Community Articles

TimothySpann · ‎07-11-2018

Capture Images from PicSum.com Free Images

Process All the Images via TensorFlow Processor, SSD Predict via MMS and SqueezeNet v1.1 via MMS

Apache Zeppelin SQL Against tblsqueeze11

Example Output from Squeeze v1.1

Storing Generic Data in HDFS via Schema

Example SSD Data JSON

High Level Flow From Server

Apache NiFi Server Flows to Store

Convert to Apache ORC

Extract Attributes

Convert JSON Arrays to Other

Example Data Derived From TensorFlow Processor

Schemas in Schema Registry

Create Table in Zeppelin

Query Table in Zeppelin

Python Libraries

git clone https://github.com/awslabs/mxnet-model-server.git


pip install opencv-python  -U
pip install scikit-learn -U
pip install easydict -U
pip install scikit-image -U
pip install numpy -U
pip install mxnet -U

pip3.6 install opencv-python -U
pip3.6 install scikit-learn -U
pip3.6 install easydict -U
pip3.6 install scikit-image -U
pip3.6 install numpy -U
pip3.6 install mxnet -U

Example Runs - Squeeze v1.1

mxnet-model-server --models squeezenet=squeezenet_v1.1.model --service mms/model_service/mxnet_vision_service.py --port 9999

[INFO 2018-07-10 16:50:26,840 PID:7730 /usr/local/lib/python3.6/site-packages/mms/request_handler/flask_handler.py:jsonify:159] Jsonifying the response: {'prediction': [[{'probability': 0.3365139067173004, 'class': 'n03710193 mailbox, letter box'}, {'probability': 0.1522996574640274, 'class': 'n03764736 milk can'}, {'probability': 0.08760709315538406, 'class': 'n03000134 chainlink fence'}, {'probability': 0.08103135228157043, 'class': 'n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin'}, {'probability': 0.04956872761249542, 'class': 'n02795169 barrel, cask'}]]}
[INFO 2018-07-10 16:50:26,842 PID:7730 /usr/local/lib/python3.6/site-packages/werkzeug/_internal.py:_log:88] 127.0.0.1 - - [10/Jul/2018 16:50:26] "POST /squeezenet/predict HTTP/1.1" 200 -
[INFO 2018-07-10 16:50:46,904 PID:7730 /usr/local/lib/python3.6/site-packages/mms/serving_frontend.py:predict_callback:467] Request input: data should be image with jpeg format.
[INFO 2018-07-10 16:50:46,960 PID:7730 /usr/local/lib/python3.6/site-packages/mms/request_handler/flask_handler.py:get_file_data:137] Getting file data from request.
[INFO 2018-07-10 16:50:47,020 PID:7730 /usr/local/lib/python3.6/site-packages/mms/serving_frontend.py:predict_callback:510] Response is text.
[INFO 2018-07-10 16:50:47,020 PID:7730 /usr/local/lib/python3.6/site-packages/mms/request_handler/flask_handler.py:jsonify:159] Jsonifying the response: {'prediction': [[{'probability': 0.1060439869761467, 'class': 'n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch'}, {'probability': 0.06582894921302795, 'class': 'n01930112 nematode, nematode worm, roundworm'}, {'probability': 0.05008145794272423, 'class': 'n01751748 sea snake'}, {'probability': 0.03847070038318634, 'class': 'n01737021 water snake'}, {'probability': 0.03614763543009758, 'class': 'n09229709 bubble'}]]}
[INFO 2018-07-10 16:50:47,021 PID:7730 /usr/local/lib/python3.6/site-packages/werkzeug/_internal.py:_log:88] 127.0.0.1 - - [10/Jul/2018 16:50:47] "POST /squeezenet/predict HTTP/1.1" 200 -


mxnet-model-server --models SSD=resnet50_ssd_model.model --service ssd_service.py --port 9998

Apache MXNet Model Server Model Zoo

https://github.com/awslabs/mxnet-model-server/blob/master/docs/model_zoo.md

Connect to MMS

/opt/demo/curl.sh
curl -X POST http://127.0.0.1:9998/SSD/predict -F "data=@$1" 2>/dev/null

/opt/demo/curl2.sh
curl -X POST http://127.0.0.1:9999/squeezenet/predict -F "data=@$1" 2>/dev/null

Flows

mxnetserverlocal.xml
mxnetmodelserver.xml

Reference

Cloudera Community

Community Articles

Integrating Apache MXNet Model Server with Apache NiFi - Updated

Apache NiFi