Need to work on POC where cognite data fusion timeseries can be added as dataset/data source in Azure MLopsIs there any way if this is possible
We are having performance issues for event retrieval whenever there are more events even below 500.Can you help me to understand how partitioning can be done or pagination, as we need to perform operation for each event after retrieval and send it as a api response.I tried below: for unstructre_insight in client.events(type="Insight", limit=None,partitions=10): (“do something with unstructre_insight in each partition in parallel to reduce response time”)I observed that if there are 64 events, then all 64 events are retrieved in one execution, how we can get one partition at a time and perform something for first partition in parallel while retrieving second partition to reduce time.
We are getting error while deployment. We have tried reformatting in local with black. Also while committing we are getting error fatal: Authentication failed for 'https://gitlab.com/pycqa/flake8.git/'. Project is in repo of team member, I have already given access to the samefatal: Authentication failed for 'https://gitlab.com/pycqa/flake8.git/'
Request you to share on how to add environment specific URL(dev/prod/test) in handler.py in AIR. Also need to understand on how to get msal_token (bearer) authorization in AIR functions.Example:msal_token = request.headers.get("authorization")requests.get(sequence_generator_api, headers={"authorization": msal_token})where sequence_generator_api is environment specific API
I am getting error for black while deploying code to AIR. Please find attached screenshot for reference. So in turn code standards are getting failed after pushing the same changes in AIR.
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