- 3243 Views
- 1 replies
- 1 kudos
Unable to install sf and rgeos R packages on the cluster
Got following errorjava.lang.RuntimeException: Installation failed with message:Error installing R package: Could not install package with error: installation of package ‘rgdal’ had non-zero exit status Full error log available at /databricks/drive...
- 3243 Views
- 1 replies
- 1 kudos
- 1 kudos
We can use the below init script to install the packages in the cluster:%scala dbutils.fs.put("dbfs:/databricks/init_scripts/rlib.sh", """ #!/bin/bash sudo apt-get install -y libudunits2-dev sudo add-apt-repository ppa:ubuntugis/ubuntugis-uns...
- 1 kudos
- 5824 Views
- 1 replies
- 0 kudos
Error importing pip package s3fs
A job recently began failing with the following error when a python notebook imports the pip package s3fs.ImportError: cannot import name 'maybe_sync' from 'fsspec.asyn' (/databricks/python/lib/python3.8/site-packages/fsspec/asyn.py) ImportError Tr...
- 5824 Views
- 1 replies
- 0 kudos
- 0 kudos
While checking the init script is installing the s3fs version 0.5.2.This version has issues at the moment from the pypi. I have tested version 0.6.0 that works fine. please change your requirement.txt file with a newer version of s3fs. Below is the p...
- 0 kudos
- 2083 Views
- 1 replies
- 1 kudos
- 2083 Views
- 1 replies
- 1 kudos
- 1 kudos
You can find a lot more info on this at this MLflow product page, including a comparison table at the bottom. I'd summarize that comparison as: Databricks provides three key things in its managed MLflow service.Security: MLflow experiments, models, ...
- 1 kudos
- 884 Views
- 0 replies
- 0 kudos
Feature Discovery
How would one discover features here and also know how to make sense of these features?Ideally, we can trace the usage of features in code as well.
- 884 Views
- 0 replies
- 0 kudos
- 2528 Views
- 1 replies
- 0 kudos
- 2528 Views
- 1 replies
- 0 kudos
- 0 kudos
You can find the MLflow version in the runtime release notes, along with a list of every other library provided. E.g., for DBR 8.3 ML, you can look at the release notes for AWS, Azure, or GCP.The MLflow client API (i.e., the API provided by installi...
- 0 kudos
- 1654 Views
- 1 replies
- 0 kudos
Muliple Where condition vs AND && in Pyspark
.where((col('state')==state) & (col('month')>startmonth)I can do the where conditions both ways. I think the one below add readability. Is there any other difference and which is the best?.where(col('state')==state).where(col('month')>startmonth)
- 1654 Views
- 1 replies
- 0 kudos
- 0 kudos
You can use explain to see what type of physical and logical plans are getting created . This is the best way to see difference , but as mentioned in the question , it should give the same physical plan
- 0 kudos
- 1497 Views
- 2 replies
- 0 kudos
How do I efficiently read image data for a deep learning model?
How do I efficiently read image data for a deep learning model?
- 1497 Views
- 2 replies
- 0 kudos
- 0 kudos
Our documentation provides nice examples of preparing image data for training and inference.Training: See docs for AWS, Azure, GCPInference: See reference solution for AWS, Azure, GCP
- 0 kudos
- 2068 Views
- 4 replies
- 0 kudos
How do you control the cost of provisioning a cluster?
How do you govern the cost of running clusters in Databricks so you're not sticker shocked?
- 2068 Views
- 4 replies
- 0 kudos
- 0 kudos
Less use of Interactive cluster and more use of job cluster can one of the way above others
- 0 kudos
- 2167 Views
- 1 replies
- 0 kudos
- 2167 Views
- 1 replies
- 0 kudos
- 0 kudos
Follow the instruction at Share models across workspaces.
- 0 kudos
- 2936 Views
- 1 replies
- 0 kudos
When should we use offline store vs online store for Feature Store?
Looking at the docs we see both options, can we use both e.g.?
- 2936 Views
- 1 replies
- 0 kudos
- 0 kudos
Online store is for real time inferencing, in most case you will use the offline store.
- 0 kudos
- 2271 Views
- 2 replies
- 0 kudos
What is Databricks' model deployment framework?
How do you do deploy a model in Databricks.
- 2271 Views
- 2 replies
- 0 kudos
- 0 kudos
The following resources provide more detail on this:Databricks model registry example notebook: https://docs.databricks.com/_static/notebooks/mlflow/mlflow-model-registry-example.htmlDatabricks model lifecycle - https://docs.databricks.com/applicatio...
- 0 kudos
- 1699 Views
- 1 replies
- 0 kudos
- 1699 Views
- 1 replies
- 0 kudos
- 0 kudos
https://databricks.com/blog/2017/05/18/taking-apache-sparks-structured-structured-streaming-to-production.html
- 0 kudos
- 4675 Views
- 1 replies
- 0 kudos
Text length limitations in the display() function
Is there a way to change the limit to the length of strings that can be shown using the display() function in notebooks? If I'm noticing truncation, what can I do?
- 4675 Views
- 1 replies
- 0 kudos
- 0 kudos
There is a 500 character limit to strings in columns which is non-configurable. To see the full contents of the column, you can either use the tooltip to expand the cell or download the full results.
- 0 kudos
- « Previous
- Next »
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
21 -
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
2 -
Copy into
2 -
CosmosDB
1 -
Cost
1 -
Courses
2 -
CSV
6 -
Csv files
1 -
DAIS2023
1 -
Dashboards
1 -
Data
9 -
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
12 -
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
48 -
Feature Store API
2 -
Feature Store Table
1 -
Feature Table
6 -
Feature Tables
4 -
Features
2 -
FeatureStore
2 -
File
5 -
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 -
Hadoop
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 -
List
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
25 -
Machine Learning Associate
1 -
Managed Table
1 -
Maven
1 -
Max Retries
1 -
Maximum Number
1 -
Medallion Architecture
1 -
Memory
3 -
Merge
4 -
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
29 -
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
4 -
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
22 -
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
15 -
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
14 -
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 |