environment_grpc . seldon_openvino_base:latest /imagenet_combiner:0.1" ] }, { "cell_type": "code", "execution_count": 50, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [ { "name": "stdout", @@ -438,14 +381,15 @@ } ], "source": [ - "!eval $(minikube docker-env) && cd resources/transformer && s2i build -E environment_grpc . seldonio/seldon-core-s2i-python36:0.5-SNAPSHOT seldonio/imagenet_transformer:0.1" + "!cd resources/transformer && s2i build -E environment_grpc . seldon_openvino_base:latest seldonio/imagenet_transformer:0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Deploy Seldon Intel OpenVINO Graph" + "## Deploy Seldon Intel OpenVINO Graph\n", + "\n" ] }, { @@ -590,314 +534,24 @@ } ], "source": [ - "get_graph(\"seldon_openvino_ensemble.json\")" + "get_graph(\"seldon_ov_predict_ensemble.json\")" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \u001b[34;01m\"apiVersion\"\u001b[39;49;00m: \u001b[33m\"machinelearning.seldon.io/v1alpha2\"\u001b[39;49;00m,\r\n", - " \u001b[34;01m\"kind\"\u001b[39;49;00m: \u001b[33m\"SeldonDeployment\"\u001b[39;49;00m,\r\n", - " \u001b[34;01m\"metadata\"\u001b[39;49;00m: {\r\n", - " \u001b[34;01m\"labels\"\u001b[39;49;00m: {\r\n", - " \u001b[34;01m\"app\"\u001b[39;49;00m: \u001b[33m\"seldon\"\u001b[39;49;00m\r\n", - " },\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"openvino-model\"\u001b[39;49;00m,\r\n", - "\t\u001b[34;01m\"namespace\"\u001b[39;49;00m: \u001b[33m\"seldon\"\u001b[39;49;00m\t\r\n", - " },\r\n", - " \u001b[34;01m\"spec\"\u001b[39;49;00m: {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"openvino\"\u001b[39;49;00m,\r\n", - " \u001b[34;01m\"predictors\"\u001b[39;49;00m: [\r\n", - " {\r\n", - " \u001b[34;01m\"componentSpecs\"\u001b[39;49;00m: [{\r\n", - " \u001b[34;01m\"spec\"\u001b[39;49;00m: {\r\n", - " \u001b[34;01m\"containers\"\u001b[39;49;00m: [\r\n", - " {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-itransformer\"\u001b[39;49;00m,\t\t\t\t\r\n", - " \u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"seldonio/imagenet_transformer:0.1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t\t },\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\r\n", - " },\r\n", - " {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-otransformer\"\u001b[39;49;00m,\t\t\t\t\r\n", - " \u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"seldonio/imagenet_transformer:0.1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t\t },\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\t\t\t\t\r\n", - " },\r\n", - " {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-combiner\"\u001b[39;49;00m,\t\t\t\t\r\n", - " \u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"seldonio/imagenet_combiner:0.1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t\t },\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\t\t\t\t\r\n", - " },\r\n", - " {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"tfserving-proxy1\"\u001b[39;49;00m,\t\t\t\t\r\n", - " \u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"seldonio/tfserving-proxy:0.2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t\t },\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\t\t\t\t\t\t\t\t\r\n", - " },\r\n", - " {\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"tfserving-proxy2\"\u001b[39;49;00m,\t\t\t\r\n", - " \u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"seldonio/tfserving-proxy:0.2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t\t },\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\t\t\t\t\t\t\t\t\r\n", - " },\r\n", - "\t\t\t {\r\n", - "\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"openvino-model-server1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"intelaipg/openvino-model-server:0.2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"command\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t \u001b[33m\"/ie-serving-py/start_server.sh\"\u001b[39;49;00m\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"args\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t \u001b[33m\"ie_serving\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"model\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--model_path\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"/opt/ml/squeezenet\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--model_name\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"squeezenet1.1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--port\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"8001\"\u001b[39;49;00m\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"ports\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"grpc\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"containerPort\"\u001b[39;49;00m: \u001b[34m8001\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"protocol\"\u001b[39;49;00m: \u001b[33m\"TCP\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"LOG_LEVEL\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"DEBUG\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"resources\"\u001b[39;49;00m: {},\r\n", - "\t\t\t\t\u001b[34;01m\"volumeMounts\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"modelstore\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"mountPath\"\u001b[39;49;00m: \u001b[33m\"/opt/ml\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\r\n", - "\t\t\t },\r\n", - "\t\t\t {\r\n", - "\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"openvino-model-server2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"image\"\u001b[39;49;00m: \u001b[33m\"intelaipg/openvino-model-server:0.2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"command\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t \u001b[33m\"/ie-serving-py/start_server.sh\"\u001b[39;49;00m\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"args\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t \u001b[33m\"ie_serving\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"model\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--model_path\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"/opt/ml/squeezenet\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--model_name\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"squeezenet1.1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"--port\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[33m\"8002\"\u001b[39;49;00m\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"ports\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"grpc\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"containerPort\"\u001b[39;49;00m: \u001b[34m8002\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"protocol\"\u001b[39;49;00m: \u001b[33m\"TCP\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"LOG_LEVEL\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"DEBUG\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t],\r\n", - "\t\t\t\t\u001b[34;01m\"resources\"\u001b[39;49;00m: {},\r\n", - "\t\t\t\t\u001b[34;01m\"volumeMounts\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t {\r\n", - "\t\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"modelstore\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t\u001b[34;01m\"mountPath\"\u001b[39;49;00m: \u001b[33m\"/opt/ml\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t]\r\n", - "\t\t\t }\r\n", - "\t\t\t],\r\n", - "\t\t\t\u001b[34;01m\"terminationGracePeriodSeconds\"\u001b[39;49;00m: \u001b[34m1\u001b[39;49;00m,\r\n", - "\t\t\t\u001b[34;01m\"volumes\"\u001b[39;49;00m: [\r\n", - "\t\t\t {\r\n", - "\t\t\t\t\u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"modelstore\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\u001b[34;01m\"volumeSource\"\u001b[39;49;00m: {\r\n", - "\t\t\t\t \u001b[34;01m\"persistentVolumeClaim\"\u001b[39;49;00m: {\r\n", - "\t\t\t\t\t\u001b[34;01m\"claimName\"\u001b[39;49;00m: \u001b[33m\"model-store-pvc\"\u001b[39;49;00m\r\n", - "\t\t\t\t }\r\n", - "\t\t\t\t}\r\n", - "\t\t\t }\r\n", - "\t\t\t]\r\n", - "\t\t }\r\n", - "\t\t}],\r\n", - " \u001b[34;01m\"graph\"\u001b[39;49;00m: {\r\n", - "\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-otransformer\"\u001b[39;49;00m,\r\n", - "\t\t \u001b[34;01m\"endpoint\"\u001b[39;49;00m: { \u001b[34;01m\"type\"\u001b[39;49;00m : \u001b[33m\"GRPC\"\u001b[39;49;00m },\r\n", - "\t\t \u001b[34;01m\"type\"\u001b[39;49;00m: \u001b[33m\"OUTPUT_TRANSFORMER\"\u001b[39;49;00m,\r\n", - "\t\t \u001b[34;01m\"children\"\u001b[39;49;00m: [\r\n", - "\t\t\t{\r\n", - "\r\n", - "\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-itransformer\"\u001b[39;49;00m,\r\n", - "\t\t \u001b[34;01m\"endpoint\"\u001b[39;49;00m: { \u001b[34;01m\"type\"\u001b[39;49;00m : \u001b[33m\"GRPC\"\u001b[39;49;00m },\r\n", - "\t\t \u001b[34;01m\"type\"\u001b[39;49;00m: \u001b[33m\"TRANSFORMER\"\u001b[39;49;00m,\r\n", - "\t\t \u001b[34;01m\"children\"\u001b[39;49;00m: [\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"imagenet-combiner\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"endpoint\"\u001b[39;49;00m: { \u001b[34;01m\"type\"\u001b[39;49;00m : \u001b[33m\"GRPC\"\u001b[39;49;00m },\r\n", - "\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m: \u001b[33m\"COMBINER\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"children\"\u001b[39;49;00m: [\r\n", - "\t\t\t\t{\r\n", - "\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"tfserving-proxy1\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[34;01m\"endpoint\"\u001b[39;49;00m: { \u001b[34;01m\"type\"\u001b[39;49;00m : \u001b[33m\"GRPC\"\u001b[39;49;00m },\r\n", - "\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m: \u001b[33m\"MODEL\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[34;01m\"children\"\u001b[39;49;00m: [],\r\n", - "\t\t\t\t \u001b[34;01m\"parameters\"\u001b[39;49;00m:\r\n", - "\t\t\t\t [\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"grpc_endpoint\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"localhost:8001\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_name\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"squeezenet1.1\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_output\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"prob\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_input\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"data\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t}\r\n", - "\t\t\t\t ]\r\n", - "\t\t\t\t},\r\n", - "\t\t\t\t{\r\n", - "\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"tfserving-proxy2\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[34;01m\"endpoint\"\u001b[39;49;00m: { \u001b[34;01m\"type\"\u001b[39;49;00m : \u001b[33m\"GRPC\"\u001b[39;49;00m },\r\n", - "\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m: \u001b[33m\"MODEL\"\u001b[39;49;00m,\r\n", - "\t\t\t\t \u001b[34;01m\"children\"\u001b[39;49;00m: [],\r\n", - "\t\t\t\t \u001b[34;01m\"parameters\"\u001b[39;49;00m:\r\n", - "\t\t\t\t [\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"grpc_endpoint\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"localhost:8002\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_name\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"squeezenet1.1\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_output\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"prob\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t},\r\n", - "\t\t\t\t\t{\r\n", - "\t\t\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m:\u001b[33m\"model_input\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"type\"\u001b[39;49;00m:\u001b[33m\"STRING\"\u001b[39;49;00m,\r\n", - "\t\t\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m:\u001b[33m\"data\"\u001b[39;49;00m\r\n", - "\t\t\t\t\t}\r\n", - "\t\t\t\t ]\r\n", - "\t\t\t\t}\t\t\t\r\n", - "\t\t\t ]\r\n", - "\t\t\t}\r\n", - "\t\t ]\r\n", - "\t\t\t}\r\n", - "\t\t ]\r\n", - "\t\t},\r\n", - " \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"openvino\"\u001b[39;49;00m,\r\n", - " \u001b[34;01m\"replicas\"\u001b[39;49;00m: \u001b[34m1\u001b[39;49;00m,\r\n", - "\t\t\u001b[34;01m\"srvOrchSpec\"\u001b[39;49;00m : {\r\n", - "\t\t \u001b[34;01m\"env\"\u001b[39;49;00m: [\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"TRACING\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t},\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_HOST\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"jaeger-agent\"\u001b[39;49;00m\r\n", - "\t\t\t},\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_AGENT_PORT\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"5775\"\u001b[39;49;00m\r\n", - "\t\t\t},\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_SAMPLER_TYPE\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"const\"\u001b[39;49;00m\r\n", - "\t\t\t},\r\n", - "\t\t\t{\r\n", - "\t\t\t \u001b[34;01m\"name\"\u001b[39;49;00m: \u001b[33m\"JAEGER_SAMPLER_PARAM\"\u001b[39;49;00m,\r\n", - "\t\t\t \u001b[34;01m\"value\"\u001b[39;49;00m: \u001b[33m\"1\"\u001b[39;49;00m\r\n", - "\t\t\t}\r\n", - "\t\t ]\t\t\t\t\r\n", - "\t\t}\r\n", - " }\r\n", - " ]\r\n", - " }\r\n", - "}\r\n" - ] - } - ], + "outputs": [], "source": [ - "!pygmentize seldon_openvino_ensemble.json" + "!pygmentize seldon_ov_predict_ensemble.json" ] }, { "cell_type": "code", "execution_count": 51, - "metadata": {}, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stdout", @@ -910,153 +564,34 @@ } ], "source": [ - "!kubectl apply -f pvc.json\n", - "!kubectl apply -f seldon_openvino_ensemble.json" + "!kubectl apply -f seldon_ov_predict_ensemble.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Serve Requests\n", + "## Testing the pipeline\n", "\n", - "**Ensure you port forward ambassador:**\n", + "Expose ambassador API endpoint outside of the Kubernetes cluster or connect to it via `kubectl port-forward`.