'sharding' 검색 결과

Investigate Apache ShardingSphere issues using official documentation, code, tests, and issue discussions. Distinguish bugs, unsupported usage, duplicates, and enhancement requests, then draft a concise maintainer-style reply explaining the evidence and next action.

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• Distinguish bugs from unsupported configurations
• Check whether an issue duplicates an earlier report or fix
• Draft a GitHub issue reply with clear next steps

Create or improve unit tests for Apache ShardingSphere classes. Cover the target class’s behavior and affected branches, verify explicitly requested coverage goals, and consolidate suitable scenarios into parameterized tests, which run the same checks with different inputs.

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• Add missing unit tests for a target class
• Verify an explicitly requested coverage target
• Consolidate suitable tests with different input data

Review Apache ShardingSphere pull requests or authorized downstream changes using repository evidence. Assess code correctness, merge readiness, or CI failures, then produce consolidated findings or a ready-to-copy reply to a review discussion.

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• Review code changes for correctness and regression risks
• Assess whether a pull request is ready to merge
• Investigate CI failures

Apply Apache ShardingSphere’s written coding standards during implementation or review, covering naming, Java constructs, tests, and Maven dependencies. Also audit a specified scope without editing files, combining automated checks with manual inspection of applicable rules.

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• Check changed code against written repository standards
• Review Java naming and declaration conventions
• Verify changed Maven dependency declarations

Implement and debug large-model training with PyTorch FSDP, which splits training state across devices. Consult documentation and code patterns for sharding choices, mixed precision, and checkpoints when building or reviewing distributed training code.

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• Implement distributed training for large models
• Compare FSDP wrapping and sharding patterns
• Review mixed-precision and checkpoint examples

Get guidance on distributed training with PyTorch FSDP, which splits model parameters across devices. The skill covers implementation, mixed precision, moving data to CPU memory, communication setup, and debugging distributed training code.

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• Implement distributed training with FSDP
• Review mixed precision and CPU offloading settings
• Choose a distributed communication backend

Add PyTorch FSDP2 to training scripts so model state can be split across GPUs. Covers distributed initialization, mixed precision, CPU offloading, and checkpoint saving and loading for models that exceed a single GPU's memory.

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• Training a model that exceeds one GPU's memory
• Adding FSDP2 to an existing training script
• Configuring mixed precision and CPU offloading

Orientação especializada para treinamento com Fully Sharded Data Parallel do PyTorch (FSDP) - sharding de parâmetros, precisão mista, CPU offloading, FSDP2

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Database architect v2 — schema design, normalization, denormalization, polyglot persistence, sharding

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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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Expert guidance for Fully Sharded Data Parallel training with PyTorch FSDP - parameter sharding, mixed precision, CPU offloading, FSDP2

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Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.

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• Reading selected regions of large scientific arrays
• Choosing data blocks and compression settings
• Working with scientific arrays stored on S3 or GCS

Implement, fix, refactor, or remove repository code through a defined workflow. It connects each change to an acceptance criterion, checks effects on existing behavior, and guides focused testing, change review, and verification.

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• Implementing a feature within an agreed scope
• Fixing bugs while preserving existing behavior
• Checking impacts before removing code

Production-ready CI/CD configurations for Playwright — GitHub Actions, GitLab CI, CircleCI, Azure DevOps, Jenkins, Docker, parallel sharding, reporting, code coverage, and global setup/teardown.

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Official NVIDIA-authored guidance for PhysicsNeMo ShardTensor domain parallelism — integrate domain parallelism into training/inference scripts (new or existing) with DDP or FSDP2, write and register shard patches to enable new layers/ops, and bootstrap multi-GPU correctness tests. Use when working with ShardTensor, scatter_tensor, domain parallelism, sequence/spatial sharding, ring attention, DeviceMesh + DDP/FSDP2 hybrid parallelism, or physicsnemo.domain_parallel. Do NOT use for generic PyTorch DDP/FSDP setup without domain parallelism, picking a PhysicsNeMo model or example (use physicsnemo-discover), or non-distributed training questions.

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In-memory caching in Golang using samber/hot — eviction algorithms (LRU, LFU, TinyLFU, W-TinyLFU, S3FIFO, ARC, TwoQueue, SIEVE, FIFO), TTL, cache loaders, sharding, stale-while-revalidate, missing key caching, and Prometheus metrics. Apply when using or adopting samber/hot, when the codebase imports github.com/samber/hot, or when the project repeatedly loads the same medium-to-low cardinality resources at high frequency and needs to reduce latency or backend pressure.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

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Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production modes.

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Administer MongoDB databases. Configure replica sets, sharding, and backups. Use when managing MongoDB deployments.

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Expert knowledge for Azure Managed Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when using Redis tiers, Entra ID auth, clustering/sharding, persistence/geo-replication, or ARM/Bicep deployments, and other Azure Managed Redis related development tasks. Not for Azure Cache for Redis (use azure-cache-redis).

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Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".

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Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.

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Core distributed systems patterns - load balancing, caching, sharding, consistent hashing, message queues, rate limiting, CDN, Bloom filters, ID generation, replication, conflict resolution, CAP theorem

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