Spice.ai is an open source, portable runtime for training and using deep learning on time series data.

Spice.ai

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Spice.ai is an open source, portable runtime for training and using deep learning on time series data.

⚠️ 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.

The vision for Spice.ai is to make creating intelligent applications as easy as building a modern website. Spice.ai brings AI development to your editor, in any language or framework with a fast, iterative, inner development loop, with continuous-integration (CI) and continuous-deployment (CD) workflows.

Spice.ai is written in Golang and Python and runs as a container or microservice with applications calling a simple HTTP API. It's deployable to any public cloud, on-premises, and edge.

📢 Read the Spice.ai announcement blog post at blog.spiceai.org.

📺 View a 60 second demo of Spice.ai in action here.

Community-driven data components

The Spice.ai runtime also includes a library of community-driven data components for streaming and processing time series data, enabling developers to quickly and easily combine data with learning to create intelligent models.

Spice.ai pod registry

Modern developers also build with the community by leveraging registries such as npm, NuGet, and pip. The registry for sharing and using Spice.ai packages is spicerack.org. As the community shares more and more AI building blocks, developers can quickly build intelligence into their applications, initially with definitions of AI projects and eventually by sharing and reusing fully-trained models.

Pre-release software

⚠️ The vision to bring intelligent application development to the maturity of modern web development is a vast undertaking. We haven't figured it all out or solved all the problems yet. We're looking for feedback on the direction. Spice.ai is not finished, in fact, we only just started in June, and we invite you on the journey.

Spice.ai and spicerack.org are both pre-release, early, alpha software. Spice.ai v0.1-alpha has many gaps, including limited deep learning algorithms and training scale, streaming data, simulated environments, and offline learning modes. Packages aren't searchable or even listed on spicerack.org yet.

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.

Join us!

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 [email protected].

Thank you for sharing this journey with us! 🙏

Getting started with Spice.ai

First, ⭐️ star this repo! Thank you for your support! 🙏

Then, follow this guide to get started quickly with Spice.ai. For a more comprehensive getting started guide, see the full online documentation.

Current hosting limitations

  • Docker is required. We are targeting self-host support in v0.3.0-alpha.
  • 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 :-)

⭐️ 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 install is not required and you may skip to the Getting Started with Codespaces section.

Installation (local machine)

  1. Install Docker
  2. Install the Spice CLI

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. Install the Spice CLI: Run 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. Continue on to train your first pod.

Create your first Spice.ai Pod and train it

A Spice.ai Pod is simply a collection of configuration and data that is used to train and deploy your own AI.

We will add intelligence to a sample application, ServerOps, by creating and training a Spice.ai pod that offers recommendations to the application for different server operations, such as performing server maintenance.

If you are using GitHub Codespaces, skip Step 1. and continue with Step 2., as the repository will already be cloned.

Step 1. Clone the Spice.ai quickstarts repository:

cd $HOME
git clone https://github.com/spiceai/quickstarts
cd quickstarts/serverops

Step 2. Start the Spice runtime with spice run:

cd $HOME/quickstarts/serverops
spice run

Step. 3. In a new terminal, add the ServerOps quickstart pod:

So that we can leave Spice.ai running, add the quickstart pod in a new terminal tab or window. If you are running in GitHub Codespaces, you can open a new terminal by clicking the split-terminal button in VS Code.

spice add quickstarts/serverops

The Spice.ai CLI will download the ServerOps quickstart pod and add the pod manifest to your project at spicepods/serverops.yaml.

The Spice runtime will then automatically detect the pod 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. If training does not start, follow the command to retrain the pod below.

Observe the 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 serverops pod to see a chart of the pod's training progress.

Retrain the pod

In addition to automatic training upon manifest changes, training can be started by using the Spice CLI from within your app directory.

spice train serverops

Get a recommendation

After training the pod, you can now get a recommendation for an action from it!

curl http://localhost:8000/api/v0.1/pods/serverops/recommendation

Run the ServerOps application

To see how Spice.ai makes creating intelligent applications easy, try running and reviewing the sample ServerOps Node or Powershell apps, serverops.js and serverops.ps1.

Node:

npm install
node serverops.js

Powershell:

./serverops.ps1

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 the journey with Spice.ai. Next, try one of the quickstart tutorials or in-depth samples for creating intelligent applications.

Try:

  • ServerOps sample - a more in-depth version of the quickstart you just completed, using CPU metrics from your own machine
  • Gardener - Intelligently water a simulated garden
  • Trader - a basic Bitcoin trading bot

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!

Contributing to Spice.ai

See CONTRIBUTING.md.

Owner
Spice.ai
Powerful and easy-to-use time series AI designed for developers.
Spice.ai
https://github.com/spiceai/spiceai https://spiceai.org
Comments
  • Dataspace Interpolation/Sparse data

    Dataspace Interpolation/Sparse data

    Some data aren't mean to be interpolated, the interpolation can be an option. If such option is chosen categories shouldn't be interpolated. Having a warning stating the given names will be ignored as the data interpolation is enable can prevent future misunderstanding.

