config_design.md

📜 Configuration Design

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.

🧑‍💻 Explore Further

• 🎉 Getting Stared
• 💡 Understanding the Overall Design Convention of BasicTS
• 📦 Exploring the Dataset Convention and Customizing Your Own Dataset
• 🛠️ Navigating The Scaler Convention and Designing Your Own Scaler
• 🧠 Diving into the Model Convention and Creating Your Own Model
• 📉 Examining the Metrics Convention and Developing Your Own Loss & Metrics
• 🏃‍♂️ Mastering The Runner Convention and Building Your Own Runner
• 📜 Interpreting the Config File Convention and Customizing Your Configuration
• 🔍 Exploring a Variety of Baseline Models