System landscape#

Tributo system landscape showing users, framework workloads and contracts, the Ray execution runtime, external systems, and platform concerns that are outside the framework boundary.

Tributo is a Ray-native ML Framework/SDK. It owns portable contracts for job submission, bounded ingestion and writing, algorithm execution, model bundles, inference, explainability, serving, and Lance vector-index operations. Ray 2.55.1 remains the distributed execution runtime for Jobs, Data, Train, Tune, Serve, tasks, actors, and scheduling.

Reading the landscape#

The main flow runs from top to bottom:

  • Users and applications use the CLI or Python API. Persisted configuration uses strict JSON, while TributoClient wraps submission, status, logs, and stop operations over the Ray Jobs API.

  • Scenario workloads cover training and tuning, custom batch inference, online serving, and streaming input. Their stability is not uniform: legacy trainers remain Beta compatibility paths, portable algorithm execution is Alpha, vector indexing and explainability are Alpha, and Kafka is an Alpha StreamSource rather than a complete service loop.

  • The portable contract layer separates bounded ingestion, algorithm dispatch, and Bundle-based model delivery. Providers, engine bindings, exporters, validators, model importers, flavors, hooks, runtime adapters, and sinks extend these contracts without changing the core orchestration path.

  • Ray executes the resolved work. Tributo does not replace Ray’s scheduler or distributed runtime.

External systems#

Data systems, Bundle stores, MLflow, Lance namespaces, and Kafka remain outside the framework boundary:

  • Data systems supply bounded inputs. Support varies by source and engine; adapter presence alone is not a support claim.

  • Bundle stores persist immutable artifacts and manifests. Current publication supports local paths, file://, and S3; HDFS Bundle storage is not implemented.

  • MLflow is an optional model-import, tracking, provenance, and registry integration. It is not the source of truth for Bundle readability.

  • Lance stores vector data and index metadata. Tributo validates requests and receipts; Lance-Ray executes distributed index and maintenance tasks.

  • Kafka provides a fail-closed microbatch input protocol. Tributo does not ship a built-in Kafka-to-inference long-running service loop.

The support matrix is the authoritative view of verified, Alpha, Beta, adapter-only, and unsupported paths. The diagram groups architectural responsibilities; it does not promote every extension contract to a supported product capability.

Framework boundary#

Tributo is intentionally not a multi-tenant ML platform. Kubernetes control planes, custom scheduling, tenant isolation, quota and RBAC, approval and canary workflows, and a centralized operations UI are outside the current framework scope. See Product Scope for the complete set of goals, non-goals, and re-evaluation triggers.