  • Remove table_lock during a training run

    Remove table_lock during a training run

    Part of #392

    To improve the performance (speed) of training, it was important to remove acquiring the lock table_lock on every step of the training run. This lock was used to control when data was added to the data_manager for the pod to prevent conflicts where the table is being updated at the same time we are querying it during training.

    The solution proposed here will remove the need for the table_lock during training by copying the data to be used during a training run. Data that is added to the data_manager will continue to be made available immediately for inferencing.

    A secondary change that speeds up the processing of the AddData request is to not take a lock when adding data. Incoming data will be resampled to match the indexes of the existing table. Merging of the table has been improved to use pandas.concat followed by a resample which will not touch existing indexes.

  • Add algorithm selection for training/importing

    Add algorithm selection for training/importing

    I haven't check the test yet but I think this is a good base to see the direction of this feature and discuss. Everything is working from my manual tests but I might missed some things.

    Resolves #327

    Summary of the commit :

    • Add 'algorithm' key in aiengine protos (StartTrainingRequest, ImportModelRequest)
    • Add 'algorithm' key in runtime protos (TrainModel, ImportModel)
    • Clean code (PEP8, flake) for ai scripts : main.py and train.py
    • Reduce Tensorflow verbose before import to avoid unecessary info logs (main.py l.318)
    • Add 'algorithm' flag for import and train command (CLI)
  • Support ingestion of transaction/correlation ids

    Support ingestion of transaction/correlation ids

    Enable the engine to ingest transaction or correlation ids, like order_id or trace_id.

    To ingest unique ids today, they would need to be set as categories, which is not scalable beyond a few ids. The initial version of this feature would simply enable them to be ingested, and round-tripped through the data APIs. In the second iteration, they would be available for reference in reward functions, and the third the potential to automatically correlate and flatten multiple rows of data on the id.

    Proposed manifest

    dataspaces:
  - from: coinbase
    name: btcusd
    identifiers:
      - transaction_id
      - tick_id
    measurements:
      ...

    alternative proposal:

    dataspaces:
  - from: coinbase
    name: btcusd
    transactions:
      identifiers:
        - transaction_id
        - tick_id
    measurements:
      ...

    alternative proposal:

    dataspaces:
  - from: coinbase
    name: btcusd
    transactions:
      identifiers:
        - transaction_id
        - tick_id
      operator: and | or
    measurements:
      ...

    alternative proposal:

    dataspaces:
  - from: coinbase
    name: btcusd
    correlation_ids:
      - transaction_id
      - tick_id
    measurements:
      ...
  • Wrong algorithm name silently fails at training

    Wrong algorithm name silently fails at training

    Using the --learning-algorithm CLI parameter if the name is not either vpg or dql the training will say it is starting but will silently fail.

    Reference: https://docs.spiceai.org/deep-learning-ai/

    Validation should be done server-side (in spiced), with the error being passed back to the CLI over the HTTP call.

  • spice pod train can return 'Not found' error if pod not loaded in runtime

    spice pod train can return 'Not found' error if pod not loaded in runtime

    Hey,

    Just encountered this error, I had ran spice run in one terminal, and went to spice train gardener in another, but got:

    failed to start training: 404 Not Found

    The source of the problem is: https://github.com/spiceai/spiceai/blob/45a7979a5741b58514076042b10c34dee5c2c0f7/pkg/cli/cmd/train.go#L78

    If the pod is not loaded by the runtime (which it wasn't because on WSL2 it seems I need to restart runtime after adding a pod via CLI) I got this 404 error which was a bit confusing.

    Perhaps another error (pod xxx may not be added?) or something could be helpful. Cheers.

  • Error on getting a recommendation on v0.3 compatible quickstarts/tweet-recommendation

    Error on getting a recommendation on v0.3 compatible quickstarts/tweet-recommendation

    Debug this. The issue is passing a negative value to np.exp() in the soft_max function. The input array is the result of model.predict().

    Console output:

    /workspaces/spiceai/ai/src/algorithms/dql/agent.py:34: RuntimeWarning: overflow encountered in exp
  exp_q_values = np.exp(q_values)
/workspaces/spiceai/ai/src/algorithms/dql/agent.py:35: RuntimeWarning: invalid value encountered in true_divide
  return exp_q_values / np.sum(exp_q_values)

    Callstack:

    image

    Overflow line:

    image

    Input/Output objects to np.exp:

    image

  • Update to TensorFlow 2.6.1 and freeze Keras to 2.6.0

    Update to TensorFlow 2.6.1 and freeze Keras to 2.6.0

    Also, tweak the waitForTrainingComplete timeout for ImportExport to fix flaky e2e test.

