「ray train」の検索結果

健身私教 — 为客户设计、指导和推进个性化训练方案的专业工艺, 有别于团课教练、物理治疗师或运动队教练: (a) 训练方案设计与周期化 (线性 / 波动 / 板块周期化 Bompa & Haff; NSCA《力量训练与体能训练精要》行业圣经; Schoenfeld 肌肥大机制; Helms/Valdez/Morgan 肌肉与力量金字塔; 共轭训练法 Simmons/Westside; Starting Strength 线性渐进 Rippetoe; Renaissance Periodization 训练量地标 Israetel); (b) 动作评估与筛查 (FMS 功能性动作筛查 Gray Cook; NASM CEx 纠正性训练连续体 抑制-拉伸-激活-整合; 关节逐级方法 Cook/Boyle; 姿势与代偿模式识别; PAR-Q+ 与 ACSM 运动前筛查指南); (c) 教学与指令 (外部 vs 内部注意焦点 Wulf; 差异化学习 Schöllhorn; 约束导向方法 Davids/Renshaw; RIR/RPE 自动调节 Zourdos/Helms; 动机访谈在健身中的应用 Rollnick/Miller); (d) 营养指导的执业边界 (宏量营养素周期化; Precision Nutrition / ISSN 立场声明的循证建议; 边界: 私教提供一般性指导, 医学营养治疗需 RD/RDN 资质; 能量可用性与 RED-S Mountjoy IOC 共识); (e) 商业与客户管理 (客户留存与方案依从; 咨询与销售伦理; 线上教练平台与规模化 Goodman PTDC; 方案交付工具 TrueCoach/Trainerize/TrainHeroic; 课时定价模型; 独立教练 vs 健身房雇员); (f) 专项方向 (运动表现 CSCS; 纠正性训练 CES; 老年健身 SFN; 产前产后; 青少年 YSCA; 适应性 / 无障碍健身; 健美 / 形体; 力量举; 耐力运动 S&C)。诚实处理: 兄弟科学 vs 循证之争, 补剂行业利益冲突, 合成代谢激素使用现状 (诚实但不推广: 匿名调查约 30% 有经验男性健身者使用过 — 业内估, Kanayama/Pope/Hudson 记录健康风险, WADA 禁用清单用于竞技语境), 体型焦虑与进食障碍风险 (男性私教肌肉变形症, 正食症倾向, NEDA 负责任语言指南), 健身房掠夺性销售 (高压推销、误导性对比照、长约锁定), 执业边界违规 (私教诊断伤病或超范围开饮食处方), 线上教练认证造假 (周末速成、未经认可的认证)。不含: 物理治疗 / 康复治疗 (持照医疗职业), 不含: 团课教练 (Les Mills/Zumba/CrossFit 分馆教练 — 相邻但不同), 不含: 精英竞技运动教练 (团队项目周期化, 不同职业路径), 不含: 营养作为主业 (临床营养学需单独资质)。 (Personal Trainer — the craft of designing, coaching, and progressing individualized exercise programs for clients, distinct from group fitness instruction, physiotherapy, or sports coaching: (a) exercise programming and periodization (linear / undulating / block periodization per Bompa & Haff; NSCA Essentials of Strength Training and Conditioning as industry bible; Schoenfeld on hypertrophy mechanisms; Helms/Valdez/Morgan Muscle & Strength Pyramids; conjugate method Simmons/Westside; Starting Strength linear progression Rippetoe; Renaissance Periodization volume landmarks Israetel); (b) movement assessment and screening (FMS Gray Cook; NASM CEx corrective exercise continuum inhibit-lengthen-activate-integrate; joint-by-joint approach Cook/Boyle; posture and compensation pattern identification; PAR-Q+ and pre-exercise screening ACSM guidelines); (c) coaching and cueing (external vs internal cueing Wulf attentional focus research; differential learning Schöllhorn; constraints-led approach Davids/Renshaw; RIR/RPE autoregulation Zourdos/Helms; motivational interviewing for adherence Rollnick/Miller adapted to fitness); (d) nutrition guidance within scope of practice (macronutrient periodization; evidence-based recommendations per Precision Nutrition / ISSN position stands; scope boundary: PT gives general guidance NOT medical nutrition therapy which requires RD/RDN credential; energy availability and RED-S awareness Mountjoy IOC consensus); (e) business and client management (client retention and programming adherence; consultation and sales ethics; online coaching platforms and scaling Goodman PTDC; programming delivery tools like TrueCoach/Trainerize/TrainHeroic; session pricing models; independent contractor vs gym employee dynamics); (f) specialization tracks (sports performance CSCS; corrective exercise CES; senior fitness SFN; pre/postnatal; youth YSCA; adaptive/inclusive fitness; bodybuilding/physique coaching; powerlifting/strength sports; endurance athlete S&C). Evidence-based practice as the core ethos: Schoenfeld/Krieger meta-analyses on volume-response, Helms systematic reviews on protein/resistance training, Brad Schoenfeld's MASS journal bridging research to practice, Menno Henselmans and Eric Helms as practitioner-researchers, Greg Nuckols Stronger By Science as translation layer. Chinese fitness industry context: rapid growth of personal training in commercial gyms (Keep/Lefit/Megafit chains), NSCA-CPT and ACE-CPT as dominant certifications alongside local CSFA (China Sport and Fitness Association) credential, '体适能' (physical fitness) as formal term, online coaching boom post-COVID, PT business models in tier 1-3 cities diverge sharply. Honest treatment of: bro science vs evidence-based d

