Skip to main content

2 posts tagged with "upgrade"

View All Tags

Spice.ai v0.5-alpha

· 3 min read
Phillip LeBlanc
Phillip LeBlanc
Co-Founder and CTO of Spice AI

We are excited to announce the release of Spice.ai v0.5-alpha! 🥇

Highlights include a new learning algorithm called "Soft Actor-Critic" (SAC), fixes to the behavior of spice upgrade, and a more consistent authoring experience for reward functions.

If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.

Highlights in v0.5-alpha​

Soft Actor-Critic (Discrete) (SAC) Learning Algorithm​

The addition of the Soft Actor-Critic (Discrete) (SAC) learning algorithm is a significant improvement to the power of the AI engine. It is not set as the default algorithm yet, so to start using it pass the --learning-algorithm sacd parameter to spice train. We'd love to get your feedback on how its working!

Consistent reward authoring experience​

With the addition of the reward function files that allow you to edit your reward function in a Python file, the behavior of starting a new training session by editing the reward function code was lost. With this release, that behavior is restored.

In addition, there is a breaking change to the variables used to access the observation state and interpretations. This change was made to better reflect the purpose of the variables and make them easier to work with in Python

Previous (Type)New (Type)
prev_state (SimpleNamespace)current_state (dict)
prev_state.interpretations (list)current_state_interpretations (list)
new_state (SimpleNamespace)next_state (dict)
new_state.interpretations (list)next_state_interpretations (list)

Improved spice upgrade behavior​

The Spice.ai CLI will no longer recommend "upgrading" to an older version. An issue was also fixed where trying to upgrade the Spice.ai CLI using spice upgrade on Linux would return an error.

New in this release​

  • Adds a new learning algorithm called "Soft-Actor Critic" (SAC).
  • Updates the reward function parameters for the YAML code blocks from prev_state and new_state to current_state and next_state to be consistent with the reward function files.
  • Fixes an issue where editing a reward functions file would not automatically trigger training.
  • Fixes the normalization of values for the Deep-Q Learning algorithm to handle larger values.
  • Fixes an issue where the Spice.ai CLI would not upgrade on Linux with the spice upgrade command.
  • Fixes an issue where the Spice.ai CLI would recommend an "upgrade" to an older version.

Resources​

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 also be starting a community call series soon!

Spice.ai v0.4-alpha

· 4 min read
Phillip LeBlanc
Phillip LeBlanc
Co-Founder and CTO of Spice AI

We are excited to announce the release of Spice.ai v0.4-alpha! 🏄‍♂️

Highlights include support for authoring reward functions in a code file, the ability to specify the time of recommendation, and ingestion support for transaction/correlation ids. Authoring reward functions in a code file is a significant improvement to the developer experience than specifying functions inline in the YAML manifest, and we are looking forward to your feedback on it!

If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.

Highlights in v0.4-alpha​

Upgrade using spice upgrade​

The spice upgrade command was added in the v0.3.1-alpha release, so you can now upgrade from v0.3.1 to v0.4 by simply running spice upgrade in your terminal. Special thanks to community member @Adm28 for contributing this feature!

Reward Function Files​

In addition to defining reward code inline, it is now possible to author reward code in functions in a separate Python file.

The reward function file path is defined by the reward_funcs property.

A function defined in the code file is mapped to an action by authoring its name in the with property of the relevant reward.

Example:

training:
  reward_funcs: my_reward.py
  rewards:
    - reward: buy
      with: buy_reward
    - reward: sell
      with: sell_reward
    - reward: hold
      with: hold_reward

Learn more in the documentation: docs.spiceai.org/concepts/rewards/external

Time Categories​

Spice.ai can now learn from cyclical patterns, such as daily, weekly, or monthly cycles.

To enable automatic cyclical field generation from the observation time, specify one or more time categories in the pod manifest, such as a month or weekday in the time section.

For example, by specifying month the Spice.ai engine automatically creates a field in the AI engine data stream called time_month_{month} with the value calculated from the month of which that timestamp relates.

Example:

time:
  categories:
    - month
    - dayofweek

Supported category values are: month dayofmonth dayofweek hour

Learn more in the documentation: docs.spiceai.org/reference/pod/#time

Get recommendation for a specific time​

It is now possible to specify the time of recommendations fetched from the /recommendation API.

Valid times are from pod epoch_time to epoch_time + period.

Previously the API only supported recommendations based on the time of the last ingested observation.

Requests are made in the following format: GET http://localhost:8000/api/v0.1/pods/{pod}/recommendation?time={unix_timestamp}

An example for quickstarts/trader

GET http://localhost:8000/api/v0.1/pods/trader/recommendation?time=1605729600

Specifying {unix_timestamp} as 0 will return a recommendation based on the latest data. An invalid {unix_timestamp} will return a result that has the valid time range in the error message:

{
  "response": {
    "result": "invalid_recommendation_time",
    "message": "The time specified (1610060201) is outside of the allowed range: (1610057600, 1610060200)",
    "error": true
  }
}

New in this release​

  • Adds time categories configuration to the pod manifest to enable learning from cyclical patterns in data - e.g. hour, day of week, day of month, and month
  • Adds support for defining reward functions in a rewards functions code file.
  • Adds the ability to specify recommendation time making it possible to now see which action Spice.ai recommends at any time during the pod period.
  • Adds support for ingestion of transaction/correlation identifiers (e.g. order_id, trace_id) in the pod manifest.
  • Adds validation for invalid dataspace names in the pod manifest.
  • Adds the ability to resize columns to the dashboard observation data grid.
  • Updates to TensorFlow 2.7 and Keras 2.7
  • Fixes a bug where data processors were using data connector params
  • Fixes a dashboard issue in the pod observations data grid where a column might not be shown.
  • Fixes a crash on pod load if the training section is not included in the manifest.
  • Fixes an issue where data manager stats errors were incorrectly being printed to console.
  • Fixes an issue where selectors may not match due to surrounding whitespace.

Resources​

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 also be starting a community call series soon!