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Top Tasks
Creating and deploying a model
Registering a model using the AI Registry user interface
Deploying a model from the AI Registry page
▶︎
Release Notes
What's new in Cloudera AI on premises 1.5.5
Known issues for Cloudera AI on premises 1.5.5
Fixed issues in Cloudera AI on premises 1.5.5
▶︎
Cumulative Hotfixes: Cloudera AI on premises
Cloudera AI on premises 1.5.5 CHF1
▶︎
ML Runtimes Release Notes
▶︎
ML Runtimes What's New
What's new in ML Runtimes version 2025.01.3
What's new in ML Runtimes version 2025.01.2
What's new in ML Runtimes version 2025.01.1
What's New in ML Runtimes version 2024.10.2
What's New in ML Runtimes version 2024.10.1
▶︎
What's New in ML Runtimes older releases
What's New in ML Runtimes version 2024.05.2
What's New in ML Runtimes version 2024.05.1
What's New in ML Runtimes version 2024.02.1
What's New in ML Runtimes version 2023.12.1
What's new in ML Runtimes version 2023.08.2
What's new in ML Runtimes version 2023.08.1
What's new in ML Runtimes version 2023.05.2
What's new in ML Runtimes version 2023.05.1
What's new in ML Runtimes version 2022.11.2
What's new in ML Runtimes version 2022.11
What's new in ML Runtimes version 2022.04
What's new in ML Runtimes version 2021.12
What's new in ML Runtimes version 2021.09.02
What's new in ML Runtimes version 2021.09
What's new in ML Runtimes version 2021.06
What's new in ML Runtimes version 2021.04
What's new in ML Runtimes version 2021.02
What's new in ML Runtimes version 2020.11
▶︎
ML Runtimes Known Issues and Limitations
Known Issues and Limitations in ML Runtimes version 2025.01.3
Known Issues and Limitations in ML Runtimes version 2025.01.2
Known Issues and Limitations in ML Runtimes version 2025.01.1
Known Issues and Limitations in ML Runtimes version 2024.10.1
Known Issues and Limitations in ML Runtimes version 2024.05.2
Known Issues and Limitations in ML Runtimes older releases
▶︎
ML Runtimes Pre-installed Packages
ML Runtimes Pre-installed Packages overview
▶︎
ML Runtimes 2025.01.3
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Python 3.12 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2025.01.2
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Python 3.12 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2025.01.1
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Python 3.12 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2024.10.2
Python 3.10 Libraries for Conda
Python 3.12 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.12 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2024.10.1
Python 3.10 Libraries for Conda
Python 3.12 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.12 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2024.05.2
Python 3.10 Libraries for Conda
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2024.05.1
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.4 Libraries
▶︎
ML Runtimes 2024.02.1
Python 3.10 Libraries for Conda
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.3 Libraries
▶︎
ML Runtimes 2023.12.1
Python 3.10 Libraries for Conda
Python 3.11 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.3 Libraries
▶︎
ML Runtimes 2023.08.2
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.10 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.3 Libraries
R 4.1 Libraries
R 4.0 Libraries
R 3.6 Libraries
▶︎
ML Runtimes 2022.11
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.9.6 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.1 Libraries
R 4.0 Libraries
R 3.6 Libraries
Python 3.9 Libraries for PBJ Workbench
Python 3.8 Libraries for PBJ Workbench
Python 3.7 Libraries for PBJ Workbench
PBJ R 4.1 Libraries
PBJ R 4.0 Libraries
PBJ R 3.6 Libraries
▶︎
ML Runtimes 2022.04
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.9.6 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.0 Libraries
R 4.1 Libraries
R 3.6 Libraries
▶︎
ML Runtimes 2021.12
Python 3.9 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.7 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Python 3.9.6 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
R 4.0 Libraries
R 4.1 Libraries
R 3.6 Libraries
ML Runtimes 2020.11
▶︎
About Cloudera AI
Cloudera AI Overview
▶︎
Requirements
Introduction to Cloudera on premises
Requirements for Cloudera AI on Openshift Container Platform
▶︎
Requirements for Cloudera AI on Cloudera Embedded Container Service
▶︎
Certification Manager service for increased security
Configuring cluster issuer for Certificate Manager
Certified Scale Limitations for Cloudera AI Workbenches on Cloudera Embedded Container Service
Getting Started with Cloudera AI on premises
Testing your connectivity to the Cloudera-Data Center Cluster
Differences Between Cloudera on cloud and Cloudera on premises
Limitations on Cloudera on premises
▶︎
Network File System (NFS)
NFS options for on premises
Portworx storage provisioner
Internal Network File System on Cloudera Embedded Container Service
Using an External NFS Server
Deploying a Cloudera AI Workbench with support for TLS
Replacing a Certificate
Setting up the GPU node
▶︎
User
▶︎
Starting a Project in Cloudera AI Workbench
▶︎
Collaboration models
Collaboration Models
Sharing Job and Session Console Outputs
▶︎
User Roles and Team Accounts
Managing your Personal Account
Creating a Team
Managing a Team Account
Managing a Synced Team
▶︎
Managing Projects
▶︎
Managing Projects
Creating a Project with ML Runtimes variants
Creating a project from a password-protected Git repo
Configuring Project-level Runtimes
Adding Project Collaborators
Modifying Project settings
Managing Project Files
Deleting a Project
▶︎
Native Workbench Console and Editor
Launch a Session
Run Code
Access the Terminal
Stop a Session
Workbench editor file types
Environmental Variables
▶︎
Third-party Editors
Modes of configuration
▶︎
Configure a browser-based IDE as an Editor
Testing a browser-based IDE in a Session
Configuring a browser-based IDE at the Project level
Configuring a local IDE using an SSH gateway
▶︎
Configure PyCharm as a local IDE
Add Cloudera AI as an Interpreter for PyCharm
Configure PyCharm to use Cloudera AI as the remote console
(Optional) Configure the Sync between Cloudera AI and PyCharm
▶︎
Configure VS Code as a local IDE
Download cdswctl and add an SSH Key
Initialize an SSH connection to Cloudera AI for VS code
Setting up VS Code
(Optional) Using VS Code with Python
(Optional) Using VS Code with R
(Optional) Using VS Code with Jupyter
(Optional) Using VS Code with Git integration
Limiting files in Explorer view
▶︎
Git for Collaboration