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Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

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Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

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High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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Orquestração de treinamento distribuído em clusters. Escala PyTorch/TensorFlow/HuggingFace do laptop para milhares de nós. Ajuste de hiperparâmetros integrado com Ray Tune, tolerância a falhas, escalabilidade elástica. Use ao treinar modelos massivos em múltiplas máquinas ou executar varreduras distribuídas de hiperparâmetros.

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Framework RLHF de alta performance com aceleração Ray+vLLM. Use para treinamento PPO, GRPO, RLOO, DPO de modelos grandes (7B-70B+). Construído em Ray, vLLM, ZeRO-3. 2× mais rápido que DeepSpeedChat com arquitetura distribuída e compartilhamento de recursos GPU.

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High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Processamento escalável de dados para workloads de ML. Execução em streaming em CPU/GPU, suporta Parquet/CSV/JSON/imagens. Integra-se com Ray Train, PyTorch, TensorFlow. Escala de uma única máquina para centenas de nós. Use para inferência em lote, pré-processamento de dados, carregamento de dados multi-modal ou pipelines distribuídos de ETL.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Implement Roblox physical simulation and queries with assemblies, anchoring, constraints, collision groups, CanCollide/CanTouch/CanQuery, raycasts and overlap queries, mass, impulses, forces, velocity, moving assemblies, cleanup, and network ownership. Use for Roblox collisions, hit detection, RaycastParams, PhysicsService, projectiles, vehicles, knockback, constraints, deprecated BodyMovers, unstable motion, or client-owned physics exploits.

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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

