azure-machine-learning-service

问题 I am building an Azure ML pipeline with the azureml Python SDK. The pipeline calls a PythonScriptStep which stores data on the workspaceblobstore of the AML workspace. I would like to extend the pipeline to export the pipeline data to an Azure Data Lake (Gen 1). Connecting the output of the PythonScriptStep directly to Azure Data Lake (Gen 1) is not supported by Azure ML as far as I understand. Therefore, I added an extra DataTransferStep to the pipeline, which takes the output from the

问题 I'm trying to write the output of batch scoring into datalake: parallel_step_name = "batchscoring-" + datetime.now().strftime("%Y%m%d%H%M") output_dir = PipelineData(name="scores", datastore=def_ADL_store, output_mode="upload", output_path_on_compute="path in data lake") parallel_run_config = ParallelRunConfig( environment=curated_environment, entry_script="use_model.py", source_directory="./", output_action="append_row", mini_batch_size="20", error_threshold=1, compute_target=compute_target,

问题 I'm trying to write the output of batch scoring into datalake: parallel_step_name = "batchscoring-" + datetime.now().strftime("%Y%m%d%H%M") output_dir = PipelineData(name="scores", datastore=def_ADL_store, output_mode="upload", output_path_on_compute="path in data lake") parallel_run_config = ParallelRunConfig( environment=curated_environment, entry_script="use_model.py", source_directory="./", output_action="append_row", mini_batch_size="20", error_threshold=1, compute_target=compute_target,

问题 I would like to register a dataset from ADLS Gen2 in my Azure Machine Learning workspace ( azureml-core==1.12.0 ). Given that service principal information is not required in the Python SDK documentation for .register_azure_data_lake_gen2() , I successfully used the following code to register ADLS gen2 as a datastore: from azureml.core import Datastore adlsgen2_datastore_name = os.environ['adlsgen2_datastore_name'] account_name=os.environ['account_name'] # ADLS Gen2 account name file_system

问题 I hope I am not missing something obvious here. I am using the new azure ml studio designer. I am able to use to create datasets, train models and use them just fine. azure ml studio allows creation of Jupyter notebooks (also) and use them to do machine learning. I am able to do that too. So, now, I am wondering, can I build my ML pipeline/experiment in ML studio designer, and once it is in good shape, export it as a python and jupyter notebook? then, use it in the same designer provided

来源： https://stackoverflow.com/questions/63187116/call-azure-stream-analytics-udf-with-multi-dimensional-array-of-last-5-records

来源： https://stackoverflow.com/questions/62995906/azure-stream-analytics-ml-service-function-call-in-cloud-job-results-in-no-outp

来源： https://stackoverflow.com/questions/62995906/azure-stream-analytics-ml-service-function-call-in-cloud-job-results-in-no-outp

问题 We created a webservice endpoint and tested it with the following code, and also with POSTMAN. We deployed the service to an AKS in the same resource group and subscription as the AML resource. UPDATE: the attached AKS had a custom networking configuration and rejected external connections. import numpy import os, json, datetime, sys from operator import attrgetter from azureml.core import Workspace from azureml.core.model import Model from azureml.core.image import Image from azureml.core

问题 During a training script executed on a compute target, we're trying to download a registered Dataset from an ADLS2 Datastore. The problem is that it takes hours to download ~1.5Gb (splitted into ~8500 files) to the compute target with the following method : from azureml.core import Datastore, Dataset, Run, Workspace # Retrieve the run context to get Workspace RUN = Run.get_context(allow_offline=True) # Retrieve the workspace ws = RUN.experiment.workspace # Creating the Dataset object based on