azure-machine-learning-studio

问题 Currently in Azure ML's while executing python script, with following code. (Python 2.7.11) In which results obtained from the mongoDB are trying to return in DataFrame using pyMongo. I got an error like :: "C:\pyhome\lib\site-packages\pymongo\topology.py", line 97, in select_servers self._error_message(selector)) ServerSelectionTimeoutError: ... ('The write operation timed out',) Please let me know if you know about the cause of the error and what to improve. My Source code : import pymongo

问题 In Azure ML Studio, we have the option of choosing a number of inbuilt ML models like Classification, Regression, etc. , which we can drag and drop to our workflow. My question is, can I upload a custom ML model that I have built locally on my system in Python, and add it to the workflow? 回答1: Take the model.pkl file, zip it, and upload it into Azure Machine Learning Studio. Click the “New” icon in the bottom left: In the pane that comes up, click on dataset, and then “From Local File”:

问题 Can we customize the Named Entity Recognition (NER) model in Azure ML Studio with a separate training dataset? What I want to do is to find out non-English names from a text. (Training dataset includes the set of names that going to use for training) 回答1: Unfortunately, this module's ability to perform NER with a custom set of entities is planned for the future, but not currently available. If you're familiar with Python and willing to put in the extra footwork, you might consider using the

问题 I can't seem to get the logger to work in the online azure notesbooks workspace. I'm using python 3.6 environment. Running this import: from azureml.logging import get_azureml_logger gives me the following error: ModuleNotFoundError: No module named 'azureml.logging 回答1: The solution is pip install "https://azuremldownloads.blob.core.windows.net/wheels/latest/azureml.logging-1.0.79-py3-none-any.whl?sv=2016-05-31&si=ro-2017&sr=c&sig=xnUdTm0B%2F%2FfknhTaRInBXyu2QTTt8wA3OsXwGVgU%2BJk%3D" I found

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/54072811/how-to-change-the-python-version-in-azure-machine-learning-sdk-containerimage-wi

来源： https://stackoverflow.com/questions/54072811/how-to-change-the-python-version-in-azure-machine-learning-sdk-containerimage-wi

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 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