Add a custom subtensor endpoint

app.py

@@ -31,6 +31,7 @@ BENCHMARK_WANDB_PROJECT = "raofoundation/pretraining-leaderboard-data"
 H4_TOKEN = os.environ.get("H4_TOKEN", None)
 API = HfApi(token=H4_TOKEN)
 WANDB_TOKEN = os.environ.get("WANDB_API_KEY", None)
+SUBTENSOR_ENDPOINT=os.environ.get("SUBTENSOR_ENDPOINT", None)
 REPO_ID = "RaoFoundation/pretraining-leaderboard"
 MAX_AVG_LOSS_POINTS = 1
 RETRIES = 5

artifacts/run-zwsn6wun-benchmarks:v0/benchmarks.table.json

@@ -0,0 +1 @@
+{"_type": "table", "column_types": {"params": {"type_map": {"Falcon Refined Web (PPL)": {"params": {"allowed_types": [{"wb_type": "none"}, {"wb_type": "number"}]}, "wb_type": "union"}, "Model": {"params": {"allowed_types": [{"wb_type": "none"}, {"wb_type": "string"}]}, "wb_type": "union"}, "Size": {"params": {"allowed_types": [{"wb_type": "none"}, {"wb_type": "string"}]}, "wb_type": "union"}, "Wikitext103 (PPL)": {"params": {"allowed_types": [{"wb_type": "none"}, {"wb_type": "number"}]}, "wb_type": "union"}}}, "wb_type": "typedDict"}, "columns": ["Model", "Size", "Wikitext103 (PPL)", "Falcon Refined Web (PPL)"], "data": [["Lucia-no/net9_key3", "183M", 27.377832412719727, 20.48467254638672], ["gpt2", "124M", 29.881267547607422, 34.4271125793457]], "ncols": 4, "nrows": 2}

requirements.txt

@@ -5,3 +5,4 @@ python-dotenv==1.0.1
 APScheduler==3.10.1
 huggingface-hub>=0.18.0
 pandas==2.2.0
+gradio==3.41.0