fix apis, including os.path, shell and multiprocess (#3650)

PaddleRec/ctr/Paddle_baseline_KDD2019/generate_test.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import argparse
 import logging
 import numpy as np
@@ -22,12 +21,12 @@ import os
 os.environ["CUDA_VISIBLE_DEVICES"] = ""
 import paddle
 import paddle.fluid as fluid
-logging.basicConfig(
-    format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
 num_context_feature = 22
 
+
 def parse_args():
     parser = argparse.ArgumentParser(description="PaddlePaddle DeepFM example")
     parser.add_argument(
@@ -59,6 +58,7 @@ def parse_args():
 
     return parser.parse_args()
 
+
 def to_lodtensor(data, place):
     seq_lens = [len(seq) for seq in data]
     cur_len = 0
@@ -72,7 +72,6 @@ def to_lodtensor(data, place):
     res.set(flattened_data, place)
     res.set_lod([lod])
 
-
     return res
 
 
@@ -91,7 +90,8 @@ def data2tensor(data, place):
         sparse_data = to_lodtensor([x[1 + i] for x in data], place)
         feed_dict["context" + str(i)] = sparse_data
 
-    context_fm = to_lodtensor(np.array([x[-2] for x in data]).astype("float32"), place)
+    context_fm = to_lodtensor(
+        np.array([x[-2] for x in data]).astype("float32"), place)
 
     feed_dict["context_fm"] = context_fm
     y_data = np.array([x[-1] for x in data]).astype("int64")
@@ -99,6 +99,7 @@ def data2tensor(data, place):
     feed_dict["label"] = y_data
     return feed_dict
 
+
 def test():
     args = parse_args()
 
@@ -112,13 +113,14 @@ def test():
     exe = fluid.Executor(place)
 
     whole_filelist = ["./out/normed_test_session.txt"]
-    test_files = whole_filelist[int(0.0 * len(whole_filelist)):int(1.0 * len(whole_filelist))]
-
+    test_files = whole_filelist[int(0.0 * len(whole_filelist)):int(1.0 * len(
+        whole_filelist))]
 
     epochs = 1
 
     for i in range(epochs):
-        cur_model_path = args.model_path + "/epoch" + str(1) + ".model"
+        cur_model_path = os.path.join(args.model_path,
+                                      "epoch" + str(1) + ".model")
         with open("./testres/res" + str(i), 'w') as r:
             with fluid.scope_guard(test_scope):
                 [inference_program, feed_target_names, fetch_targets] = \
@@ -129,9 +131,11 @@ def test():
                 for batch_id, data in enumerate(test_reader()):
                     print(len(data[0]))
                     feed_dict = data2tensor(data, place)
-                    loss_val, auc_val, accuracy, predict, _ = exe.run(inference_program,
+                    loss_val, auc_val, accuracy, predict, _ = exe.run(
+                        inference_program,
                         feed=feed_dict,
-                                                fetch_list=fetch_targets, return_numpy=False)
+                        fetch_list=fetch_targets,
+                        return_numpy=False)
 
                     x = np.array(predict)
                     for j in range(x.shape[0]):

PaddleRec/ctr/Paddle_baseline_KDD2019/infer.py

@@ -12,8 +12,7 @@ import paddle.fluid as fluid
 import map_reader
 from network_conf import ctr_deepfm_dataset
 
-logging.basicConfig(
-    format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)
 
@@ -91,15 +90,20 @@ def infer():
     place = fluid.CPUPlace()
     inference_scope = fluid.core.Scope()
 
-    filelist = ["%s/%s" % (args.data_path, x) for x in os.listdir(args.data_path)]
+    filelist = [
+        "%s/%s" % (args.data_path, x) for x in os.listdir(args.data_path)
+    ]
     from map_reader import MapDataset
     map_dataset = MapDataset()
     map_dataset.setup(args.sparse_feature_dim)
     exe = fluid.Executor(place)
 
-    whole_filelist = ["raw_data/part-%d" % x for x in range(len(os.listdir("raw_data")))]
+    whole_filelist = [
+        "raw_data/part-%d" % x for x in range(len(os.listdir("raw_data")))
+    ]
     #whole_filelist = ["./out/normed_train09",  "./out/normed_train10",  "./out/normed_train11"]
-    test_files = whole_filelist[int(0.0 * len(whole_filelist)):int(1.0 * len(whole_filelist))]
+    test_files = whole_filelist[int(0.0 * len(whole_filelist)):int(1.0 * len(
+        whole_filelist))]
 
