我正在使用以下Dockerfile创建一个码头映像:
FROM python:3.7
RUN apt-get update && pip install sagemaker boto3 numpy sagemaker-training
# Copies the training code inside the container
COPY cv.py /opt/ml/code/train.py
COPY scikit_learn_iris.py /opt/ml/code/scikit_learn_iris.py
# Defines train.py as script en
当我尝试在Sagemaker Studio中使用PySpark运行Sagemaker时,提供了一些示例 import os
from pyspark import SparkContext, SparkConf
from pyspark.sql import SparkSession
import sagemaker
from sagemaker import get_execution_role
import sagemaker_pyspark
role = get_execution_role()
# Configure Spark to use the SageMaker Sp
我试图使用SageMaker使用自己的病态学习ML模型,并使用github示例。
python代码如下:
# Define IAM role import boto3
import re
import os
import numpy as np
import pandas as pd
from sagemaker import get_execution_role
import sagemaker as sage from time
import gmtime, strftime
role = get_execution_role()
ess = sage.Session(
在本地SageMaker笔记本(使用VS代码)中运行XGBoost是没有问题的,但使用AWS承载的容器来训练XGBoost模型会导致错误(容器名:246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-xgboost:1.0-1-cpu-py3)。
Jupyter笔记本
import sagemaker
session = sagemaker.LocalSession()
# Load and prepare the training and validation data
...
# Upload the training and