📜 Configuration Design

August 29, 2025 · View on GitHub

The design philosophy of BasicTS is to be entirely configuration-based. Our goal is to allow users to focus on their models and data, without getting bogged down by the complexities of pipeline construction.

The configuration file is a .py file where you can import your model and runner, and set all necessary options. BasicTS uses EasyDict as a parameter container, making it easy to extend and flexible to use.

The configuration file typically includes the following sections:

  • General Options: Describes general settings such as configuration description, GPU_NUM, RUNNER, etc.
  • Environment Options: Includes settings like TF32, SEED, CUDNN, DETERMINISTIC, etc.
  • Dataset Options: Specifies NAME, TYPE (Dataset Class), PARAMS (Dataset Parameters), etc.
  • Scaler Options: Specifies NAME, TYPE (Scaler Class), PARAMS (Scaler Parameters), etc.
  • Model Options: Specifies NAME, TYPE (Model Class), PARAMS (Model Parameters), etc.
  • Metrics Options: Includes FUNCS (Metric Functions), TARGET (Target Metrics), NULL_VALUE (Handling of Missing Values), etc.
  • Train Options:
    • General: Specifies settings like EPOCHS, LOSS, EARLY_STOPPING, etc.
    • Optimizer: Specifies TYPE (Optimizer Class), PARAMS (Optimizer Parameters), etc.
    • Schduler: Specifies TYPE (Scheduler Class), PARAMS (Scheduler Parameters), etc.
    • Curriculum Learning: Includes settings like CL_EPOHS, WARMUP_EPOCHS, STEP_SIZE, etc.
    • Data: Specifies settings like BATCH_SIZE, NUM_WORKERS, PIN_MEMORY, etc.
  • Valid Options:
    • General: Includes INTERVAL for validation frequency.
    • Data: Specifies settings like BATCH_SIZE, NUM_WORKERS, PIN_MEMORY, etc.
  • Test Options:
    • General: Includes INTERVAL for testing frequency.
    • Data: Specifies settings like BATCH_SIZE, NUM_WORKERS, PIN_MEMORY, etc.

For a complete guide on all configuration options and examples, refer to examples/complete_config.py.

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