在为Sagemaker的因式分解机器实现培训准备我的数据时,我成功地使用function write_spmatrix_to_sparse_tensor ()将我的数据从稀疏矩阵转换为Sagemaker的因式分解机器实现所期望的recordio格式。
示例中,我将import语句限制在所提供的函数中:
import os
import io
import boto3
import sagemaker.amazon.common as smac
def write_recordio(array, y, prefix, f):
# Convert to record protobuf
我正在尝试在一个简单的数据集上运行线性学习器。我的csv数据被上传到存储桶中。问题是,当我运行它时,我得到了以下错误:
UnexpectedStatusException: Error for Training job linear-learner-2020-05-23-22-31-40-894: Failed. Reason: ClientError: Unable to read data channel 'train'. Requested content-type is 'application/x-recordio-protobuf'. Please
我在SageMaker中有一个jupyter笔记本,我想在其中运行XGBoost算法。数据必须匹配3个条件:第一列中的-No标题行-Outcome变量,其余列中的要素-All列需要为数字 我得到的错误如下: Error for Training job xgboost-2019-03-13-16-21-25-000:
Failed Reason: ClientError: Blankspace and colon not found in firstline
'0.0,0.0,99.0,314.07,1.0,0.0,0.0,0.0,0.48027846,0.0..
亚马逊网络服务SageMaker批处理转换错误如下: bare " in non quoted field found near: "['R627' 'Q2739' 'D509' 'S37009A' 'E860' 'D72829' 'R9431' 'J90' 'R7989' 在SageMaker工作室笔记本中,我使用Pandas将数据输出到csv: data.to_csv(my_file, index=False, header=Fa