     # file_groups = [whole_filelist[i:i+train_thread_num] for i in range(0, len(whole_filelist), train_thread_num)]
 
@@ -110,7 +114,8 @@ def infer():
 
     epochs = 2
     for i in range(epochs):
-        cur_model_path = args.model_path + "/epoch" + str(i + 1) + ".model"
+        cur_model_path = os.path.join(args.model_path,
+                                      "epoch" + str(i + 1) + ".model")
         with fluid.scope_guard(inference_scope):
             [inference_program, feed_target_names, fetch_targets] = \
                 fluid.io.load_inference_model(cur_model_path, exe)
@@ -120,9 +125,11 @@ def infer():
 
             test_reader = map_dataset.infer_reader(test_files, 1000, 100000)
             for batch_id, data in enumerate(test_reader()):
-                loss_val, auc_val, accuracy, predict, label = exe.run(inference_program,
+                loss_val, auc_val, accuracy, predict, label = exe.run(
+                    inference_program,
                     feed=data2tensor(data, place),
-                                            fetch_list=fetch_targets, return_numpy=False)
+                    fetch_list=fetch_targets,
+                    return_numpy=False)
 
                 #print(np.array(predict))
                 #x = np.array(predict)

PaddleRec/ctr/Paddle_baseline_KDD2019/local_train.py

@@ -6,12 +6,12 @@ import paddle.fluid as fluid
 import sys
 from network_confv6 import ctr_deepfm_dataset
 
-
 NUM_CONTEXT_FEATURE = 22
 DIM_USER_PROFILE = 10
 DIM_DENSE_FEATURE = 3
 PYTHON_PATH = "/home/yaoxuefeng/whls/paddle_release_home/python/bin/python"  # this is mine change yours
 
+
 def train():
     args = parse_args()
     if not os.path.isdir(args.model_output_dir):
@@ -23,15 +23,18 @@ def train():
     dense_feature = fluid.layers.data(
         name="dense_feature", shape=[DIM_DENSE_FEATURE], dtype='float32')
     context_feature = [
-        fluid.layers.data(name="context" + str(i), shape=[1], lod_level=1, dtype="int64")
-        for i in range(0, NUM_CONTEXT_FEATURE)]
+        fluid.layers.data(
+            name="context" + str(i), shape=[1], lod_level=1, dtype="int64")
+        for i in range(0, NUM_CONTEXT_FEATURE)
+    ]
     context_feature_fm = fluid.layers.data(
         name="context_fm", shape=[1], dtype='int64', lod_level=1)
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
     print("ready to network")
     #self define network 
-    loss, auc_var, batch_auc_var, accuracy, predict = ctr_deepfm_dataset(dense_feature, context_feature, context_feature_fm, label,
+    loss, auc_var, batch_auc_var, accuracy, predict = ctr_deepfm_dataset(
+        dense_feature, context_feature, context_feature_fm, label,
         args.embedding_size, args.sparse_feature_dim)
 
