'distributed' 검색 결과

Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Build Python code for Azure Cosmos DB's NoSQL API to create, read, update, delete, and query documents. Covers authentication, containers for storing data, partition keys for distributing records, throughput settings, asynchronous operations, and error handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implementing document operations in Python
• Writing parameterized queries for filtered searches
• Configuring containers, partition keys, and throughput

Implement Azure Cosmos DB document operations in Rust using its NoSQL SDK. The skill covers authentication, database and container clients, creating and reading records, replacing or patching fields, and deleting items with partition keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connecting a Rust application to Azure Cosmos DB
• Creating and reading documents
• Replacing documents or patching selected fields

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services

Configure Turborepo builds for projects containing multiple apps and shared packages. Set task dependencies, reuse saved build outputs locally or remotely, limit runs to affected packages, and investigate why cached results are not being reused.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up build tasks in a new Turborepo project
• Sharing cached build outputs between local machines and CI
• Running builds only for affected packages

Build GitLab workflows that automate building, testing, and deployment. The skill covers pipeline stages, dependency caching, runners that execute jobs, and manual approval before production deployment, with examples for Docker, Kubernetes, and Terraform.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automating builds and tests in GitLab
• Reviewing caches to improve pipeline performance
• Separating staging and production deployments

Build a workflow that extracts and deduplicates threat indicators such as URLs and IP addresses, creates readable reports with links disabled, and prepares standardized STIX 2.1 data for sharing through MISP or TAXII.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing threat reports with links disabled
• Consolidating indicators from multiple sources
• Distributing threat indicators through MISP or TAXII

Plan backend services and APIs, from service responsibilities and interface definitions to authentication, failure handling, monitoring, and rollout. Produces architecture diagrams, API specifications, and documented tradeoffs based on business and operational requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an order management API
• Defining responsibilities across multiple services
• Planning retries and timeouts for external integrations

Set up model training with torchforge for reinforcement learning, where rewards guide improvement. Covers training configurations, custom reward and loss functions, and distributed execution with Monarch and TorchTitan, keeping algorithm experiments separate from infrastructure concerns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom reward and loss functions
• Scale training across multiple GPUs or nodes

Approach engineering problems by checking assumptions, defining performance constraints, and choosing suitable tools before implementation. The skill guides algorithm analysis, complete code, performance optimization, and correctness checks through tests, assertions, or formal verification, meaning mathematical checks of correctness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking algorithm complexity and design assumptions
• Choosing languages for numerical or distributed systems
• Implementing concurrent code with explicit error handling

Set up reinforcement learning, which trains models using rewards, with verl. Follow workflows for preparing data, configuring GRPO or PPO training, scaling across GPUs, and addressing memory errors or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a math reasoning model with GRPO
• Configuring PPO training with a critic model
• Scaling large model training across multiple nodes

Guides setup and operation of Lambda Labs GPU instances for machine learning training and inference. Covers SSH access, persistent storage, API automation, and multi-GPU or multi-node training, including how to retain checkpoints and terminate idle instances.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up a GPU instance for LLM fine-tuning
• Keeping datasets and checkpoints between sessions
• Automating instance launch and termination through the API

Get guidance on adapting large language models with Axolotl. Covers YAML configuration files, LoRA and QLoRA methods for training fewer parameters, distributed training settings, dataset formats, and debugging training code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Write YAML settings for model fine-tuning
• Explore training with LoRA or QLoRA
• Configure training across multiple GPUs

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Configure large language model training with NVIDIA Megatron-Core across multiple GPUs. This skill helps choose how to split computation, set training parameters, monitor GPU efficiency, and address memory limits or unstable training.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning multi-GPU training for a LLaMA-style model
• Distributing MoE model experts across GPUs
• Tuning training throughput and GPU utilization

Build quarterly product plans by translating company goals into product and team OKRs, meaning objectives with measurable results. Generate linked goal hierarchies, check alignment across teams, and review how responsibilities are distributed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting quarterly product goals
• Translating company goals into team OKRs
• Reviewing conflicts and gaps between team goals

Audit and upgrade NemoClaw dependencies, including Hermes and base images. Trace upstream changes to affected code, implement required migrations, and verify tests, distributed artifacts, and the versions actually selected for use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assess the impact of a dependency upgrade
• Plan migrations for a Hermes upgrade
• Verify base images and selected artifacts

Investigate slow or failing requests in Google Cloud using Cloud Trace. Filter traces by duration or error status, inspect spans representing individual operations, and retrieve related Cloud Logging entries to investigate bottlenecks and exceptions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find requests exceeding a latency threshold
• Inspect operation hierarchies within a trace
• Investigate failures using related application logs

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Collect logs from multiple projects in one central bucket
• Query distributed logs without copying their data
• Set log access boundaries by project or log type

Install and configure OpenSearch for storing, searching, and analyzing KubeSphere logs, events, audit records, and notification history. Adjust storage and memory, enable a visualization interface, and manage scheduled cleanup of old search indexes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing storage for logs and event history
• Enabling OpenSearch Dashboards
• Adjusting storage capacity and memory

Create and manage Volcano jobs and scheduling queues in KubeSphere. Use YAML templates for PyTorch, TensorFlow, MPI, and batch workloads, configure resource requests and execution policies, and investigate jobs that remain pending or fail to create pods.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare distributed training jobs for PyTorch or TensorFlow
• Create MPI and batch processing jobs
• Configure queue capacity and scheduling weight

Guides reinforcement learning, where models learn from rewards, using torchforge and PyTorch. Covers reward design, custom loss functions, and distributed training so researchers can experiment with algorithms while keeping infrastructure concerns separate.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train mathematical reasoning models with GRPO
• Experiment with custom loss functions
• Scale training across multiple GPUs or nodes

Configure machine learning training and batch jobs across multiple clouds with SkyPilot. The skill guides GPU selection, lower-cost cloud and region choices, and automatic recovery when discounted spot instances are interrupted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a lower-cost cloud for GPU training
• Run long training jobs with recovery after interruptions
• Configure distributed training across multiple machines

Guides LLM fine-tuning with Axolotl, from YAML configuration and dataset formats to distributed training across GPUs. Covers LoRA and QLoRA, methods that train on preferences, multimodal support, and troubleshooting using documentation references and code examples.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare YAML settings for LLM fine-tuning
• Explore LoRA, QLoRA, and preference training options
• Check dataset formats and input handling