    See Issue thread for details on 2.6.1 + Keras: https://github.com/tensorflow/tensorflow/issues/52922

  • Name of algorithm used in the dashboard training run

    Name of algorithm used in the dashboard training run

    When a training run is in-progress or has been completed, the name of the learning algorithm used is not displayed. If training with different algorithms, it would be useful to compare the results of one algorithm with the other, and for that, we need to know which algorithm was used.

    Screen Shot 2021-11-03 at 10 23 04 AM
  • Can't get 'spice run'

    Can't get 'spice run'

    Hi, I just followed the instruction but after running spice run

    it downloads docker images and then fail into this error Error: Failed to verify health of localhost:8004 after 121 attempts exit status 1

    I'm running on

    • Ubuntu 18
    • Docker 20.10.6, build 370c289
  • Explicit development and production runtime modes

    Explicit development and production runtime modes

    In production, no file-watcher for changes in pod manifest or configuration. Expected to deploy and use specific pre-provided manifests in production.

    Development mode will be set on spiced with -development defaulting to "production" if not provided. spice CLI will always set the -development flag when it starts spiced.

  • Bump json5 from 1.0.1 to 1.0.2 in /dashboard

    Bump json5 from 1.0.1 to 1.0.2 in /dashboard

    Bumps json5 from 1.0.1 to 1.0.2.

    Release notes

    Sourced from json5's releases.

    v1.0.2

    • Fix: Properties with the name __proto__ are added to objects and arrays. (#199) This also fixes a prototype pollution vulnerability reported by Jonathan Gregson! (#295). This has been backported to v1. (#298)
    Changelog

    Sourced from json5's changelog.

    Unreleased [code, diff]

    v2.2.3 [code, diff]

    v2.2.2 [code, diff]

    • Fix: Properties with the name __proto__ are added to objects and arrays. (#199) This also fixes a prototype pollution vulnerability reported by Jonathan Gregson! (#295).

    v2.2.1 [code, diff]

    • Fix: Removed dependence on minimist to patch CVE-2021-44906. (#266)

    v2.2.0 [code, diff]

    • New: Accurate and documented TypeScript declarations are now included. There is no need to install @types/json5. (#236, #244)

    v2.1.3 [code, diff]

    • Fix: An out of memory bug when parsing numbers has been fixed. (#228, #229)

    v2.1.2 [code, diff]

    ... (truncated)

    Commits

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  • Bump certifi from 2021.5.30 to 2022.12.7 in /ai/src/requirements

    Bump certifi from 2021.5.30 to 2022.12.7 in /ai/src/requirements

    Bumps certifi from 2021.5.30 to 2022.12.7.

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  • Bump decode-uri-component from 0.2.0 to 0.2.2 in /dashboard

    Bump decode-uri-component from 0.2.0 to 0.2.2 in /dashboard

    Bumps decode-uri-component from 0.2.0 to 0.2.2.

    Release notes

    Sourced from decode-uri-component's releases.

    v0.2.2

    • Prevent overwriting previously decoded tokens 980e0bf

    https://github.com/SamVerschueren/decode-uri-component/compare/v0.2.1...v0.2.2

    v0.2.1

    • Switch to GitHub workflows 76abc93
    • Fix issue where decode throws - fixes #6 746ca5d
    • Update license (#1) 486d7e2
    • Tidelift tasks a650457
    • Meta tweaks 66e1c28

    https://github.com/SamVerschueren/decode-uri-component/compare/v0.2.0...v0.2.1

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  • Bump tensorflow from 2.9.1 to 2.9.3 in /ai/src/requirements

    Bump tensorflow from 2.9.1 to 2.9.3 in /ai/src/requirements

    Bumps tensorflow from 2.9.1 to 2.9.3.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.9.3

    Release 2.9.3

    This release introduces several vulnerability fixes:

    TensorFlow 2.9.2

    Release 2.9.2

    This releases introduces several vulnerability fixes:

    ... (truncated)

    Changelog

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    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

    ... (truncated)

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    • a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2
    • 258f9a1 Update py_func.cc
    • cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474
    • 3e75385 Update version numbers to 2.9.3
    • bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695
    • 3506c90 Update RELEASE.md
    • 8dcb48e Update RELEASE.md
    • 4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...
    • 6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple
    • 5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4
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  • Bump loader-utils from 2.0.2 to 2.0.4 in /dashboard

    Bump loader-utils from 2.0.2 to 2.0.4 in /dashboard

    Bumps loader-utils from 2.0.2 to 2.0.4.

    Release notes

    Sourced from loader-utils's releases.

    v2.0.4

    2.0.4 (2022-11-11)

    Bug Fixes

    v2.0.3

    2.0.3 (2022-10-20)

    Bug Fixes

    • security: prototype pollution exploit (#217) (a93cf6f)
    Changelog

    Sourced from loader-utils's changelog.

    2.0.4 (2022-11-11)

    Bug Fixes

    2.0.3 (2022-10-20)

    Bug Fixes

    • security: prototype pollution exploit (#217) (a93cf6f)
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