Deepspeed Finetune

This skill appears to do what it says (DeepSpeed fine-tuning, including remote training), but review and operate cautiously: - Review remote_train.py before use: it orchestrates SSH, installs, and key setup — verify there is no unexpected network exfiltration or telemetry. The source is listed in the SKILL.md homepage, but the registry source is 'unknown' so confirm code provenance. - Prefer SSH key auth over password-based automation (sshpass). The skill supports generating/uploading keys, but auto-generating a private key with no passphrase creates a persistent credential—store the private key securely and revoke it if the host is compromised. - Be aware StrictHostKeyChecking=no is used for automation: this disables SSH host-key validation and makes MITM attacks possible. If you must use password automation initially, add the host key to known_hosts afterwards (ssh-keyscan >> ~/.ssh/known_hosts) and switch to key-based auth. - Passwords passed via environment variables are common for ephemeral automation but can leak in process listings or logs on misconfigured systems. Provide REMOTE_SSH_PASSWORD only on trusted machines and avoid storing it on disk. - The skill will create a session file (.remote_train_session.json) and temporary ControlMaster sockets in /tmp — clean these up periodically (rm -rf /tmp/deepspeed_remote_ssh/ and remove the session file) if you are concerned about lingering access. - Test first on a non-sensitive or disposable remote VM to validate behavior and side effects (install steps, file writes, ports opened) before using on production hosts or with sensitive data. If you want higher assurance, ask the publisher for an auditable release (named maintainer, commit hashes) or run the skill code in a sandbox and review remote_train.py, ds_train.py, and monitor_training.py for any unexpected network connections or data uploads beyond standard model/dataset transfer.