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Machine Learning
Dive into the world of machine learning on the Databricks platform. Explore discussions on algorithms, model training, deployment, and more. Connect with ML enthusiasts and experts.
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Forum Posts

Idan
by New Contributor II
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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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Anonymous
Not applicable
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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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shane
by New Contributor II
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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...

Screen Shot 2023-02-02 at 3.53.16 PM
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Anonymous
Not applicable
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Hi @Shane Piesik​ Thank you for your question! To assist you better, please take a moment to review the answer and let me know if it best fits your needs.Please help us select the best solution by clicking on "Select As Best" if it does.Your feedback...

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Joseph_B
by New Contributor III
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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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Joseph_B
New Contributor III
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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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User15787040559
by New Contributor III
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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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Mooune_DBU
Valued Contributor
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There are many ways you can retrieve experiments results using the mlflow API (see example if you want to retrieve and display for only a specific model (assuming you have the `model_name`:best_models = mlflow.search_runs(filter_string=f'tags.model="...

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