Backend.AI is a streamlined, container-based computing cluster platform that hosts popular computing/ML frameworks and diverse programming languages, with pluggable heterogeneous accelerator support including CUDA GPU, ROCm GPU, Gaudi NPU, Google TPU, GraphCore IPU and other NPUs.
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Guide for implementing REST and GraphQL APIs (create, get, search, update, delete, purge, scope prefix patterns, admin prefix, SearchScope, BaseFilterAdapter, @apifunction, Click CLI).
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./bai v2 CLI usage for testing/verifying API endpoints and managing resources — entity-command reference, login/config, search patterns, testing workflow (REST API client, NOT the v1 backend.ai CLI).
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Guide for writing and managing BEPs (Backend.AI Enhancement Proposals) - creation workflow, document segmentation, context-for-ai blocks, Decision Log.
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Guide for implementing Backend.AI client SDK and CLI (Session, BaseFunction, @apifunction, Click commands, Pydantic models, FieldSpec, output handlers, APIConfig, testing).
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Trace a feature across the layered architecture (REST v2, GraphQL, Service, Repository, DB, Errors) and explore the entity source tree — includes how to read the 21k-line supergraph.graphql without loading it whole.
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Inspect and apply database schema migrations (alembic current/heads, upgrade, downgrade, diverged heads) for Backend.AI components (manager, accountmgr, appproxy).
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Diagnose and fix Docker Compose halfstack issues — config mapping, service health, DB/Valkey/etcd inspection, supergraph regeneration.
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Search and author KNOWLEDGE.md background-knowledge documents - frontmatter schema, search/check scripts, and the generated/verified lifecycle rules.
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Local server management — ./dev start/stop/restart via tmux, startup-crash debugging, pre-flight infra checks, raw component start-server commands, post-change restart workflow.
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Observe Backend.AI logs, metrics, and traces during local development via the Grafana MCP (Loki logs, Prometheus metrics, Tempo traces, Pyroscope profiles). Use after restarting a service to verify behavior instead of tailing console output.
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Guide the Backend.AI release process - run release.sh, generate changelog via towncrier, consolidate RC entries for final releases with subsection grouping.
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Guide for implementing Backend.AI repository patterns (create, get, search, update, delete, purge, batch operations, Querier, BatchQuerier, Creator, Updater, Purger, SearchScope, withtables).
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Guide for implementing Backend.AI service layer (create, get, search, update, delete, purge, batch operations, Actions, ActionResults, Processors, ActionProcessor, Service methods).
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Complete submission workflow - quality checks, commit, PR creation, changelog generation, and final push. Use after finishing implementation work.
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Test-writing guide for Backend.AI — propose success/exception/edge scenarios first, refine them with the user, then implement while reporting per-scenario verification status. Covers fixtures, withtables, mock repositories, pants test, optional TDD cadence.