- 2051 Views
- 1 replies
- 2 kudos
Resolved! Store a secret only accessible to the current user
During an interactive notebook session, I want a user to be able to retrieve a secret specific to that user. I haven't decided on storage mechanisms, but I'm open to storage mechanisms that can scalably authorize access to a single user and that I ca...
- 2051 Views
- 1 replies
- 2 kudos
- 2 kudos
I ended up using Databricks Secrets as the storage mechanism after learning from my account rep that the limit is soft and we can request a higher scope limit. In this case, each user gets a dedicated scope and no other users have access.
- 2 kudos
- 2722 Views
- 4 replies
- 1 kudos
Resolved! ML Practioner | ml 09 - automl notebook | error on importing databricks.automl
executing the following code...from databricks import automlsummary = automl.regress(train_df, target_col="price", primary_metric="rmse", timeout_minutes=5, max_trials=10)generates the error...ImportError: cannot import name 'automl' from 'databricks...
- 2722 Views
- 4 replies
- 1 kudos
- 1 kudos
I'm happy to see a particularly subject.
- 1 kudos
- 1939 Views
- 2 replies
- 1 kudos
How to input initial centroids to K-Means or GMM Clustering in SparkML ?
Hi, I want to use KMeans Model or Gaussian Mixture Model algorithm for clustering using the SparkML library, in which I want to specify the initial centroids. The option of giving initial centroids is there in the Spark MLlib (RDD based APIs) however...
- 1939 Views
- 2 replies
- 1 kudos
- 1 kudos
@Kaniz Fatma​ I still haven't got an answer to my question!!!
- 1 kudos
- 4965 Views
- 4 replies
- 7 kudos
Resolved! No Module named 'mlflow'
I new to the scalable machine learning with apache spark course. I am in the notebook ML 00a - Install Datasets it includes one cell (attached) which throws an error 'no module named 'mlflow''. It attempts to run the Classroom-Setup file. Error is th...
- 4965 Views
- 4 replies
- 7 kudos
- 7 kudos
@Myles Pember​ I hope the suggestions above helped out! If so, please select one as 'best' for us!If you still need assistance, let us know!
- 7 kudos
- 2483 Views
- 2 replies
- 3 kudos
Resolved! ML Practioner | ML 10 - Feature Store notebook | feature_store import error
the following code...from pyspark.sql.functions import monotonically_increasing_id, lit, expr, randimport uuidfrom databricks import feature_storefrom pyspark.sql.types import StringType, DoubleTypefrom databricks.feature_store import feature_table, ...
- 2483 Views
- 2 replies
- 3 kudos
- 3 kudos
Hope that was an easy fix - @Tobias Cortese​ ! Thanks for marking the "best answer"!
- 3 kudos
- 4327 Views
- 4 replies
- 5 kudos
Getting Py4J "Could not find py4j jar" error when trying to use pypmml, solution doesn't work
I'm trying to use pypmml in a DB notebook, but I'm getting the known `Error : Py4JError: Could not find py4j jar at` error. I've followed the solution here: https://kb.databricks.com/libraries/pypmml-fail-find-py4j-jar.html. However, this has not wor...
- 4327 Views
- 4 replies
- 5 kudos
- 5 kudos
I've been struggling myslef with it, but after installing pypmml for spark, I can use the other library, maybe it will work for you:runtime 10.4 LTS MLinstall pypmml-spark (https://github.com/autodeployai/pypmml-spark)install pmml4s-spark (org.pmml4s...
- 5 kudos
- 1622 Views
- 2 replies
- 1 kudos
Is it possible to load MLFlow artifacts and models from local diretory to databricks DBFS?
I have been working locally and created a few models and now I want to move those to databricks/DBFS. Is it possible to do that?
- 1622 Views
- 2 replies
- 1 kudos
- 1 kudos
Hi @Direo Direo​, can you check these docs and see if it helps-https://docs.databricks.com/applications/mlflow/access-hosted-tracking-server.html#access-the-mlflow-tracking-server-from-outside-databrickshttps://docs.databricks.com/applications/mlflow...
- 1 kudos
- 2075 Views
- 3 replies
- 1 kudos
ML Model serving cluster tags?
Is there a way to add tags automatically to ML Model serving clusters? I see we can add tags to the model itself which persist but any tags I add to the cluster serving it do not after the endpoint is stopped. This would be important to track billing...
