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Installation

Select the installation mode that best suits your needs

Usage Scenario Installation Mode
Run FeatureByte tutorials Tutorials
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

Software

  • Python 3.9 or higher

Step 1: Obtain an API Token

  1. Visit the FeatureByte Hosted Tutorials page
  2. Follow the instructions to sign up for an API token.

This token will grant you access to the hosted tutorials server running on a Snowflake data warehouse, where the tutorial data is already populated.

Step 2: Set up a virtual environment.

Using a virtual environment helps avoid potential conflicts with other packages. Here's how you can set one up:

# 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
Further reading: venv documentation.

# Create a new virtual environment
conda create --name featurebyte-env python=3.9 -y
# Activate the virtual environment
conda activate featurebyte-env
Further reading: conda documentation.

Step 3: Download the tutorial notebooks.

Download the end-to-end notebooks archive to a location on your computer.

Step 4: Load the tutorials in your preferred notebook environment.

Install your preferred notebook environment, like JupyterLab and start with the 00_Setup_Environment.ipynb notebook.

(featurebyte-env) ~/featurebyte_tutorials> pip install jupyterlab
(featurebyte-env) ~/featurebyte_tutorials> jupyter-lab 00_Setup_Environment.ipynb

(featurebyte-env) ~/featurebyte_tutorials> conda install -c conda-forge jupyterlab
(featurebyte-env) ~/featurebyte_tutorials> jupyter-lab 00_Setup_Environment.ipynb

Step 5: Run 00_Setup_Environment.ipynb

  1. Replace <api_token> with the API token you received after registering

    setup

  2. Run the notebook.

You are now all set to immerse yourself in the tutorials and the end-to-end workflow!

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 3.9 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 3.9 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 Production Ready FeatureByte service must be customized to fit the needs of your organization. Please contact us at FeatureByte for more information.

Kubernetes Host Requirements

3 Nodes of the following configuration:

Hardware

  • x86_64 architecutre with 4 cpu cores
  • 16GB of RAM

Network Attached Storage (NAS)

  • 200GB of storage space

Networking

  • Egress access to ChatGPT
  • Access to Blob Storage e.g. AWS S3, Azure Blob Storage, Google Cloud Storage