Mamokari/observability #307
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…ly if the env variable is set
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Looks good to me |
| value: diabetes_regression | ||
| value: . |
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Why is this needed? This environment variable is used to keep the name of the folder, since the name will likely change during bootstrap. For this reason, we should avoid hardcoding this name in the pipeline and code.
| @@ -26,7 +26,11 @@ | |||
| from azureml.core import Run | |||
| import argparse | |||
| import traceback | |||
| from util.model_helper import get_model | |||
| from diabetes_regression.util.model_helper import get_model | |||
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As @j-so mentioned, all the references to diabetes_regression by name will not work when someone bootstraps and renames the project. We should find a way to reference this using a variable. Both in the python files and in the pipelines.
| # Your application should run for at least this amount | ||
| # of time so the exporter will meet this interval | ||
| # Sleep can fulfill this https://pypi.org/project/opencensus-ext-azure/ | ||
| time.sleep(self.export_interval) |
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Hey @mokarian , I am not sure if you should block each log_metric call here. Probably would be better to add some runtime check at the end of the scripts using the log_metric feature?! In the sample you are referencing they wait for the main script to be executed for 60s not each method call.
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Hello @mokarian , thanks for your great work here! I am working on a fork of MLOpsPython for industry specific scenarios and we also want to integrate some observability features. I was so free to copy your work and slightly modified it. You may want to have a look at it here. I jumped started on it, so I couldn't discuss it so far with my team in detail, maybe we will change things further. |
This PR contains modules and documentation of Observability modules. Observability is an abstract solution to send logs and metrics to AzureML and Application Insights.
Components of this PR: