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Search Parallelism , found 7 results

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DeepSpeed Training Assistant

Orchestra-Research/AI-Research-SKILLs

Provides expert guidance for DeepSpeed distributed training, covering ZeRO optimizations, pipeline parallelism, mixed precision, DeepNVMe I/O, and best practices for debugging or deploying large-scale models.

TorchTitan LLM Pretraining

distributed-llm-pretraining-torchtitan

Orchestra-Research/AI-Research-SKILLs

TorchTitan delivers PyTorch-native distributed LLM pretraining with composable 4D parallelism (FSDP2, TP, PP, CP), Float8, torch.compile, and checkpointing so you can scale Llama 3.1, DeepSeek V3, or custom models across 8 to 512+ GPUs.

Nx Task Runner

tech-leads-club/agent-skills

Runs build, test, lint, serve, and other tasks in Nx workspaces via run, run-many, and affected flows, honoring the detected package manager and target metadata while exposing filters, parallelism, and caching controls for CI or local workflows.

Parallel Subagent Execution

parallel-execution

CloudAI-X/claude-workflow-v2

Guides orchestrating multiple Task tool subagents with run_in_background for concurrent work, covering when to trigger parallelism, how to craft prompts, launch in one message, synchronize results, and integrate TodoWrite status updates.

HighThroughput LLMS

serving-llms-vllm

Orchestra-Research/AI-Research-SKILLs

Deploy LLMs with vLLM to maximize throughput and minimize latency through PagedAttention, continuous batching, quantization, and tensor parallelism for production APIs or batch inference on constrained GPUs.

Megatron LLM Training

training-llms-megatron

Orchestra-Research/AI-Research-SKILLs

Megatron-Core orchestrates training of 2B–462B parameter LLMs with tensor/pipeline/context/expert parallelism, maximizing H100 MFU for production-grade distributed training pipelines.

Compute Resource Monitor

get-available-resources

K-Dense-AI/claude-scientific-skills

Assesses available CPU, GPU, memory, and disk before computationally intensive scientific work, emitting a JSON summary with strategic recommendations on parallelism, memory handling, and GPU backends so you can choose the optimal compute pattern.

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