I have registered a custom model which loads another model in the load_context method. Everything works fine when I load (with mlflow.pyfunc.load_model) and use the model in a notebook. When I try to create a serving endpoint for it I keep becoming t...
I am performing a time series analysis, using a XGBoostRegressor with rolling predictions. I am doing so using the FeatureEngineeringClient (in combination with Unity Catalog), where I create and load in my features during training and inference, as ...
I am trying to create a vector search index for a table, but it stops at 45406 rows. I can see that the writeback table has all the records but the indexing stops. Is there a hard limit on index?
There are some limits that you can be hitting: Row Size for Delta Sync Index: The maximum row size is 100KB.Embedding Source Column Size for Delta Sync Index: The maximum size is 32764 bytes.Bulk Upsert Request Size Limit for Direct Vector Index: The...
After the Data Exploration notebook runs successfully, all AutoML trials fail without providing a source notebook. I have ensured that the training data labels have no null values or any labels with 16 or less occurrences associated with them. I cann...
I have a notebook that is running extremely slowly even when I try to do pretty basic python functions. It is running far slower than locally no matter what I try, this is in spite of using a 32gb 4 core cluster with 4-8 workers. For context, my data...
Please share more information, for example: Type of data sourceType of operations being executed (sharing code if possible)Timings of local runs and Databricks runs
I'm looking to scale xgboost to large datasets which won't fit in memory on a single large EC2 instance (billions to tens of billions of rows scale). I also require many of the bells & whistles of regular in-memory xgboost slash lightgbm including:Th...
tl; dr:When the AutoML run realizes it needs to do sampling because the driver / worker node memory is not enough to load / process the entire dataset, it fails. A sample weight column is NOT provided by me, but I believe somewhere in the process the...
I am trying to configure the Gemini Vertex API in Databricks. In simple Python code, everything works fine, which indicates that I have correctly set up the API and credentials. Error message: {"error_code":"INVALID_PARAMETER_VALUE","message":"INVALI...
Hi,I can train, registered a ML Model in my Datbricks Workspace.Then, to deploy it on AKS, I need to register the model in Azure ML, and then, deploy to AKS.Is it possible to skip the Azure ML step?I would like to deploy directly into my AKS instance...
Is it still the case, can't we serve the model in Databricks. I am new to this, so I am just wondering the capabilities.
For my RAG use case, I've registered my langchain chain as a model to Unity Catalog. When I'm trying to serve the model, container image creation fails with the following error in the build log:[...] #16 178.1 Downloading langchain_core-0.3.17-py3-no...
I was able to solve the problem by adding python-snappy==0.7.3 to the requirements.
Hi, I've got a question about deploying an endpoint for Llama 3.1 8b. The following code should create the endpoint without scale-to-zero. The endpoint is being created, but with scale-to-zero, although scale_to_zero_enabled is set to False. Instead ...
Thanks for the reply @Walter_C. This didn't quite work, since it used a CPU and didn't consider the max_provisioned_throughput, but I finally got it to work like this: from mlflow.deployments import get_deploy_client client = get_deploy_client("data...
Hi I have created a model and pipeline using xgboost.spark's sparkXGBregressor and pyspark.ml's Pipeline instance. However, i run into a "RuntimeError: _get_spark_session should not be invoked from executor side." when i try to save the predictions i...
The error you're encountering is due to attempting to access the Spark session on the executor side, which is not allowed in Spark's distributed computing model. This typically happens when trying to use Spark-specific functionality within a UDF or d...
I'm trying to delete rows from a table with the same date or id as records in another table. I'm using the below query and get the error 'Multi-column In predicates are not supported in the DELETE condition'. delete from cost_model.cm_dispatch_consol...
I seem to get this error on some DeltaTables and not others:df.createOrReplaceTempView("channels_to_delete") spark.sql(""" delete from lake.something.earnings where TenantId = :tenantId and ChannelId = in ( select ChannelId ...
Hi EveryoneI tried to follow the same steps in Topic from Text on similar data as example. However, when I tri to fit the model with data I get this error.IllegalArgumentException: requirement failed: Column features must be of type equal to one of t...
Hi @amirA ,The LDA model expects the features column to be of type Vector from the pyspark.ml.linalg module, specifically either a SparseVector or DenseVector, whereas you have provided Row type.You need to convert your Row object to SparseVector.Che...
Hello, I trained a model using MLFlow, and saved the model as an artifact. I can load the model from a notebook and it works as expected (i.e. I can load the model using its URI).However, when I want to deploy it using Databricks endpoints, container...
@ivan_calvo The problem still exists. Surely there has to be some other option than downgrading the ML cluster to DBR 14.3 LTS ML?
