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Installation

Select the installation mode that best suits your needs

Usage Scenario Installation Mode
Run FeatureByte tutorials Tutorials
Prototype using data warehouse without deployment Standalone
Deploy features Hosted Service
Collaborate with other users Hosted Service
Scalable production High Availability

Installation modes

Local Service Installation Get started quickly with the FeatureByte tutorials.

Installation Steps

Requirements

Hardware

  • Intel, AMD or Apple Silicon processor with 4 cpu cores
  • 8GB of RAM

Software

  • Python 3.8 or higher
  • Docker service

Step 1: Activate a virtual environment.

Using a virtual environment will help avoid potential conflicts with other packages.

# Create a new virtual environment
python -m venv featurebyte-env

# Activate the virtual environment
## On Windows, run:
featurebyte-env\Scripts\activate.bat
## On Unix or MacOS, run:
source featurebyte-env/bin/activate
Check out the venv docs for more details.

# Create a new virtual environment
conda create --name featurebyte-env python=3.9 -y
# Activate the virtual environment
conda activate featurebyte-env
Check out the conda docs for more details.

Step 2: Install the featurebyte python package using pip. 

pip install featurebyte

Step 3: Launch the playground environment with python, or from the command shell.

Docker service required

Ensure that your docker service is running.

import featurebyte as fb
fb.playground()

featurebyte playground

This starts the local FeatureByte and Spark services, import tutorial datasets and creates a Feature Store thats connects to the local Spark data warehouse.

Once the environment is ready, you can download and run notebooks from the tutorials.

Cleaning Up

Stop all services when you are done using it.

Warning

Deployed feature jobs will be stopped when the service is stopped.

fb.stop()

featurebyte stop

Local Service Installation Local installation of the FeatureByte service is the easiest way to get started with the FeatureByte SDK. It is a single-user installation that can be used to prototype features locally with your data warehouse.

Installation Steps

Requirements

Hardware

  • Intel, AMD or Apple Silicon processor with 4 cpu cores
  • 8GB of RAM

Software

  • Python 3.8 or higher
  • Docker service

Step 1: Activate a virtual environment.

Using a virtual environment will help avoid potential conflicts with other packages.

# Create a new virtual environment
python -m venv featurebyte-env

# Activate the virtual environment
## On Windows, run:
featurebyte-env\Scripts\activate.bat
## On Unix or MacOS, run:
source featurebyte-env/bin/activate
Check out the venv docs for more details.

# Create a new virtual environment
conda create --name featurebyte-env python=3.9 -y
# Activate the virtual environment
conda activate featurebyte-env
Check out the conda docs for more details.

Step 2: Install the featurebyte python package using pip. 

pip install featurebyte

Step 3: Launch the environment with python, or from the command shell.

Docker service required

Ensure that your docker service is running.

import featurebyte as fb
fb.start()

featurebyte start

Follow instructions to create a FeatureStore that connect to your data warehouse.

Cleaning Up

Stop all services when you are done using it.

Warning

Deployed feature jobs will be stopped when the service is stopped.

fb.stop()

featurebyte stop

Single Host FeatureByte service hosted on a single server provides a light-weight option to support collaboration and job scheduling with limited scalability and availability. Multiple users can connect to the service using the FeatureByte SDK, and deploy features for production.

Installation Steps for Service Host

Service Host Requirements

Hardware

  • Intel, AMD or Apple Silicon processor with 4 cpu cores
  • 8GB of RAM
  • Up all the time, so that scheduled tasks can be executed on time
  • Has access to data warehouses to perform computation queries

Software

  • Python 3.8 or higher
  • Docker service

Step 1: Activate a virtual environment.

Using a virtual environment will help avoid potential conflicts with other packages.

# Create a new virtual environment
python -m venv featurebyte-env

# Activate the virtual environment
## On Windows, run:
featurebyte-env\Scripts\activate.bat
## On Unix or MacOS, run:
source featurebyte-env/bin/activate
Check out the venv docs for more details.

# Create a new virtual environment
conda create --name featurebyte-env python=3.9 -y
# Activate the virtual environment
conda activate featurebyte-env
Check out the conda docs for more details.

Step 2: Install the featurebyte python package using pip. 

pip install featurebyte

Step 3: Start the service with python, or from the command shell.

Docker service required

Ensure that your docker service is running.

import featurebyte as fb
fb.start()

featurebyte

This will start the services as docker containers in daemon mode.

Installation Steps for Python SDK (Individual User)

Python SDK Requirements

Hardware

  • Intel, AMD or Apple Silicon processor with 2 cpu cores
  • 4GB of RAM

Software

  • Python 3.8 or higher

Step 1: Activate a virtual environment.

Using a virtual environment will help avoid potential conflicts with other packages.

# Create a new virtual environment
python -m venv featurebyte-env

# Activate the virtual environment
## On Windows, run:
featurebyte-env\Scripts\activate.bat
## On Unix or MacOS, run:
source featurebyte-env/bin/activate
Check out the venv docs for more details.

# Create a new virtual environment
conda create --name featurebyte-env python=3.9 -y
# Activate the virtual environment
conda activate featurebyte-env
Check out the conda docs for more details.

Step 2: Install the FeatureByte SDK for each user that needs to connect to the service. 

pip install featurebyte
Update the FeatureByte configuration file to specify how to connect to the FeatureByte service.
~/.featurebyte/config.yaml
profile:

 - name: local
   api_url: http://localhost:8088
 - name: featurebyte-svc
   api_url: http://<featurebyte-svc-hostname>:8088
Modify the existing entry or create a new one with the correct api_url value.

If you have more than one profile, specify the profile to use when connecting to the service. 

import featurebyte as fb
fb.use_profile("featurebyte-svc")
Follow instructions to create a FeatureStore that connect to your data warehouse.

High Availability High availability installation of the FeatureByte service is the recommended way to run the service in production. Scale to a large number of users and deployed features, and provide highly available services.

Hardware Requirements

  • Kubernetes Cluster

Software Requirements

  • kubectl
  • helm
  • Cloud provided specific software for cloud-based Kubernetes deployment

Installation Steps (More information coming soon)

Use helm chart provided in the featurebyte GitHub repository. You can make use of cloud hosted storage, MongoDB and Redis services for greater resilience and data protection.