'distributed' 검색 결과

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.

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• 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.

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

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.

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

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.

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

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.

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• 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.

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

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

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• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Set up application monitoring with structured logs, performance metrics, and traces that follow requests across services. Build dashboards and alerts, run load tests, investigate CPU and memory bottlenecks, and estimate future infrastructure capacity.

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• Set up application dashboards and alerts
• Investigate production issues using logs, metrics, and traces
• Run load tests before traffic increases

Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.

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• Learn how Pipeline, PCollection, and PTransform fit together
• Understand grouping, aggregation, merging, and splitting
• Study time windows, output timing, and late data

Assess weaknesses in the software supply chain, from dependency packages to build pipelines and distributed artifacts. Covers authorized proof-of-concept testing, package confusion, container image risks, signing abuse, and defensive checks for tampering.

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• Assess dependency confusion risks in package registry settings
• Review tampering risks in CI/CD artifacts and container images
• Plan a non-destructive proof of concept and remediation report

Compare batching, request scheduling, and memory management for a single model serving instance. Assess concurrent request capacity, response times, generation speed, and GPU time per result that meets the required quality and deadline.

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• Comparing batching and request scheduling options
• Checking memory capacity for concurrent requests
• Assessing cache reuse and offloading tradeoffs

Compare ways to distribute AI inference across multiple instances, including replicas and separate input-processing and generation stages. Estimate capacity, throughput, delays, state-transfer costs, and recovery overhead to support routing and scaling decisions.

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• Compare replicas with separate inference stages
• Plan routing around compute capacity and cached state
• Evaluate expert placement and load imbalance

Estimate cross-server traffic, network bottlenecks, and synchronization waits for AI training or inference. Map communication onto physical network paths and assess how topology or communication changes affect the time needed to complete the job.

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• Find communication bottlenecks in distributed AI workloads
• Compare network topologies and communication paths
• Estimate synchronization waits under concurrent traffic

Build chat, image generation, and speech transcription features on 0G Compute Network, a distributed GPU marketplace. This skill guides SDK integration, model fine-tuning, response processing for billing, and account funding through code patterns and CLI commands.

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• Integrating chat and image generation into an application
• Preparing model fine-tuning jobs
• Checking balances and funding provider accounts

Find the relevant architectural guidance for API design, security, cloud infrastructure, message queues, and caching. This skill acts as an index to specialized references and lists principles for structuring software, distributed systems, and access controls.

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• Finding guidance for API design
• Reviewing security architecture topics
• Exploring containers, K8s, and Serverless

Set up file-based coordination between agent teams on different machines or across organizations. The skill creates connection settings, installs bundled sync scripts, and can initialize a shared Git repository for exchanging team status.

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• Sharing status between laptop and CI agent teams
• Reading a partner team's published status
• Setting up a shared Git repository for team updates

Guide Peekaboo releases through GitHub Actions, from version and changelog preparation to a publication rehearsal and authorized release. Verify published downloads, npm packages, Homebrew details, and follow-up updates against the hosted build.

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• Prepare version and changelog updates for a release
• Rehearse the release workflow without publishing
• Publish an explicitly authorized release

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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• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents

Publish files and static sites to IPFS, a distributed storage network, or create and deploy a web app with a frontend, backend APIs, and a database. The skill also guides email sending and updates through PinMe commands.

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• Publish a PDF or folder to IPFS
• Deploy a built static site
• Create a web app with APIs and a database

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.

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

Compute statistics across distributed NVFLARE sites without sharing raw data. Generate and validate jobs for numeric tables or image datasets, then report site-level and global summaries, missing data rates, and applied privacy settings.

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• Comparing numeric data distributions across sites
• Computing means, variances, and quantiles across sites
• Summarizing image counts and pixel intensities

Package reusable NeMo Relay behavior as an embedded component or an independently distributed plugin. Design shared configuration, validate it before activation, and register behavior with rollback handling so failed setup does not leave partial changes active.

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• Share runtime behavior across Relay applications
• Validate plugin configuration before activation
• Package a native Rust plugin or a local gRPC worker

Run training with mock or real data in Megatron-LM and Megatron Bridge, match their settings, and compare loss curves, which show training error over time. Includes recipes and multi-GPU examples for checking training behavior.

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• Compare Megatron-LM and Bridge loss curves
• Translate MLM command-line arguments into Bridge settings
• Check training on multiple GPUs

Configure CPU offloading in Megatron Bridge to reduce GPU memory use by moving intermediate training data or optimizer states to CPU memory. Check compatibility, adjust the offloaded fraction, and investigate memory errors or slowdowns.

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• Reduce GPU memory use during model training
• Tune how much optimizer state moves to CPU memory
• Check compatibility with parallelism and CUDA graphs