'workload' 검색 결과

Implement large parallel workloads in Java with Azure Batch. Manage groups of computing machines, organize jobs and tasks, inspect progress and output, adjust computing capacity, and schedule recurring jobs using SDK examples.

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• Running many computing tasks in parallel
• Adjusting machine counts to match pending work
• Checking task progress, exit codes, and output

Follow guidance to create and operate Kubernetes clusters on Azure Kubernetes Service. Configure groups of worker machines, automatic scaling, networking, monitoring, identity integration, and upgrades, with command examples and Terraform configuration for repeatable setup.

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• Create an AKS cluster on Azure
• Add GPU or spot node pools and configure automatic scaling
• Connect monitoring, Key Vault, and Azure Container Registry

Store database credentials and API keys in AWS Secrets Manager, configure automatic credential changes, and connect applications to stored secrets. Follow examples for access policies, Lambda rotation functions, and integration with ECS and EKS.

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• Move hardcoded credentials into AWS Secrets Manager
• Configure automatic database credential rotation
• Provide stored secrets to ECS and EKS workloads

Deploy container applications on AWS using ECS and Fargate, which runs containers without managing servers. Covers image publishing, task configuration, load balancing, automatic scaling, deployment updates, and troubleshooting failed or unhealthy containers.

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• Deploy Docker applications on Fargate
• Configure load balancing and automatic scaling
• Investigate failed tasks and health checks

Plan memory use and monitor long training jobs on NVIDIA DGX Spark. It guides troubleshooting for out-of-memory failures, tracks temperature and power alongside training logs, and helps assess competition between training and inference workloads.

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• Estimating available memory before training
• Troubleshooting out-of-memory failures
• Investigating slowdowns during long training jobs

Guides Kubernetes security configuration with network restrictions, Pod execution rules, and role-based access control. Use its configuration examples to separate workloads, limit permissions, and enforce deployment requirements across a cluster.

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• Restricting communication between application components
• Giving ServiceAccounts only the permissions they need
• Running Pods with restricted privileges

Set up machine learning training and inference on NVIDIA DGX Spark. Get guidance on choosing containers and packages, resolving CUDA compatibility errors, and checking that PyTorch can access the GPU before starting a workload.

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• Setting up a new DGX Spark for training or inference
• Diagnosing CUDA compatibility and import errors
• Choosing between containers and direct pip installation

Plan systems across AWS, Azure, GCP, and OCI by comparing services and architecture patterns. Helps guide service selection, disaster recovery, migration planning, and cost decisions while considering ways to reduce dependence on a single provider.

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• Planning a system across multiple cloud providers
• Designing disaster recovery in another cloud
• Comparing cloud services for a workload

Review cloud spending across AWS, Azure, GCP, and OCI. Guides resource sizing, discount commitments, storage policies, cost allocation tags, and budget alerts to identify waste and establish ongoing cost management while considering performance and reliability.

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• Finding oversized or idle cloud resources
• Comparing pricing models for steady workloads
• Reviewing storage tiers and retention policies

Turn a business use case into a Dataverse solution using Python. Get table and relationship designs, implementation code, usage instructions, and recommendations for error recovery, logging, monitoring, and performance based on the expected workload.

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• Designing customer and document management solutions
• Building data migration or scheduled synchronization jobs
• Implementing order processing and approval workflows

Review Azure infrastructure code and deployed resources against five Well-Architected Framework pillars. Identify configuration drift, classify risks, and produce remediation guidance and an architecture diagram, with GitHub issues created after user approval.

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• Review Azure reliability and security risks
• Compare infrastructure code with deployed resources
• Assess opportunities to improve cost and performance

Review AWS infrastructure code and deployed resources against the six Well-Architected pillars. Produce risk-ranked findings, remediation examples, and an architecture diagram, then create GitHub issues for tracking improvements after user confirmation.

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• Review AWS infrastructure code and deployed resources
• Prioritize findings by risk
• Prepare infrastructure code remediation examples

Understand and modify SGLang's CI pipelines, including test stage order, start conditions, early stopping after failures, and workload splitting. Use the guide to add jobs and investigate failed, skipped, or cancelled checks.

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• Add a test job to an SGLang CI stage
• Investigate failed or skipped CI checks
• Update test partitions and waiting job counts

Investigate excessive privileges inside Kubernetes pods by reviewing security settings, runtime events, and audit logs. Use admission policies to restrict risky configurations and Falco monitoring to detect suspicious privilege changes and container activity.

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• Finding pods running as root or in privileged mode
• Reviewing host filesystem access and dangerous Linux permissions
• Detecting privilege changes while containers are running

Assess serverless functions, primarily AWS Lambda, for unsafe execution of external input and unauthorized changes to code or added libraries. Combine source inspection, activity logs, and permission reviews to identify injection risks and recommend detection and prevention measures.

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• Reviewing Lambda event handlers for injection vulnerabilities
• Investigating unauthorized function or layer changes
• Checking privilege escalation through Lambda execution roles

Design PostgreSQL tables around actual queries and data volumes. Choose data types, validation rules, and indexes, then plan large table partitioning, access rules for individual rows, and schema changes with migration and rollback considerations.

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• Designing tables for users, orders, and related data
• Reviewing data types, constraints, and indexes
• Planning large tables and frequent update workloads

Build Python applications that keep other tasks moving while waiting for network, file, or database operations. The skill guides asyncio patterns, task cancellation, timeouts, workload limits, and testing for asynchronous APIs, scrapers, and background processing.

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• Building asynchronous web APIs
• Sending concurrent requests in web scrapers
• Designing background tasks and queues

Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.

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• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps

Design PostgreSQL tables by choosing data types, validation rules, and indexes that match actual queries. The skill also guides JSONB usage, partitioning large tables, and planning schema changes with attention to read and write workloads.

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• Designing tables for users and orders
• Reviewing indexes against actual queries
• Choosing between relational columns and JSONB

Create and manage MySQL, PostgreSQL, and SQL Server databases on Google Cloud. Covers instance setup, users, backups, availability, and secure connections, with a PostgreSQL walkthrough and references for application connectors and Terraform configuration.

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• Create a PostgreSQL database on Google Cloud
• Manage database users and passwords
• Connect through Cloud SQL Auth Proxy

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 data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Investigate growing memory usage in Qwen Code CLI by comparing heap snapshots, records of objects in memory. Trace what keeps objects alive, identify likely causes, and repeat the workload to verify a fix.

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• Investigating steadily growing memory usage
• Tracing references that keep objects alive
• Finding growing arrays, caches, or event listeners

Analyze sprint history to forecast delivery ranges, score team health, and track retrospective actions. Python tools turn structured sprint data into reports that support planning, highlight recurring problems, and show whether improvement efforts are progressing.

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• Forecasting sprint workload from past delivery data
• Assessing team health and prioritizing improvements
• Tracking retrospective actions and recurring themes