Linking an existing Project to a Git remote
▶︎
Embedded Web Applications
Example: A Shiny Application
Example: Flask application
▶︎
Applied ML Prototypes (AMP)
Cloudera Accelerators for Machine Learning Projects
Creating New AMPs
AMP Project Specification
Restarting a failed AMP setup
▶︎
Discovering and Exploring Data in Cloudera AI Workbench
▶︎
Exploratory Data Science and Visualization
Exploratory Data Science and Visualization
Prerequisites for Cloudera AI Discovery and Exploration
▶︎
Starting Data Discovery and Visualization
Workarounds for Cloudera Data Visualization with Hive and Impala
Working with Data Discovery and Visualization
Data connection management
Managing default and backup data connections
API permissions for Projects
Troubleshooting: 401 Unauthorized
Troubleshooting: 401 Unauthorized when accessing Hive or Impala virtual warehouses
Troubleshooting: Existing connection name
Troubleshooting: Empty data page
▶︎
Data Access
Uploading and working with local files
Auto discovering data sources
Using data connection snippets
▶︎
Manual data connections to connect to data sources
Connecting to Cloudera Data Warehouse
Setting up a Hive or Impala data connection manually
Connecting to Hive and Impala services on Cloudera on premises Base
Setting up a Spark data connection
▶︎
Accessing data with Spark
Using JDBC Connection with PySpark
Connecting to Iceberg tables
Connecting to Hive tables via HWC
Connecting to Ozone filesystem
▶︎
Accessing Ozone storage
Creating an Ozone data connection
Connecting to Ozone filesystem
Accessing local files in Ozone
Connecting to external Amazon S3 buckets
Connect to External SQL Databases
▶︎
Visualizations
▶︎
Built-in Cloudera AI Visualizations
Simple Plots
Saved Images
HTML Visualizations
IFrame Visualizations
Grid Displays
Documenting Your Analysis
Cloudera Data Visualization for Cloudera AI
▶︎
Developing for Data Science, ML and AI in Cloudera AI Workbench
▶︎
Runtimes
▶︎
Managing ML Runtimes
Adding new ML Runtimes
Updating ML Runtime images on Cloudera AI installations
Adding Custom ML Runtimes through the Runtime Catalog
Adding ML Runtimes using Runtime Repo files
ML Runtimes versus Legacy Engine
Using Runtime Catalog
Disabling and Deleting Runtimes
▶︎
PBJ Workbench
Dockerfile compatible with PBJ Workbench
PBJ Runtimes and Models
Example models with PBJ Runtimes
▶︎
Using ML Runtimes addons
Adding Hadoop CLI to ML Runtime sessions
Adding Spark to ML Runtime Sessions
Turning off ML Runtimes Addons
▶︎
ML Runtimes NVIDIA GPU Edition
Testing ML Runtime GPU Setup
ML Runtimes NVIDIA RAPIDS Edition
▶︎
Using Editors for ML Runtimes
▶︎
Using JupyterLab with ML Runtimes
Installing a Jupyter extension
Installing a Jupyter kernel
Installing R Kernel in JupyterLab Runtimes of Cloudera AI
Using Conda Runtime
Installing additional ML Runtimes Packages
Restrictions for upgrading R and Python packages
Custom Runtime addons with Cloudera AI
▶︎
ML Runtimes environment variables
ML Runtimes Environment Variables List
Accessing Environmental Variables from projects
▶︎
Customized Runtimes
▶︎
Creating customized ML Runtimes
Creating a Dockerfile for the custom Runtime Image
Metadata for custom ML Runtimes
Customizing the editor
Building the new Docker Image
Distributing the ML Runtime Image
Adding a new customized ML Runtime through the Runtime Catalog
Limitations to customized ML Runtime images
ML Runtimes Pre-installed Packages overview
▶︎
Using Cloudera Copilot
▶︎
Cloudera Copilot Overview
Using Cloudera Copilot
▶︎
Legacy Engines
▶︎
Managing Engines
Creating Resource profiles
Configuring the engine environment
Set up a custom repository location
Burstable CPUs
▶︎
Installing additional packages
Using Conda to manage dependencies
▶︎
Engine environment variables
Engine environment variables
Accessing environmental variables from projects
▶︎
Customized engine images
▶︎
Creating a customized engine image
Create a Dockerfile for the custom image
Build the new Docker image
Distribute the image
Including images in allowlist for Cloudera AI projects
Limitations with customized engines
End-to-end example: MeCab
Legacy Engine level configuration
▶︎
Pre-Installed Packages in engines
Base Engine 15-cml-2021.09-1
Base Engine 14-cml-2021.05-1
Base Engine 13-cml-2020.08-1
Base Engine 12-cml-2020.06-2
Base Engine 11-cml1.4
Base Engine 10-cml1.3
Base Engine 9-cml1.2
▶︎
GPUs
Using GPUs for Cloudera AI projects
Heterogeneous GPU clusters
Testing GPU Setup
▶︎
Distributed Computing
▶︎
Distributed Computing with Workers
Workers API
Worker Network Communication
▶︎
Spark
Spark on Cloudera AI
Apache Spark supported versions
Spark configuration files
Managing memory available for Spark drivers
Managing dependencies for Spark 2 jobs
Spark Log4j configuration
Setting up an HTTP Proxy for Spark 2
Spark web UIs
Using Spark 3 from R
▶︎
Using Spark 2 from Scala
Managing dependencies for Spark 2 and Scala
Running Spark with Yarn on the Cloudera base cluster
▶︎
Experiments
Experiments with MLflow
Cloudera AI Experiment Tracking through MLflow API
Running an Experiment using MLflow
Visualizing Experiment Results
Using an MLflow Model Artifact in a Model REST API
Deploying an MLflow model as a Cloudera AI Model REST API
Automatic Logging
Setting Permissions for an Experiment
MLflow transformers
▶︎
Evaluating LLM with MLFlow
Using Heuristic-based metrics
Using LLM-as-a-Judge metrics
Known issues and limitations
▶︎
Packaging and Deployment
▶︎
Cloudera AI Workbench Models
▶︎
Managing Models
▶︎
Models overview
Models - Concepts and Terminology
Cloudera AI Project Lifecycle
▶︎
Challenges with Machine Learning in production
Challenges with model deployment and serving
Challenges with model monitoring
▶︎
Challenges with model governance
Model visibility
Model explainability, interpretability, and reproducibility
Model governance using Apache Atlas
▶︎
Using Cloudera AI Registry
▶︎
Registering and deploying models with Cloudera AI Registry
Creating a model using MLflow
Registering a model using the AI Registry user interface
▶︎
Registering a model using MLflow SDK
Using MLflow SDK to register customized models
Viewing registered model information
Creating a new version of a registered model
▶︎
Deploying a model from the AI Registry page
Deploying a model from the Cloudera AI Registry using APIv2
Deploying a model from the destination Project page
Viewing and synchronizing the Cloudera AI Registry instance
Deleting a model from Cloudera AI Registry
Disabling Cloudera AI Registry
▶︎
Cloudera AI Registry standalone API
Prerequisites for Cloudera AI Registry standalone API
