Create README.md

README.md

@@ -0,0 +1,106 @@
+---
+license: mit
+tags:
+- regression
+- text regression
+- NAS
+- neural architecture search
+---
+# GraphArch-Regression
+
+A unified regression dataset collated from multiple graph/architecture search sources (FBNet, Hiaml, Inception, NB101, NB201, NDS, OfaMB, OfaPN, OfaRN, SNAS, Twopath) for training and evaluating models that map **ONNX-readable graph strings** to a target metric.
+
+## Schema
+- **identifier** *(string)*: Source key for the example, e.g. `FBNet_0`, `SNAS_42`.
+- **space** *(string)*: Logical dataset source (`FBNet`, `Hiaml`, `Inception`, `NB101`, `NB201`, `NDS`, `OfaMB`, `OfaPN`, `OfaRN`, `SNAS`, `Twopath`).
+- **uid** *(string)*: Original UID, if provided by the source.
+- **arch_str** *(string)*: Architecture identity; first non-empty among `arch_str`, `hash`, `uid`.
+- **input** *(string)*: ONNX-readable graph string (`onnx_readable`).
+- **target_metric** *(string)*: Always `val_accuracy`.
+- **val_accuracy** *(number | null)*: Primary regression target (Accuracy)
+- **flops** *(number | null)*: FLOPs for the architecture (if available).
+- **params** *(number | null)*: Parameter count (if available).
+- **metadata** *(string)*: Python-dict-like string including **only** keys that start with `zcp_` or `lat_` (e.g., zero-cost proxies and latency measurements). **Not populated for `SNAS`.** These can be used for multi-objective regression.
+- **metainformation** *(string)*: Only for `SNAS`; Python-dict-like string of selected fields `{arch_str, macro, train_time_sec, steps_ran, precision, batch_size}`.
+
+## Dataset Size
+With this dataset, we provide ONNX text for universal-NAS regression training over 611931 architectures:
+  - Amoeba: 4983
+  - DARTS: 5000
+  - DARTS_fix-w-d: 5000
+  - DARTS_lr-wd: 5000
+  - ENAS: 4999
+  - ENAS_fix-w-d: 5000
+  - FBNet: 5000
+  - Hiaml: 4629
+  - Inception: 580
+  - NASBench101: 423624
+  - NASBench201: 15625
+  - NASNet: 4846
+  - OfaMB: 7491
+  - OfaPN: 8206
+  - OfaRN: 10000
+  - PNAS: 4999
+  - PNAS_fix-w-d: 4559
+  - SNAS: 85500
+  - TwoPath: 6890
+
+> Tip: turn `metadata` or `metainformation` back into a dict with:
+> ```python
+> from ast import literal_eval
+> meta = literal_eval(row["metadata"])
+> ```
+
+## How to load with 🤗 Datasets
+```python
+from datasets import load_dataset
+
+# After you upload this folder to a dataset repo, e.g. your-username/GraphArch-Regression
+ds = load_dataset("your-username/GraphArch-Regression")
+
+# Or from a local clone:
+# ds = load_dataset("json", data_files="GraphArch-Regression/data.jsonl", split="train")
+```
+
+Credits
+
+This dataset was collated from several graph/NAS sources, along with our own profiling where applicable. Please credit and cite the original datasets accordingly.
+
+Inception, Hiaml, Ofa-MB/PN/RN, Twopath: `
+Mills, K. G., Han, F. X., Zhang, J., Chudak, F., Mamaghani, A. S., Salameh, M., Lu, W., Jui, S., & Niu, D. (2023). Gennape: Towards generalized neural architecture performance estimators. Proceedings of the AAAI Conference on Artificial Intelligence, 37(8), 9190–9199.`
+
+NDS: `Radosavovic, Ilija, et al. "On network design spaces for visual recognition." Proceedings of the IEEE/CVF international conference on computer vision. 2019.`
+
+NB101: `Ying, Chris, et al. "Nas-bench-101: Towards reproducible neural architecture search." International conference on machine learning. PMLR, 2019.`
+
+NB201: `Dong, Xuanyi, and Yi Yang. "Nas-bench-201: Extending the scope of reproducible neural architecture search." arXiv preprint arXiv:2001.00326 (2020).`
+
+FBNet: `Wu, Bichen, et al. "Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.`
+
+Further, multi-objective latency and zero cost proxies were sourced from 
+
+```
+Krishnakumar, Arjun, et al. "Nas-bench-suite-zero: Accelerating research on zero cost proxies." Advances in Neural Information Processing Systems 35 (2022): 28037-28051.
+
+Akhauri, Yash, and Mohamed S. Abdelfattah. "Encodings for prediction-based neural architecture search." arXiv preprint arXiv:2403.02484 (2024).
+
+Akhauri, Yash, and Mohamed Abdelfattah. "On latency predictors for neural architecture search." Proceedings of Machine Learning and Systems 6 (2024): 512-523.
+
+Lee, Hayeon, et al. "Help: Hardware-adaptive efficient latency prediction for nas via meta-learning." arXiv preprint arXiv:2106.08630 (2021).
+```
+
+
+Citations
+
+If you found this dataset useful for your research, please cite the original sources above as well as:
+
+```
+@article{akhauri2025performance,
+  title={Performance Prediction for Large Systems via Text-to-Text Regression},
+  author={Akhauri, Yash and Lewandowski, Bryan and Lin, Cheng-Hsi and Reyes, Adrian N and Forbes, Grant C and Wongpanich, Arissa and Yang, Bangding and Abdelfattah, Mohamed S and Perel, Sagi and Song, Xingyou},
+  journal={arXiv preprint arXiv:2506.21718},
+  year={2025}
+}
+```
+
+(Original Paper Coming Soon!)