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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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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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.
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...
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="...
