Category : amazon-sagemaker

I have built an anomaly detection model using AWS SageMaker inbuilt model: random cut forest. rcf = RandomCutForest( role=execution_role, instance_count=1, instance_type="ml.m5.xlarge", num_samples_per_tree=1000, num_trees=100, encrypt_inter_container_traffic=True, enable_network_isolation=True, enable_sagemaker_metrics=True) and created the endpoint:- rcf_inference = rcf.deploy( initial_instance_count=4, instance_type="ml.m5.xlarge", endpoint_name=’RCF-container2′, enable_network_isolation=True) But when I tried to get the prediction using the endpoint I am running into the following error:- ..

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My setting I have developed an environment for ML experiments that looks like the following: training happens in the AWS cloud with SageMaker Training Jobs. The trained model is stored in the /opt/ml/model directory, which is reserved by SageMaker to pack models as a .tar.gz in SageMaker’s own S3 bucket. Several evaluation metrics are computed ..

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I have the output file from PCA batch transform job "processed_features.csv.out" looks like in JSON format {"projection":[0.248282819986343 -0.494019 -0.23275601863861]} in S3. I can also retrieve this file in this location ‘s3://path1/path2/path3/model_artifacts/pca/transform/’, and the location can also be retrieved by pca_transformer.output_path However, when I try to use this file to train a K-Means model using the ..

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We are currently moving our models from single model endpoints to multi model endpoints within AWS SageMaker. After deploying the Multi Model Endpoint using prebuilt TensorFlow containers I receive the following error when calling the predict() method: {"error": "JSON Parse error: The document root must not be followed by other value at offset: 17"} I ..

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I have deployed a sagemaker endpoint and want to run predictions on the endpoint now. The endpoint represents a sagemaker pipeline and model. I followed the tutorial here. My code to set up the predictor and make the predictions is as follows: from sagemaker.predictor import Predictor predictor = Predictor(endpoint_name=endpoint_name) data_df = data_df.drop("LABEL_NAME", axis=1) pred_count = ..

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I’m trying to run a SageMaker kernel with Python 3.8 in SageMaker Studio, and the notebook appears to use a separate distribution of Python 3.7. The running app is indicated as tensorflow-2.6-cpu-py38-ubuntu20.04-v1. When I run !python3 -V I get Python 3.8.2. However, the Python instance inside the notebook is different: import sys sys.version gives ‘3.7.12 ..

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