82 skills · sorted by relevance
Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.
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Routes Chainer workflows for training, export, distributed learning, ChainerX, and checkout maintenance.
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Use MosaicML Composer for PyTorch training loops, speedup methods, loggers, checkpoints, distributed launch, profiling, and model export.
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Use ColossalAI for distributed PyTorch training, large-model parallelism, Booster plugins, ShardFormer, Colossal-Inference, and first-party application workflow routing.
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Routes agents working with Axolotl config-driven LLM fine-tuning, datasets, SFT, preference tuning, GRPO/EBFT, model adapters, distributed training, CLI operations, and troubleshooting.
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Routes BEVFormer camera-only 3D detection workflows, from install/import and config inspection through nuScenes data prep, distributed training and evaluation, and log analysis.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Use AReaL for large-scale asynchronous LLM reinforcement learning, agentic RL, distributed post-training, inference/agent services, backend planning, and customization workflows.
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API de treinamento distribuído mais simples. 4 linhas para adicionar suporte distribuído a qualquer script PyTorch. API unificada para DeepSpeed/FSDP/Megatron/DDP. Posicionamento automático de device, precisão mista (FP16/BF16/FP8). Config interativo, comando de launch único. Padrão do ecossistema HuggingFace.
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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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