Authenticating clients for interacting with Cloudera AI Registry API
Role-based authorization
Using the REST Client
Cloudera AI Registry CLI Client
Known issues with Cloudera AI Registry standalone API
▶︎
Troubleshooting issues with Cloudera AI Registry API
Debugging the model import failure
Importing a Hugging Face Model (Technical Preview)
▶︎
Creating and deploying a model
Hosting an LLM as a Cloudera AI Workbench model
Deploying the Cloudera AI Workbench model
Usage guidelines for deploying models with Cloudera AI
Known Issues and Limitations with Model Builds and Deployed Models
Request/Response Formats (JSON)
Testing calls to a Model
▶︎
Securing Models
Access Keys for Models
▶︎
API Key for Models
Enabling authentication
Generating an API key
Managing API Keys
Workflows for active Models
Technical metrics for Models
Debugging issues with Models
Deleting a Model
Configuring model request payload size
▶︎
Example - Model training and deployment (Iris)
Training the Model
Deploying the Model
▶︎
Securing Models
Access Keys for Models
▶︎
API Key for Models
Enabling authentication
Generating an API key
Managing API Keys
▶︎
Model Governance
Enabling model governance
ML Governance Requirements
Registering training data lineage using a linking file
Viewing lineage for a model deployment in Atlas
▶︎
Model Metrics
Enabling model metrics
Tracking model metrics without deploying a model
Tracking metrics for deployed models
▶︎
Using Registered Models
Deploying a model from Registered Models
Viewing details of a registered model
Editing model visibility
Deleting a registered model version
▶︎
Model Hub [Technical Preview]
▶︎
Using Model Hub
Role-based authorization in Model Hub
Model Hub in air-gapped installations
Importing models from NVIDIA NGC
Importing models from Hugging Face (Technical Preview)
Downloading and uploading Model Repositories for an air-gapped environment
▶︎
Cloudera AI Inference service [Technical Preview]
▶︎
Cloudera AI Inference service Overview
Key Features
Key Applications
Terminology
Limitations and Restrictions
Supported Model Artifact Formats
Cloudera AI Inference service Concepts
Authenticating Cloudera AI Inference service
▶︎
Managing Model Endpoints using UI
Creating a Model Endpoint using the UI
Listing Model Endpoints using UI
Viewing details of a Model Endpoint using UI
Editing a Model Endpoint Configuration using UI
▶︎
Managing Model Endpoints using API
Preparing to interact with the Cloudera AI Inference service API
Creating a Model Endpoint using API
Listing Model Endpoints using API
Describing a Model Endpoint using API
Deleting a Model Endpoint using API
Autoscaling Model Endpoints using API
Tuning auto-scaling sensitivity using the API
Running Models on GPU
Deploying models with Canary deployment using API
▶︎
Interacting with Model Endpoints
▶︎
Making an inference call to a Model Endpoint with an OpenAI API
Cloudera AI Inference service using OpenAI Python SDK client in a Cloudera AI Workbench Session
Cloudera AI Inference service using OpenAI Python SDK client on a local machine
OpenAI Inference Protocol Using Curl
▶︎
Making an inference call to a Model Endpoint with Open Inference Protocol
Open Inference Protocol Using Python SDK
Open Inference Protocol Using Curl
Deploying Predictive Models
Accessing Cloudera AI Inference service Metrics
▶︎
Deploying Applications
▶︎
Applications
Analytical Applications
Securing Applications
Limitations with Analytical Applications
Monitoring applications
▶︎
Using Cloudera AI Studios
AI Studios Overview (Technical Preview)
▶︎
Using RAG Studio
RAG Studio Overview
Key features for RAG Studio
Using the RAG Studio
Use cases for RAG Studio
▶︎
Using Fine Tuning Studio
Fine Tuning Studio Overview
Key Features for Fine Tuning Studio
Using Fine Tuning Studio
Use case for Fine Tuning Studio - Event ticketing support
▶︎
Using Synthetic Data Studio
Synthetic Data Studio Overview
Synthetic Data Studio use cases
Key Features of Synthetic Data Studio
Launching Synthetic Data Studio within a project
Generating synthetic data for fine-tuning models
Evaluating generated datasets for fine-tuning LLMs
Managing generated datasets
▶︎
Use case: Data generation for ticketing system using Synthetic Data Studio
Generating synthetic data for a ticketing use case using the Supervised Fine-Tuning workflow
Evaluating the generated dataset
▶︎
Using Agent Studio
Agent Studio Overview
Key Features of Agent Studio
Use Cases of Agent Studio
Launching Agent Studio within a Project
Use case: Sequential Idea Generation Workflow
Limitations of AI Studios
▶︎
Jobs and Pipelines
Creating a Job
Creating a Pipeline
Viewing Job History
Jobs API
▶︎
Reference
▶︎
API
Cloudera AI API v2
API v2 usage
REST API v2 Reference
REST API v2 for Cloudera AI Inference service
REST API v2 for AI Registry
▶︎
CLI
Command Line Tools in Cloudera AI
▶︎
cdswctl Command Line Interface client
Downloading and configuring cdswctl
Initializing an SSH endpoint
Logging into cdswctl
Preparing to manage models for using the model CLI
Creating a model using the model CLI
Build and deployment commands for models
Deploying a new model with updated resources
Viewing replica logs for a model
▶︎
Using ML Runtimes with cdswctl
Querying the engine type
Listing ML Runtimes
Starting sessions and creating SSH endpoints
Creating a model
cdswctl command reference
▶︎
Jupyter Magics
▶︎
Jupyter Magic Commands
Python
Scala
▶︎
Troubleshooting for the Data Scientist
▶︎
Troubleshooting issues with workloads
Troubleshooting Kerberos issues
Exit codes for Cloudera AI jobs and sessions
Troubleshooting for ML Runtimes
Troubleshooting Custom Runtime Addons
▶︎
Administrator
▶︎
Managing your Cloudera AI Workbench service
▶︎
Cloudera AI Workbenches
Provisioning a Cloudera AI Workbench
Monitoring Cloudera AI Workbenches
Removing Cloudera AI Workbenches
Upgrading Cloudera AI Workbenches
▶︎
Backing up Cloudera AI Workbenches
Workbench backup and restore prerequisites
Backing up a Cloudera AI Workbench
Cleaning up and backing up the Cloudera AI Workbench database manually
Restoring a Cloudera AI Workbench
▶︎
Setting up Cloudera AI Studios
AI Studios Overview (Technical Preview)
▶︎
Managing RAG Studio
RAG Studio Overview
Key features for RAG Studio
Launching RAG Studio within a project
Configuring RAG Studio
▶︎
Managing Fine Tuning Studio
Fine Tuning Studio Overview
Key Features for Fine Tuning Studio
Launching Fine Tuning Studio within a project
Managing AI Studios
Host names and endpoints required for AI Studios
Limitations of AI Studios
▶︎
GPU usage
GPU nodes setup as worker nodes
Configuring GPU usage