- 2075 Views
- 3 replies
- 1 kudos
- 1 kudos
Hey there @Deep Kalra​ Hope all is well! Just wanted to check in if you were able to resolve your issue and would you be happy to share the solution or mark an answer as best? Else please let us know if you need more help. We'd love to hear from you....
- 1 kudos
- 971 Views
- 0 replies
- 3 kudos
Java Error for installation rasterframes
Hi all,I have followed the steps in this notebook to install rasterframes on my databricks cluster.Eventually I am able to import the following:from pyrasterframes import rf_ipython from pyrasterframes.utils import create_rf_spark_session from pyspar...
- 971 Views
- 0 replies
- 3 kudos
- 779 Views
- 0 replies
- 1 kudos
I should the same issue about this: https://community.databricks.com/s/topic/0TO3f000000CjVqGAK/py4jjavaerror
Could you pls help take a look at it? Thanks!
- 779 Views
- 0 replies
- 1 kudos
- 2983 Views
- 4 replies
- 0 kudos
Model serving keep relaunching
Hello, I tried to serve my model realtime. Model process keeps relaunching.I am getting this error in the logs, TypeError: Descriptors cannot not be created directly. If this call came from a _pb2.py file, your generated code is out of date and must ...
- 2983 Views
- 4 replies
- 0 kudos
- 0 kudos
Hey there @Hulma Abdul Rahman​ Hope you are well. Just wanted to see if you were able to find an answer to your question and would you like to mark an answer as best? It would be really helpful for the other members too.Cheers!
- 0 kudos
- 1103 Views
- 1 replies
- 1 kudos
- 1103 Views
- 1 replies
- 1 kudos
- 1 kudos
Hi @Chiraag Lathia​ Gentle reminder on the answer provided by @Kaniz Fatma​ . Please let us know if you have more doubts or queries.
- 1 kudos
- 1272 Views
- 1 replies
- 3 kudos
Is there a latency difference between querying from the feature store and delta table?
Our team ran a benchmarking experiment comparing queries from the MLFlow feature store vs directly from delta table, and we found querying delta table was ~2-3 times faster than the feature store. So I’m wondering if someone else has done a similar b...
- 1272 Views
- 1 replies
- 3 kudos
- 3 kudos
Hi @Mike Lo​ Gentle reminder on the answer provided by @Kaniz Fatma​ . Please let us know if you have more doubts or queries.
- 3 kudos
- 2076 Views
- 3 replies
- 1 kudos
How to track features used and filters in MLFlow?
Hello everyone,We are experimenting with several approaches in a Machine Learning project ( binary classification), and we would like to keep track of those using MLFlow. We are using the feature store to build, store, and retrieve the features, and ...
- 2076 Views
- 3 replies
- 1 kudos
- 1 kudos
Thanks for the information, I will try to figure it out for more. Keep sharing such informative post keep suggesting such post.
- 1 kudos
- 17652 Views
- 9 replies
- 5 kudos
Access multiple .mdb files using Python
Hi, I wanted to access multiple .mdb access files which are stored in the Azure Data Lake Storage(ADLS) or on Databricks File System using Python. Is it possible to guide me how can I achieve it? It would be great if you can share some code snippets ...
- 17652 Views
- 9 replies
- 5 kudos
- 5 kudos
@Dhara Mandal​ Can you please try below?# cmd 1 %pip instal pandas_access # cmd 2 import pandas_access as mdb db_filename = '/dbfs/FileStore/Campaign_Template.mdb' # Listing the tables. for tbl in mdb.list_tables(db_filename): print(tbl) ...
- 5 kudos
Connect with Databricks Users in Your Area
Join a Regional User Group to connect with local Databricks users. Events will be happening in your city, and you won’t want to miss the chance to attend and share knowledge.
If there isn’t a group near you, start one and help create a community that brings people together.
