AiAuN
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- AlicenseNot gradedqualityBmaintenanceEnables AI agents and their users to submit, route, and monitor training jobs across free-tier GPU clouds (Kaggle, Colab, Lightning, local MPS) through a local daemon that tracks quota, explains routing decisions, and gates long or secret-reading agent jobs behind human approval.MIT
- FlicenseNot gradedqualityBmaintenanceEnables 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.-
- AlicenseNot gradedqualityBmaintenanceEnables ML researchers to manage experiments across local and remote AutoDL GPU instances, including experiment creation, training launch, run polling, and report writing via Claude Code.1MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to autonomously manage Google Colab GPU sessions, submit and monitor training jobs, and debug/fix issues via an encrypted tunnel without requiring a browser tab.MIT
- AlicenseNot gradedqualityDmaintenanceEnables running GPU-accelerated Python code on Kaggle from any MCP-compatible AI assistant without local GPU hardware.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to orchestrate multi-system LLM training, quantization, and evaluation workloads across local, Colab, Kaggle, and SSH targets, with persistent monitoring, historical analysis, and visualization via MCP tools.MIT
TDQS
Scored across 14 tools
Each tool targets a distinct step in the Kaggle/Drive experiment workflow: validation, local inspection, dataset/kernel operations, script source, tracking, and Drive transfer. Only the two smoke-script tools are close variants, but their names and descriptions clearly separate color-class vs. ResNet50 GPU usage.
Names are readable but grammatically inconsistent: some are verb-first like inspect_local_dir, some are object-verb like kaggle_kernel_push, and others are noun phrases like experiment_tracking_links or drive_folder_info. The kaggle_* and drive_* prefixes provide some grouping, but there is no single predictable verb_noun convention.
14 tools is a reasonable size for an experiment pipeline spanning local inspection, Kaggle dataset/kernel lifecycle, and Drive output transfer. Each tool maps to a needed step, so the count feels well-scoped.
The set covers the main loop: validate request, inspect data, push dataset, push kernel, poll logs, move outputs to Drive, and provide tracking links. Minor gaps exist—configs can be listed but not read, and there is no update/cancel for kernels—but agents can generally work around them.