     print("ready to optimize")
@@ -42,7 +45,8 @@ def train():
     exe.run(fluid.default_startup_program())
     #use dataset api for much faster speed
     dataset = fluid.DatasetFactory().create_dataset()
-    dataset.set_use_var([dense_feature] + context_feature + [context_feature_fm] + [label])
+    dataset.set_use_var([dense_feature] + context_feature +
+                        [context_feature_fm] + [label])
     #self define how to process generated training insatnces in map_reader.py
     pipe_command = PYTHON_PATH + "  map_reader.py %d" % args.sparse_feature_dim
     dataset.set_pipe_command(pipe_command)
@@ -50,26 +54,35 @@ def train():
     thread_num = 1
     dataset.set_thread(thread_num)
     #self define how to split training files for example:"split -a 2 -d -l 200000 normed_train.txt normed_train"
-    whole_filelist = ["./out/normed_train%d" % x for x in range(len(os.listdir("out")))]
-    whole_filelist = ["./out/normed_train00", "./out/normed_train01", "./out/normed_train02", "./out/normed_train03",
-                      "./out/normed_train04", "./out/normed_train05", "./out/normed_train06", "./out/normed_train07",
-                      "./out/normed_train08",
-                      "./out/normed_train09", "./out/normed_train10", "./out/normed_train11"]
+    whole_filelist = [
+        "./out/normed_train%d" % x for x in range(len(os.listdir("out")))
+    ]
+    whole_filelist = [
+        "./out/normed_train00", "./out/normed_train01", "./out/normed_train02",
+        "./out/normed_train03", "./out/normed_train04", "./out/normed_train05",
+        "./out/normed_train06", "./out/normed_train07", "./out/normed_train08",
+        "./out/normed_train09", "./out/normed_train10", "./out/normed_train11"
+    ]
     print("ready to epochs")
     epochs = 10
     for i in range(epochs):
         print("start %dth epoch" % i)
         dataset.set_filelist(whole_filelist[:int(len(whole_filelist))])
         #print the informations you want by setting fetch_list and fetch_info
-        exe.train_from_dataset(program=fluid.default_main_program(),
+        exe.train_from_dataset(
+            program=fluid.default_main_program(),
             dataset=dataset,
             fetch_list=[auc_var, accuracy, predict, label],
             fetch_info=["auc", "accuracy", "predict", "label"],
             debug=False)
-        model_dir = args.model_output_dir + '/epoch' + str(i + 1) + ".model"
+        model_dir = os.path.join(args.model_output_dir,
+                                 '/epoch' + str(i + 1) + ".model")
         sys.stderr.write("epoch%d finished" % (i + 1))
         #save model
-        fluid.io.save_inference_model(model_dir, [dense_feature.name] + [x.name for x in context_feature] + [context_feature_fm.name] + [label.name],
+        fluid.io.save_inference_model(
+            model_dir,
+            [dense_feature.name] + [x.name for x in context_feature] +
+            [context_feature_fm.name] + [label.name],
             [loss, auc_var, accuracy, predict, label], exe)
 
 

PaddleRec/ctr/dcn/README.md

@@ -32,7 +32,7 @@ DCN模型介绍可以参阅论文[Deep & Cross Network for Ad Click Predictions]
 
 数据下载命令
 ```bash
-cd data && sh download.sh
+cd data && python download.py
 ```
 
 ## 数据处理
@@ -70,13 +70,14 @@ loss: [0.44703564]      auc_val: [0.80654419]
 ## 多机训练
 首先使用命令下载并预处理小规模样例数据集：
 ```bash
-cd dist_data && sh dist_download.sh && cd ..
+cd dist_data && python dist_download.py && cd ..
 ```
 运行命令本地模拟多机场景，默认使用2 X 2，即2个pserver，2个trainer的方式组网训练。
 
 **注意：在多机训练中，建议使用Paddle 1.6版本以上或[最新版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/install/Tables.html#whl-dev)。**
 
 ```bash
+# 该sh不支持Windows
 sh cluster_train.sh
 ```
 参数说明：

PaddleRec/ctr/dcn/cluster_train.py

@@ -162,7 +162,8 @@ def train():
                 fetch_info=['total_loss', 'avg_logloss', 'auc'],
                 debug=False,
                 print_period=args.print_steps)
-            model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+            model_dir = os.path.join(args.model_output_dir,
+                                     'epoch_' + str(epoch_id + 1))
             sys.stderr.write('epoch%d is finished and takes %f s\n' % (
                 (epoch_id + 1), time.time() - start))
             if args.trainer_id == 0:  # only trainer 0 save model

PaddleRec/ctr/dcn/config.py

-#!/usr/bin/env python
-# coding: utf-8
 import argparse
 """
 global params

PaddleRec/ctr/dcn/data/download.py

+import os
+import sys
+import io
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress
+
+if __name__ == '__main__':
+    trainfile = 'train.txt'
+    url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url)
+    print("download and extract finished")
+
+    count = 0
+    for _ in io.open(trainfile, 'r', encoding='utf-8'):
+        count += 1
+
+    print("total records: %d" % count)
+    print("done")

PaddleRec/ctr/dcn/data/download.sh

-#!/bin/bash
-
-workdir=$(cd $(dirname $0); pwd)
-
-cd $workdir
-
-trainfile='train.txt'
-
-echo "data dir:" ${workdir}
-
-cd $workdir
-
-echo "download data starting..."
-wget --no-check-certificate -c https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
-echo "download finished"
-
-echo "extracting ..."
-tar xzf dac.tar.gz >/dev/null 2>&1
-wc -l $trainfile | awk '{print $1}' > line_nums.log
-
-echo "extract finished"
-echo "total records: "`cat line_nums.log`
-echo "done"

PaddleRec/ctr/dcn/data/preprocess.py

-#!/usr/bin/env python
-# coding: utf-8
 from __future__ import print_function, absolute_import, division
 
 import os
@@ -19,7 +17,6 @@ VOCAB_DIR = 'vocab'
 TRAIN_DIR = 'train'
 TEST_VALID_DIR = 'test_valid'
 SPLIT_RATIO = 0.9
-LINE_NUMS = "line_nums.log"
 FREQ_THR = 10
 
