Hi there, I'm trying to run a streaming inference with Delta Live Tables with tables and a model registered in Unity Catalog, but it fails for unclear reasons. The DLT pipeline is based on a notebook, the channel is set to 'Preview', presumably runni...
I am trying to load a simple Minmaxscaler model that was logged as a run through spark's ML Pipeline api for reuse. On average it takes 40+ seconds just to load the model with the following example: This is fine and the model transforms my data corre...
Hello,Any solutions found for this issue?I'm serving up a large number of models at a time, but since we converted to PySpark (due to our data demands), the mlflow.spark.load_model() is taking hours.Part of the reason to switch to spark was to help w...
Hi fellows, I encountered memory(?) error when sending POST requests to my real-time endpoint, and I'm unable to find hardware setting to increase memory, as suggested by the Service Logs (below). Steps to Repro:(1) I registered a custom MLFlow model...
Thank you for your responses, @Annapurna_Hiriy and @Retired_modIndeed, it appeared that my original model (~800MB) was too big for the current server. Based on your suggestion, I made a simpler/smaller model for this project, and then I was able to d...
Hi, We provisioned the endpoint with 4 DBUs and also disabled the scale_to_zero option. For some reason, it randomly drops to 0 provisioned concurrency. Logs available in the serving endpoint service are not insightful. Currently, we are provisioning...
Hi,I apologize if my question wasn't clear; let me clarify it.We are not using the scale_to_zero option and we are not doing any warmup requests so it should never scale to zero despite traffic or zero traffic right?
I have been trying to deploy spark ML Models from the experiement page via UI, the deployment gets aborted after a long run, any particular reason for why this might be happening? I have also taken care of dependencies still it is failing.Dependency ...
@Kumaran Thanks for the reply kumaram The deployment was finally successful for Random Forest algorithm, failing for sparkxgbregressor.Sharing code snippet:from xgboost.spark import SparkXGBRegressor vec_assembler = VectorAssembler(inputCols=train_df...
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 am using Databricks AutoML ( Python SDK) to forecast bed occupancy. (Actually, Databricks used MLflow experiments for AutoML run). After training with different iterations, I registered the best model in the Databricks Model registry. Now I am tryi...
Hi, It can be a bug if the python version is 3.9.5 and still the error is on compatibility. Could you please raise a support case to look into it further?
Similar to this other question: https://community.databricks.com/s/question/0D58Y00008hahwuSAA/cant-edit-the-cluster-created-by-mlflow-model-servingWe're using Azure Databricks, and have a model that requires a WHL to be downloaded from a private add...
I know that in the documentation about model serving says.The cluster is maintained as long as serving is enabled, even if no active model version exists. To terminate the serving cluster, disable model serving for the registered model.The cluster is...
ML flow model serving in Databricks docs details the options to enable and disable from the UIhttps://docs.databricks.com/applications/mlflow/model-serving.html
Please find below the REST APIs to enable and disable Model-ServingBelow are the examples in PythonYou need to use the token to interact with Rest APItoken = "dxxxxxx"instance = "https://<workspacexxx>.cloud.databricks.com"headers = {'Authorization':...