\n", "\n", - "```\n", - "kubectl port-forward $(kubectl get pods -n seldon -l service=ambassador -o jsonpath='{.items[0].metadata.name}') -n seldon 8003:8080\n", - "```" + "Install python dependencies:" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import tensorflow as tf\n", - "from seldon_core.proto import prediction_pb2\n", - "from seldon_core.proto import prediction_pb2_grpc\n", - "import grpc\n", - "\n", - "def grpc_request_ambassador_tensor(deploymentName,namespace,endpoint=\"localhost:8004\",data=None):\n", - " datadef = prediction_pb2.DefaultData(\n", - " names = 'x',\n", - " tftensor = tf.make_tensor_proto(data)\n", - " )\n", - " request = prediction_pb2.SeldonMessage(data = datadef)\n", - " channel = grpc.insecure_channel(endpoint)\n", - " stub = prediction_pb2_grpc.SeldonStub(channel)\n", - " if namespace is None:\n", - " metadata = [('seldon',deploymentName)]\n", - " else:\n", - " metadata = [('seldon',deploymentName),('namespace',namespace)]\n", - " response = stub.Predict(request=request,metadata=metadata)\n", - " return response\n", - "\n", - "def grpc_request_ambassador_bindata(deploymentName,namespace,endpoint=\"localhost:8004\",data=None):\n", - " request = prediction_pb2.SeldonMessage(binData = data)\n", - " channel = grpc.insecure_channel(endpoint)\n", - " stub = prediction_pb2_grpc.SeldonStub(channel)\n", - " if namespace is None:\n", - " metadata = [('seldon',deploymentName)]\n", - " else:\n", - " metadata = [('seldon',deploymentName),('namespace',namespace)]\n", - " response = stub.Predict(request=request,metadata=metadata)\n", - " return response\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "135.026\n", - "Eskimo dog, husky\n" - ] - } - ], - "source": [ - "%matplotlib inline\n", - "import numpy as np\n", - "from keras.applications.imagenet_utils import preprocess_input, decode_predictions\n", - "from keras.preprocessing import image\n", - "import sys\n", - "import json\n", - "import matplotlib.pyplot as plt\n", - "import datetime\n", - "API_AMBASSADOR=\"localhost:8003\"\n", - "\n", - "def getImage(path):\n", - " img = image.load_img(path, target_size=(227, 227))\n", - " x = image.img_to_array(img)\n", - " plt.imshow(x/255.)\n", - " x = np.expand_dims(x, axis=0)\n", - " x = preprocess_input(x)\n", - " return x\n", - "\n", - "def getImageRaw(path):\n", - " img = image.load_img(path, target_size=(227, 227))\n", - " x = image.img_to_array(img)\n", - " plt.imshow(x/255.)\n", - " return x\n", - "\n", - "def getImageBytes(path):\n", - " with open(path, mode='rb') as file: \n", - " fileContent = file.read()\n", - " return fileContent\n", - "\n", - "#X = getImage(\"car.png\")\n", - "#X = X.transpose((0,3,1,2))\n", - "\n", - "#X = getImageRaw(\"car.png\")\n", - "#X = getImageRaw(\"dog.jpeg\")\n", - "#print(X.shape)\n", - "X = getImageBytes(\"dog.jpeg\")\n", - "start_time = datetime.datetime.now()\n", - "response = grpc_request_ambassador_bindata(\"openvino-model\",\"seldon\",API_AMBASSADOR,data=X)\n", - "end_time = datetime.datetime.now()\n", - "duration = (end_time - start_time).total_seconds() * 1000\n", - "print(duration)\n", - "\n", - "print(response.strData)\n" + "!pip install -y sedon-core grpcio" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Send multiple requests to get average response time." - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "130.20825\n" - ] - } - ], - "source": [ - "durations = []\n", - "for i in range(100):\n", - " X = getImageBytes(\"dog.jpeg\")\n", - " start_time = datetime.datetime.now()\n", - " response = grpc_request_ambassador_bindata(\"openvino-model\",\"seldon\",API_AMBASSADOR,data=X)\n", - " end_time = datetime.datetime.now()\n", - " duration = (end_time - start_time).total_seconds() * 1000\n", - " durations.append(duration)\n", - "print(sum(durations)/float(len(durations)))" + "Optionally expand `input_images.txt` to include bigger of a complete imagenet dataset in the same format: path to the image separated by the imagenet class number." ] }, { @@ -1064,7 +599,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "!python seldon_grpc_client.py" + ] } ], "metadata": { diff --git a/examples/models/openvino_imagenet_ensemble/pelican.jpeg b/examples/models/openvino_imagenet_ensemble/pelican.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..149fc6f65654be89e5be8c0edddcb906b91679a1 GIT binary patch literal 10901 zcmb7pbyO5i)b`RP-5oBmbcmz~3+V1r%Mva~BOOvA(jW~IOD!F;z!K630@5hmEnPoA z38nMn{l0VF|G#I>oHH}`InTX+&AoHye(ru1Km~gNeF4D10RV6w2H<`X5C$M2B%-CI zp{J##rDvdLWMX7xW@2Ju<>X}LWMN@vVSSh^oB~{|EW8g3ZV7H)kdUaTC=;iYtdy{f zfQYCN4k;-q1vv!^6&0%pFDI{vh=|z3{Qtmx2Y{LwFaWs1!{GtoQsdxJV&yJ8;$Arsq>Eu8)fyiw6nU7xa^5h9;=kLucqQhheiSbdK0{eg8uE6lLv!x|pE40D?Pljm9dej_JU 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HcmV?d00001 diff --git a/examples/models/openvino_imagenet_ensemble/pvc.json b/examples/models/openvino_imagenet_ensemble/pvc.json deleted file mode 100644 index 159bddcce3..0000000000 --- a/examples/models/openvino_imagenet_ensemble/pvc.json +++ /dev/null @@ -1,43 +0,0 @@ ---- -{ - "kind": "PersistentVolume", - "apiVersion": "v1", - "metadata": { - "name": "hostpath-pvc" - }, - "spec": { - "capacity": { - "storage": "1Gi" - }, - "hostPath": { - "path": "/opt/ml", - "type": "" - }, - "accessModes": [ - "ReadWriteOnce" - ], - "persistentVolumeReclaimPolicy": "Retain", - "storageClassName": "manual" - } -} - ---- -{ - "kind": "PersistentVolumeClaim", - "apiVersion": "v1", - "metadata": { - "name": "model-store-pvc" - }, - "spec": { - "accessModes": [ - "ReadWriteOnce" - ], - "resources": { - "requests": { - "storage": "1Gi" - } - }, - "storageClassName": "manual" - } -} - diff --git a/examples/models/openvino_imagenet_ensemble/resources/combiner/ImageNetCombiner.py b/examples/models/openvino_imagenet_ensemble/resources/combiner/ImageNetCombiner.py index c6c2a118ce..9fe61dfb51 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/combiner/ImageNetCombiner.py +++ b/examples/models/openvino_imagenet_ensemble/resources/combiner/ImageNetCombiner.py @@ -1,10 +1,11 @@ import logging +import numpy as np logger = logging.getLogger(__name__) class ImageNetCombiner(object): def aggregate(self, Xs, features_names): print("ImageNet Combiner aggregate called") - logger.info(Xs) - return (Xs[0]+Xs[1])/2.0 + logger.debug(Xs) + return (np.reshape(Xs[0],(1,-1)) + np.reshape(Xs[1], (1,-1)))/2.0 diff --git a/examples/models/openvino_imagenet_ensemble/resources/combiner/Makefile b/examples/models/openvino_imagenet_ensemble/resources/combiner/Makefile index 09b08c4508..d881143b27 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/combiner/Makefile +++ b/examples/models/openvino_imagenet_ensemble/resources/combiner/Makefile @@ -1,4 +1,4 @@ build: - s2i build -E environment_grpc . seldonio/seldon-core-s2i-python36:0.4-SNAPSHOT seldonio/imagenet_combiner:0.1 + s2i build -E environment_grpc . seldon_openvino_base:latest seldonio/imagenet_combiner:0.1 diff --git a/examples/models/openvino_imagenet_ensemble/resources/combiner/README.md b/examples/models/openvino_imagenet_ensemble/resources/combiner/README.md new file mode 100644 index 0000000000..f0fa036a85 --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/combiner/README.md @@ -0,0 +1,8 @@ +# Combiner components for two models' results + + +### Building +```bash +s2i build -E environment_grpc . seldon_openvino_base:latest seldonio/imagenet_combiner:0.1 + +``` diff --git a/examples/models/openvino_imagenet_ensemble/resources/model/Prediction.py