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/Amazon SageMaker Pipelines/

Questions tagged with Amazon SageMaker Pipelines

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Invoking endpoint outputs empty prediction data

Hello, I am able to invoke my endpoint using the following command template: > aws --profile ‘insert_profile_name’ sagemaker-runtime invoke-endpoint --endpoint-name 'insert_endpoint_name' --body fileb://'insert_image_file_path' --region ‘insert_region’ --content-type application/x-image output.txt However, this produces an output text file that contains the following: > {prediction": []} Also, this appears in the terminal after running the command: > { "ContentType": "application/json", "InvokedProductionVariant": "variant-name-1" } The image I used to invoke my endpoint was also used for training the model. Here is my training job configuration (values that I've modified or added): > **Job Settings:** > Algorithm - Object Detection | Input Mode - Pipe > **Hyperparameters:** > num_classes - 1 | mini_batch_size - 1 | num_training_samples - 1 > **Input data configuration:** > *First channel:* > Name - validation | Input Mode - Pipe | Content Type - application/x-recordio | Record Wrapper - RecordIO | S3 Data Type - AugmentedManifestFile | Attribute Names - source-ref, bounding-box > *Second channel:* > Name - train | Input Mode - Pipe | Content Type - application/x-recordio | Record Wrapper - RecordIO | S3 Data Type - AugmentedManifestFile | Attribute Names - source-ref, bounding-box Any help would be appreciated. I can provide more information if needed. Thanks!
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asked 2 months ago

How can I feed outputed augmented manifest file as input to blazingtext in a pipeline?

I'm creating a pipeline with multiple steps One to preprocess a dataset and the other one takes the preprocessed one as an input to train a BlazingText model for classification My first `ProcessingStep` outputs augmented manifest files step_process = ProcessingStep( name="Nab3Process", processor=sklearn_processor, inputs=[ ProcessingInput(source=raw_input_data, destination=raw_dir), ProcessingInput(source=categories_input_data, destination=categories_dir) ], outputs=[ ProcessingOutput(output_name="train", source=train_dir), ProcessingOutput(output_name="validation", source=validation_dir), ProcessingOutput(output_name="test", source=test_dir), ProcessingOutput(output_name="mlb_train", source=mlb_data_train_dir), ProcessingOutput(output_name="mlb_validation", source=mlb_data_validation_dir), ProcessingOutput(output_name="mlb_test", source=mlb_data_test_dir), ProcessingOutput(output_name="le_vectorizer", source=le_vectorizer_dir), ProcessingOutput(output_name="mlb_vectorizer", source=mlb_vectorizer_dir) ], code=preprocessing_dir) But I'm having a hard time when I try to feed my `train` output as a `TrainingInput` to the model step to use it to train. step_train = TrainingStep( name="Nab3Train", estimator=bt_train, inputs={ "train": TrainingInput( step_process.properties.ProcessingOutputConfig.Outputs[ "train" ].S3Output.S3Uri, distribution="FullyReplicated", content_type="application/x-recordio", s3_data_type='AugmentedManifestFile', attribute_names=['source', 'label'], input_mode='Pipe', record_wrapping='RecordIO' ), "validation": TrainingInput( step_process.properties.ProcessingOutputConfig.Outputs[ "validation" ].S3Output.S3Uri, distribution="FullyReplicated", content_type='application/x-recordio', s3_data_type='AugmentedManifestFile', attribute_names=['source', 'label'], input_mode='Pipe', record_wrapping='RecordIO' ) }) And I'm getting the following error 'FailureReason': 'ClientError: Could not download manifest file with S3 URL "s3://sagemaker-us-east-1-xxxxxxxxxx/Nab3Process-xxxxxxxxxx/output/train". Please ensure that the bucket exists in the selected region (us-east-1), that the manifest file exists at that S3 URL, and that the role "arn:aws:iam::xxxxxxxxxx:role/service-role/AmazonSageMakerServiceCatalogProductsUseRole" has "s3:GetObject" permissions on the manifest file. Error message from S3: The specified key does not exist.' What Should I do?
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asked 2 months ago
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