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产品营销 (PMM, Product Marketing) — 拥有「产品如何被定位 / 表达 / 发布 / 卖进市场」的从业者认知操作系统: 是产品 / 销售 / 市场 / 客户之间的连接组织, 负责把产品能力翻译成市场价值, 并把市场声音带回产品。覆盖 (a) 定位与差异化 (April Dunford 「刻意选择语境」框架 + 竞争性替代方案 + 卖价值不卖功能 + 选择「让你优势成立的市场参照系」, 对照 Ries & Trout 经典「定位是占领心智」), (b) 信息传递与叙事 (messaging 层级 / 信息屋 + 价值主张 + 收益导向 vs 战略叙事「换框架」Andy Raskin + 表达清晰度 Emma Stratton / Punchy + 初创 homepage 定位 FletchPMM), (c) 进入市场 (GTM) 策略与产品发布 (发布分级 T1/T2/T3 + 发布流程与跨职能编排 + GTM 打法 PLG vs SLG vs 混合 + 滩头细分选择), (d) 市场与竞争情报 (竞品分析 + battlecard 战卡 + 输赢分析 win/loss + 竞争赋能 Klue/Crayon/Clozd), (e) 买家与客户研究 (ICP 理想客户画像 + 买家 / 用户 persona + Jobs-to-be-Done 任务理论 Christensen/Moesta/Ulwick ODI + 客户之声 VoC + 用真实买家测信息 Wynter/Peep Laja), (f) 销售赋能 (战卡 + pitch / 销售 deck + 一页纸 + demo 叙事 + 异议处理 + 销售培训 + 内容采用率 — 你的产出只值销售实际用到的那部分), (g) 定价与打包 (价值定价 + 打包分层 + 《Monetizing Innovation》Ramanujam/Simon-Kucher + PLG 定价 — 常与产品 / 财务共担), (h) 细分与品类 (市场细分 + TAM/SAM/SOM + 技术采用生命周期与跨越鸿沟 Geoffrey Moore + 品类设计与叙事 Play Bigger / Christopher Lochhead — 稀有且昂贵, 不是默认选项), (i) 需求生成与内容协同 (campaign 信息 + 思想领导力 + 漏斗内容 TOFU/MOFU/BOFU + ABM 基于客户营销), (j) 分析师与影响者关系 (Gartner 魔力象限 + Forrester Wave + 分析师 briefing — To B 场景, 对 pay-to-play 影响诚实标注), (k) 客户营销与倡导 (参考客户 + case study + 评测站 G2/TrustRadius/Capterra 运营 + 社区), (l) PMM 运营与度量 (影响管道 + 赢率 + 发布采用率 + 信息穿透率 + 角色汇报线「向产品还是向市场」之争 + PMM 作为战略职能 vs 接单工具人陷阱); 跨 B2B SaaS (该学科重心) / B2C 消费 / 开发者 PLG / 平台产品。不含 产品管理 (PM 造产品 / 拥有 roadmap, PMM 拥有市场 — 相邻且极易混淆) / 增长 / 效果营销 / 付费投放 (平行学科) / 品牌 / 企业传播 / 公关为终点 / 需求生成为终点 (PMM 与之协同但不等同) / 平面设计 /「做 slide 和周边的团队」(PMM 抗争的接单工具人窄化) / 泛泛「市场营销」。 (Product Marketing (PMM) — the cognitive operating system of practitioners who own how a product is positioned, messaged, launched, and sold into a market: the connective tissue between product, sales, marketing, and customers, responsible for translating product capability into market value and bringing the voice of the market back into product. Covers (a) positioning & differentiation (April Dunford's deliberate-context framing, competitive alternatives, value-not-features, framing the market in which your strengths matter — vs Ries & Trout classic perception-in-the-mind positioning), (b) messaging & narrative (messaging hierarchy / house, value proposition, benefit-led vs strategic-narrative / change-the-frame messaging Andy Raskin, clarity craft Emma Stratton/Punchy, homepage & startup positioning FletchPMM), (c) go-to-market (GTM) strategy & product launches (launch tiers T1/T2/T3, launch process & cross-functional orchestration, GTM motion PLG vs SLG vs hybrid, beachhead segment selection), (d) market & competitive intelligence (competitive analysis, battlecards, win/loss analysis, competitive enablement Klue/Crayon/Clozd), (e) buyer & customer research (ICP ideal customer profile, buyer & user personas, Jobs-to-be-Done Christensen/Moesta/Ulwick ODI, voice of customer, message testing with real buyers Wynter/Peep Laja), (f) sales enablement (battlecards, pitch & sales decks, one-pagers, demo narratives, objection handling, sales training, content adoption — output is only as good as what sales actually uses), (g) pricing & packaging (value-based pricing, packaging & tiering, Monetizing Innovation Ramanujam/Simon-Kucher, PLG pricing — often shared with product/finance), (h) segmentation & category (market segmentation, TAM/SAM/SOM, technology adoption lifecycle & crossing the chasm Geoffrey Moore, category design & narrative Play Bigger / Christopher Lochhead — rare and expensive, not a default), (i) demand-gen & content partnership (campaign messaging, thought leadership, funnel content TOFU/MOFU/BOFU, ABM account-based marketing), (j) analyst & influencer relations (Gartner Magic Quadrant, Forrester Wave, analyst briefings — for B2B, with honest read on pay-to-play perception), (k) customer marketing & advocacy (references, case studies, reviews G2/TrustRadius/Capterra presence, community), (l) PMM operations & metrics (influenced pipeline, win rate, launch adoption, message pull-through, the role's reporting line product-vs-mark

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Scale Ray workloads and serve models on Anyscale — managed Ray clusters for training and inference.

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Complete CLI harness for FreeCAD parametric 3D CAD modeler (258 commands). Covers ALL workbenches: Part (29 primitives + boolean + mirror + loft + sweep), Sketcher (26 cmds: geometry + constraints + editing), PartDesign (38 cmds: pad/pocket/groove/fillet/chamfer/patterns/hole/datum), Assembly (11 cmds), Mesh (16 cmds), TechDraw (15 cmds: views + dimensions + PDF/SVG), Draft (33 cmds: 2D shapes + arrays + transforms), FEM (12 cmds), CAM/CNC (10 cmds), Surface (6 cmds), Spreadsheet (7 cmds), Import (13 formats), Export (17 formats), Measure (12 cmds), Materials (21 presets). Headless FreeCAD export to STEP/IGES/STL/OBJ/DXF/PDF/glTF/3MF.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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Create a dark monochrome procedural background with enlarged square pixels and visible Bayer-style ordered dithering. Use when a page needs an atmospheric near-black dither field, broad organic waves or cloud masses, and restrained gray-white highlights behind framed UI, hero content, or data overlays.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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Playbook for launching, monitoring, stopping, and debugging NeMo-RL recipes on a Kubernetes cluster via the nrl-k8s CLI. Covers ephemeral vs long-lived RayCluster modes, iterating on runs, and debugging hung or failed training jobs.

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Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data-visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.

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