b/examples/models/openvino_imagenet_ensemble/resources/model/Prediction.py new file mode 100644 index 0000000000..cacf54dbcb --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/model/Prediction.py @@ -0,0 +1,100 @@ +import numpy as np +import logging +import datetime +import os +import sys +from urllib.parse import urlparse +from google.cloud import storage +from openvino.inference_engine import IENetwork, IEPlugin + + +def get_logger(name): + logger = logging.getLogger(name) + log_formatter = logging.Formatter("%(asctime)s - %(name)s - " + "%(levelname)s - %(message)s") + logger.setLevel('DEBUG') + + console_handler = logging.StreamHandler() + console_handler.setFormatter(log_formatter) + logger.addHandler(console_handler) + + return logger + +logger = get_logger(__name__) + + +def gs_download_file(path): + if path is None: + return None + parsed_path = urlparse(path) + bucket_name = parsed_path.netloc + file_path = parsed_path.path[1:] + gs_client = storage.Client() + bucket = gs_client.get_bucket(bucket_name) + blob = bucket.blob(file_path) + tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) + blob.download_to_filename(tmp_path) + return tmp_path + + +def s3_download_file(path): + if path is None: + return None + s3_endpoint = os.getenv('S3_ENDPOINT') + s3_client = boto3.client('s3', endpoint_url=s3_endpoint) + parsed_path = urlparse(path) + bucket_name = parsed_path.netloc + file_path = parsed_path.path[1:] + tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) + s3_transfer = boto3.s3.transfer.S3Transfer(s3_client) + s3_transfer.download_file(bucket_name, file_path, tmp_path) + return tmp_path + + +def GetLocalPath(requested_path): + parsed_path = urlparse(requested_path) + if parsed_path.scheme == '': + return requested_path + elif parsed_path.scheme == 'gs': + return gs_download_file(path=requested_path) + elif parsed_path.scheme == 's3': + return s3_download_file(path=requested_path) + + +class Prediction(object): + def __init__(self): + try: + xml_path = os.environ["XML_PATH"] + bin_path = os.environ["BIN_PATH"] + + except KeyError: + print("Please set the environment variables XML_PATH, BIN_PATH") + sys.exit(1) + + xml_local_path = GetLocalPath(xml_path) + bin_local_path = GetLocalPath(bin_path) + print('path object', xml_local_path) + + CPU_EXTENSION = os.getenv('CPU_EXTENSION', "/usr/local/lib/libcpu_extension.so") + + plugin = IEPlugin(device='CPU', plugin_dirs=None) + if CPU_EXTENSION: + plugin.add_cpu_extension(CPU_EXTENSION) + net = IENetwork(model=xml_local_path, weights=bin_local_path) + self.input_blob = next(iter(net.inputs)) + self.out_blob = next(iter(net.outputs)) + self.batch_size = net.inputs[self.input_blob].shape[0] + self.inputs = net.inputs + self.outputs = net.outputs + self.exec_net = plugin.load(network=net, num_requests=self.batch_size) + + + def predict(self,X,feature_names): + start_time = datetime.datetime.now() + results = self.exec_net.infer(inputs={self.input_blob: X}) + predictions = results[self.out_blob] + end_time = datetime.datetime.now() + duration = (end_time - start_time).total_seconds() * 1000 + logger.debug("Processing time: {:.2f} ms".format(duration)) + return predictions.astype(np.float64) + diff --git a/examples/models/openvino_imagenet_ensemble/resources/model/README.md b/examples/models/openvino_imagenet_ensemble/resources/model/README.md new file mode 100644 index 0000000000..994e69511e --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/model/README.md @@ -0,0 +1,41 @@ +# OpenVINO prediction component + +Model configuration is implemented using environment variables: + +`XML_PATH` - s3, gs or local path to xml file in OpenVINO model server + +`BIN_PATH` - s3, gs or local path to bin file in OpenVINO model server + +When using GCS make sure you added also GOOGLE_APPLICATION_CREDENTIALS variable and mounted correspondent token file. + +In case is S3 or Minio storage add appropriate environment variables with the credentials. + +Component is executing inference operation. Processing time is included in the components debug logs. + +Model input and output tensors are determined automatically. There is assumed only one input tensor and output tensor. + +### Building example: + +```bash +s2i build -E environment_grpc . seldon_openvino_base:latest seldon-openvino-prediction:0.1 +``` +The base image `seldon_openvino_base:latest` should be created according to this [procedure](../../../../../wrappers/s2i/python_openvino) + + +### Local testing example: + +```bash +docker run -it -v $GOOGLE_APPLICATION_CREDENTIALS:/etc/gcp.json -e GOOGLE_APPLICATION_CREDENTIALS=/etc/gcp.json \ + -e XML_PATH=gs://inference-eu/models_zoo/resnet_V1_50/resnet_V1_50.xml \ + -e BIN_PATH=gs://inference-eu/models_zoo/resnet_V1_50/resnet_V1_50.bin +starting microservice +2019-02-05 11:13:32,045 - seldon_core.microservice:main:261 - INFO: Starting microservice.py:main +2019-02-05 11:13:32,047 - seldon_core.microservice:main:292 - INFO: Annotations: {} +path object /tmp/resnet_V1_50.xml + net = IENetwork(model=xml_local_path, weights=bin_local_path) +2019-02-05 11:14:19,870 - seldon_core.microservice:main:354 - INFO: Starting servers +2019-02-05 11:14:19,906 - seldon_core.microservice:grpc_prediction_server:333 - INFO: GRPC microservice Running on port 5000 +``` + + + diff --git a/examples/models/openvino_imagenet_ensemble/resources/model/environment_grpc b/examples/models/openvino_imagenet_ensemble/resources/model/environment_grpc new file mode 100644 index 0000000000..43c84db3fc --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/model/environment_grpc @@ -0,0 +1,5 @@ +MODEL_NAME=Prediction +API_TYPE=GRPC +SERVICE_TYPE=MODEL +PERSISTENCE=0 + diff --git a/examples/models/openvino_imagenet_ensemble/resources/model/requirements.txt b/examples/models/openvino_imagenet_ensemble/resources/model/requirements.txt new file mode 100644 index 0000000000..e931a7a540 --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/model/requirements.txt @@ -0,0 +1,2 @@ +google-cloud-storage==1.13.0 +boto3==1.9.34 \ No newline at end of file diff --git a/examples/models/openvino_imagenet_ensemble/resources/transformer/ImageNetTransformer.py b/examples/models/openvino_imagenet_ensemble/resources/transformer/ImageNetTransformer.py index ce49e070a6..a65007c267 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/transformer/ImageNetTransformer.py +++ b/examples/models/openvino_imagenet_ensemble/resources/transformer/ImageNetTransformer.py @@ -1,11 +1,10 @@ import numpy as np -from keras.applications.imagenet_utils import preprocess_input, decode_predictions -from keras.preprocessing import image from seldon_core.proto import prediction_pb2 import tensorflow as tf import logging -import sys -import io +import datetime +import cv2 +import os logger = logging.getLogger(__name__) @@ -14,26 +13,48 @@ def __init__(self, metrics_ok=True): print("Init called") f = open('imagenet_classes.json') self.cnames = eval(f.read()) - + self.size = os.getenv('SIZE', 224) + self.dtype = os.getenv('DTYPE', 'float') + self.classes = os.getenv('CLASSES', 