 INT_COLUMN_NAMES = ['I' + str(i) for i in range(1, 14)]
@@ -113,11 +110,13 @@ def split_data():
             fout.close()
             data_dir = TEST_VALID_DIR
             cur_part_idx = int(line_idx / 200000)
-            fout = open(data_dir + '/part-' + str(cur_part_idx), 'w')
+            fout = open(
+                os.path.join(data_dir, 'part-' + str(cur_part_idx)), 'w')
         if line_idx % 200000 == 0 and line_idx != 0:
             fout.close()
             cur_part_idx = int(line_idx / 200000)
-            fout = open(data_dir + '/part-' + str(cur_part_idx), 'w')
+            fout = open(
+                os.path.join(data_dir, 'part-' + str(cur_part_idx)), 'w')
         fout.write(line)
     fout.close()
     fin.close()

PaddleRec/ctr/dcn/dist_data/dist_download.py

+from __future__ import print_function
+import os
+import sys
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress
+
+if __name__ == '__main__':
+    url = "https://paddlerec.bj.bcebos.com/deepfm%2Fdist_data_demo.tar.gz"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url, savename="dist_data_demo.tar.gz")
+    print("download and extract finished")
+
+    print("preprocessing...")
+    os.system("python dist_preprocess.py")
+    print("preprocess done")
\ No newline at end of file

PaddleRec/ctr/dcn/dist_data/dist_download.sh

-#!/bin/bash
-
-# download small demo dataset
-wget --no-check-certificate https://paddlerec.bj.bcebos.com/deepfm%2Fdist_data_demo.tar.gz -O dist_data_demo.tar.gz
-tar xzvf dist_data_demo.tar.gz
-# preprocess demo dataset
-python dist_preprocess.py

PaddleRec/ctr/dcn/dist_data/dist_preprocess.py

-#!/usr/bin/env python
-# coding: utf-8
 from __future__ import print_function, absolute_import, division
 
 import os
@@ -21,7 +19,6 @@ TEST_DIR = 'dist_test_valid_data'
 TRAIN_FILE = os.path.join(TRAIN_DIR, 'tr')
 TEST_FILE = os.path.join(TEST_DIR, 'ev')
 SPLIT_RATIO = 0.9
-LINE_NUMS = "line_nums.log"
 FREQ_THR = 10
 
 INT_COLUMN_NAMES = ['I' + str(i) for i in range(1, 14)]

PaddleRec/ctr/dcn/infer.py

-#!/usr/bin/env python
-# coding: utf-8
 import logging
 import random
 
@@ -46,7 +44,8 @@ def infer():
 
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
-    cur_model_path = args.model_output_dir + '/epoch_' + args.test_epoch
+    cur_model_path = os.path.join(args.model_output_dir,
+                                  'epoch_' + args.test_epoch)
 
     with fluid.scope_guard(inference_scope):
         with fluid.framework.program_guard(test_program, startup_program):

PaddleRec/ctr/dcn/local_train.py

-#!/usr/bin/env python
-# coding: utf-8
 from __future__ import print_function, absolute_import, division
 import os
 import random
@@ -75,7 +73,8 @@ def train(args):
             fetch_info=['total_loss', 'avg_logloss', 'auc'],
             debug=False,
             print_period=args.print_steps)
-        model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+        model_dir = os.path.join(args.model_output_dir,
+                                 'epoch_' + str(epoch_id + 1))
         sys.stderr.write('epoch%d is finished and takes %f s\n' % (
             (epoch_id + 1), time.time() - start))
         fluid.io.save_persistables(

PaddleRec/ctr/dcn/network.py

-#!/usr/bin/env python
-# coding: utf-8
 from __future__ import print_function, absolute_import, division
 import paddle.fluid as fluid
 from collections import OrderedDict

PaddleRec/ctr/dcn/reader.py

-#!/usr/bin/env python
-# coding: utf-8
 """
 dataset and reader
 """

PaddleRec/ctr/deepfm/README.md

@@ -25,7 +25,7 @@ To preprocess the raw dataset, we min-max normalize continuous features to [0, 1
 
 Download and preprocess data:
 ```bash
-cd data && sh download_preprocess.sh && cd ..
+cd data && python download_preprocess.py && cd ..
 ```
 
