Update dataset card

README.md

@@ -1,3 +1,142 @@
----
-license: mit
----
+---
+license: cc-by-4.0
+task_categories:
+  - tabular-regression
+language:
+  - en
+tags:
+  - gpu
+  - cloud-computing
+  - pricing
+  - market-microstructure
+  - h100
+  - a100
+pretty_name: GPU Price Tracker
+size_categories:
+  - 1M<n<10M
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: prices/**/*.parquet
+---
+
+# GPU Price Tracker
+
+A continuously-updated dataset of **cross-cloud GPU rental pricing**
+covering 12+ public cloud providers (AWS, GCP, Azure, Lambda Labs,
+RunPod, Vast.ai, DataCrunch, Cudo Compute, TensorDock, Vultr, Oracle,
+Nebius, CloudRift). Snapshots are collected twice daily by scraping
+provider pricing surfaces via the
+[`gpuhunt`](https://github.com/dstackai/gpuhunt) library and published
+as Hive-partitioned Parquet files (`prices/dt=YYYY-MM-DD/*.parquet`).
+
+The dataset is intended for:
+
+- **Researchers** studying cloud-market microstructure, GPU price
+  dynamics, and the spot–on-demand spread as a utilization proxy.
+- **Practitioners** comparing GPU rental costs across providers for
+  capacity planning, procurement, and ML-training cost estimation.
+
+A full dashboard view is at [the hosted Streamlit app](https://github.com/alex-hubbard/gpu_price_tracker)
+(see the GitHub README for the deploy URL).
+
+## Quick start
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("afhubbard/gpu-prices", split="train")
+print(ds[0])
+# {'timestamp': '2026-05-07T09:17:00Z', 'provider': 'aws',
+#  'instance_type': 'p4d.24xlarge', 'gpu_type': 'A100', 'gpu_count': 8,
+#  'gpu_memory_gb': 40, 'vcpus': 96, 'ram_gb': 1152.0,
+#  'region': 'us-east-1', 'price_per_hour': 32.7726, 'is_spot': False,
+#  'available': True, 'availability_zone': None}
+```
+
+Or with DuckDB directly (no `datasets` install required):
+
+```python
+import duckdb
+con = duckdb.connect()
+con.sql("INSTALL httpfs; LOAD httpfs;")
+con.sql("""
+SELECT gpu_type,
+       AVG(price_per_hour / gpu_count) AS avg_price_per_gpu_hour,
+       COUNT(*) AS listings
+FROM read_parquet('hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
+                  hive_partitioning = true)
+WHERE timestamp = (SELECT MAX(timestamp) FROM read_parquet(
+                       'hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
+                       hive_partitioning = true))
+  AND gpu_count > 0
+GROUP BY gpu_type
+ORDER BY avg_price_per_gpu_hour
+LIMIT 10
+""").show()
+```
+
+## Schema
+
+| Column | Type | Description |
+| --- | --- | --- |
+| `timestamp` | timestamp (UTC) | When the snapshot was taken |
+| `provider` | string | Cloud provider id |
+| `instance_type` | string | Provider SKU |
+| `gpu_type` | string | Normalized accelerator family (`H100`, `A100`, …) |
+| `gpu_count` | int32 | GPUs per SKU |
+| `gpu_memory_gb` | int32 (nullable) | VRAM per GPU |
+| `vcpus` | int32 | Host vCPUs |
+| `ram_gb` | float32 | Host RAM in GB |
+| `region` | string | Provider's raw region (not canonicalized) |
+| `price_per_hour` | float32 | USD/hr for the full SKU |
+| `is_spot` | bool | Spot/preemptible flag (semantics vary; see methodology) |
+| `available` | bool (nullable) | Listed and offerable at scrape time |
+| `availability_zone` | string (nullable) | Zone within the region, where applicable |
+
+Compute `price_per_gpu_hour = price_per_hour / gpu_count` for fair
+cross-SKU comparison.
+
+## Collection cadence
+
+Twice daily (~09:00 and 21:00 UTC) via a GitHub Actions cron. Files
+are append-only — each run produces a new immutable Parquet file under
+`prices/dt=<UTC date>/`.
+
+## Limitations (read before modeling)
+
+- **Region strings are raw** — not canonicalized across providers.
+  Use a separate lookup if doing cross-cloud regional comparisons.
+- **Spot semantics differ** by provider (AWS auction vs. Vast.ai P2P,
+  etc.). See the methodology document.
+- **No customer telemetry** — the data is supply/listing prices only.
+- **CPU/Unknown rows** — a non-trivial fraction of upstream rows have
+  `gpu_count = 0` or `gpu_type = 'Unknown'`. Filter these out for
+  most analyses.
+- **12-hour cadence** — too coarse for intraday auction analyses.
+
+Full methodology, provider-by-provider notes, and a list of analytical
+questions the data does and does not support:
+[methodology.md](https://github.com/alex-hubbard/gpu_price_tracker/blob/main/methodology.md)
+and
+[MODELING_GPU_USAGE_TRENDS.md](https://github.com/alex-hubbard/gpu_price_tracker/blob/main/MODELING_GPU_USAGE_TRENDS.md).
+
+## License
+
+CC BY 4.0. Suggested citation:
+
+```bibtex
+@misc{hubbard2026gpuprices,
+  author       = {Alex Hubbard},
+  title        = {GPU Price Tracker},
+  year         = {2026},
+  howpublished = {\url{https://github.com/alex-hubbard/gpu_price_tracker}},
+  note         = {Dataset and software, MIT (code) / CC BY 4.0 (data)}
+}
+```
+
+## Source code
+
+Collection pipeline, dashboard, and migration scripts live at
+<https://github.com/alex-hubbard/gpu_price_tracker>.