如何将 boto3 客户端与 Python 多处理一起使用？

Question

代码看起来像这样：

import multiprocessing as mp
from functools import partial

import boto3
import numpy as np


s3 = boto3.client('s3')

def _something(**kwargs):
    # Some mixed integer programming stuff related to the variable archive
    return np.array(some_variable_related_to_archive)


def do(s3):
    archive = np.load(s3.get_object('some_key')) # Simplified -- details not relevant
    pool = mp.pool()
    sub_process = partial(_something, slack=0.1)
    parts = np.array_split(archive, some_int)
    target_parts = np.array(things)

    out = pool.starmap(sub_process, [x for x in zip(parts, target_parts)] # Error occurs at this line

    pool.close()
    pool.join()

do(s3)

错误：

_pickle.PicklingError: Can't pickle <class 'botocore.client.S3'>: attribute lookup S3 on botocore.client failed

我对 Python 多处理库的经验非常有限。我不确定为什么当 S3 客户端不是任何函数中的参数时，它会抛出上述错误。请注意，如果存档文件是从磁盘而不是 S3 加载的，代码能够运行没问题。

任何 help/guidance 将不胜感激。

Answer 1

传递给 mp.starmap() 的对象必须是可腌制的，而 S3 客户端不可腌制。将 S3 客户端的操作带到调用 mp.starmap() 的函数之外可以解决问题：

import multiprocessing as mp
from functools import partial

import boto3
import numpy as np


s3 = boto3.client('s3')
archive = np.load(s3.get_object('some_key')) # Simplified -- details not relevant # Move the s3 call here, outside of the do() function

def _something(**kwargs):
    # Some mixed integer programming stuff related to the variable archive
    return np.array(some_variable_related_to_archive)


def do(archive): # pass the previously loaded archive, and not the s3 object into the function
    pool = mp.pool()
    sub_process = partial(_something, slack=0.1)
    parts = np.array_split(archive, some_int)
    target_parts = np.array(things)

    out = pool.starmap(sub_process, [x for x in zip(parts, target_parts)] # Error occurs at this line

    pool.close()
    pool.join()

do(archive) # pass the previously loaded archive, and not the s3 object into the function

Answer 2

好吧，我用一种非常直接的方式解决了它。也就是说，使用更简化、更不复杂的对象而不是 .我用了 class Bucket.

但是，您应该考虑以下 post：Can't pickle when using multiprocessing Pool.map()。我将与 boto3 相关的每个对象都放在任何 class 函数之外。其他一些 post 建议将 s3 对象和函数放入您要并行化的函数中以避免开销，不过我还没有尝试过。事实上，我将与您分享一个代码，其中可以将信息保存到 msgpack 文件类型中。

我的代码示例如下（在任何 class 或函数之外）。希望对你有帮助。

import pandas as pd
import boto3
from pathos.pools import ProcessPool

s3 = boto3.resource('s3')
s3_bucket_name = 'bucket-name'
s3_bucket = s3.Bucket(s3_bucket_name)

def msgpack_dump_s3 (df, filename):
    try:
        s3_bucket.put_object(Body=df.to_msgpack(), Key=filename)
        print(module, filename + " successfully saved into s3 bucket '" + s3_bucket.name + "'")
    except Exception as e:
        # logging all the others as warning
        print(module, "Failed deleting bucket. Continuing. {}".format(e))

def msgpack_load_s3 (filename):
    try:
        return s3_bucket.Object(filename).get()['Body'].read()
    except ClientError as ex:
        if ex.response['Error']['Code'] == 'NoSuchKey':
            print(module, 'No object found - returning None')
            return None
        else:
            print(module, "Failed deleting bucket. Continuing. {}".format(ex))
            raise ex
    except Exception as e:
        # logging all the others as warning
        print(module, "Failed deleting bucket. Continuing. {}".format(e))
    return

def upper_function():

    def function_to_parallelize(filename):
        file = msgpack_load_s3(filename)
        if file is not None:
            df = pd.read_msgpack(file)
        #do somenthing

        print('\t\t\tSaving updated info...')
        msgpack_dump_s3(df, filename)


        pool = ProcessPool(nodes=ncpus)
        # do an asynchronous map, then get the results
        results = pool.imap(function_to_parallelize, files)
        print("...")
        print(list(results))
        """
        while not results.ready():
            time.sleep(5)
            print(".", end=' ')

如何将 boto3 客户端与 Python 多处理一起使用？

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