Skill UI

Guides engineers through DeepSpeed distributed training and NVMe I/O optimizations, covering ZeRO, pipeline parallelism, mixed precision, and DeepNVMe handles for synchronous and asynchronous tensor storage.

HuggingFace Accelerate adds distributed support to any PyTorch script with just four lines, providing a unified API for DDP, DeepSpeed, FSDP, and Megatron, plus automatic device placement, mixed precision, and simple launch/config commands for fast prototyping.

Guides production-grade miles RL training for massive MoE models, covering FP8/INT4 low precision, train-inference alignment, and speculative rollout workflows to maximize throughput in enterprise deployments.

Mastering the full lifecycle of machine learning models from data ingestion to deployment. This expertise covers building robust, production-grade MLOps pipelines using orchestrators like Kubeflow, Airflow, or Prefect. Key skills include feature store implementation with Feast, rigorous data validation, experiment tracking (e.g., MLflow), model registry management, and automated deployment workflows. Essential for automating model training and ensuring reproducibility at scale.

Flash Attention optimizer delivers 2-4x speedup and 10-20x memory savings for transformer attention, ideal when training or running long-context models that hit GPU memory or latency limits; supports PyTorch native scaled_dot_product_attention, the flash-attn library, H100 FP8 precision, and sliding-window attention.

Guides adding PyTorch FSDP2’s fully_shard integration to training loops, covering correct distributed init, bottom-up sharding, mixed precision/offload policies, DTensor-aware optimizers, and distributed checkpointing so oversized models or DeviceMesh sharding goals work reliably.

High-level PyTorch Lightning streamlines training loops with automatic distributed support (DDP/FSDP/DeepSpeed), callbacks, checkpointing, logging, and device management so you can scale from a laptop to multi-node clusters without boilerplate.

SkyPilot orchestrates ML training, batch jobs, and serving across 20+ clouds with automatic cost-aware region selection, spot-instance recovery, managed clusters, and GPU-aware distributed training, letting you optimize spend and avoid vendor lock-in.

This comprehensive runbook provides rapid, structured procedures for incident response on Vast.ai cloud GPU instances. It covers common failure scenarios like spot preemption, training job crashes, and GPU hardware issues. Users can follow the steps for triage, mitigation, recovery, and postmortem analysis to minimize downtime and ensure computational continuity.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

Creates security-focused skills for automation or training environments, enabling teams to define and deploy safety-oriented routines quickly.