 After executing these commands, 3 folders "train_data", "test_data" and "aid_data" will be generated. The folder "train_data" contains 90% of the raw data, while the rest 10% is in "test_data". The folder "aid_data" contains a created feature dictionary "feat_dict.pkl2".
@@ -58,12 +58,13 @@ We emulate distributed training on a local machine. In default, we use 2 X 2，i
 ### Download and preprocess distributed demo dataset
 This small demo dataset(a few lines from Criteo dataset) only test if distributed training can train.
 ```bash
-cd dist_data && sh dist_data_download.sh && cd ..
+cd dist_data && python dist_data_download.py && cd ..
 ```
 
 ### Distributed Train and Infer
 Train
 ```bash
+# 该sh不支持Windows
 sh cluster_train.sh
 ```
 params of cluster_train.sh：

PaddleRec/ctr/deepfm/cluster_train.py

@@ -133,7 +133,7 @@ def train():
         dataset.set_batch_size(args.batch_size)
         dataset.set_thread(args.num_thread)
         train_filelist = [
-            args.train_data_dir + '/' + x
+            os.path.join(args.train_data_dir, x)
             for x in os.listdir(args.train_data_dir)
         ]
 
@@ -156,7 +156,8 @@ def train():
                 fetch_info=['epoch %d batch loss' % (epoch_id + 1)],
                 print_period=5,
                 debug=False)
-            model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+            model_dir = os.path.join(args.model_output_dir,
+                                     'epoch_' + str(epoch_id + 1))
             sys.stderr.write('epoch%d is finished and takes %f s\n' % (
                 (epoch_id + 1), time.time() - start))
             if args.trainer_id == 0:  # only trainer 0 save model

PaddleRec/ctr/deepfm/data/download_preprocess.py

+import os
+import shutil
+import sys
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress, download_file
+
+if __name__ == '__main__':
+    url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
+    url2 = "https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url)
+    download_file(url2, "./aid_data/feat_dict_10.pkl2", True)
+    print("download and extract finished")
+
+    print("preprocessing...")
+    os.system("python preprocess.py")
+    print("preprocess done")
+
+    shutil.rmtree("raw_data")
+    print("done")

PaddleRec/ctr/deepfm/data/download_preprocess.sh

-#!/bin/bash
-
-wget --no-check-certificate https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
-wget --no-check-certificate https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2 -O ./aid_data/feat_dict_10.pkl2 || rm -f ./aid_data/feat_dict_10.pkl2
-tar zxf dac.tar.gz >/dev/null 2>&1
-rm -f dac.tar.gz
-
-python preprocess.py
-rm *.txt
-rm -r raw_data

PaddleRec/ctr/deepfm/dist_data/dist_data_download.py

+import os
+import shutil
+import sys
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress
+
+if __name__ == '__main__':
+    url = "https://paddlerec.bj.bcebos.com/deepfm%2Fdist_data_demo.tar.gz"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url, savename="dist_data_demo.tar.gz")
+    print("download and extract finished")
+
+    print("preprocessing...")
+    os.system("python preprocess_dist.py")
+    print("preprocess done")
+
+    print("done")
\ No newline at end of file

PaddleRec/ctr/deepfm/dist_data/dist_data_download.sh

-#!/bin/bash
-
-# download small demo dataset
-wget --no-check-certificate https://paddlerec.bj.bcebos.com/deepfm%2Fdist_data_demo.tar.gz -O dist_data_demo.tar.gz
-tar xzvf dist_data_demo.tar.gz
-# preprocess dataset
-python preprocess_dist.py

PaddleRec/ctr/deepfm/infer.py

@@ -25,7 +25,8 @@ def infer():
     inference_scope = fluid.Scope()
 
     test_files = [
-        args.test_data_dir + '/' + x for x in os.listdir(args.test_data_dir)
+        os.path.join(args.test_data_dir, x)
+        for x in os.listdir(args.test_data_dir)
     ]
     criteo_dataset = CriteoDataset()
     criteo_dataset.setup(args.feat_dict)
@@ -34,7 +35,8 @@ def infer():
 
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
-    cur_model_path = args.model_output_dir + '/epoch_' + args.test_epoch
+    cur_model_path = os.path.join(args.model_output_dir,
+                                  'epoch_' + args.test_epoch)
 
     with fluid.scope_guard(inference_scope):
         with fluid.framework.program_guard(test_program, startup_program):