▶︎
Site Administration
Managing Users
▶︎
Service Accounts
Creating a machine user and synchronizing to workbench
Synchronizing machine users from the Synced team
Running workloads using a service account
Authenticating Hadoop for Cloudera AI service accounts
Configuring Quotas
▶︎
Non-user dependent Resource Usage Limiting for workloads
Setting Resource Usage Limiting for workloads
Creating Resource profiles
Disable or deprecate Runtime addons
Onboarding Business Users
Adding a Collaborator
▶︎
User roles
Business Users and Cloudera AI
Managing your Personal Account
Creating a Team
Managing a Team Account
▶︎
Monitoring user activity
Tracked user events
Monitoring user events
Collecting project size information
Monitoring active Models across the Workbench
Monitoring and alerts
Application polling endpoint
Choosing default engine
Controlling User access to features
Cloudera AI email notifications
Web session timeouts
Project garbage collection
How to make base cluster configuration changes
Ephemeral storage
▶︎
NTP proxy setup on Cloudera AI
Updating proxy configuration in an existing workbench
Export Usage List
Disable addons
Optimized queries with Dashboards Archive table
Host name required by Learning Hub
▶︎
Configuring HTTP Headers for Cloudera AI
Enable HTTP security headers
Enable HTTP Strict Transport Security (HSTS)
Enable Cross-Origin Resource Sharing (CORS)
▶︎
SSH Keys
Personal key
Team key
Adding an SSH key to GitHub
Creating an SSH tunnel
Hadoop authentication for Cloudera AI Workbenches
Cloudera AI and outbound network access
▶︎
Managing Cloudera Accelerators for Machine Learning Projects
Cloudera Accelerators for Machine Learning Projects
HuggingFace Spaces and Community AMPs
Creating New AMPs using API
Custom AMP Catalog
Adding a catalog
Catalog File Specification
Host names required by AMPs
AMPs in airgapped environments
Configuring Traefik readTimeout for large file uploads
▶︎
Managing ML Runtimes
▶︎
Managing ML Runtimes
Adding new ML Runtimes
Adding Custom ML Runtimes through the Runtime Catalog
Adding ML Runtimes using Runtime Repo files
Updating ML Runtime images on Cloudera AI installations
ML Runtimes versus Legacy Engine
Using Runtime Catalog
Changing Docker credential setting for ML Runtime
Disabling and Deleting Runtimes
▶︎
Using ML Runtimes addons
Adding Hadoop CLI to ML Runtime sessions
Adding Spark to ML Runtime Sessions
Turning off ML Runtimes Addons
▶︎
Customized Runtimes
▶︎
Creating customized ML Runtimes
Creating a Dockerfile for the custom Runtime Image
Metadata for custom ML Runtimes
Customizing the editor
Building the new Docker Image
Distributing the ML Runtime Image
Adding a new customized ML Runtime through the Runtime Catalog
Limitations to customized ML Runtime images
Adding Docker registry credentials and certificates
▶︎
Managing Cloudera Copilot
▶︎
Cloudera Copilot Overview
Configuring Cloudera AI Inference service and Amazon Bedrock to set up Cloudera Copilot
Configuring Cloudera Copilot
▶︎
Security
▶︎
Configuring HTTP Headers for Cloudera AI
Enable HTTP security headers
Enable HTTP Strict Transport Security (HSTS)
Enable Cross-Origin Resource Sharing (CORS)
▶︎
SSH Keys
Personal key
Team key
Adding an SSH key to GitHub
Creating an SSH tunnel
Hadoop authentication for Cloudera AI Workbenches
Cloudera AI and outbound network access
▶︎
Quota Management [Technical Preview]
Quota Management overview
Enabling Quota Management in Cloudera AI
▶︎
Provisioning a workbench
Upgrade Cloudera AI Workbench to the latest version
Quota for Cloudera AI workloads
Resource Usage Dashboard
Limitations for Quota Management
Yunikorn Gang scheduling
▶︎
Cloudera AI Metrics Collector Service
Cloudera AI Metrics Collector Service overview
▶︎
Visualization in Cloudera AI quota management
Cloudera AI Quota Management dashboard
Troubleshooting for Cloudera AI Metrics Collector Service
▶︎
Managing your Model Hub [Technical Preview]
▶︎
Importing models in an air-gapped environment
▶︎
Downloading and uploading Model Repositories for an air-gapped environment
Prerequisites for downloading and uploading Model artifacts in air-gapped environment
Understanding NVIDIA NGC file
Downloading Model Repositories for an air-gapped environment
Uploading Model Repositories for an air-gapped environment
Creating the Model entry in Cloudera AI Registry in air-gapped environment
Importing Model to Cloudera AI Registry in air-gapped environment
▶︎
Managing your Cloudera AI Registry service
▶︎
Setting up Cloudera AI Registry
▶︎
Setting up Cloudera AI Registry
Prerequisites for creating Cloudera AI Registry
Creating a Cloudera AI Registry
Setting up certificates for Cloudera AI Registry
Synchronizing Cloudera AI Registry with a workbench
Viewing details for Cloudera AI Registries
Cloudera AI Registry permissions
Model access control
▶︎
Upgrading Cloudera AI Registry
Upgrading Cloudera AI Registry
▶︎
Deleting Cloudera AI Registry
▶︎
Deleting Cloudera AI Registry
Force delete a Cloudera AI Registry
▶︎
Managing your Cloudera AI Inference service [Technical Preview]
▶︎
Setting up Cloudera AI Inference service
▶︎
Cloudera AI Inference service Overview
Key Features
Key Applications
Terminology
Limitations and Restrictions
Supported Model Artifact Formats
Cloudera AI Inference service Configuration and Sizing
▶︎
Prerequisites for setting up Cloudera AI Inference service
Importing Models
Register an ONNX model to Cloudera AI Registry
▶︎
Managing Cloudera AI Inference service
▶︎
Managing Cloudera AI Inference service using the UI
Creating a Cloudera AI Inference service instance using the UI
Listing Cloudera AI Inference service instances using the UI
Viewing details of a Cloudera AI Inference service instances using the UI
Deleting Cloudera AI Inference service instances using the UI
Obtaining Control Plane Audit Logs for Cloudera AI Inference service using the UI
▶︎
Managing Cloudera AI Inference service using Cloudera CLI
Listing Cloudera AI Inference service instances
Describing Cloudera AI Inference service instance
Deleting Cloudera AI Inference service instance
Setting up certificates for Cloudera AI Inference Service
▶︎
Supporting your Cloudera AI service
▶︎
Troubleshooting for the Administrator
Recommended troubleshooting workflow
Downloading diagnostic bundles for a workbench
Handling project volume size increase in Cloudera AI
(Optional) Configure the Sync between Cloudera AI and PyCharm
(Optional) Using VS Code with Git integration
(Optional) Using VS Code with Jupyter
(Optional) Using VS Code with Python
(Optional) Using VS Code with R
About Cloudera AI
Access Keys for Models
