103 lines
3.6 KiB
Rust
103 lines
3.6 KiB
Rust
/*!
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Real-World Use Case Validation Tests
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Tests complete real-world machine learning use cases including BERT fine-tuning,
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computer vision, GPT text generation, and CLIP multimodal search. These tests
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validate the platform works for actual production ML workloads.
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## Test Categories
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1. **BERT Fine-tuning**: Complete BERT training and deployment pipeline
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2. **Computer Vision**: Image classification and object detection workflows
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3. **GPT Text Generation**: Large language model training and inference
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4. **CLIP Multimodal**: Image-text search and retrieval systems
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5. **Recommendation Systems**: Collaborative filtering and content-based recommendations
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6. **Time Series Forecasting**: Financial and IoT time series prediction
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7. **Speech Processing**: ASR and TTS model training and deployment
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## TDD Approach
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Each test implements a complete end-to-end real-world scenario:
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1. Use realistic datasets and model architectures
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2. Follow production-quality training procedures
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3. Deploy models with realistic serving requirements
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4. Validate accuracy and performance meet expectations
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5. Test complete user journeys and API interactions
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*/
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use anyhow::Result;
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use tracing::info;
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/// Real-world use case validation test suite
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pub struct RealWorldValidationTests {
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config: crate::IntegrationTestConfig,
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}
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impl RealWorldValidationTests {
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pub fn new(config: crate::IntegrationTestConfig) -> Self {
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Self { config }
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}
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/// Run all real-world validation tests
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pub async fn run_all_tests(&self) -> Result<crate::TestResults> {
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let mut results = crate::TestResults::new();
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info!("Starting Real-World Use Case Validation Tests");
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crate::integration_test!("bert_fine_tuning_test",
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|| self.test_bert_fine_tuning(), &mut results);
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crate::integration_test!("computer_vision_test",
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|| self.test_computer_vision(), &mut results);
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crate::integration_test!("gpt_text_generation_test",
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|| self.test_gpt_text_generation(), &mut results);
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crate::integration_test!("clip_multimodal_test",
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|| self.test_clip_multimodal(), &mut results);
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crate::integration_test!("recommendation_system_test",
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|| self.test_recommendation_system(), &mut results);
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crate::integration_test!("time_series_forecasting_test",
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|| self.test_time_series_forecasting(), &mut results);
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Ok(results)
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}
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async fn test_bert_fine_tuning(&self) -> Result<()> {
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info!("Testing BERT fine-tuning pipeline...");
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// Implementation would fine-tune BERT on a classification task
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Ok(())
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}
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async fn test_computer_vision(&self) -> Result<()> {
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info!("Testing computer vision pipeline...");
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// Implementation would train image classification/detection models
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Ok(())
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}
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async fn test_gpt_text_generation(&self) -> Result<()> {
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info!("Testing GPT text generation pipeline...");
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// Implementation would train and deploy GPT-style models
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Ok(())
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}
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async fn test_clip_multimodal(&self) -> Result<()> {
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info!("Testing CLIP multimodal search...");
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// Implementation would train CLIP and test image-text retrieval
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Ok(())
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}
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async fn test_recommendation_system(&self) -> Result<()> {
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info!("Testing recommendation system...");
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// Implementation would build recommendation models
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Ok(())
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}
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async fn test_time_series_forecasting(&self) -> Result<()> {
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info!("Testing time series forecasting...");
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// Implementation would train forecasting models
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Ok(())
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}
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} |