Request a New Group-
Academy
1 -
Access
4 -
Access control
3 -
Access Data
2 -
AccessKeyVault
1 -
Account
4 -
ADB
2 -
Adf
1 -
ADLS
2 -
AI Summit
4 -
Airflow
1 -
Amazon
2 -
Apache
1 -
Apache spark
3 -
API
5 -
APILimit
1 -
Artifacts
1 -
Audit
1 -
Autoloader
6 -
Autologging
2 -
Automation
2 -
Automl
23 -
AWS
7 -
Aws databricks
1 -
Aws s3
2 -
AWSSagemaker
1 -
Azure
32 -
Azure active directory
1 -
Azure blob storage
2 -
Azure data factory
1 -
Azure data lake
1 -
Azure Data Lake Storage
3 -
Azure data lake store
1 -
Azure databricks
32 -
Azure DevOps
2 -
Azure event hub
1 -
Azure key vault
1 -
Azure sql database
1 -
Azure Storage
2 -
Azure synapse
1 -
Azure Unity Catalog
1 -
Azure vm
1 -
AzureML
2 -
Bar
1 -
Best practice
6 -
Best Practices
8 -
Best Way
1 -
Beta
1 -
Better Way
1 -
Bi
1 -
BI Integrations
1 -
BI Tool
1 -
Billing and Cost Management
1 -
Blob
1 -
Blog
1 -
Blog Post
1 -
Broadcast variable
1 -
Bug
2 -
Bug Report
2 -
Business Intelligence
1 -
Catalog
3 -
CatalogDDL
1 -
CatalogPricing
1 -
Centralized Model Registry
1 -
Certification
2 -
Certification Badge
1 -
Change
1 -
Change Logs
1 -
Chatgpt
2 -
Check
2 -
CHUNK
1 -
CICD
3 -
Classification Model
1 -
Cli
1 -
Clone
1 -
Cloud Storage
1 -
Cluster
10 -
Cluster Configuration
2 -
Cluster management
5 -
Cluster policy
1 -
Cluster Start
1 -
Cluster Tags
1 -
Cluster Termination
2 -
Clustering
1 -
ClusterMemory
1 -
Clusters
5 -
ClusterSpecification
1 -
CNN HOF
1 -
Code
3 -
Column names
1 -
Community
4 -
Community Account
1 -
Community Edition
1 -
Community Edition Password
1 -
Community Members
1 -
Company Email
1 -
Concat Ws
1 -
Conda
1 -
Condition
1 -
Config
1 -
Configure
3 -
Confluent Cloud
1 -
Connect
1 -
Container
2 -
ContainerServices
1 -
Control Plane
1 -
ControlPlane
1 -
Copy
1 -
Copy into
2 -
CosmosDB
1 -
Cost
1 -
Courses
2 -
CSV
6 -
Csv files
1 -
DAIS2023
1 -
Dashboards
1 -
Data
8 -
Data Engineer Associate
1 -
Data Engineer Certification
1 -
Data Explorer
1 -
Data Ingestion
2 -
Data Ingestion & connectivity
11 -
Data Quality
1 -
Data Quality Checks
1 -
Data Science
11 -
Data Science & Engineering
2 -
databricks
5 -
Databricks Academy
3 -
Databricks Account
1 -
Databricks AutoML
9 -
Databricks Cluster
3 -
Databricks Community
5 -
Databricks community edition
4 -
Databricks connect
1 -
Databricks dbfs
1 -
Databricks Environment
1 -
Databricks Feature Store
1 -
Databricks JDBC
1 -
Databricks Job
1 -
Databricks jobs
1 -
Databricks Lakehouse
1 -
Databricks Lakehouse Platform Accreditation
1 -
Databricks Mlflow
4 -
Databricks Model
2 -
Databricks notebook
10 -
Databricks ODBC
1 -
Databricks Platform
1 -
Databricks Pyspark
1 -
Databricks Python Notebook
1 -
Databricks Repos
2 -
Databricks Repos Api
1 -
Databricks Runtime
9 -
Databricks SQL
8 -
Databricks SQL Permission Problems
1 -
Databricks Terraform
1 -