Access Keys for Models
Access the Terminal
Accessing Cloudera AI Inference service Metrics
Accessing data with Spark
Accessing Environmental Variables from projects
Accessing environmental variables from projects
Accessing local files in Ozone
Accessing Ozone storage
Add Cloudera AI as an Interpreter for PyCharm
Adding a catalog
Adding a Collaborator
Adding a new customized ML Runtime through the Runtime Catalog
Adding a new customized ML Runtime through the Runtime Catalog
Adding an SSH key to GitHub
Adding an SSH key to GitHub
Adding Custom ML Runtimes through the Runtime Catalog
Adding Custom ML Runtimes through the Runtime Catalog
Adding Docker registry credentials and certificates
Adding Hadoop CLI to ML Runtime sessions
Adding Hadoop CLI to ML Runtime sessions
Adding ML Runtimes using Runtime Repo files
Adding ML Runtimes using Runtime Repo files
Adding new ML Runtimes
Adding new ML Runtimes
Adding Project Collaborators
Adding Spark to ML Runtime Sessions
Adding Spark to ML Runtime Sessions
Administrator
Agent Studio Overview
AI Studios Overview (Technical Preview)
AI Studios Overview (Technical Preview)
All roles
AMP Project Specification
AMPs in airgapped environments
Analytical Applications
Apache Spark supported versions
API
API Key for Models
API Key for Models
API permissions for Projects
API v2 usage
Application polling endpoint
Applications
Applied ML Prototypes (AMP)
Authenticating clients for interacting with Cloudera AI Registry API
Authenticating Cloudera AI Inference service
Authenticating Hadoop for Cloudera AI service accounts
Auto discovering data sources
Automatic Logging
Autoscaling Model Endpoints using API
Backing up a Cloudera AI Workbench
Backing up Cloudera AI Workbenches
Base Engine 10-cml1.3
Base Engine 11-cml1.4
Base Engine 12-cml-2020.06-2
Base Engine 13-cml-2020.08-1
Base Engine 14-cml-2021.05-1
Base Engine 15-cml-2021.09-1
Base Engine 9-cml1.2
Build and deployment commands for models
Build the new Docker image
Building the new Docker Image
Building the new Docker Image
Built-in Cloudera AI Visualizations
Burstable CPUs
Business Users and Cloudera AI
Catalog File Specification
cdswctl Command Line Interface client
cdswctl command reference
Certification Manager service for increased security
Certified Scale Limitations for Cloudera AI Workbenches on Cloudera Embedded Container Service
Challenges with Machine Learning in production
Challenges with model deployment and serving
Challenges with model governance
Challenges with model monitoring
Changing Docker credential setting for ML Runtime
Choosing default engine
Cleaning up and backing up the Cloudera AI Workbench database manually
CLI
Cloudera Accelerators for Machine Learning Projects
Cloudera Accelerators for Machine Learning Projects
Cloudera AI and outbound network access
Cloudera AI and outbound network access
Cloudera AI API v2
Cloudera AI email notifications
Cloudera AI Experiment Tracking through MLflow API
Cloudera AI Inference service Concepts
Cloudera AI Inference service Configuration and Sizing
Cloudera AI Inference service Overview
Cloudera AI Inference service Overview
Cloudera AI Inference service using OpenAI Python SDK client in a Cloudera AI Workbench Session
Cloudera AI Inference service using OpenAI Python SDK client on a local machine
Cloudera AI Inference service [Technical Preview]
Cloudera AI Metrics Collector Service
Cloudera AI Metrics Collector Service overview
Cloudera AI on premises 1.5.5 CHF1
Cloudera AI Overview
Cloudera AI Project Lifecycle
Cloudera AI Quota Management dashboard
Cloudera AI Registry CLI Client
Cloudera AI Registry permissions
Cloudera AI Registry standalone API
Cloudera AI Workbench Models
Cloudera AI Workbenches
Cloudera Copilot Overview
Cloudera Copilot Overview
Cloudera Data Visualization for Cloudera AI
Collaboration models
Collaboration Models
Collecting project size information
Command Line Tools in Cloudera AI
Configure a browser-based IDE as an Editor
Configure PyCharm as a local IDE
Configure PyCharm to use Cloudera AI as the remote console
Configure VS Code as a local IDE
Configuring a browser-based IDE at the Project level
Configuring a local IDE using an SSH gateway
Configuring Cloudera AI Inference service and Amazon Bedrock to set up Cloudera Copilot
Configuring Cloudera Copilot
Configuring cluster issuer for Certificate Manager
Configuring GPU usage
Configuring HTTP Headers for Cloudera AI
Configuring HTTP Headers for Cloudera AI
Configuring model request payload size
Configuring Project-level Runtimes
Configuring Quotas
Configuring RAG Studio
Configuring the engine environment
Configuring Traefik readTimeout for large file uploads
Connect to External SQL Databases
Connecting to Cloudera Data Warehouse
Connecting to external Amazon S3 buckets
Connecting to Hive and Impala services on Cloudera on premises Base
Connecting to Hive tables via HWC
Connecting to Iceberg tables
Connecting to Ozone filesystem
Connecting to Ozone filesystem
Controlling User access to features
Create a Dockerfile for the custom image
Creating a Cloudera AI Inference service instance using the UI
Creating a Cloudera AI Registry
Creating a customized engine image
Creating a Dockerfile for the custom Runtime Image
Creating a Dockerfile for the custom Runtime Image
Creating a Job
Creating a machine user and synchronizing to workbench
Creating a model
Creating a Model Endpoint using API
Creating a Model Endpoint using the UI
Creating a model using MLflow
Creating a model using the model CLI
Creating a new version of a registered model
Creating a Pipeline
Creating a project from a password-protected Git repo
Creating a Project with ML Runtimes variants
Creating a Team
Creating a Team
Creating an Ozone data connection
Creating an SSH tunnel
Creating an SSH tunnel
Creating and deploying a model
Creating and deploying a model
Creating customized ML Runtimes
Creating customized ML Runtimes
Creating New AMPs
Creating New AMPs using API
Creating Resource profiles
Creating Resource profiles
Creating the Model entry in Cloudera AI Registry in air-gapped environment
Cumulative Hotfixes: Cloudera AI on premises
Custom AMP Catalog
Custom Runtime addons with Cloudera AI
Customized engine images
Customized Runtimes
Customized Runtimes
Customizing the editor
Customizing the editor
Data Access
Data connection management
Debugging issues with Models
Debugging the model import failure
Deleting a Model
Deleting a Model Endpoint using API
Deleting a model from Cloudera AI Registry
Deleting a Project
Deleting a registered model version
