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Can we retrieve experiment results via MLflow API or is this only possible using UI?
Yes, you can use the API https://www.mlflow.org/docs/latest/python_api/index.html
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And how about tracing data? Do you know how to read likespark.read.format("mlflow-experiment").load() ?
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- 3317 Views
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Using code_path in mlflow.pyfunc models on Databricks
We are using Databricks over AWS infra, registering models on mlflow. We write our in-project imports as from src.(module location) import (objects).Following examples online, I expected that when I use mlflow.pyfunc.log_model(...code_path=['PROJECT_...
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- 2 replies
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Hi @Idan Reshef​ Thank you for posting your question in our community! We are happy to assist you.To help us provide you with the most accurate information, could you please take a moment to review the responses and select the one that best answers y...
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- 3015 Views
- 3 replies
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Not able to configure cluster settings instance type using mlflow api 2.0 to enable model serving.
I'm able to enable model serving by using the mlflow api 2.0 with the following code...instance = f'https://{workspace}.cloud.databricks.com' headers = {'Authorization': f'Bearer {api_workflow_access_token}'} # Enable Model Serving import request...
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- 3 replies
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- 1581 Views
- 1 replies
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Resolved! MLflow API return Description of a run
Is there a way to get the description of a run using MLflow API?
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- 1 replies
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Ok, just found it, it's called 'tags.mlflow.note.content'
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- 1728 Views
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What can I do to reduce the number of MLflow API calls I make?
I'm fitting multiple models in parallel. For each one, I'm logging lots of params and metrics to MLflow. I'm hitting rate limits, causing problems in my jobs.
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The first thing to try is to log in batches. If you are logging each param and metric separately, you're making 1 API call per param and 1 per metric. Instead, you should use the batch logging APIs; e.g. use "log_params" instead of "log_param" http...
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