README
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Spice.ai
Spice.ai is an open source runtime and distribution system for time series AI built for developers.
⚠️ DEVELOPER PREVIEW ONLY Spice.ai is under active alpha stage development and is not intended to be used in production until its 1.0-stable release.
Welcome to the Spice.ai Developer Preview and thank you for your engagement in early Spice.ai development.
Our intention with this preview is to work with developers early to co-define and co-develop the developer experience, aligning to the goal of making AI easy for developers. 🚀 Thus, due to the stage of development and as we focus, there are currently several limitations on the general Roadmap to v1.0-stable.
We greatly appreciate and value your feedback. Please feel free to file an issue and get in touch with the team through Discord or by sending us mail at team@spiceai.io.
Thank you for sharing this journey with us! 🙏
Getting started with Spice.ai
Follow this guide to get started quickly with Spice.ai. For a more comphrehensive getting started guide, see the full online documentation.
Current Limitations
- Running in Docker is required. We will support a baremetal experience at launch.
- Only macOS and Linux are natively supported. WSL 2 is required for Windows.
- arm64 is not yet supported (i.e. Apple's M1 Macs). We use M1s ourselves, so we hope to support this very soon :-)
Prerequisites (Developer Preview only)
⭐️ We highly recommend using GitHub Codespaces to get started. Codespaces enables you to run Spice.ai in a virtual environment in the cloud. If you use Codespaces, the following prerequisites are not required and you may skip to the Getting Started with Codespaces section.
To continue with installation on your local machine, follow these steps:
- Install Docker
- Generate and export a GitHub PAT
Step 1. Install Docker: While self-hosting on baremetal hardware will be supported, the Developer Preview currently requires Docker. To install Docker, please follow these instructions.
Step 2. Generate and export a GitHub PAT: To access private repositories and resources, you will need to generate a GitHub Personal Access Token with repo and read:packages scopes.
Once you have created a token, use it to log in to the Spice.ai Docker Repository:
docker login ghcr.io/spiceai
Username: <your GitHub username>
Password: <your token>
Add the token to an environment variable named SPICE_GH_TOKEN:
export SPICE_GH_TOKEN=<your token>
You will need to set the SPICE_GH_TOKEN in each terminal you use, so you may want to add to your terminal configuration, E.g. .bashrc or .zshrc. You can manually edit the file or use a command like:
cat "export SPICE_GH_TOKEN=<your token>" >> ~/.bashrc
These steps won't be required after public release.
Installation (local machine)
Install the Spice CLI by running the following curl command in your terminal.
curl https://install.spiceai.org | /bin/bash
You may need to restart your terminal for the spice command to be added to your PATH.
Getting started with Codespaces
The recommended way to get started with Spice.ai is to use GitHub Codespaces.
Create a new GitHub Codespace in the spiceai/quickstarts repo at github.com/spiceai/quickstarts/codespaces.
Once you open the Codespace, Spice.ai and everything you need to get started will already be installed. You may continue on below.
Create your first Spice.ai Pod and train it
A Spice.ai Pod is simply a collection of configuration and data that you use to train and deploy your own AI.
The first Spice.ai Pod you will create and train is based off of a problem that many system administrators are familiar with: server maintenance. Application and system logging is critical part of running a production service, but letting those logs build up can cause other issues, especially if those logs end up filling the entire disk! It is simple enough to run a utility at a certain time every day to ensure this doesn't happen, but what if we choose to run the cleanup in the middle of peak traffic to the server?
We will use Spice.ai to train a pod that can intelligently learn when the best times are to run a cleanup job on a server. Let's call this example the LogPruner.
Clone the Spice.ai quickstarts repo in a directory where you would normally put your code. E.g.
cd $HOME
git clone https://github.com/spiceai/quickstarts
cd quickstarts/logpruner
In a new terminal window or tab, navigate to the directory and start the Spice runtime in development mode with spice run.
cd $HOME/quickstarts/logpruner
spice run
In the original terminal instance, add the LogPruner sample pod:
spice add samples/LogPruner
The Spice CLI will download the LogPruner sample pod manifest and add it to your project at .spice/pods/logpruner.yaml.
The Spice runtime will then automatically detect the manifest and start your first training run!
Note, automatic training relies on your system's filewatcher. In some cases, this might be disabled or not work as expected, especially when using containers. If training does not start, follow the command to retrain your pod below.
Observe your pod training
Navigate to http://localhost:8000 in your favorite browser. You will see an overview of your pods. From here, you can click on the logpruner pod to see a chart of the pod's training progress.
Retrain your pod
The runtime will automatically detect changes to your pod manifest and start training. In addition, you can trigger training by using the Spice CLI from within your app directory.
spice train logpruner
Get a recommendation from your pod
After training your pod, you can now get a recommendation for an action from it!
curl http://localhost:8000/api/v0.1/pods/logpruner/recommendation
Conclusion and next steps
Congratulations! In just a few minutes you downloaded and installed the Spice.ai CLI and runtime, created your first Spice.ai Pod, trained it, and got a recommendation from it.
This is just the start of your journey with AI. Next, try one of the quickstart tutorials or in-depth samples for creating intelligent applications with Spice.ai.
Try:
- Log Pruner sample - a more in-depth version of the quickstart you just completed, using CPU metrics from your own machine
- Trader - a basic Bitcoin trading bot
Kubernetes:
Spice.ai can be deployed to Kubernetes! Try out the Kubernetes sample.
Community
Spice.ai started with the vision to make AI easy for developers. We are building Spice.ai in the open and with the community. Reach out on Discord or by email to get involved. We will be starting a community call series soon!
- Discord:
- Reddit:
- Twitter: @SpiceAIHQ
- Email: team@spiceai.io
Contributing to Spice.ai
See CONTRIBUTING.md.
Directories