1000) + + def crop_resize(self, img,cropx,cropy): + y,x,c = img.shape + if y < cropy: + img = cv2.resize(img, (x, cropy)) + y = cropy + if x < cropx: + img = cv2.resize(img, (cropx,y)) + x = cropx + startx = x//2-(cropx//2) + starty = y//2-(cropy//2) + return img[starty:starty+cropy,startx:startx+cropx,:] + def transform_input_grpc(self, request): - logger.debug("Transform called") - b = io.BytesIO(request.binData) - img = image.load_img(b, target_size=(227, 227)) - X = image.img_to_array(img) - X = np.expand_dims(X, axis=0) - X = preprocess_input(X) - X = X.transpose((0,3,1,2)) + logger.info("Transform called") + start_time = datetime.datetime.now() + X = np.frombuffer(request.binData, dtype=np.uint8) + X = cv2.imdecode(X, cv2.IMREAD_COLOR) # BGR format + X = self.crop_resize(X, self.size, self.size) + X = X.astype(self.dtype) + X = X.transpose(2,0,1).reshape(1,3,self.size,self.size) + logger.info("Shape: %s; Dtype: %s; Min: %s; Max: %s",X.shape,X.dtype,np.amin(X),np.amax(X)) + jpeg_time = datetime.datetime.now() + jpeg_duration = (jpeg_time - start_time).total_seconds() * 1000 + logger.info("jpeg preprocessing: %s ms", jpeg_duration) datadef = prediction_pb2.DefaultData( names = 'x', tftensor = tf.make_tensor_proto(X) ) + end_time = datetime.datetime.now() + duration = (end_time - start_time).total_seconds() * 1000 + logger.info("Total transformation: %s ms", duration) request = prediction_pb2.SeldonMessage(data = datadef) return request def transform_output_grpc(self, request): - logger.debug("Transform output called") + logger.info("Transform output called") result = tf.make_ndarray(request.data.tftensor) - result = result.reshape(1,1000) + result = result.reshape(1,self.classes) single_result = result[[0],...] ma = np.argmax(single_result) diff --git a/examples/models/openvino_imagenet_ensemble/resources/transformer/Makefile b/examples/models/openvino_imagenet_ensemble/resources/transformer/Makefile index 91062b05f5..0aa81684e0 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/transformer/Makefile +++ b/examples/models/openvino_imagenet_ensemble/resources/transformer/Makefile @@ -1,3 +1,3 @@ build: - s2i build -E environment_grpc . seldonio/seldon-core-s2i-python36:0.5-SNAPSHOT seldonio/imagenet_transformer:0.1 + s2i build -E environment_grpc . seldon_openvino_base:latest seldonio/imagenet_transformer:0.1 diff --git a/examples/models/openvino_imagenet_ensemble/resources/transformer/README.md b/examples/models/openvino_imagenet_ensemble/resources/transformer/README.md new file mode 100644 index 0000000000..7f2a962c2b --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/resources/transformer/README.md @@ -0,0 +1,24 @@ +## Transformer component + +Exemplary implementation of data transformation tasks. + +Input transformation function accepts as input the binary representation of jpeg content. +It performs the following operations: +- convert compressed jpeg content to numpy array (BGR format) +- crop/resize the image to the square shape set in the environment variable `SIZE` (by default 224) +- transpose the data to NCWH + + +Output transformation function is consuming the imagenet classification models. +It is converting the array including probability for each imagenet classes into the class name. +It is returning 'human readable' string with most likely class name. +The function is using `CLASSES` environment variable to define the expected number of classes in the output. +Depending on the model it could be 1000 (the default value) or 1001. + + +### Building example: +```bash +s2i build -E environment_grpc . seldon_openvino_base:latest seldonio/imagenet_transformer:0.1 +``` + +The base image `seldon_openvino_base:latest` should be created according to this [procedure](../../../../../wrappers/s2i/python_openvino) \ No newline at end of file diff --git a/examples/models/openvino_imagenet_ensemble/resources/transformer/environment_grpc b/examples/models/openvino_imagenet_ensemble/resources/transformer/environment_grpc index 8fb23e694e..bc883fb1fd 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/transformer/environment_grpc +++ b/examples/models/openvino_imagenet_ensemble/resources/transformer/environment_grpc @@ -2,3 +2,4 @@ MODEL_NAME=ImageNetTransformer API_TYPE=GRPC SERVICE_TYPE=TRANSFORMER PERSISTENCE=0 + diff --git a/examples/models/openvino_imagenet_ensemble/resources/transformer/requirements.txt b/examples/models/openvino_imagenet_ensemble/resources/transformer/requirements.txt index b75cccccae..e69de29bb2 100644 --- a/examples/models/openvino_imagenet_ensemble/resources/transformer/requirements.txt +++ b/examples/models/openvino_imagenet_ensemble/resources/transformer/requirements.txt @@ -1,3 +0,0 @@ -numpy>=1.8.2 -keras -pillow diff --git a/examples/models/openvino_imagenet_ensemble/seldon_grpc_client.py b/examples/models/openvino_imagenet_ensemble/seldon_grpc_client.py new file mode 100644 index 0000000000..44c79135ef --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/seldon_grpc_client.py @@ -0,0 +1,60 @@ +from seldon_core.proto import prediction_pb2 +from seldon_core.proto import prediction_pb2_grpc +import grpc +import datetime + + +API_AMBASSADOR="localhost:8080" + + +def grpc_request_ambassador_bindata(deploymentName,namespace,endpoint="localhost:8004",data=None): + request = prediction_pb2.SeldonMessage(binData = data) + channel = grpc.insecure_channel(endpoint) + stub = prediction_pb2_grpc.SeldonStub(channel) + if namespace is None: + metadata = [('seldon',deploymentName)] + else: + metadata = [('seldon',deploymentName),('namespace',namespace)] + response = stub.Predict(request=request,metadata=metadata) + return response + + +def getImage(path): + img = image.load_img(path, target_size=(227, 227)) + x = image.img_to_array(img) + x = np.expand_dims(x, axis=0) + x = preprocess_input(x) + return x + +def getImageBytes(path): + with open(path, mode='rb') as file: + fileContent = file.read() + return fileContent + + +fc = open('imagenet_classes.json') +cnames = eval(fc.read()) +print(type(cnames)) + +input_images = "input_images.txt" +with open(input_images) as f: + lines = f.readlines() + +i = 0 +matched = 0 +durations = [] +for j in range(20): # repeat the sequence of requests + for line in lines: + path, label = line.strip().split(" ") + X = getImageBytes(path) + start_time = datetime.datetime.now() + response = grpc_request_ambassador_bindata("openvino-model","default",API_AMBASSADOR,data=X) + end_time = datetime.datetime.now() + duration = (end_time - start_time).total_seconds() * 1000 + durations.append(duration) + print("Duration",duration) + i += 1 + if response.strData == cnames[int(label)]: + matched += 1 +print("average duration:",sum(durations)/float(len(durations))) +print("average accuracy:",matched/float(len(durations))*100) \ No newline at end of file diff --git a/examples/models/openvino_imagenet_ensemble/seldon_ov_predict_ensemble.json b/examples/models/openvino_imagenet_ensemble/seldon_ov_predict_ensemble.json new file mode 100644 index 0000000000..ec67c2d336 --- /dev/null +++ b/examples/models/openvino_imagenet_ensemble/seldon_ov_predict_ensemble.json @@ -0,0 +1,235 @@ +{ + "apiVersion": "machinelearning.seldon.io/v1alpha2", + "kind": "SeldonDeployment", + "metadata": { + "labels": { + "app": "seldon" + }, + "name": "openvino-model", + "namespace": "seldon" + }, + "spec": { + "name": "openvino", + "predictors": [ + { + "componentSpecs": [{ + "spec": { + "containers": [ + { + "name": "imagenet-itransformer", + "image": "seldon/imagenet_transformer:0.1", + "env": [ + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + } + ] + }, + { + "name": "imagenet-otransformer", + "image": "seldon/imagenet_transformer:0.1", + "env": [ + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + } + ] + }, + { + "name": "imagenet-combiner", + "image": "seldon/imagenet_combiner:0.1", + "env": [ + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + } + ] + }, + { + "name": "prediction1", + "image": "seldon/seldon-openvino-prediction:0.1", + "resources": { + "requests": { + "cpu": "1" + }, + "limits": { + "cpu": "32" + } + }, + "env": [ + { + "name": "XML_PATH", + "value": "gs://inference-eu/serving/densenet_169_int8/1/densenet_169_i8.xml" + }, + { + "name": "BIN_PATH", + "value": "gs://inference-eu/serving/densenet_169_int8/1/densenet_169_i8.bin" + }, + { + "name": "KMP_SETTINGS", + "value": "1" + }, + { + "name": "KMP_AFFINITY", + "value": "granularity=fine,verbose,compact,1,0" + }, + { + "name": "KMP_BLOCKTIME", + "value": "1" + }, + { + "name": "OMP_NUM_THREADS", + "value": "16" + }, + { + "name": "http_proxy", + "value": "" + }, + { + "name": "https_proxy", + "value": "" + }, + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + } + ] + }, + { + "name": "prediction2", + "image": "seldon/seldon-openvino-prediction:0.1", + "resources": { + "requests": { + "cpu": "1" + }, + "limits": { + "cpu": "32" + } + }, + "env": [ + { + "name": "XML_PATH", + "value": "gs://inference-eu/serving/resnet_50_int8/1/resnet_50_i8.xml" + }, + { + "name": "BIN_PATH", + "value": "gs://inference-eu/serving/resnet_50_int8/1/resnet_50_i8.bin" + }, + { + "name": "KMP_SETTINGS", + "value": "1" + }, + { + "name": "KMP_AFFINITY", + "value": "granularity=fine,verbose,compact,1,0" + }, + { + "name": "KMP_BLOCKTIME", + "value": "1" + }, + { + "name": "OMP_NUM_THREADS", + "value": "16" + }, + { + "name": "http_proxy", + "value": "" + }, + { + "name": "https_proxy", + "value": "" + }, + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + } + ] + } + ], + "terminationGracePeriodSeconds": 1 + } + }], + "graph": { + "name": "imagenet-otransformer", + "endpoint": { "type" : "GRPC" }, + "type": "OUTPUT_TRANSFORMER", + "children": [ + { + + "name": "imagenet-itransformer", + "endpoint": { "type" : "GRPC" }, + "type": "TRANSFORMER", + "children": [ + { + "name": "imagenet-combiner", + "endpoint": { "type" : "GRPC" }, + "type": "COMBINER", + "children": [ + { + "name": "prediction1", + "endpoint": { "type" : "GRPC" }, + "type": "MODEL", + "children": [] + }, + { + "name": "prediction2", + "endpoint": { "type" : "GRPC" }, + "type": "MODEL", + "children": [] + } + ] + } + ] + } + ] + }, + "name": "openvino", + "replicas": 0, + "svcOrchSpec" : { + "env": [ + { + "name": "TRACING", + "value": "1" + }, + { + "name": "JAEGER_AGENT_HOST", + "value": "jaeger-agent" + }, + { + "name": "JAEGER_AGENT_PORT", + "value": "5775" + }, + { + "name": "JAEGER_SAMPLER_TYPE", + "value": "const" + }, + { + "name": "JAEGER_SAMPLER_PARAM", + "value": "1" + } + ] + } + } + ] + } +} diff --git a/examples/models/openvino_imagenet_ensemble/seldon_openvino_ensemble.json b/examples/models/openvino_imagenet_ensemble/seldon_ovms_ensemble.json similarity index 98% rename from examples/models/openvino_imagenet_ensemble/seldon_openvino_ensemble.json rename to examples/models/openvino_imagenet_ensemble/seldon_ovms_ensemble.json index 2303741e16..226ea6f224 100644 --- a/examples/models/openvino_imagenet_ensemble/seldon_openvino_ensemble.json +++ b/examples/models/openvino_imagenet_ensemble/seldon_ovms_ensemble.json @@ -87,7 +87,7 @@ }, { "name": "openvino-model-server1", - "image": "intelaipg/openvino-model-server:0.2", + "image": "intelaipg/openvino-model-server:latest", "command": [ "/ie-serving-py/start_server.sh" ], @@ -124,7 +124,7 @@ }, { "name": "openvino-model-server2", - "image": "intelaipg/openvino-model-server:0.2", + "image": "intelaipg/openvino-model-server:latest", "command": [ "/ie-serving-py/start_server.sh" ], diff --git 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zr#z=l=Qb`@DvRug*$IesNI(*$xfa5)=LA_?Ud_4K6wNJ5E!S%}wKWCew&N{#SOek7 z@9x7Bd(cxN5@bsn*E;H`tFV=(v+St0+%jC20F?03Q5dHjsoo9j|qZQY;Chh5%~JUl_1czd#srZ z^i)U;lI`*qmbwWJod-sv7E3D|T0XY|XXKioY%@{5%@x;UB(^rCIPo}?tFB0HQDSPE z8%!Dey+oW2!AP4k%Xr`CC~2XuLLmc>XxG)lHbwP*xT2w;b)v~&f-*nP`yU6LxS#2~&MtPIeV*XXy<;@^5v#cQXT*2TiWk z7M>4OMkt)PSs@iW6)|bEeg7b}M z1!sDU|2Q2s;ByWME*h7+4o42&&oV&*J(AV}RcE%--m>%scPraC_|x!CsB2u2~s4S|^Rh0dFCB(u|s-$=u}p zODpzx?^jeboJO8G!JX%%q0KoIiF9TC@C<3>GSvhl=KS6oW9$C|u2g!nrUBby00000 LNkvXXu0mjfSkaGI literal 0 HcmV?d00001 From a950a0f1dc0d06398b8b5884d2e2a935ae45a64d Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Tue, 5 Feb 2019 22:51:52 +0100 Subject: [PATCH 4/6] drop outdated changes --- examples/models/openvino_R4/Makefile | 12 -- examples/models/openvino_R4/Prediction.py | 100 ----------------- examples/models/openvino_R4/README.md | 8 -- examples/models/openvino_R4/client.py | 103 ------------------ examples/models/openvino_R4/dog.jpeg | Bin 7694 -> 0 bytes examples/models/openvino_R4/environment_grpc | 8 -- examples/models/openvino_R4/requirements.txt | 2 - wrappers/s2i/python/Dockerfile_model_openvino | 25 ----- wrappers/s2i/python/Dockerfile_openvino_base | 63 ----------- wrappers/s2i/python/Makefile | 4 - wrappers/s2i/python/README.md | 4 - 11 files changed, 329 deletions(-) delete mode 100644 examples/models/openvino_R4/Makefile delete mode 100644 examples/models/openvino_R4/Prediction.py delete mode 100644 examples/models/openvino_R4/README.md delete mode 100644 examples/models/openvino_R4/client.py delete mode 100644 examples/models/openvino_R4/dog.jpeg delete mode 100644 examples/models/openvino_R4/environment_grpc delete mode 100644 examples/models/openvino_R4/requirements.txt delete mode 100644 wrappers/s2i/python/Dockerfile_model_openvino delete mode 100644 wrappers/s2i/python/Dockerfile_openvino_base diff --git a/examples/models/openvino_R4/Makefile b/examples/models/openvino_R4/Makefile deleted file mode 100644 index 22c5045cb1..0000000000 --- a/examples/models/openvino_R4/Makefile +++ /dev/null @@ -1,12 +0,0 @@ - - -build_image: - s2i build -E environment_grpc . seldon_python_openvino seldon-openvino-prediction - - -clean: - rm -rf model - rm -rf proto/__pycache__ - rm -f proto/*.py - rm -r proto/*.proto - rm -rf tensorflow diff --git a/examples/models/openvino_R4/Prediction.py b/examples/models/openvino_R4/Prediction.py deleted file mode 100644 index 6997961a29..0000000000 --- a/examples/models/openvino_R4/Prediction.