'pipelines' 검색 결과

Create CI/CD configurations, which automate build, test, and deployment steps, alongside Terraform infrastructure templates and Kubernetes deployment files. Generate health-check and rollback instructions to review before applying changes to cloud infrastructure or application environments.

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• Setting up GitHub Actions or CircleCI pipelines
• Creating Terraform modules for AWS, GCP, or Azure
• Preparing blue/green or rolling deployments

Design iii backends by combining functions, queues, and shared state. Choose patterns for workflows with retries, live updates, cooperating AI agents, and event-driven processing, with examples showing how the pieces connect.

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• Designing order workflows with retries and step tracking
• Updating screens or caches when stored data changes
• Passing tasks between specialized AI agents

Design MongoDB data structures around how data is accessed, write queries, and build staged data transformations for aggregation. Review indexes and query execution statistics to identify performance issues and choose suitable approaches for related or time-series data.

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• Choosing between embedded and referenced data
• Writing search queries and aggregation pipelines
• Reviewing slow queries and index usage

Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.

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• Creating architecture diagrams for technical papers
• Illustrating workflows and processing pipelines
• Adjusting diagram layouts through JSON specifications

Set up and troubleshoot automated build, test, and deployment workflows in KubeSphere DevOps. Covers Jenkins integration, project management, multi-cluster installation, and ArgoCD integration for deployments managed through Git.

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• Set up build, test, and deployment pipelines
• Configure Jenkins integration
• Install DevOps across multiple clusters

Manage credentials for Git access and deployments in KubeSphere DevOps. Covers SSH keys, passwords, API tokens, and Kubernetes connection settings, including their use in Jenkins pipelines and troubleshooting synchronization failures.

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• Register credentials for private Git repositories
• Use deployment credentials in Jenkins pipelines
• Investigate credentials missing from Jenkins

Create and run KubeSphere DevOps pipelines connected to Jenkins to automate builds and tests. Covers branch-specific workflows, required input parameters, private repository credentials, execution monitoring, logs, and downloading build output files.

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• Create a pipeline that builds and tests a Go application
• Run builds for individual repository branches
• Configure credentials for a private repository

Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.

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• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark execution settings

Turn plain-language descriptions or Mermaid diagrams into interactive, standalone HTML diagrams of systems, workflows, API exchanges, data flows, and state changes. Validate layouts, explore relationships, switch themes, and export diagrams as images or video.

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• Explain system components and their relationships
• Visualize workflows and approval steps
• Map API calls and data pipelines

Build isolated code execution environments on Cloudflare Workers for AI-generated code, scripts, and build pipelines. Covers running commands, managing files, preserving interpreter state, exposing service previews, and cleaning up sandbox resources.

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• Build an execution environment for AI-generated code
• Run build and test pipelines in isolation
• Preview a web service running in a sandbox

Inspect CircleCI runs, identify failing steps, and validate configuration locally using the CircleCI CLI, its command-line tool. It also guides targeted reruns and pipeline launches, reporting results, permission gaps, and next steps.

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• Investigating failed CircleCI runs
• Validating configuration before committing changes
• Rerunning a specific workflow or job

Plan and carry out agent deployments on Google Cloud using agents-cli. The guide covers deployment targets, permissions, secrets, automated build and deployment pipelines, verification, and recovery procedures for agents built with supported frameworks.

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• Comparing Agent Runtime, Cloud Run, and GKE
• Setting up automated build and deployment pipelines
• Configuring secrets and service account permissions

Write and review GitHub Actions workflows for syntax and structural correctness. Check quoting around expressions and special characters, then validate with actionlint, a workflow checker, to catch errors that ordinary YAML parsing can miss.

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• Reviewing new or edited GitHub Actions workflow files
• Checking quotes around expressions containing special characters
• Investigating workflow failures before any jobs start

Upgrade an existing .NET 10 project to .NET 11 by updating framework targets and packages, fixing compatibility issues, reviewing runtime behavior changes, and verifying builds, tests, and deployment configuration.

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• Upgrading a project from net10.0 to net11.0
• Fixing build errors after an SDK upgrade
• Reviewing changes in runtime and EF Core behavior

Move .NET tests from VSTest to Microsoft.Testing.Platform, the test execution platform. Update project settings, test commands, and CI pipelines for the selected SDK, then verify test counts and expected reports or diagnostic files.

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• Migrate .NET test projects from VSTest to MTP
• Update test commands in CI pipelines
• Translate xUnit.net v3 test filters

Build Python machine learning workflows for classification, numerical prediction, and grouping similar records. Use scikit-learn guidance and examples to prepare data, compare algorithms, tune model settings, and evaluate results with repeatable processing steps.

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• Building classification and numerical prediction models
• Grouping customers by similar characteristics
• Preparing data with missing values and categorical fields

Build research workflows that regenerate results from raw data with a single command. Organize project files, configure workflow tools, manage software environments and data versions, and prepare replication packages for journal submission.

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• Setting up folders for a new research project
• Automating analysis and figure generation
• Debugging results that differ across computers

Review academic slide decks or audit empirical analysis pipelines. It checks slide arguments, layout, and compilation, or reproduces numerical results in R, Stata, and Python to diagnose discrepancies and produce a formal report with prioritized recommendations.

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• Reviewing academic slide arguments and layout
• Checking LaTeX compilation warnings
• Comparing empirical results across R, Stata, and Python

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

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Design and implement repeatable preprocessing pipelines for cleaning, encoding, transforming, and validating ML input data.

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Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

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Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.

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Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and evaluation of trained checkpoints or existing/hosted endpoints. Use when a request names a Nemotron step or workflow, or asks to clean, translate, train, fine-tune, align, convert, optimize, evaluate, or compose these into a pipeline. Do NOT use for frontend/dashboard/visualization work, generic ML advice, billing/access, or non-Nemotron coding tasks.

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Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).

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