“distributed”的搜索结果

Foundational distributed computing algorithms for consensus, coordination, and fault tolerance

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Decompose systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

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ABP Framework cross-cutting patterns including authorization, background jobs, distributed events, multi-tenancy, and module configuration. Use when: (1) defining permissions, (2) creating background jobs, (3) publishing/handling distributed events, (4) configuring modules.

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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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Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

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Foundational distributed computing algorithms for consensus, coordination, and fault tolerance

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ABP Framework cross-cutting patterns including authorization, background jobs, distributed events, multi-tenancy, and module configuration. Use when: (1) defining permissions, (2) creating background jobs, (3) publishing/handling distributed events, (4) configuring modules.

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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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设计使用Redis的缓存、请求频率限制、并发协调和消息分发，结合代码示例选择数据结构，并检查键的有效期与连接管理。

• 为API响应添加缓存
• 按用户限制请求次数
• 防止多个工作进程同时执行同一任务

将PyTorch训练代码整理为LightningModule和Trainer结构，配置验证、日志记录、模型保存及多GPU分布式训练。

• 迁移现有PyTorch训练代码
• 添加验证指标记录和模型保存
• 配置多GPU训练

帮助实现和排查使用PyTorch FSDP的大模型分布式训练代码。通过源自官方文档的说明与示例，比较分片策略，并查看混合精度和训练状态保存的实现。

• 实现大模型分布式训练
• 比较FSDP应用方法与分片策略
• 查看混合精度与检查点示例

将调研、开发、测试和系统分析分配给多个AI智能体，设计角色与任务顺序，支持并行执行、共享信息、监控进度，并将结果汇总成报告。

• 将并行调研结果汇总成报告
• 衔接应用设计、实现与审查
• 并行执行不同类型的测试

协助实现和管理Raft，通过领导者选举、日志复制、节点增删及网络中断后的重新同步，让分布式系统中的多个节点保持状态一致。

• 实现分布式节点的领导者选举
• 保持复制日志的一致性
• 添加或移除集群节点

设计和调整分布式系统中节点之间的信息交换机制，处理更新传播、状态同步、并发冲突和故障检测，并检查通信量与状态收敛情况。

• 设计分布式节点间的更新传播
• 同步状态并解决并发更新冲突
• 管理节点加入与检测故障

帮助实现多个设备或服务器之间的数据同步，使用CRDT合并并发更新，涵盖计数器、集合等结构，以及仅发送变更的增量同步和操作因果关系追踪。

• 汇总多个节点的计数
• 合并共享集合的并发更新
• 实现仅传输变更的增量同步

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

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• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template

Set up monitoring for communication between services in Istio or Linkerd environments. Combine metrics, logs, and distributed traces, which follow requests across services, to investigate delays and errors, visualize dependencies, and define service quality targets.

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• Trace requests across multiple services
• Create communication metrics and dashboards
• Investigate latency, errors, and connectivity issues

Design and implement Saga workflows across services, using compensating actions to undo completed work after failures. Define step deadlines, handle repeated messages safely, and monitor stalled order, booking, or other long-running processes.

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• Coordinating orders across inventory, payment, and shipping services
• Designing compensation for failed bookings
• Investigating workflows stuck during compensation

Check investor statements against a fund's NAV pack, the records of its net asset value. Recalculate each investor's capital balance, compare statement lines, and report discrepancies with their causes before statements are distributed.

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• Checking investor statements before distribution
• Tracing differences in allocated profit or loss
• Checking capital balances across reporting periods

Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.

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• Creating an Aspire application from a template
• Connecting services written in different languages
• Diagnosing services through logs and traces

Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.

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• Investigate failed health checks and growing queues
• Reproduce request-specific timeouts or crashes
• Investigate output changes after deployment