我试图在pyspark应用程序中使用boto3客户机将我的numpy数组上传到s3,但是它给了我pickling错误消息。下面是我的代码。
def write_features3(model,key,obj,output_path, format_name):
try:
LOGGER.info('executing vgg16 feature extractor...')
img = image.load_img(BytesIO(obj), target_size=(224, 224,3))
img_data = image.img_to_array(img)
img_data = np.expand_dims(img_data, axis=0)
img_data = preprocess_input(img_data)
vgg16_feature = model.predict(img_data)[0]
LOGGER.info('++++++++++++++++++++++++++++',vgg16_feature.shape)
file_name_without_ext = get_file_name_without_ext(key)
rest_of_path = OUTPUT.split('/', 1)[1]
s3_full_path = rest_of_path + '/' + file_name_without_ext + '.' + '.npy'
LOGGER.info("Saving to S3....")
feature_dir = '/home/hadoop'
s3 = boto3.client('s3', region_name='us-east-1')
local_dir_full_path = feature_dir + '/' + file_name_without_ext + '.npy'
np.save(local_dir_full_path, vgg16_feature)
s3.upload_file(local_dir_full_path, 'test', s3_full_path)
os.remove(local_dir_full_path)
except Exception as e:
print('Error......{}'.format(e.args))
return []
def write_features_(xs):
model_data = initVGG16()
for k, v in xs:
yield k, write_features3(model_data, k,v,OUTPUT, FORMAT_NAME)
driver program:-
s3_files_rdd = sc.binaryFiles('s3n://....')
features_rdd = s3_files_rdd.foreachPartition(write_features_)
当我尝试这个程序,我得到以下错误。甚至我也尝试将s3客户机放在write\u features\u partition方法中,但没有成功。同样的错误。
spark版本-2.2.1
错误:-
n save_reduce
save(state)
File "/usr/lib64/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib64/python2.7/pickle.py", line 655, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib64/python2.7/pickle.py", line 687, in _batch_setitems
save(v)
File "/usr/lib64/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib64/python2.7/pickle.py", line 606, in save_list
self._batch_appends(iter(obj))
File "/usr/lib64/python2.7/pickle.py", line 642, in _batch_appends
save(tmp[0])
File "/usr/lib64/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/cloudpickle.py", line 600, in save_reduce
save(state)
File "/usr/lib64/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib64/python2.7/pickle.py", line 655, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib64/python2.7/pickle.py", line 687, in _batch_setitems
save(v)
File "/usr/lib64/python2.7/pickle.py", line 331, in save
self.save_reduce(obj=obj, *rv)
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/cloudpickle.py", line 600, in save_reduce
save(state)
File "/usr/lib64/python2.7/pickle.py", line 286, in save
f(self, obj) # Call unbound method with explicit self
File "/usr/lib64/python2.7/pickle.py", line 655, in save_dict
self._batch_setitems(obj.iteritems())
File "/usr/lib64/python2.7/pickle.py", line 687, in _batch_setitems
save(v)
File "/usr/lib64/python2.7/pickle.py", line 306, in save
rv = reduce(self.proto)
TypeError: can't pickle thread.lock objects
Traceback (most recent call last):
File "six_file_boto3_write1.py", line 249, in <module>
run()
File "six_file_boto3_write1.py", line 227, in run
s3_files_rdd.foreachPartition(write_features_)
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 799, in foreachPartition
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 1041, in count
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 1032, in sum
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 906, in fold
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 809, in collect
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 2455, in _jrdd
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 2388, in _wrap_function
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/rdd.py", line 2374, in _prepare_for_python_RDD
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/serializers.py", line 464, in dumps
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/cloudpickle.py", line 704, in dumps
File "/mnt/yarn/usercache/hadoop/appcache/application_1541683970451_0004/container_1541683970451_0004_01_000001/pyspark.zip/pyspark/cloudpickle.py", line 162, in dump
pickle.PicklingError: Could not serialize object: TypeError: can't pickle thread.lock objects
