ea339a54a7
- Initialize DVC with local storage backend (task 1.6) - Create PostgreSQL schema for training_runs table (task 1.7) - Add SQLAlchemy database connection setup (task 1.8) - Create Pydantic config models for pipeline.yaml (task 2.1) - Add migration runner for database setup - Fix pyproject.toml package discovery config
27 lines
1.1 KiB
SQL
27 lines
1.1 KiB
SQL
-- Create training_runs table for tracking ML training runs
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CREATE TABLE IF NOT EXISTS training_runs (
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id SERIAL PRIMARY KEY,
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run_id VARCHAR(255) NOT NULL UNIQUE,
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model_type VARCHAR(100) NOT NULL,
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experiment_name VARCHAR(255) NOT NULL,
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pipeline_config_hash VARCHAR(64) NOT NULL,
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dataset_version VARCHAR(100),
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metrics_summary JSONB,
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status VARCHAR(50) NOT NULL DEFAULT 'running',
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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completed_at TIMESTAMP WITH TIME ZONE,
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CONSTRAINT valid_status CHECK (status IN ('running', 'completed', 'failed', 'cancelled'))
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);
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-- Create index on run_id for faster lookups
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CREATE INDEX idx_training_runs_run_id ON training_runs(run_id);
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-- Create index on experiment_name for filtering by experiment
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CREATE INDEX idx_training_runs_experiment ON training_runs(experiment_name);
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-- Create index on status for filtering active runs
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CREATE INDEX idx_training_runs_status ON training_runs(status);
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-- Create index on created_at for chronological queries
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CREATE INDEX idx_training_runs_created_at ON training_runs(created_at DESC);
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