Adding CPU support (float32) and some additional comments.

app.py

@@ -27,7 +27,7 @@ from src.display.utils import (
     WeightType,
     Precision
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.envs import API, DEVICE, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
 
@@ -311,7 +311,7 @@ with demo:
                         choices=[i.value.name for i in Precision if i != Precision.Unknown],
                         label="Precision",
                         multiselect=False,
-                        value="float16",
+                        value="float16" if DEVICE != "cpu" else "float32",
                         interactive=True,
                     )
                     weight_type = gr.Dropdown(

src/envs.py

@@ -4,21 +4,31 @@ from huggingface_hub import HfApi
 
 # Info to change for your repository
 # ----------------------------------
-TOKEN = os.environ.get("TOKEN") # A read/write user token for a user that has access to your org
 
-OWNER = "Bias-Leaderboard" # Change to your org - don't forget to create a results and request file
-DEVICE = "cuda:0" # "cpu" # cuda:0 if you add compute
+# A read/write user access token for a user in the org
+# You can create these at https://huggingface.co/settings/tokens
+# This must be set as a 'Secret' in the leaderboard Space settings.
+TOKEN = os.environ.get("TOKEN")
+
+# Change to your org name
+OWNER = "Bias-Leaderboard"
+DEVICE = "cuda:0" # "cpu" or "cuda:0" if you add compute
 LIMIT = 20 # !!!! Should be None for actual evaluations!!!
 # ----------------------------------
 
+# Define some input/output variables.
+# Don't forget to create a results and requests Dataset for your org
+# Leaderboard Space
 REPO_ID = f"{OWNER}/leaderboard"
+# Leaderboard input Dataset
 QUEUE_REPO = f"{OWNER}/requests"
+# Leaderboard output Dataset
 RESULTS_REPO = f"{OWNER}/results"
 
-# If you setup a cache later, just change HF_HOME
+# If you setup a cache, set HF_HOME.
 CACHE_PATH=os.getenv("HF_HOME", ".")
 
-# Local caches
+# Local caches to read previously-submitted/computed results
 EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
 EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
 EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")