Deleting Cloudera AI Inference service instance
Deleting Cloudera AI Inference service instances using the UI
Deleting Cloudera AI Registry
Deleting Cloudera AI Registry
Deploying a Cloudera AI Workbench with support for TLS
Deploying a model from Registered Models
Deploying a model from the AI Registry page
Deploying a model from the AI Registry page
Deploying a model from the Cloudera AI Registry using APIv2
Deploying a model from the destination Project page
Deploying a new model with updated resources
Deploying an MLflow model as a Cloudera AI Model REST API
Deploying models with Canary deployment using API
Deploying Predictive Models
Deploying the Cloudera AI Workbench model
Deploying the Model
Describing a Model Endpoint using API
Describing Cloudera AI Inference service instance
Differences Between Cloudera on cloud and Cloudera on premises
Disable addons
Disable or deprecate Runtime addons
Disabling and Deleting Runtimes
Disabling and Deleting Runtimes
Disabling Cloudera AI Registry
Distribute the image
Distributed Computing
Distributed Computing with Workers
Distributing the ML Runtime Image
Distributing the ML Runtime Image
Dockerfile compatible with PBJ Workbench
Documenting Your Analysis
Download cdswctl and add an SSH Key
Downloading and configuring cdswctl
Downloading and uploading Model Repositories for an air-gapped environment
Downloading and uploading Model Repositories for an air-gapped environment
Downloading diagnostic bundles for a workbench
Downloading Model Repositories for an air-gapped environment
Editing a Model Endpoint Configuration using UI
Editing model visibility
Embedded Web Applications
Enable Cross-Origin Resource Sharing (CORS)
Enable Cross-Origin Resource Sharing (CORS)
Enable HTTP security headers
Enable HTTP security headers
Enable HTTP Strict Transport Security (HSTS)
Enable HTTP Strict Transport Security (HSTS)
Enabling authentication
Enabling authentication
Enabling model governance
Enabling model metrics
Enabling Quota Management in Cloudera AI
End-to-end example: MeCab
Engine environment variables
Engine environment variables
Environmental Variables
Ephemeral storage
Evaluating generated datasets for fine-tuning LLMs
Evaluating LLM with MLFlow
Evaluating the generated dataset
Example - Model training and deployment (Iris)
Example models with PBJ Runtimes
Example: A Shiny Application
Example: Flask application
Exit codes for Cloudera AI jobs and sessions
Experiments
Experiments with MLflow
Exploratory Data Science and Visualization
Exploratory Data Science and Visualization
Export Usage List
Fine Tuning Studio Overview
Fine Tuning Studio Overview
Fixed issues in Cloudera AI on premises 1.5.5
Force delete a Cloudera AI Registry
Generating an API key
Generating an API key
Generating synthetic data for a ticketing use case using the Supervised Fine-Tuning workflow
Generating synthetic data for fine-tuning models
Getting Started with Cloudera AI on premises
Git for Collaboration
GPU nodes setup as worker nodes
GPU usage
GPUs
Grid Displays
Hadoop authentication for Cloudera AI Workbenches
Hadoop authentication for Cloudera AI Workbenches
Handling project volume size increase in Cloudera AI
Heterogeneous GPU clusters
Host name required by Learning Hub
Host names and endpoints required for AI Studios
Host names required by AMPs
Hosting an LLM as a Cloudera AI Workbench model
How to make base cluster configuration changes
HTML Visualizations
HuggingFace Spaces and Community AMPs
IFrame Visualizations
Importing a Hugging Face Model (Technical Preview)
Importing Model to Cloudera AI Registry in air-gapped environment
Importing Models
Importing models from Hugging Face (Technical Preview)
Importing models from NVIDIA NGC
Importing models in an air-gapped environment
Including images in allowlist for Cloudera AI projects
Initialize an SSH connection to Cloudera AI for VS code
Initializing an SSH endpoint
Installing a Jupyter extension
Installing a Jupyter kernel
Installing additional ML Runtimes Packages
Installing additional packages
Installing R Kernel in JupyterLab Runtimes of Cloudera AI
Interacting with Model Endpoints
Internal Network File System on Cloudera Embedded Container Service
Introduction to Cloudera on premises
Jobs and Pipelines
Jobs API
Jupyter Magic Commands
Jupyter Magics
Key Applications
Key Applications
Key Features
Key Features
Key Features for Fine Tuning Studio
Key Features for Fine Tuning Studio
Key features for RAG Studio
Key features for RAG Studio
Key Features of Agent Studio
Key Features of Synthetic Data Studio
Known issues and limitations
Known Issues and Limitations in ML Runtimes older releases
Known Issues and Limitations in ML Runtimes version 2024.05.2
Known Issues and Limitations in ML Runtimes version 2024.10.1
Known Issues and Limitations in ML Runtimes version 2025.01.1
Known Issues and Limitations in ML Runtimes version 2025.01.2
Known Issues and Limitations in ML Runtimes version 2025.01.3
Known Issues and Limitations with Model Builds and Deployed Models
Known issues for Cloudera AI on premises 1.5.5
Known issues with Cloudera AI Registry standalone API
Launch a Session
Launching Agent Studio within a Project
Launching Fine Tuning Studio within a project
Launching RAG Studio within a project
Launching Synthetic Data Studio within a project
Legacy Engine level configuration
Legacy Engines
Limitations and Restrictions
Limitations and Restrictions
Limitations for Quota Management
Limitations of AI Studios
Limitations of AI Studios
Limitations on Cloudera on premises
Limitations to customized ML Runtime images
Limitations to customized ML Runtime images
Limitations with Analytical Applications
Limitations with customized engines
Limiting files in Explorer view
Linking an existing Project to a Git remote
Listing Cloudera AI Inference service instances
Listing Cloudera AI Inference service instances using the UI
Listing ML Runtimes
Listing Model Endpoints using API
Listing Model Endpoints using UI
Logging into cdswctl
Making an inference call to a Model Endpoint with an OpenAI API
Making an inference call to a Model Endpoint with Open Inference Protocol
Managing a Synced Team
Managing a Team Account
Managing a Team Account
Managing AI Studios
Managing API Keys
Managing API Keys
Managing Cloudera Accelerators for Machine Learning Projects
Managing Cloudera AI Inference service
Managing Cloudera AI Inference service using Cloudera CLI
Managing Cloudera AI Inference service using the UI
Managing Cloudera Copilot
Managing default and backup data connections
Managing dependencies for Spark 2 and Scala
