Update README.md

README.md

@@ -1,39 +1,78 @@
----
-dataset_info:
-  features:
-  - name: depth
-    dtype: int64
-  - name: width
-    dtype: int64
-  - name: tokens
-    dtype: int64
-  - name: FLOPs_per_token
-    dtype: float64
-  - name: FLOPs
-    dtype: float64
-  - name: params
-    dtype: float64
-  - name: params_with_embeds
-    dtype: float64
-  - name: FLOPs_6N
-    dtype: float64
-  - name: params_pred_loss
-    dtype: float64
-  - name: wd_ratio
-    dtype: float64
-  - name: wd_pred_loss
-    dtype: float64
-  - name: bucket
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 1772
-    num_examples: 13
-  download_size: 6825
-  dataset_size: 1772
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: mins_1e-3/mins_lr_ablation_hot_width_depth_params_relaxed_params/train-*
----
+---
+dataset_info:
+  features:
+  - name: depth
+    dtype: int64
+  - name: width
+    dtype: int64
+  - name: tokens
+    dtype: int64
+  - name: FLOPs_per_token
+    dtype: float64
+  - name: FLOPs
+    dtype: float64
+  - name: params
+    dtype: float64
+  - name: params_with_embeds
+    dtype: float64
+  - name: FLOPs_6N
+    dtype: float64
+  - name: params_pred_loss
+    dtype: float64
+  - name: wd_ratio
+    dtype: float64
+  - name: wd_pred_loss
+    dtype: float64
+  - name: bucket
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1772
+    num_examples: 13
+  download_size: 6825
+  dataset_size: 1772
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: mins_1e-3/mins_lr_ablation_hot_width_depth_params_relaxed_params/train-*
+license: mit
+---
+This dataset is my cache for the [scaling-laws](https://github.com/mcleish7/gemstone-scaling-laws) related to the [gemstone models](https://huggingface.co/collections/tomg-group-umd/gemstone-models-679408ee3f19f1d4d00e8b10).
+
+In `data_cache` is the approach 3 data cache with the mins for `delta=1e-4`, the mins for `delta=1e-3` are in `mins_1e-3`.
+
+This is the code I used to upload it:
+```
+import pandas as pd
+from datasets import Dataset
+import os
+import gc
+
+
+def get_data_dict(path):
+    contents = os.listdir(path)
+
+    ds_store = {}
+    for i, file in enumerate(contents):
+        gc.collect()
+        df = pd.read_parquet(f"{path}{file}")
+        for col in df.columns:
+            if pd.api.types.is_interval_dtype(df[col]):
+                df[col] = df[col].astype(str)
+
+        hf_dataset = Dataset.from_pandas(df)
+        ds_store[file.replace(".parquet", "")] = hf_dataset
+        hf_dataset.push_to_hub(
+            "smcleish/scaling-laws-cache",
+            private=True,
+            data_dir=path.split("/")[1] + "/" + file.replace(".parquet", ""),
+        )
+        gc.collect()
+
+
+ds_1 = get_data_dict("plotters/data_cache/")
+ds_2 = get_data_dict("plotters/mins_1e-3/")
+```
+To download it do the oppostite of this. The cache is very large, so maybe target specific files you would like. The approach 3 code is expecting pandas `.parquet` files.
+Please open a discussion with any questions as this is currently very experimental.