PaddleRec/ctr/deepfm/local_train.py

@@ -36,7 +36,8 @@ def train():
     dataset.set_batch_size(args.batch_size)
     dataset.set_thread(args.num_thread)
     train_filelist = [
-        args.train_data_dir + '/' + x for x in os.listdir(args.train_data_dir)
+        os.path.join(args.train_data_dir, x)
+        for x in os.listdir(args.train_data_dir)
     ]
 
     print('---------------------------------------------')
@@ -50,7 +51,8 @@ def train():
             fetch_info=['epoch %d batch loss' % (epoch_id + 1)],
             print_period=1000,
             debug=False)
-        model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+        model_dir = os.path.join(args.model_output_dir,
+                                 'epoch_' + str(epoch_id + 1))
         sys.stderr.write('epoch%d is finished and takes %f s\n' % (
             (epoch_id + 1), time.time() - start))
         fluid.io.save_persistables(

PaddleRec/ctr/dnn/README.cn.md

@@ -29,7 +29,7 @@ pip install -r requirements.txt
 
 下载数据集：
 ```bash
-cd data && ./download.sh && cd ..
+cd data && python download.py && cd ..
 ```
 
 ## 模型
@@ -56,6 +56,7 @@ python train.py \
 本地启动一个2 trainer 2 pserver的分布式训练任务，分布式场景下训练数据会按照trainer的id进行切分，保证trainer之间的训练数据不会重叠，提高训练效率
 
 ```bash
+# 该sh不支持Windows
 sh cluster_train.sh
 ```
 

PaddleRec/ctr/dnn/README.md

@@ -39,7 +39,7 @@ categorical features. For the test dataset, the labels are omitted.
 
 Download dataset:
 ```bash
-cd data && ./download.sh && cd ..
+cd data && python download.py && cd ..
 ```
 
 ## Model
@@ -73,6 +73,7 @@ In distributed training setting, training data is splited by trainer_id, so that
  do not overlap among trainers
 
 ```bash
+# this shell not support Windows
 sh cluster_train.sh
 ```
 

PaddleRec/ctr/dnn/data/download.py

+import os
+import shutil
+import sys
+import glob
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress
+
+if __name__ == '__main__':
+    url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
+
+    print("download and extract starting...")
+    download_file_and_uncompress(url)
+    print("download and extract finished")
+
+    if os.path.exists("raw"):
+        shutil.rmtree("raw")
+    os.mkdir("raw")
+
+    # mv ./*.txt raw/
+    files = glob.glob("*.txt")
+    for f in files:
+        shutil.move(f, "raw")
+
+    print("done")

PaddleRec/ctr/dnn/train.py

@@ -12,7 +12,6 @@ import paddle.fluid as fluid
 
 import reader
 from network_conf import ctr_dnn_model
-from multiprocessing import cpu_count
 import utils
 
 # disable gpu training for this example
@@ -106,6 +105,11 @@ def parse_args():
         type=int,
         default=1,
         help='The num of trianers, (default: 1)')
+    parser.add_argument(
+        '--cpu_count',
+        type=int,
+        default=1,
+        help='The number of cpu to use when training (default: 1)')
     parser.add_argument(
         '--enable_ce',
         action='store_true',
@@ -141,7 +145,7 @@ def train_loop(args, train_program, py_reader, loss, auc_var, batch_auc_var,
     if os.getenv("NUM_THREADS", ""):
         exec_strategy.num_threads = int(os.getenv("NUM_THREADS"))
 
-    cpu_num = int(os.environ.get('CPU_NUM', cpu_count()))
+    cpu_num = int(os.environ.get('CPU_NUM', args.cpu_count))
     build_strategy.reduce_strategy = \
         fluid.BuildStrategy.ReduceStrategy.Reduce if cpu_num > 1 \
             else fluid.BuildStrategy.ReduceStrategy.AllReduce
@@ -173,8 +177,8 @@ def train_loop(args, train_program, py_reader, loss, auc_var, batch_auc_var,
                     .format(pass_id, batch_id, loss_val / args.batch_size,
                             auc_val, batch_auc_val))
                 if batch_id % 1000 == 0 and batch_id != 0:
-                    model_dir = args.model_output_dir + '/batch-' + str(
-                        batch_id)
+                    model_dir = os.path.join(args.model_output_dir,
+                                             'batch-' + str(batch_id))
                     if args.trainer_id == 0:
                         fluid.io.save_persistables(
                             executor=exe,
@@ -188,7 +192,7 @@ def train_loop(args, train_program, py_reader, loss, auc_var, batch_auc_var,
 
         total_time += time.time() - pass_start
 
-        model_dir = args.model_output_dir + '/pass-' + str(pass_id)
+        model_dir = os.path.join(args.model_output_dir, 'pass-' + str(pass_id))
         if args.trainer_id == 0:
             fluid.io.save_persistables(
                 executor=exe,