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -import logging -import datetime -import os -import sys -from urllib.parse import urlparse -from google.cloud import storage -from openvino.inference_engine import IENetwork, IEPlugin - - -def get_logger(name): - logger = logging.getLogger(name) - log_formatter = logging.Formatter("%(asctime)s - %(name)s - " - "%(levelname)s - %(message)s") - logger.setLevel('DEBUG') - - console_handler = logging.StreamHandler() - console_handler.setFormatter(log_formatter) - logger.addHandler(console_handler) - - return logger - -logger = get_logger(__name__) - - -def gs_download_file(path): - if path is None: - return None - parsed_path = urlparse(path) - bucket_name = parsed_path.netloc - file_path = parsed_path.path[1:] - gs_client = storage.Client() - bucket = gs_client.get_bucket(bucket_name) - blob = bucket.blob(file_path) - tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) - blob.download_to_filename(tmp_path) - return tmp_path - - -def s3_download_file(path): - if path is None: - return None - s3_endpoint = os.getenv('S3_ENDPOINT') - s3_client = boto3.client('s3', endpoint_url=s3_endpoint) - parsed_path = urlparse(path) - bucket_name = parsed_path.netloc - file_path = parsed_path.path[1:] - tmp_path = os.path.join('/tmp', file_path.split(os.sep)[-1]) - s3_transfer = boto3.s3.transfer.S3Transfer(s3_client) - s3_transfer.download_file(bucket_name, file_path, tmp_path) - return tmp_path - - -def GetLocalPath(requested_path): - parsed_path = urlparse(requested_path) - if parsed_path.scheme == '': - return requested_path - elif parsed_path.scheme == 'gs': - return gs_download_file(path=requested_path) - elif parsed_path.scheme == 's3': - return s3_download_file(path=requested_path) - - -class Prediction(object): - def __init__(self): - try: - xml_path = os.environ["XML_PATH"] - bin_path = os.environ["BIN_PATH"] - - except KeyError: - print("Please set the environment variables XML_PATH, BIN, PATH, INPUT and OUTPUT") - sys.exit(1) - - xml_local_path = GetLocalPath(xml_path) - bin_local_path = GetLocalPath(bin_path) - print('path object', xml_local_path) - - CPU_EXTENSION = os.getenv('CPU_EXTENSION', '/usr/local/lib/libcpu_extension.so') - - plugin = IEPlugin(device='CPU', plugin_dirs=None) - if CPU_EXTENSION: - plugin.add_cpu_extension(CPU_EXTENSION) - net = IENetwork.from_ir(model=xml_local_path, weights=bin_local_path) - self.input_blob = next(iter(net.inputs)) - self.out_blob = next(iter(net.outputs)) - self.batch_size = net.inputs[self.input_blob].shape[0] - self.inputs = net.inputs - self.outputs = net.outputs - self.exec_net = plugin.load(network=net, num_requests=self.batch_size) - - - def predict(self,X,feature_names): - start_time = datetime.datetime.now() - results = self.exec_net.infer(inputs={self.input_blob: X}) - predictions = results[self.out_blob] - end_time = datetime.datetime.now() - duration = (end_time - start_time).total_seconds() * 1000 - logger.debug("Processing time: {:.2f} ms".format(duration)) - return predictions.astype(np.float64) - diff --git a/examples/models/openvino_R4/README.md b/examples/models/openvino_R4/README.md deleted file mode 100644 index b1f5d759dc..0000000000 --- a/examples/models/openvino_R4/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# OpenVINO prediction component - -Model configuration is implemented using environment variables: - -XML_PATH - s3, gs or local path to xml file in OpenVINO model server - -BIN_PATH - s3, gs or local path to bin file in OpenVINO model server - diff --git a/examples/models/openvino_R4/client.py b/examples/models/openvino_R4/client.py deleted file mode 100644 index fabc04a8d7..0000000000 --- a/examples/models/openvino_R4/client.py +++ /dev/null @@ -1,103 +0,0 @@ -import json -import requests -import base64 -from proto import prediction_pb2 -from proto import prediction_pb2_grpc -import grpc -import numpy as np -import pickle -import tensorflow as tf -import cv2 -import datetime -import tensorflow as tf - -def image_2_vector(input_file): - nparr = np.fromfile(input_file, dtype=np.float32) - print("nparr",nparr.dtype,nparr.shape) - img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR) - print("img",img.dtype,img.shape) - print("Initial size",img.shape) - image = cv2.resize(img, (w, h)) - print("image",image.dtype) - print("Converted size",image.shape) - - vector = image.reshape((w * h * 3)) - print("vector shape",vector.shape, "vector type", vector.dtype ) - return vector - -def image_2_bytes(input_file): - with open(input_file, "rb") as binary_file: - # Read the whole file at once - data = binary_file.read() - - #data = data.tobytes() - #print(data) - print("binary data size:", len(data), type(data)) - return data - -def run(function,image_path,iterations=1): - w = 224 - h = 224 - - # NOTE(gRPC Python Team): .close() is possible on a channel and should be - # used in circumstances in which the with statement does not fit the needs - # of the code. - with grpc.insecure_channel('localhost:5000') as channel: - stub = prediction_pb2_grpc.ModelStub(channel) - print("seldon stub", stub) - start_time = datetime.datetime.now() - processing_times = np.zeros((0),int) - - img = cv2.imread(image_path) - print("img type", type(img)) - print("img",img.shape) - print("Initial size",img.shape) - image = cv2.resize(img, (w, h)) - image = image.reshape(1, w, h, 3) - image = image.transpose((0,3,1,2)) - print("image",image.dtype) - print("Converted size",image.shape) - - if function == "tensor": - datadef = prediction_pb2.DefaultData( - names = 'x', - tensor = prediction_pb2.Tensor( - shape = image.shape, - values = image.ravel().tolist() - ) - ) - elif function == "tftensor": - print("Create tftensor") - datadef = prediction_pb2.DefaultData( - names = 'x', - tftensor = tf.make_tensor_proto(image) - ) - - GRPC_request = prediction_pb2.SeldonMessage( - data = datadef - ) - - for I in range(iterations): - start_time = datetime.datetime.now() - response = stub.Predict(request=GRPC_request) - end_time = datetime.datetime.now() - duration = (end_time - start_time).total_seconds() * 1000 - processing_times = np.append(processing_times,np.array([int(duration)])) - - print('processing time for all iterations') - for x in processing_times: - print(x,"ms") - print('processing_statistics') - print('average time:',round(np.average(processing_times),1), 'ms; average speed:', round(1000/np.average(processing_times),1),'fps') - print('median time:',round(np.median(processing_times),1), 'ms; median speed:',round(1000/np.median(processing_times),1),'fps') - print('max time:',round(np.max(processing_times),1), 'ms; max speed:',round(1000/np.max(processing_times),1),'fps') - print('min time:',round(np.min(processing_times),1),'ms; min speed:',round(1000/np.min(processing_times),1),'fps') - print('time percentile 90:',round(np.percentile(processing_times,90),1),'ms; speed percentile 90:',round(1000/np.percentile(processing_times,90),1),'fps') - print('time percentile 50:',round(np.percentile(processing_times,50),1),'ms; speed percentile 50:',round(1000/np.percentile(processing_times,50),1),'fps') - print('time standard deviation:',round(np.std(processing_times))) - print('time