Managing dependencies for Spark 2 jobs
Managing Engines
Managing Fine Tuning Studio
Managing generated datasets
Managing memory available for Spark drivers
Managing ML Runtimes
Managing ML Runtimes
Managing ML Runtimes
Managing Model Endpoints using API
Managing Model Endpoints using UI
Managing Models
Managing Project Files
Managing Projects
Managing Projects
Managing RAG Studio
Managing Users
Managing your Personal Account
Managing your Personal Account
Manual data connections to connect to data sources
Metadata for custom ML Runtimes
Metadata for custom ML Runtimes
ML Governance Requirements
ML Runtimes 2020.11
ML Runtimes 2021.12
ML Runtimes 2022.04
ML Runtimes 2022.11
ML Runtimes 2023.08.2
ML Runtimes 2023.12.1
ML Runtimes 2024.02.1
ML Runtimes 2024.05.1
ML Runtimes 2024.05.2
ML Runtimes 2024.10.1
ML Runtimes 2024.10.2
ML Runtimes 2025.01.1
ML Runtimes 2025.01.2
ML Runtimes 2025.01.3
ML Runtimes environment variables
ML Runtimes Environment Variables List
ML Runtimes Known Issues and Limitations
ML Runtimes NVIDIA GPU Edition
ML Runtimes NVIDIA RAPIDS Edition
ML Runtimes Pre-installed Packages
ML Runtimes Pre-installed Packages overview
ML Runtimes Pre-installed Packages overview
ML Runtimes Release Notes
ML Runtimes versus Legacy Engine
ML Runtimes versus Legacy Engine
ML Runtimes What's New
MLflow transformers
Model access control
Model explainability, interpretability, and reproducibility
Model Governance
Model governance using Apache Atlas
Model Hub in air-gapped installations
Model Hub [Technical Preview]
Model Metrics
Model visibility
Models - Concepts and Terminology
Models overview
Modes of configuration
Modifying Project settings
Monitoring active Models across the Workbench
Monitoring and alerts
Monitoring applications
Monitoring Cloudera AI Workbenches
Monitoring user activity
Monitoring user events
Native Workbench Console and Editor
Network File System (NFS)
NFS options for on premises
Non-user dependent Resource Usage Limiting for workloads
NTP proxy setup on Cloudera AI
Obtaining Control Plane Audit Logs for Cloudera AI Inference service using the UI
Onboarding Business Users
Open Inference Protocol Using Curl
Open Inference Protocol Using Python SDK
OpenAI Inference Protocol Using Curl
Optimized queries with Dashboards Archive table
PBJ R 3.6 Libraries
PBJ R 4.0 Libraries
PBJ R 4.1 Libraries
PBJ Runtimes and Models
PBJ Workbench
Personal key
Personal key
Portworx storage provisioner
Pre-Installed Packages in engines
Preparing to interact with the Cloudera AI Inference service API
Preparing to manage models for using the model CLI
Prerequisites for Cloudera AI Discovery and Exploration
Prerequisites for Cloudera AI Registry standalone API
Prerequisites for creating Cloudera AI Registry
Prerequisites for downloading and uploading Model artifacts in air-gapped environment
Prerequisites for setting up Cloudera AI Inference service
Project garbage collection
Provisioning a Cloudera AI Workbench
Provisioning a workbench
Python
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for Conda
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for JupyterLab
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.10 Libraries for Workbench
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for JupyterLab
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.11 Libraries for Workbench
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Conda
Python 3.12 Libraries for Conda
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for JupyterLab
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.12 Libraries for Workbench
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for JupyterLab
Python 3.7 Libraries for PBJ Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.7 Libraries for Workbench
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for JupyterLab
Python 3.8 Libraries for PBJ Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.8 Libraries for Workbench
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for JupyterLab
Python 3.9 Libraries for PBJ Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9 Libraries for Workbench
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Python 3.9.6 Libraries for JupyterLab
Querying the engine type
Quota for Cloudera AI workloads
Quota Management overview
Quota Management [Technical Preview]
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 3.6 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.0 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.1 Libraries
R 4.3 Libraries
R 4.3 Libraries
R 4.3 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
R 4.4 Libraries
RAG Studio Overview
RAG Studio Overview
Recommended troubleshooting workflow
Register an ONNX model to Cloudera AI Registry
Registering a model using MLflow SDK
Registering a model using the AI Registry user interface
Registering a model using the AI Registry user interface
Registering and deploying models with Cloudera AI Registry
Registering training data lineage using a linking file
Release Notes
Removing Cloudera AI Workbenches
Replacing a Certificate
Request/Response Formats (JSON)
Requirements
Requirements for Cloudera AI on Cloudera Embedded Container Service
Requirements for Cloudera AI on Openshift Container Platform
Resource Usage Dashboard
Restarting a failed AMP setup
Restoring a Cloudera AI Workbench
Restrictions for upgrading R and Python packages
Role-based authorization
Role-based authorization in Model Hub
Run Code
Running an Experiment using MLflow
Running Models on GPU
Running Spark with Yarn on the Cloudera base cluster
Running workloads using a service account
Runtimes
Saved Images
Scala
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.11 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Scala 2.12 Libraries for Workbench
Securing Applications
Securing Models
Securing Models
Security
Service Accounts
Set up a custom repository location
Setting Permissions for an Experiment
Setting Resource Usage Limiting for workloads
Setting up a Hive or Impala data connection manually
Setting up a Spark data connection
Setting up an HTTP Proxy for Spark 2
Setting up certificates for Cloudera AI Inference Service
Setting up certificates for Cloudera AI Registry
Setting up Cloudera AI Inference service
Setting up Cloudera AI Registry
Setting up Cloudera AI Registry
Setting up Cloudera AI Studios
Setting up the GPU node
Setting up VS Code
Sharing Job and Session Console Outputs
Simple Plots
Site Administration
Spark
Spark configuration files
Spark Log4j configuration
Spark on Cloudera AI
Spark web UIs
SSH Keys
SSH Keys
