我正在尝试解决二进制分类问题。因此，我在Keras（带有TensorFlow后端）中创建了一个模型，并在CPU上训练了该模型。我使用Keras API将模型保存为TensorFlow SavedModel格式。我正在将Kaggle Kernels与Python 3.7.6，Keras 2.4.3和TensorFlow 2.3.0一起使用。
这是我与一些模型数据一起使用的简化代码（实际上，我正在GPU上训练我的模型，但我认为我所面临的问题与该事实无关）
# setup
import numpy as np
random_state = 0
# create mockup data
train_labels = np.random.randint(2, size=(1000))
validation_labels = np.random.randint(2, size=(200))
train_features = np.random.rand(1000, 49)
validation_features = np.random.rand(200, 49)
# create and train model
from keras.models import Sequential
from keras.layers import Dense, Dropout
dim = train_features.shape[1]
model = Sequential()
model.add(Dense(dim, input_dim=dim, activation='relu'))
model.add(Dropout(0.2, seed=random_state))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics='accuracy')
model.summary()
epochs = 10
batch_size = 100
model.fit(train_features, train_labels, epochs=epochs, batch_size=batch_size, verbose=1, validation_data=(validation_features, validation_labels))
# evaluate model
from sklearn.metrics import confusion_matrix
prediction_labels = model.predict_classes(validation_features)
print(confusion_matrix(prediction_labels, validation_labels))
# save model
model.save('/kaggle/working/model')
然后在新的会话中加载模型。当我在单个CPU上进行预测时，它就可以正常工作。但是，我实际上想做的是在所有4个可用CPU上并行进行预测（与我的模型相反，我要解决的实际问题要复杂得多，涉及的数据更多）。我试图像这样使用multiprocessing来做到这一点：
# setup
import numpy as np
import pandas as pd
random_state = 0
# load model
from keras.models import load_model
model = load_model('../input/repro-model/model')
# create mockup test data
test_features = np.random.rand(1000, 49)
# make some test predictions on the entire test set and a single observation
model.predict_classes(test_features)
model.predict_classes(test_features[[0]])
# set up test dataframe for making predictions in parallel
test_df = pd.DataFrame(test_features)
seq_ids = []
for i in np.arange(1,201):
seq_id = [i] * 5
seq_ids.append(seq_id)
frm_ids = [np.arange(1,6)] * 200
test_df['seq_id'] = [item for sublist in seq_ids for item in sublist]
test_df['frm_id'] = [item for sublist in frm_ids for item in sublist]
# test the setup
seq_id = 1
frm_id = 1
model.predict_classes(test_df[(test_df.seq_id == seq_id) & (test_df.frm_id == frm_id)].drop(['seq_id', 'frm_id'], axis=1))
# create function for making predictions
def make_prediction(model, data, seq_id, frm_id):
print(seq_id)
pred = model.predict_classes(data[(data.seq_id == seq_id) & (data.frm_id == frm_id)].drop(['seq_id', 'frm_id'], axis=1))
return pred
# make test prediction
make_prediction(model, test_df, 1, 1)
# make predictions in parallel
from multiprocessing import Pool
import itertools
workers = 4
p = Pool(processes=workers)
seq_list, frm_list = np.arange(1, 201), np.arange(1, 6)
id_pair_list = list(itertools.product(seq_list, frm_list))
predictions = p.starmap(make_prediction, [(model, test_df, id_pair[0], id_pair[1]) for id_pair in id_pair_list])
p.close()
运行上面的代码时，我得到以下堆栈跟踪和错误：
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-85-52e9e388daba> in <module>
8 id_pair_list = list(itertools.product(seq_list, frm_list))
9
---> 10 predictions = p.starmap(make_prediction, [(model, test_df, id_pair[0], id_pair[1]) for id_pair in id_pair_list])
11 p.close()
/opt/conda/lib/python3.7/multiprocessing/pool.py in starmap(self, func, iterable, chunksize)
274 `func` and (a, b) becomes func(a, b).
275 '''
--> 276 return self._map_async(func, iterable, starmapstar, chunksize).get()
277
278 def starmap_async(self, func, iterable, chunksize=None, callback=None,
/opt/conda/lib/python3.7/multiprocessing/pool.py in get(self, timeout)
655 return self._value
656 else:
--> 657 raise self._value
658
659 def _set(self, i, obj):
/opt/conda/lib/python3.7/multiprocessing/pool.py in _handle_tasks(taskqueue, put, outqueue, pool, cache)
429 break
430 try:
--> 431 put(task)
432 except Exception as e:
433 job, idx = task[:2]
/opt/conda/lib/python3.7/multiprocessing/connection.py in send(self, obj)
204 self._check_closed()
205 self._check_writable()
--> 206 self._send_bytes(_ForkingPickler.dumps(obj))
207
208 def recv_bytes(self, maxlength=None):
/opt/conda/lib/python3.7/multiprocessing/reduction.py in dumps(cls, obj, protocol)
49 def dumps(cls, obj, protocol=None):
50 buf = io.BytesIO()
---> 51 cls(buf, protocol).dump(obj)
52 return buf.getbuffer()
53
TypeError: can't pickle _thread.RLock objects
阅读完有关此主题的所有内容后，我在SO（例如here，here和here）和GitHub（例如here ，以及here和here），我发现腌制顺序Keras模型和多线程存在一些问题。然后，我尝试将模型加载到预测函数中，并在每次预测函数调用后将其删除，如下所示：
def make_prediction(data, seq_id, frm_id):
print(seq_id)
from keras.models import load_model
model = load_model('../input/repro-model/model')
pred = model.predict_classes(data[(data.seq_id == seq_id) & (data.frm_id == frm_id)].drop(['seq_id', 'frm_id'], axis=1))
del model
return pred
from multiprocessing import Pool
import itertools
workers = 4
p = Pool(processes=workers)
seq_list, frm_list = np.arange(1, 201), np.arange(1, 6)
id_pair_list = list(itertools.product(seq_list, frm_list))
predictions = p.starmap(make_prediction, [(test_df, id_pair[0], id_pair[1]) for id_pair in id_pair_list])
p.close()
我再也没有收到错误，但是在进行前四个预测时执行starmap函数调用时，该过程挂起。因此，肯定还有一些问题。使用model._make_predict_function()似乎无效，here无效。关于此主题的关于SO的文章也很多，但没有得到解答：deprecated，here和here。
有人知道我如何将连续的Keras模型与CPU并行进行预测吗？这将是很棒的，因为我已经在这个问题上坚持了很长时间了。非常感谢！
答案 0 :(得分：0)
添加此代码段可帮助我解决此问题（但仅适用于tf 2.0.0版）
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