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Coastline

Coastline is a context-aware recommender system for fine-tuning LLMs1. It accounts for infrastructure constraints, workload demands, and user objectives, and recommends best-fit configurations as part of an LLM fine-tuning ecosystem.

LLM fine-tuning workloads are complex and, until now, unpredictable, making it difficult to estimate computational demands. For each workload, Coastline derives a grid of possible configurations from the tunable parameters (the number of GPUs and the batch size), filters out infeasible configurations, predicts the throughput and power of each feasible configuration, and ranks them by the user's goal.

Built with PriorLabs-TabPFN. See TabPFN.

Install

pip install coastline-recommender          # Kavier, AutoConf, the CLI, and the dashboard
pip install "coastline-recommender[ml]"    # adds the data-driven predictors
coastline --help

Python 3.11 to 3.13. The import name is coastline.

Pages

IBM ado

Coastline is integrated with IBM ado, an accelerated discovery orchestrator, as a plug-in. The plug-in exposes two experiments on recommending LLM fine-tuning workloads, one using the multi-objective recommender and the other using the min-GPU recommender. It uses the public SDK of Coastline, the same entry point as the CLI and the dashboard. Source: github.com/Radu-Nicolae/ado-coastline.

TabPFN

Built with PriorLabs-TabPFN.

The tabpfn predictor and the model file portfolio/tabpfn.pkl contain TabPFN v2 weights. TabPFN is a Tabular Prior-Fitted Network, a transformer-style model pre-trained on synthetic tabular tasks2. The weights are licensed under the Prior Labs License v1.2; a copy is in LICENSE-TabPFN.txt.

Cite

@software{coastline,
  author = {Nicolae, Radu and Lotito, Daniele and Iosup, Alexandru},
  title  = {Coastline: Exploring the impact of multi-objective, context-aware recommenders on performance and
            sustainability of datacenters under LLM fine-tuning workloads},
  year   = {2026},
  doi    = {10.5281/zenodo.23119267},
  url    = {https://github.com/atlarge-research/coastline-recommender}
}

The metadata is in CITATION.cff.

  1. R. Nicolae, D. Lotito, A. Iosup. Coastline: Exploring the impact of multi-objective, context-aware recommenders on performance and sustainability of datacenters under LLM fine-tuning workloads. MSc thesis, Vrije Universiteit Amsterdam, 2026. ↩

  2. N. Hollmann et al. Accurate predictions on small data with a tabular foundation model. Nature 637, 319-326, 2025. doi:10.1038/s41586-024-08328-6. ↩