472건 · 관련도순
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Implementing distributed tracing with W3C Trace Context — the byte-precise `traceparent` and `tracestate` header formats, OpenTelemetry's W3CTraceContextPropagator as the modern default (replacing X-B3-* and X-Datadog-*), head vs tail sampling, and HTTP→SQL trace propagation via sqlcommenter. Grounded in the W3C REC and OpenTelemetry specs. NOT for OTel metrics/logs pipelines, vendor backend setup (Datadog/Honeycomb/Jaeger/Tempo), or browser RUM tracing.
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Adds .NET Aspire cloud-native orchestration to existing .NET solutions. Analyzes solution structure to identify services (APIs, web apps, workers), creates AppHost and ServiceDefaults projects, configures service discovery, adds NuGet packages, and sets up distributed application orchestration. Use when adding Aspire to .NET solutions or creating new cloud-ready distributed applications.
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Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.
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High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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Implements Redis patterns for caching, sessions, rate limiting, pub/sub, and distributed locks with best practices. Use when users request "Redis caching", "session storage", "rate limiter", "pub/sub messaging", or "distributed locks".
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Lambda Labs에서 GPU 서버를 시작하고 머신러닝 학습과 추론을 실행하는 절차를 안내합니다. SSH 연결, 영구 저장소, Python API 조작과 여러 GPU를 활용한 분산 학습도 다룹니다.
NeMo Curator로 LLM 학습 데이터의 중복과 저품질 내용을 제거하고 개인정보를 삭제하거나 대체합니다. 처리 흐름 구성과 이미지, 영상, 음성 정리도 다룹니다.
Qdrant에 텍스트 등의 특징을 벡터로 저장해 유사 검색을 구현합니다. 속성 필터링, AI 답변용 자료 검색, 실시간 추천과 대규모 운영 설정을 지원합니다.
Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.
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Help design quorum management for distributed systems, where multiple nodes must agree on decisions. Covers adapting participation thresholds and voting weights to network conditions, performance, and failures, plus membership changes, validation, and rollback.
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Initialize a group of collaborating AI agents by choosing how they connect, allocating resources, and setting up communication. Establish shared-memory rules so agents record progress, share outputs, check dependencies, and signal completion.
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Benchmark distributed consensus protocols, methods computers use to agree on shared results. Compare processing rates, response times, scaling behavior, and resource usage across Byzantine, Raft, and Gossip, then identify bottlenecks and potential tuning changes.
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Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.
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Design and implement ways for multiple agents or distributed computers to agree on decisions. Covers weighted voting, task allocation, coordination, and protocols that handle faulty or malicious participants, with tools for mathematical analysis.
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Coordinate agreement across distributed participants when some may fail or act maliciously. It covers PBFT, a three-phase agreement protocol, along with message authentication, malicious participant detection, leader replacement, and recovery from network partitions.
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Coordinates multiple AI agents with agentic-flow to run tasks in parallel or through dependent stages. It covers team communication structures, shared memory, workload balancing, failure recovery, and performance monitoring for complex workflows.
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Coordinate multiple AI agents for research, development, testing, and system analysis using Claude Flow. Choose how agents communicate, assign specialist roles, combine parallel and sequential tasks, and monitor progress through reporting and recovery workflows.
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Audit slow page loads by measuring how static files, such as images and scripts, are delivered. Check CDN usage, a network of distributed servers, and identify caching or delivery settings that need improvement.
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Evaluate an Agent Skill bundle before release by checking structure, activation conditions, output quality, scripts, safety, file integrity, and host compatibility. Receive a detailed JSON report that distinguishes local evidence checks from production readiness backed by external attestation.
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Guide HyperFrames video projects from setup through layout checks, editable previews, and video output. Covers command-line workflows, environment troubleshooting, and distributed rendering on AWS Lambda, which splits video rendering work across cloud functions.
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Manage files and storage buckets in Tigris through its official CLI. Agents can upload and download objects, generate temporary sharing URLs, take snapshots, and manage access keys and permissions with structured JSON output.
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Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.
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