Databricks Training
2 -
Databricks Unity Catalog
1 -
Databricks V2
1 -
Databricks version
1 -
Databricks Workflow
2 -
Databricks Workflows
1 -
Databricks workspace
2 -
Databricks-connect
1 -
DatabricksContainer
1 -
DatabricksJobs
2 -
DatabricksML
6 -
Dataframe
3 -
Datalake
1 -
DataLakeGen1
1 -
DataSharing
1 -
Datatype
1 -
DataVersioning
1 -
Date
1 -
Date Column
1 -
Dateadd
1 -
DB Notebook
1 -
DB Runtime
1 -
DBFS
5 -
DBFS Rest Api
1 -
Dbt
1 -
Dbu
1 -
Dbutils
2 -
Dbx
2 -
DDL
1 -
DDP
1 -
Dear Community
1 -
DecisionTree
1 -
DecisionTreeClasifier
1 -
Deep learning
4 -
Default Location
1 -
Delete
1 -
Delt Lake
4 -
Delta
24 -
Delta lake table
1 -
Delta Live
1 -
Delta Live Tables
6 -
Delta log
1 -
Delta Sharing
3 -
Delta table
9 -
Delta-lake
1 -
Deploy
1 -
DESC
1 -
Details
1 -
Dev
1 -
Devops
1 -
Df
1 -
Difference
2 -
Different Notebook
1 -
Different Parameters
1 -
DimensionTables
1 -
Directory
3 -
Disable
1 -
Display
1 -
Distribution
1 -
DLT
6 -
DLT Pipeline
3 -
Docker
1 -
Docker image
3 -
Documentation
4 -
Dolly
5 -
Dolly Demo
2 -
Download
2 -
EC2
1 -
Emr
2 -
Endpoint
2 -
Ensemble Models
1 -
Environment Variable
1 -
Epoch
1 -
Error
18 -
Error Code
2 -
Error handling
1 -
Error log
2 -
Error Message
4 -
ETL
2 -
Eventhub
1 -
Exam
1 -
Example
1 -
Excel
2 -
Exception
3 -
Experiments
4 -
External Sources
1 -
Extract
1 -
Fact Tables
1 -
Failure
2 -
Feature
9 -
Feature Lookup
2 -
Feature Store
49 -
Feature Store API
2 -
Feature Store Table
1 -
Feature Table
6 -
Feature Tables
4 -
Features
2 -
FeatureStore
2 -
File
4 -
File Path
2 -
File Size
1 -
Files
2 -
Fine Tune Spark Jobs
1 -
Forecasting
2 -
Forgot Password
2 -
Garbage Collection
1 -
Garbage Collection Optimization
1 -
GCP
4 -
Gdal
1 -
Git
3 -
Github
2 -
Github actions
2 -
Github Repo
2 -
Gitlab
1 -
GKE
1 -
Global Init Script
1 -
Global init scripts
4 -
Google
1 -
Governance
1 -
Gpu
5 -
Graphviz
1 -
Help
5 -
Hi
1 -
Horovod
1 -
Html
1 -
Hyperopt
4 -
Hyperparameter Tuning
2 -
Iam
1 -
Image
3 -
Image Data
1 -
Import
2 -
Industry Experts
1 -
Inference Setup Error
1 -
INFORMATION
1 -
Init script
3 -
Input
1 -
Insert
1 -
Instance Profile
1 -
Int
2 -
Interactive cluster
1 -
Internal error
1 -
INVALID STATE
1 -
Invalid Type Code
1 -
IP
1 -
Ipython
1 -
Ipywidgets
1 -
Jar
1 -
Java
2 -
JDBC Connections
1 -
Jdbc driver
1 -
Jira
1 -
Job
4 -
Job Cluster
1 -
Job Parameters
1 -
Job Runs
1 -
JOBS
5 -
Jobs & Workflows
6 -
Join
1 -
Jsonfile
1 -
Jupyternotebook
2 -
K-means
1 -
K8s
1 -
Kafka consumer
1 -
Kedro
1 -
Key Management
1 -
Kinesis
1 -
Lakehouse
1 -
Large Datasets
1 -
Latest Version
1 -
Learning
1 -
Libraries
2 -
Library
3 -
LightGMB
1 -
Limit
3 -
Linear regression
1 -
LLM
3 -
LLMs
14 -
Local computer
1 -
Local Machine
1 -
Log Model
2 -
Logging
1 -
Login
1 -
Logistic regression
1 -
LogPyfunc
1 -
LogRuns
1 -
Logs
1 -
Long Time
2 -
Low Latency APIs
