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Spicepods

Overview​

A Spicepod is a configuration package that defines application-specific datasets, catalogs, machine learning (ML) models, and secrets. It functions similarly to a code packaging system (such as npm or pip), but is designed for data and AI components rather than code libraries.

Spicepods are defined in a YAML manifest file, typically named spicepod.yaml, and can be shared, versioned, and reused across projects.

Structure​

A Spicepod is described by a YAML manifest file, typically named spicepod.yaml, which includes the following key sections:

  • Metadata: Basic information about the Spicepod, such as its name and version.
  • Datasets: Definitions of datasets that are used or produced within the Spicepod.
  • Catalogs: Definitions of catalogs that are used within the Spicepod.
  • Models: Definitions of language or traditional ML models that the Spicepod manages, including their sources and associated datasets.
  • Secrets: Configuration for any secret stores used within the Spicepod.

Example Manifest​

version: v1
kind: Spicepod
name: my_spicepod

datasets:
  - from: spice.ai/spiceai/quickstart/datasets/taxi_trips
    name: taxi_trips
    acceleration:
      enabled: true

models:
  - from: openai:gpt-4o-mini
    name: openai_model
    params:
      openai_api_key: ${ env:OPENAI_API_KEY }
      tools: auto

secrets:
  - from: env
    name: env

Additional Example​

version: v1
kind: Spicepod
name: another_spicepod

datasets:
  - from: databricks:spiceai_demo.public.dataset
    name: sample_ds
    params:
      mode: delta_lake
      databricks_endpoint: dbc-a1b2345c-d6e7.cloud.databricks.com
      databricks_token: ${secrets:my_token}
      databricks_aws_access_key_id: ${secrets:aws_access_key_id}
      databricks_aws_secret_access_key: ${secrets:aws_secret_access_key}
    acceleration:
      enabled: true
      refresh_mode: full

models:
  - from: huggingface.co/microsoft/Phi-3.5-mini-instruct
    name: phi

secrets:
  - from: env
    name: env

Key Components​

Datasets​

Datasets in a Spicepod can be sourced from various locations, including local files or remote databases. They can be materialized and accelerated using different engines such as DuckDB, SQLite, or PostgreSQL to optimize performance.

Learn more at Datasets.

Catalogs​

Catalogs in a Spicepod can contain multiple schemas. Each schema, in turn, contains multiple tables where the actual data is stored.

Learn more at Catalogs.

Models​

ML models are integrated into the Spicepod similarly to datasets. The models can be specified using paths to local files or remote locations. ML inference can be performed using the models and datasets defined within the Spicepod.

Learn more at Models.

Secrets​

Spice.ai supports various secret stores to manage sensitive information such as API keys or database credentials. Supported secret store types include environment variables, files, AWS Secrets Manager, Kubernetes secrets, and keyrings.

Learn more at Secret Stores