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wengbenjue / build_tf_serving.sh
Created July 7, 2019 03:36 — forked from masroorhasan/build_tf_serving.sh
#!/bin/bash
USER=$1
TAG=$2
TF_SERVING_VERSION_GIT_BRANCH="r1.13"
git clone --branch="${TF_SERVING_VERSION_GIT_BRANCH}" https://github.com/tensorflow/serving
TF_SERVING_BUILD_OPTIONS="--copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse4.1 --copt=-msse4.2"
cd serving && \
docker build --pull -t $USER/tensorflow-serving-devel:$TAG \
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wengbenjue / optimized_tf_serving_client.py
Created July 7, 2019 03:29 — forked from masroorhasan/optimized_tf_serving_client.py
#!/usr/bin/env python
from __future__ import print_function
import argparse
import numpy as np
import time
tt = time.time()
import cv2
from grpc.beta import implementations
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wengbenjue / jg_tp33_cdh512.md
Created July 17, 2018 03:08 — forked from sjudeng/jg_tp33_cdh512.md
Testing OLAP using JanusGraph with TinkerPop 3.3.0 and Spark 2.2 on Yarn (Cloudera)
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wengbenjue / jg_tp33_cdh512.md
Created July 17, 2018 03:08 — forked from sjudeng/jg_tp33_cdh512.md
Testing OLAP using JanusGraph with TinkerPop 3.3.0 and Spark 2.2 on Yarn (Cloudera)
@wengbenjue
wengbenjue / imdb_cnn_kim_small_embedding.py
Created August 14, 2017 10:01 — forked from entron/imdb_cnn_kim_small_embedding.py
Keras implementation of Kim's paper "Convolutional Neural Networks for Sentence Classification" with a very small embedding size. The test accuracy is 0.853.
'''This scripts implements Kim's paper "Convolutional Neural Networks for Sentence Classification"
with a very small embedding size (20) than the commonly used values (100 - 300) as it gives better
result with much less parameters.
Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py
Get to 0.853 test accuracy after 5 epochs. 13s/epoch on Nvidia GTX980 GPU.
'''
from __future__ import print_function