Results for “fsdp”

Adds PyTorch FSDP2 (fully_shard) to training scripts with correct init, sharding, mixed precision/offload config, and distributed checkpointing. Use when models exceed single-GPU memory or when you need DTensor-based sharding with DeviceMesh.

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Drafts Family Limited Partnership agreements for estate planning and intergenerational wealth transfer. Ensures IRS enforceability under IRC §2036 with legitimate business purpose, valuation discounts, and senior-generation control. Use when drafting FLP agreements, family partnership documents, or estate planning partnership structures.

Flash FPGA firmware on HSB Lattice boards and Leopard Imaging VB1940 cameras connected to NVIDIA devkits, with safety checks and multi-step upgrade/downgrade procedures.

Development with Fpga: tools and best practices

Use when designing a Digital Down Converter (DDC) using dsphdl System objects. Triggers on requests involving DDC design, frequency down-conversion for FPGA/ASIC, NCO + mixer + decimation filter chains, fractional/non-integer sample rate conversion, or HDL-optimized receiver front-end signal processing.

This skill recommends monitoring desmopressin (DDAVP) doses and adjusting them as needed in patients who have recently started antiepileptic drugs (AEDs). It is triggered when a patient initiates AED therapy and requires DDAVP management for diabetes insipidus.

Maps the US federal privacy landscape including sectoral laws (HIPAA, GLBA, FERPA, COPPA, FCRA, ECPA, VPPA), FTC Section 5 enforcement, proposed federal comprehensive legislation, and the interaction between federal and state privacy regimes. Keywords: federal privacy, HIPAA, GLBA, FERPA, COPPA, FCRA, FTC, sectoral, preemption.

Comprehensive guide to mdp. Master the concepts, implementation, best practices, and real-world applications of mdp in professional environments.

JACKAL — First Jump pyramider. FOX v1.6's exact five-layer entry gauntlet combined with RHINO's pyramiding mechanic. Enter at 30% on a qualifying First Jump, add 40% at +10% ROE and final 30% at +20% ROE — but only after re-validating 4h trend, SM alignment, and volume. Failed scouts cost $15 instead of FOX's $50. Full pyramids capture the same upside. DSL High Water Mode (mandatory).

Foundational F# patterns covering functional-first programming, type providers, computation expressions, and domain modeling. Use when writing F# code, understanding functional patterns, working with type providers, or building .NET applications with F#. This is the entry point for F# development.

Drafts a Stormwater Pollution Prevention Plan (SWPPP) compliant with 40 CFR Part 122, EPA Construction General Permit (CGP), and applicable state NPDES requirements for construction projects disturbing one or more acres. Use when drafting SWPPPs, construction stormwater permits, erosion control plans, NPDES compliance documents, or BMP selection memoranda.

Provides F# testing patterns using xUnit, FsUnit, Unquote, FsCheck, and integration testing with Testcontainers and WebApplicationFactory, including test organization and anti-patterns.

Build type-safe, testable Node.js/Deno backends using fp-ts with ReaderTaskEither, functional dependency injection, and service composition patterns.

Comprehensive guide to ssd. Master the concepts, implementation, best practices, and real-world applications of ssd in professional environments.

Read BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link simulations.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Use when implementing php functionality with production-grade patterns and safeguards.

Patterns for FPGA development including RTL design (Verilog/VHDL), timing closure, clock domain crossing, high-level synthesis, and verification. Covers both traditional HDL and modern HLS approaches. Use when ", " mentioned.

Integrate the Anthropic Claude API in PHP projects using the official SDK, covering client setup, message requests, streaming, and tool use.

Provides a quick reference for fp-ts pipe and flow functions to chain functions, compose operations, and build data pipelines.

Apply functional programming patterns with fp-ts in React, covering state management, form validation, and data fetching for React 18/19 and Next.js 14/15.

SB Leader 夸夸模式 — ENFP 型领导，懂情绪有节奏。底层行为不变（三条红线、方法论），旁白从施压变成共情+鼓励+偶尔戏谑吐槽。Triggers on: '/pua:yes', '夸夸模式', '唠嗑模式', '情绪价值', 'yes', '夸我', '鼓励模式'.

FOX v2.0 — Dual-mode emerging movers scanner. All live trading lessons from Fox v1.0 applied, plus one experimental tweak: Stalker minReasons = 3. Stalker entries must have at least 3 distinct scoring reasons, not just pass the score threshold. This forces breadth of confirmation beyond the auto-awarded base climb score + one bonus. Stalker minScore 7, minTotalClimb 8, tighter Phase 1 for low-score entries, consecutive-loss streak gate. XYZ banned. Leverage 7-10x. DSL exit managed by plugin runtime via runtime.yaml.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), and distributed training (DDP, FSDP, DeepSpeed) for scalable neural network training.