Starting Data Discovery and Visualization
Starting sessions and creating SSH endpoints
Stop a Session
Supported Model Artifact Formats
Supported Model Artifact Formats
Synchronizing Cloudera AI Registry with a workbench
Synchronizing machine users from the Synced team
Synthetic Data Studio Overview
Synthetic Data Studio use cases
Team key
Team key
Technical metrics for Models
Terminology
Terminology
Testing a browser-based IDE in a Session
Testing calls to a Model
Testing GPU Setup
Testing ML Runtime GPU Setup
Testing your connectivity to the Cloudera-Data Center Cluster
Third-party Editors
Top Tasks
Tracked user events
Tracking metrics for deployed models
Tracking model metrics without deploying a model
Training the Model
Troubleshooting Custom Runtime Addons
Troubleshooting for Cloudera AI Metrics Collector Service
Troubleshooting for ML Runtimes
Troubleshooting for the Administrator
Troubleshooting for the Data Scientist
Troubleshooting issues with Cloudera AI Registry API
Troubleshooting issues with workloads
Troubleshooting Kerberos issues
Troubleshooting: 401 Unauthorized
Troubleshooting: 401 Unauthorized when accessing Hive or Impala virtual warehouses
Troubleshooting: Empty data page
Troubleshooting: Existing connection name
Tuning auto-scaling sensitivity using the API
Turning off ML Runtimes Addons
Turning off ML Runtimes Addons
Understanding NVIDIA NGC file
Updating ML Runtime images on Cloudera AI installations
Updating ML Runtime images on Cloudera AI installations
Updating proxy configuration in an existing workbench
Upgrade Cloudera AI Workbench to the latest version
Upgrading Cloudera AI Registry
Upgrading Cloudera AI Registry
Upgrading Cloudera AI Workbenches
Uploading and working with local files
Uploading Model Repositories for an air-gapped environment
Usage guidelines for deploying models with Cloudera AI
Use case for Fine Tuning Studio - Event ticketing support
Use case: Data generation for ticketing system using Synthetic Data Studio
Use case: Sequential Idea Generation Workflow
Use cases for RAG Studio
Use Cases of Agent Studio
User
User roles
User Roles and Team Accounts
Using Agent Studio
Using an External NFS Server
Using an MLflow Model Artifact in a Model REST API
Using Cloudera AI Registry
Using Cloudera AI Studios
Using Cloudera Copilot
Using Cloudera Copilot
Using Conda Runtime
Using Conda to manage dependencies
Using data connection snippets
Using Editors for ML Runtimes
Using Fine Tuning Studio
Using Fine Tuning Studio
Using GPUs for Cloudera AI projects
Using Heuristic-based metrics
Using JDBC Connection with PySpark
Using JupyterLab with ML Runtimes
Using LLM-as-a-Judge metrics
Using ML Runtimes addons
Using ML Runtimes addons
Using ML Runtimes with cdswctl
Using MLflow SDK to register customized models
Using Model Hub
Using RAG Studio
Using Registered Models
Using Runtime Catalog
Using Runtime Catalog
Using Spark 2 from Scala
Using Spark 3 from R
Using Synthetic Data Studio
Using the RAG Studio
Using the REST Client
Viewing and synchronizing the Cloudera AI Registry instance
Viewing details for Cloudera AI Registries
Viewing details of a Cloudera AI Inference service instances using the UI
Viewing details of a Model Endpoint using UI
Viewing details of a registered model
Viewing Job History
Viewing lineage for a model deployment in Atlas
Viewing registered model information
Viewing replica logs for a model
Visualization in Cloudera AI quota management
Visualizations
Visualizing Experiment Results
Web session timeouts
What's new in Cloudera AI on premises 1.5.5
What's New in ML Runtimes older releases
What's new in ML Runtimes version 2020.11
What's new in ML Runtimes version 2021.02
What's new in ML Runtimes version 2021.04
What's new in ML Runtimes version 2021.06
What's new in ML Runtimes version 2021.09
What's new in ML Runtimes version 2021.09.02
What's new in ML Runtimes version 2021.12
What's new in ML Runtimes version 2022.04
What's new in ML Runtimes version 2022.11
What's new in ML Runtimes version 2022.11.2
What's new in ML Runtimes version 2023.05.1
What's new in ML Runtimes version 2023.05.2
What's new in ML Runtimes version 2023.08.1
What's new in ML Runtimes version 2023.08.2
What's New in ML Runtimes version 2023.12.1
What's New in ML Runtimes version 2024.02.1
What's New in ML Runtimes version 2024.05.1
What's New in ML Runtimes version 2024.05.2
What's New in ML Runtimes version 2024.10.1
What's New in ML Runtimes version 2024.10.2
What's new in ML Runtimes version 2025.01.1
What's new in ML Runtimes version 2025.01.2
What's new in ML Runtimes version 2025.01.3
Workarounds for Cloudera Data Visualization with Hive and Impala
Workbench backup and restore prerequisites
Workbench editor file types
Worker Network Communication
Workers API
Workflows for active Models
Working with Data Discovery and Visualization
Yunikorn Gang scheduling
«
(Optional) Using VS Code with Git integration
▶︎
Managing Projects
Creating a Project with ML Runtimes variants
Creating a project from a password-protected Git repo
Configuring Project-level Runtimes
Adding Project Collaborators
Modifying Project settings
Managing Project Files
Deleting a Project
▶︎
Native Workbench Console and Editor
Launch a Session
Run Code
Access the Terminal
Stop a Session
Workbench editor file types
Environmental Variables
▼
Third-party Editors
Modes of configuration
▶︎
Configure a browser-based IDE as an Editor
Testing a browser-based IDE in a Session
Configuring a browser-based IDE at the Project level
Configuring a local IDE using an SSH gateway
▶︎
Configure PyCharm as a local IDE
Add Cloudera AI as an Interpreter for PyCharm
Configure PyCharm to use Cloudera AI as the remote console
(Optional) Configure the Sync between Cloudera AI and PyCharm
▼
Configure VS Code as a local IDE
Download cdswctl and add an SSH Key
Initialize an SSH connection to Cloudera AI for VS code
Setting up VS Code
(Optional) Using VS Code with Python
(Optional) Using VS Code with R
(Optional) Using VS Code with Jupyter
(Optional) Using VS Code with Git integration
Limiting files in Explorer view
▶︎
Git for Collaboration
Linking an existing Project to a Git remote
▶︎
Embedded Web Applications
Example: A Shiny Application
Example: Flask application
»
projects
(Optional) Using VS Code with Git integration
VS Code has substantial Git integration.
If you created your project from a git repo or a custom template, your changes and outside changes made to the repo will automatically appear.
Parent topic:
Configure VS Code as a local IDE
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