azure-machine-learning-service

问题 I am running pyspark from an Azure Machine Learning notebook. I am trying to move a file using the dbutil module. from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate() def get_dbutils(spark): try: from pyspark.dbutils import DBUtils dbutils = DBUtils(spark) except ImportError: import IPython dbutils = IPython.get_ipython().user_ns["dbutils"] return dbutils dbutils = get_dbutils(spark) dbutils.fs.cp("file:source", "dbfs:destination") I got this error:

问题 I'm using a free trial account on MS Azure and I'm following this tutorial. https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score I'm stuck when I try to "submit the pipeline". The reason seems to be that I can't create a compute instance or a training cluster on a free plan. I still have 200USDs of free credits. I guess there must be a solution? Error messages: Invalid graph: The pipeline compute target is invalid. 400: Compute Test3 in state

问题 I'm using a free trial account on MS Azure and I'm following this tutorial. https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score I'm stuck when I try to "submit the pipeline". The reason seems to be that I can't create a compute instance or a training cluster on a free plan. I still have 200USDs of free credits. I guess there must be a solution? Error messages: Invalid graph: The pipeline compute target is invalid. 400: Compute Test3 in state

问题 I'm using a free trial account on MS Azure and I'm following this tutorial. https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-train-score I'm stuck when I try to "submit the pipeline". The reason seems to be that I can't create a compute instance or a training cluster on a free plan. I still have 200USDs of free credits. I guess there must be a solution? Error messages: Invalid graph: The pipeline compute target is invalid. 400: Compute Test3 in state

问题 How to handle the frequent changes in the dataset in Azure Machine Learning Studio. My dataset may change over time, I need to add more rows to dataset. How will I refresh the dataset which I currently use to train the model by using the newly updated dataset . I need this work to be done programmatically(in c# or python) instead of doing it manually in the studio. 回答1: When registering an AzureML Dataset, no data is moved, just some information like where the data is and how it should be

问题 I'm working in a .NET project where I will generate a dataset. I need to load that dataset into Azure Machine Learning Studio. Is there a way to load that dataset into ML studio programmatically (perhaps with an apikey and RequestURI ) instead of manually loading dataset in the Azure ML Studio? 回答1: It may help you: local_path = 'data/prepared.csv' dataframe.to_csv(local_path) upload the local file to a datastore on the cloud # azureml-core of version 1.0.72 or higher is required # azureml

问题 I'm working in a .NET project where I will generate a dataset. I need to load that dataset into Azure Machine Learning Studio. Is there a way to load that dataset into ML studio programmatically (perhaps with an apikey and RequestURI ) instead of manually loading dataset in the Azure ML Studio? 回答1: It may help you: local_path = 'data/prepared.csv' dataframe.to_csv(local_path) upload the local file to a datastore on the cloud # azureml-core of version 1.0.72 or higher is required # azureml

问题 Background I have created an ML Workspace environment from a conda environment.yml plus some docker config and environment variables. I can access it from within a Python notebook: env = Environment.get(workspace=ws, name='my-environment', version='1') I can use this successfully to run a Python script as an experiment, i.e. runconfig = ScriptRunConfig(source_directory='script/', script='my-script.py', arguments=script_params) runconfig.run_config.target = compute_target runconfig.run_config

问题 I was trying to register an ONNX model to Azure Machine Learning service workspace in two different ways, but I am getting errors I couldn't solve. First method: Via Jupyter Notebook and python Script model = Model.register(model_path = MODEL_FILENAME, model_name = "MyONNXmodel", tags = {"onnx":"V0"}, description = "test", workspace = ws) The error is : HttpOperationError: Operation returned an invalid status code 'Service invocation failed!Request: GET https://cert-westeurope.experiments

问题 I am trying out Azure Machine Learning Service for ML deployment. I have already trained a model on a compute VM and saved it as pickle, and now would like to deploy it (I am using Python on Azure notebooks for the purpose as of now). From the guide, it looks like I need to I need a run object to be existing in my session to execute the "model registration" step: # register model model = run.register_model(model_name='my_model', model_path='outputs/my_model.pkl') print(model.name, model.id,