run-experiment

Running ML experiments on remote GPU servers involves deploying training scripts to SSH remote servers or Vast.ai instances. The workflow includes pre-flight GPU availability checks, code synchronization, and deployment with optional W&B experiment tracking.

Yes, you can deploy training jobs across local and remote GPUs simultaneously by using orchestration tools that detect your environment. This ensures GPU availability checks and code synchronization are applied uniformly across local CUDA/MPS, SSH remote servers, and Vast.ai instances.

Orchestrating ML experiments on Vast.ai requires end-to-end deployment from environment detection to monitoring. This approach ensures pre-flight GPU validation, code synchronization, and results collection with optional W&B integration and minimal manual setup.

This deployment workflow supports local CUDA and MPS environments. It provides environment-aware deployment that automatically detects local GPU availability and applies pre-flight validation before launching ML training jobs.

No, you do not need W&B integration to monitor ML experiments on remote servers. W&B integration is optional; the core deployment process handles end-to-end experiment launch, monitoring, and results collection independently of experiment tracking tools.