2 -
LTS
3 -
LTS ML
3 -
Machine
3 -
Machine Learning
24 -
Machine Learning Associate
1 -
Managed Table
1 -
Maven
1 -
Max Retries
1 -
Maximum Number
1 -
Medallion Architecture
1 -
Memory
3 -
Merge
3 -
Merge Into
1 -
Metadata
1 -
Metrics
3 -
Microsoft
1 -
Microsoft azure
1 -
ML
17 -
ML Lifecycle
4 -
ML Model
4 -
ML Pipeline
2 -
ML Practioner
3 -
ML Runtime
1 -
MLEndPoint
1 -
MlFlow
75 -
MLflow API
5 -
MLflow Artifacts
2 -
MLflow Experiment
6 -
MLflow Experiments
3 -
Mlflow Model
10 -
Mlflow project
4 -
Mlflow registry
3 -
Mlflow Run
1 -
Mlflow Server
5 -
MLFlow Tracking Server
3 -
MLModel
1 -
MLModelRealtime
1 -
MLModels
2 -
Mlops
6 -
Model
32 -
Model Deployment
4 -
Model Lifecycle
6 -
Model Loading
2 -
Model Monitoring
1 -
Model registry
5 -
Model Serving
33 -
Model Serving Cluster
2 -
Model Serving REST API
6 -
Model Size
2 -
Model Training
2 -
Model Tuning
1 -
Model Version
1 -
Models
8 -
Module
3 -
Modulenotfounderror
1 -
MongoDB
1 -
Monitoring and Visibility
1 -
Mount Point
1 -
Mounts
1 -
Multi
1 -
Multiline
1 -
Multiple users
1 -
Nested
1 -
New
2 -
New Cluster
1 -
New Feature
1 -
New Features
1 -
New Workspace
1 -
Nlp
3 -
Note
1 -
Notebook
6 -
Notebook Context
1 -
Notebooks
5 -
Notification
2 -
Object
3 -
Object Type
1 -
Odbc
1 -
Onboarding
1 -
Online Feature Store Table
1 -
OOM Error
1 -
Open source
2 -
Open Source MLflow
4 -
Optimization
2 -
Optimize
1 -
Optimize Command
1 -
OSS
3 -
Overwatch
1 -
Overwrite
2 -
Packages
2 -
Pandas
3 -
Pandas dataframe
1 -
Pandas udf
4 -
Pandas_udf
1 -
Parallel
1 -
Parallel processing
1 -
Parallel Runs
1 -
Parallelism
1 -
Parameter
2 -
PARAMETER VALUE
2 -
Partition
1 -
Partner Academy
1 -
Password
1 -
Path
1 -
Pending State
2 -
Performance
2 -
Performance Tuning
1 -
Permission
1 -
Personal access token
2 -
Photon
2 -
Photon Engine
1 -
Pickle
1 -
Pickle Files
2 -
Pip
2 -
Pipeline Model
1 -
Points
1 -
Possible
1 -
Postgres
1 -
Presentation
1 -
Pricing
2 -
Primary Key
1 -
Primary Key Constraint
1 -
PROBLEM
2 -
Production
2 -
Progress bar
2 -
Proven Practice
4 -
Proven Practices
2 -
Public
2 -
Pycharm IDE
1 -
Pyfunc Model
2 -
Pymc
1 -
Pymc3
1 -
Pymc3 Models
2 -
PyPI
1 -
Pyspark
6 -
Pyspark Dataframe
1 -
Python
21 -
Python API
1 -
Python Code
1 -
Python Error
1 -
Python Function
3 -
Python Libraries
1 -
Python notebook
2 -
Python Packages
1 -
Python Project
1 -
Python script
1 -
Python Task
1 -
Pytorch
3 -
Query
2 -
Question
2 -
R
6 -
R Shiny
1 -
Randomforest
2 -
Rate Limits
1 -
Reading-excel
2 -
Redis
2 -
Region
1 -
Remote RPC Client
1 -
Repos
1 -
Rest
1 -
Rest API
14 -
REST Endpoint
2 -
RESTAPI
1 -
Result
1 -
Rgeos Packages
1 -
Run
3 -
Runtime
2 -
Runtime 10.4 LST ML
1 -
Runtime update
1 -
S3
1 -
S3bucket
2 -
Sagemaker
1 -
Salesforce
1 -
SAP
1 -
Scalability
1 -
Scalable Machine
2 -
Schema
4 -
Schema evolution
1 -
Score Batch Support