PaddleRec/ctr/tools/tools.py

+import os
+import time
+import shutil
+import requests
+import sys
+import tarfile
+import zipfile
+import platform
+import functools
+
+lasttime = time.time()
+FLUSH_INTERVAL = 0.1
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+
+
+def get_platform():
+    return platform.platform()
+
+
+def is_windows():
+    return get_platform().lower().startswith("windows")
+
+
+def progress(str, end=False):
+    global lasttime
+    if end:
+        str += "\n"
+        lasttime = 0
+    if time.time() - lasttime >= FLUSH_INTERVAL:
+        sys.stdout.write("\r%s" % str)
+        lasttime = time.time()
+        sys.stdout.flush()
+
+
+def download_file(url, savepath, print_progress):
+    r = requests.get(url, stream=True)
+    total_length = r.headers.get('content-length')
+
+    if total_length is None:
+        with open(savepath, 'wb') as f:
+            shutil.copyfileobj(r.raw, f)
+    else:
+        with open(savepath, 'wb') as f:
+            dl = 0
+            total_length = int(total_length)
+            starttime = time.time()
+            if print_progress:
+                print("Downloading %s" % os.path.basename(savepath))
+            for data in r.iter_content(chunk_size=4096):
+                dl += len(data)
+                f.write(data)
+                if print_progress:
+                    done = int(50 * dl / total_length)
+                    progress("[%-50s] %.2f%%" %
+                             ('=' * done, float(100 * dl) / total_length))
+        if print_progress:
+            progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
+
+
+def _uncompress_file(filepath, extrapath, delete_file, print_progress):
+    if print_progress:
+        print("Uncompress %s" % os.path.basename(filepath))
+
+    if filepath.endswith("zip"):
+        handler = _uncompress_file_zip
+    elif filepath.endswith("tgz"):
+        handler = _uncompress_file_tar
+    else:
+        handler = functools.partial(_uncompress_file_tar, mode="r")
+
+    for total_num, index, rootpath in handler(filepath, extrapath):
+        if print_progress:
+            done = int(50 * float(index) / total_num)
+            progress("[%-50s] %.2f%%" %
+                     ('=' * done, float(100 * index) / total_num))
+    if print_progress:
+        progress("[%-50s] %.2f%%" % ('=' * 50, 100), end=True)
+
+    if delete_file:
+        os.remove(filepath)
+
+    return rootpath
+
+
+def _uncompress_file_zip(filepath, extrapath):
+    files = zipfile.ZipFile(filepath, 'r')
+    filelist = files.namelist()
+    rootpath = filelist[0]
+    total_num = len(filelist)
+    for index, file in enumerate(filelist):
+        files.extract(file, extrapath)
+        yield total_num, index, rootpath
+    files.close()
+    yield total_num, index, rootpath
+
+
+def _uncompress_file_tar(filepath, extrapath, mode="r:gz"):
+    files = tarfile.open(filepath, mode)
+    filelist = files.getnames()
+    total_num = len(filelist)
+    rootpath = filelist[0]
+    for index, file in enumerate(filelist):
+        files.extract(file, extrapath)
+        yield total_num, index, rootpath
+    files.close()
+    yield total_num, index, rootpath
+
+
+def download_file_and_uncompress(url,
+                                 savepath=None,
+                                 savename=None,
+                                 extrapath=None,
+                                 print_progress=True,
+                                 cover=False,
+                                 delete_file=False):
+    if savepath is None:
+        savepath = "."
+
+    if extrapath is None:
+        extrapath = "."
+
+    if savename is None:
+        savename = url.split("/")[-1]
+    savepath = os.path.join(savepath, savename)
+
+    if cover:
+        if os.path.exists(savepath):
+            shutil.rmtree(savepath)
+
+    if not os.path.exists(savepath):
+        download_file(url, savepath, print_progress)
+    _ = _uncompress_file(savepath, extrapath, delete_file, print_progress)

