9ffdb03e89
## Summary - **Total Agents**: 65 (24 coverage + 41 error fixes) - **Compilation Errors**: 194 → 0 ✅ - **New Tests**: 530+ tests (~17,500 lines) - **Success Rate**: 100% ## Phase 1: Test Coverage Expansion (Waves 1-3) - Wave 1-3: 24 agents deployed - Created comprehensive test suites across all modules - Added 530+ tests for baseline, advanced, and integration coverage ## Phase 2: Error Elimination (Waves 4-14) - Wave 4 (12 agents): Fixed 162 errors (Enum Display, tower util, borrow checker) - Wave 7 (1 agent): Fixed 52 ML proto errors (DataSource, Hyperparameters) - Wave 8 (1 agent): Fixed 33 Trading proto errors (SubmitOrderRequest) - Wave 12 (4 agents): Fixed 13 ComplianceRequirements field errors - Wave 13 (3 agents): Fixed 16 data crate test errors - Wave 14 (2 agents): Fixed final 2 data lib errors ## Infrastructure Improvements - Added MinIO Docker service for S3 E2E testing - Created S3Config::for_minio_testing() helper - Added storage test_helpers module - Fixed proto field mappings across all services - Added tower "util" feature for ServiceExt ## Key Error Patterns Fixed - Proto field name changes (120+ instances) - Enum Display trait usage (31 instances) - Borrow checker errors (20+ instances) - Missing methods/features (40+ instances) - Struct field additions (Order, ComplianceRequirements) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Storage Crate
Overview
The storage crate provides robust integration with Amazon S3 for durable and scalable storage of models, large datasets, and other critical artifacts. It includes features for caching, versioning, data integrity verification, and efficient file management.
Features
- S3 Client Integration: Seamlessly integrates with AWS S3 for uploading, downloading, and managing objects.
- Model Caching: Implements a local caching layer for frequently accessed models, reducing latency and S3 API calls.
- Model Versioning: Supports tracking and managing different versions of machine learning models or configuration files stored in S3.
- Checksum Verification: Ensures data integrity by automatically verifying checksums (e.g., MD5, SHA256) during uploads and downloads.
- Compression/Decompression Utilities: Provides built-in support for compressing and decompressing files (e.g., Gzip, Zstd) to optimize storage and transfer costs.
- File Management API: Offers a high-level API for common S3 operations like listing objects, deleting, and managing prefixes.
Usage
use storage::{S3Client, S3Config};
use std::path::PathBuf;
let config = S3Config {
bucket_name: "foxhunt-models".to_string(),
region: "us-east-1".to_string(),
// ... other AWS credentials or profile settings
};
// let client = S3Client::new(config).expect("Failed to create S3 client");
let local_file_path = PathBuf::from("./my_model.bin");
let s3_key = "models/v1/my_model.bin";
// Example: Upload a file to S3
// client.upload_file(&local_file_path, s3_key, true /* with checksum */)
// .expect("Failed to upload model");
// println!("Model uploaded to s3://{}/{}", config.bucket_name, s3_key);
// Example: Download a file from S3
// let download_path = PathBuf::from("./downloaded_model.bin");
// client.download_file(s3_key, &download_path, true /* with checksum */)
// .expect("Failed to download model");
// println!("Model downloaded to {:?}", download_path);
Testing
cargo test --package storage
Documentation
Complete API documentation is available at docs.rs/storage.