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Running
on
Zero
Running
on
Zero
File size: 2,601 Bytes
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import glob
import sys
import pandas as pd
from huggingface_hub import hf_hub_download, upload_file
from huggingface_hub.utils._errors import EntryNotFoundError
sys.path.append(".")
from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv # noqa: E402
def has_previous_benchmark() -> str:
csv_path = None
try:
csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILE)
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():
all_csvs = sorted(glob.glob(f"{BASE_PATH}/*.csv"))
collate_csv(all_csvs, FINAL_CSV_FILE)
# 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_FILE)
previous_results = pd.read_csv(csv_path)
numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
numeric_columns = [
c for c in numeric_columns if c not in ["batch_size", "num_inference_steps", "actual_gpu_memory (gbs)"]
]
for column in numeric_columns:
previous_results[column] = previous_results[column].map(lambda x: filter_float(x))
# Calculate the percentage change
current_results[column] = current_results[column].astype(float)
previous_results[column] = previous_results[column].astype(float)
percent_change = ((current_results[column] - previous_results[column]) / previous_results[column]) * 100
# Format the values with '+' or '-' sign and append to original values
current_results[column] = current_results[column].map(str) + percent_change.map(
lambda x: f" ({'+' if x > 0 else ''}{x:.2f}%)"
)
# There might be newly added rows. So, filter out the NaNs.
current_results[column] = current_results[column].map(lambda x: x.replace(" (nan%)", ""))
# Overwrite the current result file.
current_results.to_csv(FINAL_CSV_FILE, 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_FILE,
path_or_fileobj=FINAL_CSV_FILE,
repo_type="dataset",
commit_message=commit_message,
)
if __name__ == "__main__":
push_to_hf_dataset()
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