amazon-sagemaker

问题 I am getting error when i call get_execution_role() from sagemaker in python. I have attached the error for the same. I have added the SagemakerFullAccess Policy to role and user both. 回答1: get_execution_role() is a function helper used in the Amazon SageMaker Examples GitHub repository. These examples were made to be executed from the fully managed Jupyter notebooks that Amazon SageMaker provides. From inside these notebooks, get_execution_role() will return the IAM role name that was passed

问题 From this page: https://docs.aws.amazon.com/step-functions/latest/dg/input-output-contextobject.html I'm using "TransformJobName.$": "$$.Execution.Name" to define a dynamic SageMaker job name since the name needs to be unique otherwise statemachine will fail When I executed the statemachine, I got the error below: { "resourceType": "sagemaker", "resource": "createTransformJob.sync", "error": "SageMaker.AmazonSageMakerException", "cause": "2 validation errors detected: Value '808bxx-xxxx-xxxx

问题 I am working with Deepar and trying to get a better understanding of the quantile values returned. From the documentation, the likelihood hyperparameter explains that: ...provide quantiles of the distribution and return samples . If I look at a single data point the quantiles returned are linear. E.g. the 0.1 quantile has the lowest predicted value and 0.9 quantile has the highest predicted value. I am having trouble understanding this. If these are samples from the distribution, shouldn't

问题 I am trying to train a model using the sagemaker library. So far, my code is the following: container = get_image_uri(boto3.Session().region_name, 'xgboost', repo_version='0.90-1') estimator = sagemaker.estimator.Estimator(container, role = 'AmazonSageMaker-ExecutionRole-20190305TXXX', train_instance_count = 1, train_instance_type = 'ml.m4.2xlarge', output_path = 's3://antifraud/production/', hyperparameters = {'num_rounds':'400', 'objective':'binary:logistic', 'eval_metric':'error@0.1'})

问题 I am very new to AWS and the cloud environment. I am a machine learning engineer, I am planning to build a custom CNN into the AWS environment to predict a given image has an iPhone present or not. What I have done: Step 1: I have created a S3 bucket for iPhone classifier with the below folder structure : Iphone_Classifier > Train > Yes_iphone_images > 1000 images > No_iphone_images > 1000 images > Dev > Yes_iphone_images > 100 images > No_iphone_images > 100 images > Test > 30 random images

问题 I have a pre-trained model which I am loading in AWS SageMaker Notebook Instance from S3 Bucket and upon providing a test image for prediction from S3 bucket it gives me the accurate results as required. I want to deploy it so that I can have an endpoint which I can further integrate with AWS Lambda Function and AWS API GateWay so that I can use the model with real time application. Any idea how can I deploy the model from AWS Sagemaker Notebook Instance and get its endpoint? Code inside the

问题 In one of the cells of Sagemaker notebook, I've set a variable region="us-west-2" In subsequent cell, I run following 2 shell commands !echo $region Output us-west-2 However, unable to run aws shell command using this variable !aws ecr get-login-password --region $region $ variable-name doesn't help inside jupyter cell ! shell command 回答1: As answered here: https://stackoverflow.com/a/19674648/5157515 There's no direct way to access python variables with ! command. But with magic command %

问题 In one of the cells of Sagemaker notebook, I've set a variable region="us-west-2" In subsequent cell, I run following 2 shell commands !echo $region Output us-west-2 However, unable to run aws shell command using this variable !aws ecr get-login-password --region $region $ variable-name doesn't help inside jupyter cell ! shell command 回答1: As answered here: https://stackoverflow.com/a/19674648/5157515 There's no direct way to access python variables with ! command. But with magic command %

问题 AWS Sagemaker's notebook comes with Scikit-Learn version 0.19.1 I would like to use version 0.20.2. To avoid updating it every time in the notebook code, I tried using the lifecycle configurations. I created one with the following code : #!/bin/bash set -e /home/ec2-user/anaconda3/bin/conda install scikit-learn -y When I run the attached notebook instance and go to the terminal, the version of scikit-learn found with conda list is correct (0.20.2). But when I run a notebook and import sklearn

问题 I have a step function statemachine which creates SageMaker batch transform job, the definition is written in Terraform, I wanted to add the stepfunction execution id to the batch transform job names: in stepfunction terraform file: definition = templatefile("stepfuntion.json", { xxxx ) in the "stepfuntion.json": {... "TransformJobName": "jobname-$$.Execution.Id", } }, "End": true } } } But after terraform apply, it didn't generate the actual id, it gave me jobname-$$.Execution.Id , can