Enables LLMs to manage and run machine learning training jobs on a remote server, including syncing code, submitting experiments, monitoring progress, reading TensorBoard metrics, and receiving completion notifications.
Enables AI agents to manage GPU training end-to-end through natural language, including submitting and scheduling jobs, monitoring logs and metrics, diagnosing failures, comparing runs, and recommending the best checkpoints.
Enables AI agents and MCP clients to manage AutoDL GPU instances programmatically, including creating, listing, powering on/off, executing commands, transferring files, and enforcing automatic shutdown.