utility to push to the hub.

Author: sayakpaul

benchmarks/push_results.py

@@ -0,0 +1,75 @@
+import os
+
+import pandas as pd
+import torch
+from huggingface_hub import hf_hub_download, upload_file
+from huggingface_hub.utils import EntryNotFoundError
+
+
+if torch.cuda.is_available():
+    TOTAL_GPU_MEMORY = float(
+        os.getenv("TOTAL_GPU_MEMORY", torch.cuda.get_device_properties(0).total_memory / (1024**3))
+    )
+else:
+    raise
+
+REPO_ID = "diffusers/benchmarks"
+
+
+def has_previous_benchmark() -> str:
+    from run_all import FINAL_CSV_FILENAME
+
+    csv_path = None
+    try:
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
+    except EntryNotFoundError:
+        csv_path = None
+    return csv_path
+
+
+def filter_float(value):
+    if isinstance(value, str):
+        return float(value.split()[0])
+    return value
+
+
+def push_to_hf_dataset():
+    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
+
+    # If there's an existing benchmark file, we should report the changes.
+    csv_path = has_previous_benchmark()
+    if csv_path is not None:
+        current_results = pd.read_csv(FINAL_CSV_FILENAME)
+        previous_results = pd.read_csv(csv_path)
+
+        # identify the numeric columns we want to annotate
+        numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
+
+        # for each numeric column, append the old value in () if present
+        for column in numeric_columns:
+            # coerce any “x units” strings back to float
+            prev_vals = previous_results[column].map(filter_float)
+            # align indices in case rows were added/removed
+            prev_vals = prev_vals.reindex(current_results.index)
+
+            # build the new string: "current_value (previous_value)"
+            curr_str = current_results[column].astype(str)
+            prev_str = prev_vals.map(lambda x: f" ({x})" if pd.notnull(x) else "")
+
+            current_results[column] = curr_str + prev_str
+
+        # overwrite the CSV
+        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
+
+    commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
+    upload_file(
+        repo_id=REPO_ID,
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
+        repo_type="dataset",
+        commit_message=commit_message,
+    )
+
+
+if __name__ == "__main__":
+    push_to_hf_dataset()

benchmarks/run_all.py

@@ -1,12 +1,15 @@
 import glob
+import os
 import subprocess
+
 import pandas as pd
-import os
+
 
 PATTERN = "benchmarking_*.py"
 FINAL_CSV_FILENAME = "collated_results.csv"
 GITHUB_SHA = os.getenv("GITHUB_SHA", None)
 
+
 class SubprocessCallException(Exception):
     pass
 
@@ -33,7 +36,7 @@ def run_scripts():
     python_files = sorted(glob.glob(PATTERN))
 
     for file in python_files:
-       if file != "benchmarking_utils.py":
+        if file != "benchmarking_utils.py":
             print(f"****** Running file: {file} ******")
             command = f"python {file}"
             run_command(command.split())