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Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.
中文简介正在准备中。当前显示英语简介。
Set up the Graphsignal Profiler for inference workloads — vLLM, SGLang, PyTorch, and dstack services. Use when the user wants GPU profiling, tracing, or monitoring for inference, asks about `graphsignal-run` or `graphsignal.watch()`, or asks about CUPTI / Prometheus / OTLP setup.
中文简介正在准备中。当前显示原文说明。
用Manim动画解释技术原理、图表、系统架构和产品流程，先制作分镜与场景计划，再生成短视频及缩略图，并通过试渲染调整构图与节奏。
Create Mermaid diagrams from code relationships parsed by Trailmark. Visualize function calls, class inheritance, module dependencies, class members, complexity hotspots, and paths from inputs to sensitive functions, with adjustable focus, depth, and layout.
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Build a graph of functions and calls across multiple programming languages to investigate entry points, paths to sensitive operations, and potential impact. Use the results to prioritize security reviews and connect external findings with source code.
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Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.
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Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
中文简介正在准备中。当前显示原文说明。
PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.
中文简介正在准备中。当前显示原文说明。
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
中文简介正在准备中。当前显示原文说明。
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
中文简介正在准备中。当前显示原文说明。
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
中文简介正在准备中。当前显示原文说明。
通过API搜索、查看和编辑SiYuan笔记，支持创建文档、追加和更新段落、重命名及删除内容，也可将文档导出为Markdown。
制作营销和提案用的HTML幻灯片，结合Chart.js图表、自适应布局及写作框架，安排内容与页面结构，并统一颜色、字体等设计设置。
Create mathematical animations in ManimGL using Python, with guidance for equations, graphs, 3D scenes, and camera movement. Includes interactive development workflows, examples, and templates for testing changes and rendering scenes to video.
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Create mathematical and geometric animations in Python with Manim Community Edition. Use guidance, examples, and scene templates to structure animations, display equations, plot graphs, control cameras, and render two-dimensional or three-dimensional scenes.
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Choose tools and workflows for 3D modeling, dimensioned CAD parts, scan processing, rendering, and fabrication. Capability tables and recipes help connect tasks from photographs or initial designs to printable parts, CNC toolpaths, or game assets.
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Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.
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Create or edit Markdown with each prose paragraph on one source line, letting the viewer wrap the text automatically. Preserve meaningful line breaks in lists, code, and poetry while reducing unnecessary changes in file comparisons.
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Turn notes, prose, code, schemas, or tables into editable diagrams, including flowcharts, architecture diagrams, and schedules. Choose Mermaid, Graphviz DOT, PlantUML, or SVG to suit the content, with image or PDF rendering when needed.
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Draft a LinkedIn post from a topic or rough notes, matching the tone and length of past posts. Shape one clear idea into a specific opening, short paragraphs, and a gentle discussion prompt, ready for review.
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Build and search a project knowledge graph, a map of information and its relationships. Check matching entries, relationship confidence, graph freshness, and changes since the previous build to understand the project's recorded structure.
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Create static technical drawings from object descriptions, photographs, or CAD models. Produce consistent views that explain shape or assembly, plus a viewer with zoom, editable colors, and downloads in SVG, PNG, and PDF formats.
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Read or create DOCX documents when standard file tools fail. This fallback extracts text from the document's internal XML and uses python-docx through shell commands to build new files with headings, paragraphs, and tables.
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Connect static analysis results and human audit notes to a Trailmark code graph. Group findings by severity and compare them with input flow, impact, and privilege boundaries to prioritize investigation and understand findings in their code context.
中文简介正在准备中。当前显示英语简介。