「distributed」の検索結果

Provides Event-Driven Architecture (EDA) patterns for Spring Boot — creates domain events, configures ApplicationEvent and @TransactionalEventListener, sets up Kafka producers and consumers, and implements the transactional outbox pattern for reliable distributed messaging. Use when implementing event-driven systems in Spring Boot, setting up async messaging with Kafka, publishing domain events from DDD aggregates, or needing reliable event publishing with the outbox pattern.

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Applies microservices for independent deployment and per-service scaling. Use when teams need autonomous release cycles with distinct capability scaling needs.

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Applies CQRS and Event Sourcing for read/write separation and audit trails. Use when designing systems with complex domain logic or full state-change history.

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Applies modular monolith with enforced internal boundaries. Use when teams want service-level autonomy without distributed system overhead.

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Applies client-server architecture for web/mobile apps. Use when designing systems with centralized backend services, trust boundaries, or offline-first sync.

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Use FedML for setup, CLI operations, MLOps launch, distributed and federated training, model serving, and workflow orchestration.

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Operate Fugue workflows, FugueSQL, backend selection, and notebook magics for pandas and distributed dataframe execution.

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Route DGL graph learning tasks across graph APIs, datasets, message passing, GraphBolt, distributed tools, and DGL-Go workflows.

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Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.

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Routes Chainer workflows for training, export, distributed learning, ChainerX, and checkout maintenance.

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Use ColossalAI for distributed PyTorch training, large-model parallelism, Booster plugins, ShardFormer, Colossal-Inference, and first-party application workflow routing.

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Use MosaicML Composer for PyTorch training loops, speedup methods, loggers, checkpoints, distributed launch, profiling, and model export.

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Routes BEVFormer camera-only 3D detection workflows, from install/import and config inspection through nuScenes data prep, distributed training and evaluation, and log analysis.

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Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.

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Use AReaL for large-scale asynchronous LLM reinforcement learning, agentic RL, distributed post-training, inference/agent services, backend planning, and customization workflows.

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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

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Use when adding service-wide Spring Boot 3 Actuator, Micrometer metrics, distributed tracing, OpenTelemetry export, structured logging, health probes, dashboards, or alerts. Combine with ai-observability only for Spring AI-specific token, prompt, and model telemetry.

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Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.

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Automated enforcement of GDPR Article 5(1)(e) storage limitation principle. Covers TTL-based deletion, retention policy engines, archival workflows, legal hold exemptions, and lifecycle automation. Includes technical implementation patterns for automated data expiry and defensible deletion across distributed systems.

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HarmonyOS application development expert. Use when building HarmonyOS apps with ArkTS, ArkUI, Stage model, and distributed capabilities. Covers HarmonyOS NEXT (API 12+) best practices.

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Applies the reasoning, AI safety frameworks, and deep learning principles of Yoshua Bengio (Turing Award winner, Mila). Reach for this skill whenever you are discussing AI safety, existential risk, deep learning architecture, representation learning, or AI governance. Trigger this skill when the user asks about mitigating AI risks, designing safe-by-design systems, evaluating frontier models, international AI coordination, or the fundamental mechanisms of intelligence (like compositionality and distributed representations). Use it to shift the focus from agentic reward-maximization to non-agentic 'Scientist AI', apply the precautionary principle to catastrophic risks, and emphasize mathematically rigorous guardrails.

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Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.

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Extract exactly 8 context-sensitive keywords from Chinese, English, or mixed text and turn them into a distributed weighted Graph View with no center goal node. Use when Codex needs keyword extraction, blacklist filtering, co-occurrence edges, node definitions/notes, weighted graph JSON, or an online Graph View tool for text analysis.

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Automatically generate TLA+ specifications from program code, repositories, or system implementations. Use when asked to generate TLA+ spec, create TLA+ specification from code, convert program to TLA+, formalize system in TLA+, extract TLA+ model from code, or when working with formal specification of concurrent systems, distributed systems, protocols, algorithms, or state machines that need to be verified.

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