Results for “4d-parallelism”

Trains, evaluates, exports, quantizes, and runs inference for Sparse4D multi-camera temporal 3D object detection and tracking models using TAO.

Optimize MSBuild build parallelism by configuring /maxcpucount, graph build mode, project references, and analyzing binlogs to reduce multi-project solution build times.

Creates seek-safe 2D/3D keyframe animations using GSAP, CSS, Anime.js, WAAPI, FLIP, SVG morph/draw, and text trails for HyperFrames compositions.

Execute phased plans with multiple independent tasks per phase by fanning out one agent per task, reconciling outcomes per wave, gating between phases with verify commands, and emitting a phase × outcome report. Triggers include "execute this plan", "work through these phases", "swarm over this backlog", "parallelize this plan".

Build cinematic, scroll-driven 2.5D websites with parallax depth, text animations, and premium effects using CSS and JavaScript.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Provides principles for 3D game development, covering rendering pipelines, shaders, physics, cameras, lighting, and LOD optimization.

Expert guidance for architecting, developing, and operating SurrealDB 3, covering SurrealQL, multi-model data modeling, vector search, security, deployment, performance tuning, SDK integration, and ecosystem tools.

NLVR2: A Visual Reasoning Benchmark for Natural Language

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies for maximum GPU efficiency.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Use when building falling leaves that read as leaves (not confetti): each leaf tumbling on its own axis with sideways slip driven by that tumble. Covers 2D canvas and instanced 3D, recycling, density maths, depth layering, tone-mapped colour, reduced motion, and visibility pausing—for maple, sakura, blossom, ash, or snowfall shapes.

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Task Decomposition

4-stage funnel that screens all 500+ Hyperliquid perps down to the top trading opportunities. Scores setups 0-400 across smart money, market structure, technicals, and funding. BTC macro filter, hourly trend gate (counter-trend = hard skip), cross-scan momentum tracking. Near-zero LLM tokens — all computation in Python. Use when scanning for new trading opportunities on Hyperliquid, evaluating setups, or checking market conditions.

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

SVIT: Scaling up Visual Instruction Tuning

3D game development principles. Rendering, shaders, physics, cameras.

Deterministic pixel art upscaling using EPX/Scale2x, hq2x/hq4x, and xBR algorithms that add valid sub-pixels through pattern recognition. Activate on 'pixel art scaling', 'EPX', 'Scale2x', 'hq2x', 'hq4x', 'xBR', 'retro game upscaling'. NOT for AI/ML upscaling, photo enlargement, or simple nearest-neighbor.

Dask parallel computing reference for Python. Covers Dask DataFrame (parallel Pandas), Dask Array (parallel NumPy), Dask Delayed for custom parallelism, Dask Bag, distributed clusters, dashboard monitoring, and scaling best practices.

3D game development principles. Rendering, shaders, physics, cameras.

Comprehensive GLSL shader techniques for creating stunning visual effects — ray marching, SDF modeling, fluid simulation, particle systems, procedural generation, lighting, post-processing, and more.

Embeds target and source image parquets, then mines nearest-neighbour source images for augmentation in VCN AOI workflows.

Mosaic Augmentation for Detection and Segmentation

Evaluates text-to-image models on spatial relationship accuracy using the VISOR metric, separating object detection from spatial correctness to reveal biases like object priority and merging.

Flamingo: A Visual Language Model for Few-Shot Learning

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

Parallel execution engine for high-throughput task completion

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Computação paralela/distribuída. Escale pandas/NumPy além da memória disponível, DataFrames/Arrays paralelos, processamento multi-arquivo, grafos de tarefas, para datasets maiores que RAM e workflows paralelos.

Parallel execution engine for high-throughput task completion

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.