The purpose of this FAQ document is to provide users and partners with answers to common queries or concerns related to the Databricks Help Center Single Sign-On process. I am a Databricks customer. Does anything change how I access Databricks plat...
The purpose of this FAQ document is to provide users and partners with answers to common queries or concerns related to the Databricks Help Center Single Sign-On process. I am a Databricks partner. Does anything change how I access Databricks Platf...
The purpose of this FAQ document is to provide users and partners with answers to common queries or concerns related to the Databricks Community Single Sign-On process. Why am I being asked to update my primary and recovery email? Primary email: This...
If you are getting No Module named textdistance errors, you need to install the textdistance library. This can be done at the cluster level or the session level.
You should use distributed training. By distributing the training workload among GPUs or worker nodes, you can optimize resource utilization and reduce the likelihood of ConnectionException errors and out of memory (OOM) issues. A good option for di...
AutoML supports binary/multiple classification, regression, and forecasting models. For more details, please review the How Databricks AutoML works (AWS | Azure | GCP) documentation.
There is a rate limit of 100 notes per minute. To ensure you do not exceed this limit, you should make adjustments to the deployment and execution of your ML jobs. Distribute recurring workflows evenly over the planned time period To ensure complian...
To install the tkinter package, you can run the following shell command in a notebook: %sh sudo apt-get install python3-tk. To install the package automatically on every cluster start, you can add the command to a cluster-scoped init script.
The trace indicates a statusCode=401 error caused by com.databricks.mlflowdbfs.MlflowHttpException. You need to disable mlflowdbfs in the environment variable before executing log_model(). Example code: import osos.environ["DISABLE_MLFLOWDBFS"] = ...
The errors occur for resources/recipe_dag_template.html with the inspect() method and for base.html with the remaining methods. To resolve TemplateNotFound errors and ensure successful display of results while executing the regression RECIPE with Ji...
When using MLflow to log a model, be aware of warnings like the one below: WARNING mlflow.utils.requirements_utils: The following packages were not found in the public PyPI package index as of 2022-12-21; if these packages are not present in the pub...
You should review the following items: Check the request origin: Ensure you're making the request from the intended source. Verify if the request is originating from the expected location. Payload mismatch: Confirm that the payload sent in the POST ...
Identifying the root cause of worker termination involves analyzing signals that can provide insights into the issue. Typically, these problems are associated with memory pressure, but understanding the specific events, workload type, and workload s...
If you view a stack trace and it looks similar to the following: RestException Traceback (most recent call last)File <command-XXXXXXXXXXXX>:72 mlflow.sklearn.autolog()...File /databricks/python/lib/python3.9/site-packages/mlflow/tracking/fluent.py:...
A Container creation failed error message usually means there are missing dependencies. Check the pip requirements to see if there are any missing dependencies. Adding the required dependencies should resolve the error.
Feature store tables are materialized tables that use delta tables underneath. To delete data from the feature store table, you have to run a DELETE command on the rows in the underlying delta table based on the partition column.
The Databricks feature store provides a catalog that enables data scientists to search for existing features in the offline feature store. The feature store UI offers a searchable interface, allowing you to discover features and view the code used fo...
The potential root cause could be high GPU utilization while running a live experiment. This can be validated both by using the Spark UI and by using the Nvidia -smi command. If a single GPU is explicitly used, this might cause an overload and hence...
It is treated as a single request when batching 10 data points. This is because the batching process involves making a single endpoint request to score multiple data points simultaneously.
