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Wrap SINGA into a Docker image, which can run in a mesos cluster Can run in training and testing modes.
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Aaron Wuwf
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Apr 29, 2016
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python | ||
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| #/************************************************************ | ||
| #* | ||
| #* Licensed to the Apache Software Foundation (ASF) under one | ||
| #* or more contributor license agreements. See the NOTICE file | ||
| #* distributed with this work for additional information | ||
| #* regarding copyright ownership. The ASF licenses this file | ||
| #* to you 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 | ||
| #* | ||
| #* Unless required by applicable law or agreed to in writing, | ||
| #* software distributed under the License is distributed on an | ||
| #* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| #* KIND, either express or implied. See the License for the | ||
| #* specific language governing permissions and limitations | ||
| #* under the License. | ||
| #* | ||
| #*************************************************************/ | ||
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| import os, sys | ||
| import numpy as np | ||
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| current_path_ = os.path.dirname(__file__) | ||
| singa_root_=os.path.abspath(os.path.join(current_path_,'../..')) | ||
| sys.path.append(os.path.join(singa_root_,'thirdparty','protobuf-2.6.0','python')) | ||
| sys.path.append(os.path.join(singa_root_,'tool','python')) | ||
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| from model import neuralnet, updater | ||
| from singa.driver import Driver | ||
| from singa.layer import * | ||
| from singa.model import save_model_parameter, load_model_parameter | ||
| from singa.utils.utility import swap32 | ||
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| from PIL import Image | ||
| import glob,random, shutil, time | ||
| from flask import Flask, request, redirect, url_for | ||
| from singa.utils import kvstore, imgtool | ||
| app = Flask(__name__) | ||
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| def train(batchsize,disp_freq,check_freq,train_step,workspace,checkpoint=None): | ||
| print '[Layer registration/declaration]' | ||
| # TODO change layer registration methods | ||
| d = Driver() | ||
| d.Init(sys.argv) | ||
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| print '[Start training]' | ||
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| #if need to load checkpoint | ||
| if checkpoint: | ||
| load_model_parameter(workspace+checkpoint, neuralnet, batchsize) | ||
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| for i in range(0,train_step): | ||
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| for h in range(len(neuralnet)): | ||
| #Fetch data for input layer | ||
| if neuralnet[h].layer.type==kDummy: | ||
| neuralnet[h].FetchData(batchsize) | ||
| else: | ||
| neuralnet[h].ComputeFeature() | ||
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| neuralnet[h].ComputeGradient(i+1, updater) | ||
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| if (i+1)%disp_freq == 0: | ||
| print ' Step {:>3}: '.format(i+1), | ||
| neuralnet[h].display() | ||
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| if (i+1)%check_freq == 0: | ||
| save_model_parameter(i+1, workspace, neuralnet) | ||
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| print '[Finish training]' | ||
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| def product(workspace,checkpoint): | ||
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| print '[Layer registration/declaration]' | ||
| # TODO change layer registration methods | ||
| d = Driver() | ||
| d.Init(sys.argv) | ||
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| load_model_parameter(workspace+checkpoint, neuralnet,1) | ||
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| app.debug = True | ||
| app.run(host='0.0.0.0', port=80) | ||
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| @app.route("/") | ||
| def index(): | ||
| return "Hello World! This is SINGA DLAAS! Please send post request with image=file to '/predict' " | ||
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| def allowed_file(filename): | ||
| allowd_extensions_ = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif']) | ||
| return '.' in filename and \ | ||
| filename.rsplit('.', 1)[1] in allowd_extensions_ | ||
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| @app.route('/predict', methods=['POST']) | ||
| def predict(): | ||
| size_=(32,32) | ||
| pixel_length_=3*size_[0]*size_[1] | ||
| label_num_=10 | ||
| if request.method == 'POST': | ||
| file = request.files['image'] | ||
| if file and allowed_file(file.filename): | ||
| im = Image.open(file).convert("RGB") | ||
| im = imgtool.resize_to_center(im,size_) | ||
| pixel = floatVector(pixel_length_) | ||
| byteArray = imgtool.toBin(im,size_) | ||
| data = np.frombuffer(byteArray, dtype=np.uint8) | ||
| data = data.reshape(1, pixel_length_) | ||
| #dummy data Layer | ||
| shape = intVector(4) | ||
| shape[0]=1 | ||
| shape[1]=3 | ||
| shape[2]=size_[0] | ||
| shape[3]=size_[1] | ||
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| for h in range(len(neuralnet)): | ||
| #Fetch data for input layer | ||
| if neuralnet[h].is_datalayer: | ||
| if not neuralnet[h].is_label: | ||
| neuralnet[h].Feed(data,3) | ||
| else: | ||
| neuralnet[h].FetchData(1) | ||
| else: | ||
| neuralnet[h].ComputeFeature() | ||
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| #get result | ||
| #data = neuralnet[-1].get_singalayer().data(neuralnet[-1].get_singalayer()) | ||
| #prop =floatArray_frompointer(data.mutable_cpu_data()) | ||
| prop = neuralnet[-1].GetData() | ||
| print prop | ||
| result=[] | ||
| for i in range(label_num_): | ||
| result.append((i,prop[i])) | ||
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| result.sort(key=lambda tup: tup[1], reverse=True) | ||
| print result | ||
| response="" | ||
| for r in result: | ||
| response+=str(r[0])+":"+str(r[1]) | ||
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| return response | ||
| return "error" | ||
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| if __name__=='__main__': | ||
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| if sys.argv[1]=="train": | ||
| if len(sys.argv) < 6: | ||
| print "argv should be more than 6" | ||
| exit() | ||
| if len(sys.argv) > 6: | ||
| checkpoint = sys.argv[6] | ||
| else: | ||
| checkpoint = None | ||
| #training | ||
| train( | ||
| batchsize = int(sys.argv[2]), | ||
| disp_freq = int(sys.argv[3]), | ||
| check_freq = int(sys.argv[4]), | ||
| train_step = int(sys.argv[5]), | ||
| workspace = '/workspace', | ||
| checkpoint = checkpoint, | ||
| ) | ||
| else: | ||
| if len(sys.argv) < 3: | ||
| print "argv should be more than 2" | ||
| exit() | ||
| checkpoint = sys.argv[2] | ||
| product( | ||
| workspace = '/workspace', | ||
| checkpoint = checkpoint | ||
| ) | ||
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