如何在 amazonsagemaker jupyter notebook 中设置桶的路径？

Question

我是 aws 新手，如何设置我的存储桶路径和访问该存储桶的文件？

我需要用前缀更改什么吗？

import os
import boto3
import re
import copy
import time
from time import gmtime, strftime
from sagemaker import get_execution_role

role = get_execution_role()

region = boto3.Session().region_name

bucket='ltfs1' # Replace with your s3 bucket name
prefix = 'sagemaker/ltfs1' # Used as part of the path in the bucket where you store data
# bucket_path = 'https://s3-{}.amazonaws.com/{}'.format(region,bucket) # The URL to access the bucket

我正在使用上面的代码，但它显示找不到文件错误

Answer 1

如果您正在访问的文件在您的 s3 存储桶的根目录中，您可以这样访问该文件：

import pandas as pd

bucket='ltfs1'
data_key = 'data.csv'
data_location = 's3://{}/{}'.format(bucket, data_key)
training_data = pd.read_csv(data_location)

Answer 2

您需要使用 "sage.session.s3_input" 指定训练数据所在的 s3 存储桶的位置。

示例代码如下：

import sagemaker as sage
from sagemaker import get_execution_role

role = get_execution_role()
sess = sage.Session()

bucket= 'dev.xxxx.sagemaker'
prefix="EstimatorName"

s3_training_file_location = "s3://{}/csv".format(bucket) 
data_location_config = sage.session.s3_input(s3_data=s3_training_file_location, content_type="csv")

output_path="s3://{}/{}".format(bucket,prefix)


account = sess.boto_session.client('sts').get_caller_identity()['Account']
region = sess.boto_session.region_name
image = '{}.dkr.ecr.{}.amazonaws.com/CustomEstimator:latest'.format(account, region)
print(image) 
# xxxxxx.dkr.ecr.us-heast-1.amazonaws.com/CustomEstimator:latest

tree = sage.estimator.Estimator(image,
                       role, 1, 'ml.c4.2xlarge',
                       base_job_name='CustomJobName',
                       code_location=output_path,
                       output_path=output_path,
                       sagemaker_session=sess)

tree.fit(data_location_config)

如何在 amazonsagemaker jupyter notebook 中设置桶的路径？

how to set path of bucket in amazonsagemaker jupyter notebook?

amazon-s3

amazon-web-services

python-3.x

amazon-sagemaker