run-experiment

Automating machine learning experiment deployment involves detecting the target environment, synchronizing code, and managing the GPU training process. This skill handles provisioning, dependency installation, and execution across local, remote, Vast.ai, and Modal platforms.

Yes, you can run training jobs on both Vast.ai and Modal serverless infrastructure. The skill supports multi-environment deployment, allowing you to provision on-demand Vast.ai instances or utilize serverless Modal infrastructure for your machine learning tasks.

Managing a remote GPU training session lifecycle requires pre-flight GPU checks, dependency installation, and process monitoring. This skill automates execution in persistent screen sessions and handles automated cleanup or instance destruction upon job completion to save costs.

The best way to sync code and set up logging for ML experiments is through automated environment-specific setup. This skill synchronizes your local code to remote servers and integrates W&B logging to monitor training tasks without manual configuration.

Yes, Vast.ai deployment supports automated cleanup to stop incurring GPU costs. The skill manages the entire experiment lifecycle, ensuring that instances are automatically destroyed upon training completion to maintain efficient resource utilization.

Automated provisioning for machine learning experiments eliminates the complexity of manual SSH setup and environment configuration. It ensures efficient resource utilization by automating environment detection, code synchronization, and process management across diverse GPU environments.