alreadytaikeune

Install a few dependencies

sudo apt-get install python-pip apache2 libapache2-mod-wsgi

Edit Apache's configuration file

If you're going from a fresh apache install then the following command may simply work for you.

alreadytaikeune

container=$1
for i in `docker ps -a | grep $1 | awk '{split($0,a," "); print a[1]}'`; do 
    docker rm $i;
done

alreadytaikeune

# python module.py
#             |
#          (thread)
#             |
#             |
# (GIL unlocked or in a state where it can 
# be release upon request, or when doing 
#     blocking operation)
#             |

alreadytaikeune

{
    "format": {"type":"aggregate_n_features",
               "merge_channels":true},
    "features": {
        "MelSp1": {
            "feature_type": "PostProcessedMelSpectrum",
            "parameters": {
                "blockSize": 1024,
                "stepSize": 1024,
                "MelMaxFreq": 10000.0,

alreadytaikeune

#!/usr/bin/env python
# -*- encoding: utf-8 -*-

# Copyright 2018, Anis KHLIF
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0

alreadytaikeune

2018-07-23 09:35:00,063 : MainThread : INFO : running /usr/local/lib/python2.7/site-packages/gensim-3.5.0-py2.7-linux-x86_64.egg/gensim/scripts/word2vec_standalone.py -train data/text9 -output /tmp/test -window 5 -negative 5 -threads 4 -min_count 5 -iter 5 -cbow 0
2018-07-23 09:35:00,064 : MainThread : INFO : collecting all words and their counts
2018-07-23 09:35:11,715 : MainThread : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2018-07-23 09:35:29,976 : MainThread : INFO : PROGRESS: at sentence #10000, processed 100000000 words, keeping 694463 word types
2018-07-23 09:35:40,645 : MainThread : INFO : collected 833184 word types from a corpus of 124301826 raw words and 12431 sentences
2018-07-23 09:35:40,645 : MainThread : INFO : Loading a fresh vocabulary
2018-07-23 09:35:42,254 : MainThread : INFO : effective_min_count=5 retains 218316 unique words (26% of original 833184, drops 614868)
2018-07-23 09:35:42,254 : MainThread : INFO : effective_min_count=5 leaves 123353509 word corpu

alreadytaikeune

2018-07-23 09:55:28,275 : MainThread : INFO : running /usr/local/lib/python2.7/site-packages/gensim-3.5.0-py2.7-linux-x86_64.egg/gensim/scripts/word2vec_standalone.py -train data/text9 -output /tmp/test -window 5 -negative 5 -threads 4 -min_count 5 -iter 5 -cbow 0
2018-07-23 09:55:28,276 : MainThread : INFO : collecting all words and their counts
2018-07-23 09:55:39,924 : MainThread : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2018-07-23 09:55:57,855 : MainThread : INFO : PROGRESS: at sentence #10000, processed 100000000 words, keeping 694463 word types
2018-07-23 09:56:08,638 : MainThread : INFO : collected 833184 word types from a corpus of 124301826 raw words and 12431 sentences
2018-07-23 09:56:08,638 : MainThread : INFO : Loading a fresh vocabulary
2018-07-23 09:56:10,308 : MainThread : INFO : effective_min_count=5 retains 218316 unique words (26% of original 833184, drops 614868)
2018-07-23 09:56:10,308 : MainThread : INFO : effective_min_count=5 leaves 123353509 word corpu

alreadytaikeune

2018-07-23 10:34:49,659 : MainThread : INFO : running /usr/local/lib/python2.7/site-packages/gensim-3.5.0-py2.7-linux-x86_64.egg/gensim/scripts/word2vec_standalone.py -train data/text9 -output /tmp/test -window 5 -negative 5 -threads 4 -min_count 5 -iter 5 -cbow 0 -loss
2018-07-23 10:34:49,661 : MainThread : INFO : collecting all words and their counts
2018-07-23 10:35:01,095 : MainThread : INFO : PROGRESS: at sentence #0, processed 0 words, keeping 0 word types
2018-07-23 10:35:18,754 : MainThread : INFO : PROGRESS: at sentence #10000, processed 100000000 words, keeping 694463 word types
2018-07-23 10:35:29,348 : MainThread : INFO : collected 833184 word types from a corpus of 124301826 raw words and 12431 sentences
2018-07-23 10:35:29,349 : MainThread : INFO : Loading a fresh vocabulary
2018-07-23 10:35:30,889 : MainThread : INFO : effective_min_count=5 retains 218316 unique words (26% of original 833184, drops 614868)
2018-07-23 10:35:30,889 : MainThread : INFO : effective_min_count=5 leaves 123353509 word

alreadytaikeune

bazel build tensorflow/tools/graph_transforms:summarize_graph

# While you are at it, you can also build other very helpful utilities that you may need:
bazel build tensorflow/python/tools:freeze_graph
bazel build tensorflow/tools/graph_transforms:summarize_graph
bazel build -c opt tensorflow/tools/benchmark:benchmark_model

alreadytaikeune

from tensorflow.python.framework import graph_util

# Suppose you have obtained in a way or the other a graph object, and suppose
# you have a list of the output nodes names (manually created after inspectection
# with tensorboard for example). Then, one way to build a frozen graph is the following:

with tf.Session(graph=graph) as sess:
  graph_def = graph.as_graph_def()
  frozen_graph_def = graph_util.convert_variables_to_constants(
        sess, graph_def, output_node_names)