mithril-core-agent

You build a reusable storage backend for ML workflows by implementing the asynchronous StorageBackend trait, which defines interfaces for get, put, delete, exists, and list operations across concrete backends like LocalStorage, S3Storage, and GcsStorage.

The best way to standardize data compression and hashing in ML infrastructure is using pluggable Compressor and HashFunction traits with a unified error model, standardizing data handling with consistent type definitions like DType and TensorMeta.

Centralized error handling for Mithril core infrastructure works through the MithrilError type and a consolidated Result<T> pattern, formalizing error handling to promote robust, reusable components across crates.

Yes, you can use zstd and lz4_flex for compression in Mithril modules. The infrastructure specifies these as dependencies, enabling pluggable compressors within the unified type system to standardize data encoding.

Yes, you need tokio to implement asynchronous storage backends. The infrastructure lists tokio as a dependency, providing the asynchronous runtime required for the StorageBackend trait's get, put, delete, exists, and list operations.

You define core traits and types for persistent storage in ML tools to reduce duplication across Mithril modules. This shared infrastructure layer enables teams to implement new backends and formats quickly while keeping deployments consistent.