'modal' 검색 결과

Run model training, fine-tuning, inference, and batch jobs on Modal’s cloud GPUs. Estimate hardware needs and cost, generate Python launch scripts, run or deploy workloads, monitor logs, and retrieve results without managing remote servers.

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• Test CUDA code without a local GPU
• Run model training or LoRA fine-tuning on cloud GPUs
• Deploy a model inference API or vLLM service

Build Python workloads on Modal for cloud execution, including GPU model training and inference, web APIs, scheduled jobs, and parallel batch processing. Configure container environments, persistent storage, and automatic scaling through the Modal SDK.

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• Serving an AI model through a web API
• Running model training or fine-tuning on cloud GPUs
• Processing independent batch inputs in parallel

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Launch machine learning experiments on local GPUs, remote servers, Vast.ai, or Modal. It checks the configured environment, deploys code, saves logs, and verifies startup, with optional W&B metric logging and automatic Vast.ai result collection and instance cleanup.

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• Run training jobs on a local GPU
• Deploy experiments to an SSH server
• Run experiments on Vast.ai and collect results

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Review modal dialogs for keyboard and screen reader accessibility. The skill checks dialog roles, title labels, focus movement within the modal, Escape dismissal, and focus returning to the opening control, then identifies issues and ways to verify fixes.

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• Reviewing keyboard operation in modal dialogs
• Checking focus movement when opening and closing modals
• Reviewing dialog titles and screen reader labels

Launch machine learning training jobs on local or remote GPUs. The skill checks available GPU memory, synchronizes code, saves logs, and verifies startup, with optional experiment tracking and support for Vast.ai and Modal.

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• Launch training on an available remote GPU
• Run experiments locally with CUDA or MPS
• Record training metrics with W&B

Run machine learning training and predictions on cloud GPUs, then publish models as APIs that other applications can call. The skill guides Python-based setup, automatic scaling, persistent storage, parallel processing, and scheduled jobs.

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• Run machine learning jobs without managing GPU servers
• Publish a model as an automatically scaling API
• Process large batches of data in parallel

Draft or revise technical documentation in a direct, friendly style based on Modal's documentation. Lead with the concept or benefit, place a runnable example near the top, and reduce jargon and vague promotional wording.

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• Draft a guide that starts with a runnable example
• Rewrite technical prose in a direct, friendly voice
• Standardize wording and formatting across documentation

React / Next.js의 버튼, 모달, 알림, 페이지 전환에 애니메이션을 추가하고, 표시와 사라짐 및 스크롤에 연동되는 움직임을 구현합니다.

• 버튼 조작에 움직임을 추가합니다
• 모달과 알림의 표시와 사라짐을 구현합니다
• Next.js App Router 페이지 전환을 구현합니다

Review pop-ups, modals, and banners that obscure page content. Identify mobile usability and search visibility concerns, then improve dismissal controls and keyboard focus, the element currently receiving keyboard input.

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• Review promotional pop-ups on mobile
• Check keyboard dismissal of modals
• Verify focus movement when dialogs open and close

Check running experiments, collect logs and result metrics, and summarize progress alongside baseline comparisons. It supports SSH servers, Vast.ai, and Modal, with optional Weights & Biases metrics and cost reporting for the supported compute services.

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• Check whether remote experiments have finished
• Compare collected metrics with the matching baseline
• Investigate NaN values or diverging training runs

Build or update Figma pages, modals, and other composed views from code or descriptions. Reuse existing components, shared color and spacing values, and styles, assembling and checking the design section by section.

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• Recreate an application page in Figma from code
• Build a landing page using an existing design system
• Create a modal or sidebar from a description

Use when the user asks to run heavy or GPU work on Modal (the cloud compute platform) — writing a Modal function in the workspace, running it with the user's own `modal` CLI + token, and bringing results back. Data-to-compute for jobs too big for the laptop, without a Slurm cluster.

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Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.

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Compose or analyze sacred music in Hildegard von Bingen's distinctive modal style. Covers modal selection, melodic contour (wide-range melodies), text-setting (syllabic and melismatic), neumatic notation, and liturgical context for antiphons, sequences, and responsories. Use when composing a new piece in Hildegardian style, analyzing an existing chant for structure and mode, researching medieval modal music, preparing to perform or teach Hildegard's music, or setting Latin sacred texts.

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Registers a custom WooCommerce shipping method with explicit control over which fields appear in the per-zone settings modal — extend WC_Shipping_Method, declare your fields in init_form_fields (and ONLY those fields, no unset / DOM hacks / CSS hides), set the $supports array to control whether the modal opens (omit 'instance-settings' to suppress it entirely), register via the woocommerce_shipping_methods filter, load the class on woocommerce_shipping_init. Corrects the "this is React, removing fields is hard" misconception — the zone-method modal is Backbone with a PHP-rendered settings_html string; the field list is wholly PHP-controlled. Use when scaffolding a shipping method or when you want a feature-flag-only modal without WC defaults. Triggers on WC_Shipping_Method, woocommerce_shipping_methods, woocommerce_shipping_init, init_form_fields with shipping context, $supports shipping-zones, calculate_shipping, add_rate, or "remove default fields from shipping method".

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Create short video clips from text or still images with LTX-2.3 22B. Generate supporting footage, animated backgrounds, or moving portraits while specifying camera motion, lighting, dimensions, and duration, with clips around five seconds by default.

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• Generate supporting footage for narrated videos
• Add subtle motion to slide backgrounds
• Animate still portraits

Build image classification and search workflows by comparing images with text descriptions. Covers classification without additional training, similarity scoring, batch processing, and storing image representations in a vector database for retrieval.

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• Classifying images without additional training
• Searching image collections with text descriptions
• Comparing images with candidate descriptions

Build React interfaces with HeroUI v3, including buttons, forms, cards, and modals. The skill guides installation, component documentation lookup, and light or dark theme configuration using Tailwind CSS v4 and reusable interface components.

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• Set up HeroUI v3 in a React project
• Build forms and modals from documented examples
• Customize light and dark themes

Build and update Kortix web screens with established interface components and reference layouts. Guides component selection for forms, lists, modals, loading states, and notifications, including replacements for deprecated components and consistent responsive page structure.

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• Build a settings screen with existing components
• Replace deprecated interface components
• Standardize loading, empty, and error states

Guides image classification and search with OpenAI's CLIP by comparing images with text descriptions, without additional training. Includes batch processing, image-text matching, and category-based checks for potentially inappropriate images.

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• Classify images using text labels without additional training
• Search an image collection with natural-language descriptions
• Compare how closely images match text descriptions

Get guidance on adapting LLMs through additional training with LLaMA-Factory. The skill covers a visual interface requiring no code, LoRA and QLoRA training methods, and documentation-based help with implementation, features, and debugging.

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• Learning additional training through a visual interface
• Exploring LoRA and QLoRA training methods
• Checking supported models and multimodal features

Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

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