variance:',round(np.var(processing_times))) - - -#run("tensor","./dog.jpeg",iterations=100) - -run("tftensor","./dog.jpeg",iterations=100) diff --git a/examples/models/openvino_R4/dog.jpeg b/examples/models/openvino_R4/dog.jpeg deleted file mode 100644 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zEX6<-9A-Mq)sRJ=h`hm8R6NWI%=HxZ^%V6-C>|E>7Z(*!BZd02S=umCNaZ(ssO3ZHE2QG@j~*zlfy#yl%Y(**SBJx?H)(7d@f zXW9Igj;k}WT;1Z|p->$bQ96663ad}7G=gC5o=d+Cd*zEcg#o`8CMLD@9KBS Dockerfile docker build -t $(IMAGE_NAME)-candidate . IMAGE_NAME=$(IMAGE_NAME)-candidate test/run -.PHONY: build_python_openvino -build_python_openvino: - docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t openvino_base:latest . - docker build -f Dockerfile_model_openvino --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t seldon_model_openvino . .PHONY: clean clean: diff --git a/wrappers/s2i/python/README.md b/wrappers/s2i/python/README.md index dbb8a44aaa..4464081131 100644 --- a/wrappers/s2i/python/README.md +++ b/wrappers/s2i/python/README.md @@ -13,8 +13,4 @@ e.g. from 0.3-SNAPSHOT to release 0.3 and create 0.4-SNAPSHOT ``` ./update_python_version.sh X X+1```, e.g ```./update_python_version.sh 0.2 0.3``` - * building openvino python base image -```docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy -t openvino_base:latest . ``` - - From d7f71b67204b7808222eb1159f831f2587698105 Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Tue, 5 Feb 2019 22:54:00 +0100 Subject: [PATCH 5/6] add new seldon base image --- .../python_openvino/Dockerfile_openvino_base | 79 +++++++++++++++++++ wrappers/s2i/python_openvino/README.md | 45 +++++++++++ 2 files changed, 124 insertions(+) create mode 100644 wrappers/s2i/python_openvino/Dockerfile_openvino_base create mode 100644 wrappers/s2i/python_openvino/README.md diff --git a/wrappers/s2i/python_openvino/Dockerfile_openvino_base b/wrappers/s2i/python_openvino/Dockerfile_openvino_base new file mode 100644 index 0000000000..5990110dd5 --- /dev/null +++ b/wrappers/s2i/python_openvino/Dockerfile_openvino_base @@ -0,0 +1,79 @@ +FROM intelpython/intelpython3_core as DEV +RUN apt-get update && apt-get install -y \ + curl \ + ca-certificates \ + libgfortran3 \ + vim \ + build-essential \ + cmake \ + curl \ + wget \ + libssl-dev \ + ca-certificates \ + git \ + libboost-regex-dev \ + gcc-multilib \ + g++-multilib \ + libgtk2.0-dev \ + pkg-config \ + unzip \ + automake \ + libtool \ + autoconf \ + libpng-dev \ + libcairo2-dev \ + libpango1.0-dev \ + libglib2.0-dev \ + libgtk2.0-dev \ + libswscale-dev \ + libavcodec-dev \ + libavformat-dev \ + libgstreamer1.0-0 \ + gstreamer1.0-plugins-base \ + libusb-1.0-0-dev \ + libopenblas-dev + +ARG DLDT_DIR=/dldt-2018_R5 +RUN git clone --depth=1 -b 2018_R5 https://github.com/opencv/dldt.git ${DLDT_DIR} && \ + cd ${DLDT_DIR} && git submodule init && git submodule update --recursive && \ + rm -Rf .git && rm -Rf model-optimizer + +WORKDIR ${DLDT_DIR} +RUN curl -L -o ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz https://github.com/intel/mkl-dnn/releases/download/v0.17.2/mklml_lnx_2019.0.1.20180928.tgz && \ + tar -xzf ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz && rm ${DLDT_DIR}/mklml_lnx_2019.0.1.20180928.tgz +WORKDIR ${DLDT_DIR}/inference-engine +RUN mkdir build && cd build && cmake -DGEMM=MKL -DMKLROOT=${DLDT_DIR}/mklml_lnx_2019.0.1.20180928 -DENABLE_MKL_DNN=ON -DCMAKE_BUILD_TYPE=Release .. +RUN cd build && make -j4 +RUN pip install cython numpy && mkdir ie_bridges/python/build && cd ie_bridges/python/build && \ + cmake -DInferenceEngine_DIR=${DLDT_DIR}/inference-engine/build -DPYTHON_EXECUTABLE=`which python` -DPYTHON_LIBRARY=/opt/conda/lib/libpython3.6m.so -DPYTHON_INCLUDE_DIR=/opt/conda/include/python3.6m .. && \ + make -j4 + +FROM intelpython/intelpython3_core as PROD + +LABEL io.openshift.s2i.scripts-url="image:///s2i/bin" + +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl \ + ca-certificates \ + build-essential \ + python3-setuptools \ + vim + +COPY --from=DEV /dldt-2018_R5/inference-engine/bin/intel64/Release/lib/*.so /usr/local/lib/ +COPY --from=DEV /dldt-2018_R5/inference-engine/ie_bridges/python/bin/intel64/Release/python_api/python3.6/openvino/ /usr/local/lib/openvino/ +COPY --from=DEV /dldt-2018_R5/mklml_lnx_2019.0.1.20180928/lib/lib*.so /usr/local/lib/ +ENV LD_LIBRARY_PATH=/usr/local/lib +ENV PYTHONPATH=/usr/local/lib + +RUN conda create --name myenv -y +ENV PATH /opt/conda/envs/myenv/bin:$PATH +RUN conda install -y tensorflow opencv && conda clean -a -y +WORKDIR /microservice + +RUN pip install wheel +RUN pip install seldon-core +RUN pip install --upgrade setuptools + +COPY ./s2i/bin/ /s2i/bin + +EXPOSE 5000 diff --git a/wrappers/s2i/python_openvino/README.md b/wrappers/s2i/python_openvino/README.md new file mode 100644 index 0000000000..eb84839dbc --- /dev/null +++ b/wrappers/s2i/python_openvino/README.md @@ -0,0 +1,45 @@ +# Seldon base image with python and OpenVINO inference engine + +## Building +```bash + +cd ../../../wrappers/s2i/python_openvino +cp ../python/s2i . +docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy \ +-t seldon_openvino_base:latest . +``` +## Usage + +This base image can be used to Seldon components exactly the same way like with standard Seldon base images. +Use s2i tool like documented [here](https://github.com/SeldonIO/seldon-core/blob/master/docs/wrappers/python.md). +An example is presented below: + +```bash +s2i build . seldon_openvino_base:latest {component_image_name} +``` + +## References + +[OpenVINO toolkit](https://software.intel.com/en-us/openvino-toolkit) + +[OpenVINO API docs](https://software.intel.com/en-us/articles/OpenVINO-InferEngine#inpage-nav-9) + +[Seldon pipeline example](../../../examples/models/openvino_imagenet_ensemble) + + +## Notes + +This Seldon base image contains, beside OpenVINO inference execution engine python API also several other useful components. +- Intel optimized python version +- Intel optimized OpenCV package +- Intel optimized TensorFlow with MKL engine +- Configured conda package manager + +In case you would use this compoment to run inference operations using TensorFlow with MKL, make sure you configure +also the following environment variables: + +`KMP_AFFINITY`=granularity=fine,verbose,compact,1,0 + +`KMP_BLOCKTIME`=1 + +`OMP_NUM_THREADS`={number of CPU cores} \ No newline at end of file From 33eed35cf2151113cd0d92ee27400fb30ceb5094 Mon Sep 17 00:00:00 2001 From: Dariusz Trawinski Date: Tue, 5 Feb 2019 23:09:38 +0100 Subject: [PATCH 6/6] fix readme --- wrappers/s2i/python_openvino/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/wrappers/s2i/python_openvino/README.md b/wrappers/s2i/python_openvino/README.md index eb84839dbc..d55bf01506 100644 --- a/wrappers/s2i/python_openvino/README.md +++ b/wrappers/s2i/python_openvino/README.md @@ -3,7 +3,6 @@ ## Building ```bash -cd ../../../wrappers/s2i/python_openvino cp ../python/s2i . docker build -f Dockerfile_openvino_base --build-arg http_proxy=$http_proxy --build-arg https_proxy=$https_proxy \ -t seldon_openvino_base:latest .