PaddleRec/ctr/xdeepfm/README.md

@@ -8,7 +8,7 @@
 
 demo数据集，在data目录下执行命令，下载数据
 ```bash
-sh download.sh
+python download.py
 ```
 
 ## 环境
@@ -39,6 +39,7 @@ test_epoch设置加载第10轮训练的模型。
 
 数据下载同上面命令。
 ```bash
+# 该sh不支持Windows
 sh cluster_train.sh
 ```
 参数说明：

PaddleRec/ctr/xdeepfm/cluster_train.py

@@ -139,7 +139,7 @@ def train():
         dataset.set_pipe_command('python criteo_reader.py')
         dataset.set_batch_size(args.batch_size)
         dataset.set_filelist([
-            args.train_data_dir + '/' + x
+            os.path.join(args.train_data_dir, x)
             for x in os.listdir(args.train_data_dir)
         ])
 
@@ -161,7 +161,8 @@ def train():
                 fetch_info=['loss', 'auc'],
                 debug=False,
                 print_period=args.print_steps)
-            model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+            model_dir = os.path.join(args.model_output_dir,
+                                     'epoch_' + str(epoch_id + 1))
             sys.stderr.write('epoch%d is finished and takes %f s\n' % (
                 (epoch_id + 1), time.time() - start))
             if args.trainer_id == 0:  # only trainer 0 save model

PaddleRec/ctr/xdeepfm/data/download.py

+import os
+import shutil
+import sys
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
+sys.path.append(TOOLS_PATH)
+
+from tools import download_file_and_uncompress, download_file
+
+if __name__ == '__main__':
+    url_train = "https://paddlerec.bj.bcebos.com/xdeepfm%2Ftr"
+    url_test = "https://paddlerec.bj.bcebos.com/xdeepfm%2Fev"
+
+    train_dir = "train_data"
+    test_dir = "test_data"
+
+    if not os.path.exists(train_dir):
+        os.mkdir(train_dir)
+    if not os.path.exists(test_dir):
+        os.mkdir(test_dir)
+
+    print("download and extract starting...")
+    download_file(url_train, "./train_data/tr", True)
+    download_file(url_test, "./test_data/ev", True)
+    print("download and extract finished")
+
+    print("done")

PaddleRec/ctr/xdeepfm/data/download.sh

-#!/bin/bash
-
-if [ ! -d "train_data" ]; then
-    mkdir train_data
-fi
-
-if [ ! -d "test_data" ]; then
-    mkdir test_data
-fi
-
-wget --no-check-certificate https://paddlerec.bj.bcebos.com/xdeepfm%2Fev -O ./test_data/ev
-wget --no-check-certificate https://paddlerec.bj.bcebos.com/xdeepfm%2Ftr -O ./train_data/tr

PaddleRec/ctr/xdeepfm/infer.py

@@ -26,7 +26,8 @@ def infer():
     inference_scope = fluid.Scope()
 
     test_files = [
-        args.test_data_dir + '/' + x for x in os.listdir(args.test_data_dir)
+        os.path.join(args.test_data_dir, x)
+        for x in os.listdir(args.test_data_dir)
     ]
     criteo_dataset = CriteoDataset()
     test_reader = paddle.batch(
@@ -34,7 +35,8 @@ def infer():
 
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
-    cur_model_path = args.model_output_dir + '/epoch_' + args.test_epoch
+    cur_model_path = os.path.join(args.model_output_dir,
+                                  'epoch_' + args.test_epoch)
 
     with fluid.scope_guard(inference_scope):
         with fluid.framework.program_guard(test_program, startup_program):

PaddleRec/ctr/xdeepfm/local_train.py

@@ -4,6 +4,7 @@ import paddle.fluid as fluid
 import sys
 import network_conf
 import time
+import utils
 
 
 def train():
@@ -25,7 +26,8 @@ def train():
     dataset.set_pipe_command('python criteo_reader.py')
     dataset.set_batch_size(args.batch_size)
     dataset.set_filelist([
-        args.train_data_dir + '/' + x for x in os.listdir(args.train_data_dir)
+        os.path.join(args.train_data_dir, x)
+        for x in os.listdir(args.train_data_dir)
     ])
 
     if args.use_gpu == 1:
@@ -46,7 +48,8 @@ def train():
             fetch_info=['loss', 'auc'],
             debug=False,
             print_period=args.print_steps)
-        model_dir = args.model_output_dir + '/epoch_' + str(epoch_id + 1)
+        model_dir = os.path.join(args.model_output_dir,
+                                 'epoch_' + str(epoch_id + 1))
         sys.stderr.write('epoch%d is finished and takes %f s\n' % (
             (epoch_id + 1), time.time() - start))
         fluid.io.save_persistables(