1 -
Script
1 -
SDLC
1 -
Search
1 -
Search Runs
1 -
Secret scope
1 -
Secure Cluster Connectiv
1 -
Security
2 -
Security Exception
1 -
Self Service Notebooks
1 -
Server
1 -
Serverless
1 -
Serverless Inference
1 -
Serverless Real
1 -
Service Application
1 -
Service principal
1 -
Serving
1 -
Serving Status Failed
1 -
Set
1 -
Sf Username
1 -
Shap
2 -
Similar Issue
1 -
Similar Support
1 -
Simple Spark ML Model
1 -
Sin Cosine
1 -
Size
1 -
Sklearn
1 -
SKLEARN VERSION
1 -
Slow
1 -
Small Scale Experimentation
1 -
Source Table
1 -
Spark
13 -
Spark config
1 -
Spark connector
1 -
Spark Daatricks
1 -
Spark Error
1 -
Spark Means
1 -
Spark ML's CrossValidator
1 -
Spark MLlib
2 -
Spark MLlib Models
1 -
Spark Model
1 -
Spark Optimization
1 -
Spark Pandas Api
1 -
Spark Pipeline Model
1 -
Spark streaming
1 -
Spark ui
1 -
Spark Version
2 -
Spark-submit
1 -
Sparkml
1 -
SparkML Models
2 -
Sparknlp
3 -
SparkNLP Model
1 -
Sparktrials
1 -
Split Data
1 -
Spot
1 -
SQL
19 -
SQL Editor
1 -
SQL Queries
1 -
Sql query
1 -
SQL Visualisations
1 -
SQL Visualizations
1 -
Stage failure
2 -
Standard
2 -
StanModel
1 -
Storage
3 -
Storage account
1 -
Storage Space
1 -
Store Import Error
1 -
Store MLflow
1 -
Store Secret
1 -
Stream
2 -
Stream Data
1 -
Streaming
1 -
String
3 -
Stringindexer
1 -
Structtype
1 -
Structured streaming
2 -
Study Material
1 -
Substantial Performance Issues
1 -
Successful Runs
1 -
Summit22
3 -
Summit23
2 -
Support
1 -
Support Team
1 -
Synapse
1 -
Synapse ML
1 -
Table
4 -
Table access control
1 -
Tableau
1 -
Tables
3 -
Tabular Models
1 -
Task
1 -
Temporary View
1 -
Tensor flow
1 -
Tensorboard
1 -
Tensorflow Distributor
1 -
TensorFlow Model
2 -
Terraform
1 -
Test
1 -
Test Dataframe
1 -
Text Column
1 -
TF Model
1 -
TF SummaryWriter
1 -
TF SummaryWriter Flush
1 -
Threading Lock
1 -
TID
4 -
Time
1 -
Time-Series
1 -
Timeseries
1 -
Timestamps
1 -
TODAY
1 -
Tracking Server
1 -
Training
6 -
Transaction Log
1 -
Trying
1 -
Tuning
2 -
Type
1 -
Type Changes
1 -
UAT
1 -
UC
1 -
Udf
6 -
Ui
1 -
Unexpected Error
1 -
Unity Catalog
12 -
Unrecognized Arguments
1 -
Urgent Question
1 -
Use
5 -
Use Case
2 -
Use cases
1 -
User and Group Administration
1 -
Using MLflow
1 -
UTC
2 -
Utils.environment
1 -
Uuid
1 -
Val File Path
1 -
Validate ML Model
2 -
Values
1 -
Variable
1 -
Variable Explanations
1 -
Vector
1 -
Version
1 -
Version Information
1 -
Versioncontrol
1 -
Versioning
1 -
View
1 -
Visualization
2 -
WARNING
1 -
Web App Azure Databricks
1 -
Web ui
1 -
Weekly Release Notes
2 -
weeklyreleasenotesrecap
2 -
Whl
1 -
Wildcard
1 -
Worker Nodes
1 -
Workflow
2 -
Workflow Jobs
1 -
Workspace
2 -
Workspace Region
1 -
Write
1 -
Writing
1 -
XGBModel
2 -
Xgboost
2 -
Xgboost Model
2 -
Yesterday Afternoon
1 -
Z-ordering
1 -
Zorder
1
- « Previous
- Next »
User | Count |
---|---|
89 | |
39 | |
36 | |
25 | |
25 |