教程 | 从零开始：TensorFlow机器学习模型快速部署指南

(
cd "$HOME" && 
curl -O http://download.tensorflow.org/example_images/flower_photos.tgz && 
tar xzf flower_photos.tgz ;
) && 
bazel build tensorflow/examples/image_retraining:retrain 
          tensorflow/examples/image_retraining:label_image 
&& 
bazel-bin/tensorflow/examples/image_retraining/retrain 
  --image_dir "$HOME"/flower_photos 
  --how_many_training_steps=200
&& 
bazel-bin/tensorflow/examples/image_retraining/label_image 
  --graph=/tmp/output_graph.pb 
  --labels=/tmp/output_labels.txt 
  --output_layer=final_result:0 
  --image=$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg

sudo docker run -it --net=host liubowei/simple-ml-serving:latest /bin/bash

>>> cat test.sh && bash test.sh

while read line ; do
bazel-bin/tensorflow/examples/image_retraining/label_image 
--graph=/tmp/output_graph.pb --labels=/tmp/output_labels.txt 
--output_layer=final_result:0 
--image="$line" ;
done

141:  run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer,
142:        FLAGS.num_top_predictions)

141:  for line in sys.stdin:
142:    run_graph(load_image(line), labels, FLAGS.input_layer, FLAGS.output_layer,
142:        FLAGS.num_top_predictions)

def run_graph(image_data, labels, input_layer_name, output_layer_name,
              num_top_predictions, sess):
    # Feed the image_data as input to the graph.
    #   predictions will contain a two-dimensional array, where one
    #   dimension represents the input image count, and the other has
    #   predictions per class
    softmax_tensor = sess.graph.get_tensor_by_name(output_layer_name)
    predictions, = sess.run(softmax_tensor, {input_layer_name: image_data})
    # Sort to show labels in order of confidence
    top_k = predictions.argsort()[-num_top_predictions:][::-1]
    for node_id in top_k:
      human_string = labels[node_id]
      score = predictions[node_id]
      print('%s (score = %.5f)' % (human_string, score))
    return [ (labels[node_id], predictions[node_id].item()) for node_id in top_k ] # numpy floats are not json serializable, have to run item

...

  with tf.Session() as sess:
    for line in sys.stdin:
      run_graph(load_image(line), labels, FLAGS.input_layer, FLAGS.output_layer,
          FLAGS.num_top_predictions, sess)

#!/usr/bin/env python
# usage: python echo.py to launch the server ; and then in another session, do
# curl -v -XPOST 127.0.0.1:12480 -F "data=@./image.jpg"
from flask import Flask, request
app = Flask(__name__)
@app.route('/', methods=['POST'])
def classify():
    try:
        data = request.files.get('data').read()
        print repr(data)[:1000]
        return data, 200
    except Exception as e:
        return repr(e), 500
app.run(host='127.0.0.1',port=12480)

And here is the corresponding flask app hooked up to run_graph above:

#!/usr/bin/env python
# usage: bash tf_classify_server.sh
from flask import Flask, request
import tensorflow as tf
import label_image as tf_classify
import json
app = Flask(__name__)
FLAGS, unparsed = tf_classify.parser.parse_known_args()
labels = tf_classify.load_labels(FLAGS.labels)
tf_classify.load_graph(FLAGS.graph)
sess = tf.Session()
@app.route('/', methods=['POST'])
def classify():
    try:
        data = request.files.get('data').read()
        result = tf_classify.run_graph(data, labels, FLAGS.input_layer, FLAGS.output_layer, FLAGS.num_top_predictions, sess)
        return json.dumps(result), 200
    except Exception as e:
        return repr(e), 500
app.run(host='127.0.0.1',port=12480)

// Usage : node basic_proxy.js WORKER_PORT_0,WORKER_PORT_1,...
const worker_ports = process.argv[2].split(',')
if (worker_ports.length === 0) { console.err('missing worker ports') ; process.exit(1) }

const proxy = require('http-proxy').createProxyServer({})
proxy.on('error', () => console.log('proxy error'))

let i = 0
require('http').createServer((req, res) => {
  proxy.web(req,res, {target: 'http://localhost:' + worker_ports[ (i++) % worker_ports.length ]})
}).listen(12480)
console.log(`Proxying localhost:${12480} to [${worker_ports.toString()}]`)

// spin up the ML workers
const { exec } = require('child_process')
worker_ports.map(port => exec(`/bin/bash ./tf_classify_server.sh ${port}`))

// Usage : node seaport_proxy.js
const seaportServer = require('seaport').createServer()
seaportServer.listen(12481)
const proxy = require('http-proxy').createProxyServer({})
proxy.on('error', () => console.log('proxy error'))

let i = 0
require('http').createServer((req, res) => {
  seaportServer.get('tf_classify_server', worker_ports => {
    const this_port = worker_ports[ (i++) % worker_ports.length ].port
    proxy.web(req,res, {target: 'http://localhost:' + this_port })
  })
}).listen(12480)
console.log(`Seaport proxy listening on ${12480} to '${'tf_classify_server'}' servers registered to ${12481}`)

// Usage : node tf_classify_server.js
const port = require('seaport').connect(12481).register('tf_classify_server')
console.log(`Launching tf classify worker on ${port}`)
require('child_process').exec(`/bin/bash ./tf_classify_server.sh ${port}`)

// Usage : node p2p_proxy.js
const seaportServer = require('seaport').createServer()
seaportServer.listen(12481)

let i = 0
require('http').createServer((req, res) => {
  seaportServer.get('tf_classify_server', worker_ports => {
    const this_port = worker_ports[ (i++) % worker_ports.length ].port
    res.end(`${this_port}
`)
  })
}).listen(12480)
console.log(`P2P seaport proxy listening on ${12480} to 'tf_classify_server' servers registered to ${12481}`)

curl -v -XPOST localhost:`curl localhost:12480` -F"data=@$HOME/flower_photos/daisy/21652746_cc379e0eea_m.jpg"