run-experiment

Automating ML experiment deployment on a remote GPU server requires SSH access and a configured conda or virtual environment. The workflow detects the remote setup, runs pre-flight GPU checks, synchronizes code, and launches the training script.

Yes, you can run ML experiments on local GPUs. The deployment process is environment-aware, meaning it automatically detects whether you are using local GPUs or remote servers and executes the training jobs accordingly without requiring separate configurations.

Yes, SSH access is required to deploy training jobs on remote GPU servers. Additionally, you need a configured CLAUDE.md file with code sync settings, a conda or virtual environment, and compatible training scripts to ensure successful execution.

This deployment workflow integrates with Weights and Biases optionally through settings configured in your CLAUDE.md file. It provides monitoring hooks that automatically connect your launched training scripts to W&B for experiment tracking.

Pre-flight checks performed before launching ML experiments validate GPU availability and verify environment dependencies. These checks ensure the local or remote GPU setup is fully compatible and ready to execute your training scripts without interruption.

Yes, you will receive post-launch notifications when your GPU training job starts. The workflow provides monitoring hooks that send notifications through platforms like Feishu after successfully deploying and verifying the training scripts.