from huggingface_hub import HfFileSystem import pandas as pd from utils import logger from datetime import datetime import threading import traceback import json import re # NOTE: if caching is an issue, try adding `use_listings_cache=False` fs = HfFileSystem() IMPORTANT_MODELS = [ "auto", "bert", # old but dominant (encoder only) "gpt2", # old (decoder) "t5", # old (encoder-decoder) "modernbert", # (encoder only) "vit", # old (vision) - fixed comma "clip", # old but dominant (vision) "detr", # objection detection, segmentation (vision) "table-transformer", # objection detection (visioin) - maybe just detr? "got_ocr2", # ocr (vision) "whisper", # old but dominant (audio) "wav2vec2", # old (audio) "llama", # new and dominant (meta) "gemma3", # new (google) "qwen2", # new (Alibaba) "mistral3", # new (Mistral) - added missing comma "qwen2_5_vl", # new (vision) "llava", # many models from it (vision) "smolvlm", # new (video) "internvl", # new (video) "gemma3n", # new (omnimodal models) "qwen2_5_omni", # new (omnimodal models) ] KEYS_TO_KEEP = [ "success_amd", "success_nvidia", "skipped_amd", "skipped_nvidia", "failed_multi_no_amd", "failed_multi_no_nvidia", "failed_single_no_amd", "failed_single_no_nvidia", "failures_amd", "failures_nvidia", "job_link_amd", "job_link_nvidia", ] def log_dataframe_link(link: str) -> str: """ Adds the link to the dataset in the logs, modifies it to get a clockable link and then returns the date of the report. """ logger.info(f"Reading df located at {link}") # Make sure the links starts with an http adress if link.startswith("hf://"): link = "https://huggingface.co/" + link.removeprefix("hf://") # Pattern to match transformers_daily_ci followed by any path, then a date (YYYY-MM-DD format) pattern = r'transformers_daily_ci(.*?)/(\d{4}-\d{2}-\d{2})' match = re.search(pattern, link) # Failure case: if not match: logger.error("Could not find transformers_daily_ci and.or date in the link") return "9999-99-99" # Replace the path between with blob/main path_between = match.group(1) link = link.replace("transformers_daily_ci" + path_between, "transformers_daily_ci/blob/main") logger.info(f"Link to data source: {link}") # Return the date return match.group(2) def infer_latest_update_msg(date_df_amd: str, date_df_nvidia: str) -> str: # Early return if one of the dates is invalid if date_df_amd.startswith("9999") and date_df_nvidia.startswith("9999"): return "could not find last update time" # Warn if dates are not the same if date_df_amd != date_df_nvidia: logger.warning(f"Different dates found: {date_df_amd} (AMD) vs {date_df_nvidia} (NVIDIA)") # Take the latest date and format it try: latest_date = max(date_df_amd, date_df_nvidia) yyyy, mm, dd = latest_date.split("-") return f"last updated {mm}/{dd}/{yyyy}" except Exception as e: logger.error(f"When trying to infer latest date, got error {e}") return "could not find last update time" def read_one_dataframe(json_path: str, device_label: str) -> tuple[pd.DataFrame, str]: df_upload_date = log_dataframe_link(json_path) df = pd.read_json(json_path, orient="index") df.index.name = "model_name" df[f"failed_multi_no_{device_label}"] = df["failures"].apply(lambda x: len(x["multi"]) if "multi" in x else 0) df[f"failed_single_no_{device_label}"] = df["failures"].apply(lambda x: len(x["single"]) if "single" in x else 0) return df, df_upload_date def get_first_working_df(file_list: list[str]) -> str: for file in file_list: job_links = file.rsplit('/', 1)[0] + "/job_links.json" try: links = pd.read_json(f"hf://{job_links}", typ="series") has_one_working_link = any(links.values) except Exception as e: logger.error(f"Could not read job links from {job_links}: {e}") has_one_working_link = False if has_one_working_link: return file logger.warning(f"Skipping {file} as it has no working job links.") raise RuntimeError("Could not find any working dataframe in the provided list.") def get_distant_data() -> tuple[pd.DataFrame, str]: # Retrieve AMD dataframe amd_src = "hf://datasets/optimum-amd/transformers_daily_ci/**/runs/**/ci_results_run_models_gpu/model_results.json" files_amd = sorted(fs.glob(amd_src, refresh=True), reverse=True) file_amd = get_first_working_df(files_amd) df_amd, date_df_amd = read_one_dataframe(f"hf://{file_amd}", "amd") # Retrieve NVIDIA dataframe, which pattern should be: # hf://datasets/hf-internal-testing`/transformers_daily_ci/raw/main/YYYY-MM-DD/ci_results_run_models_gpu/model_results.json nvidia_src = "hf://datasets/hf-internal-testing/transformers_daily_ci/*/ci_results_run_models_gpu/model_results.json" files_nvidia = sorted(fs.glob(nvidia_src, refresh=True), reverse=True) # NOTE: should this be removeprefix instead of lstrip? nvidia_path = files_nvidia[0].lstrip('datasets/hf-internal-testing/transformers_daily_ci/') nvidia_path = "https://huggingface.co/datasets/hf-internal-testing/transformers_daily_ci/raw/main/" + nvidia_path df_nvidia, date_df_nvidia = read_one_dataframe(nvidia_path, "nvidia") # Infer and format the latest df date latest_update_msg = infer_latest_update_msg(date_df_amd, date_df_nvidia) # Join both dataframes joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer") joined = joined[KEYS_TO_KEEP] joined.index = joined.index.str.replace("^models_", "", regex=True) # Fitler out all but important models important_models_lower = [model.lower() for model in IMPORTANT_MODELS] filtered_joined = joined[joined.index.str.lower().isin(important_models_lower)] # Warn for ach missing important models for model in IMPORTANT_MODELS: if model not in filtered_joined.index: print(f"[WARNING] Model {model} was missing from index.") return filtered_joined, latest_update_msg def get_sample_data() -> tuple[pd.DataFrame, str]: # Retrieve sample dataframes df_amd, _ = read_one_dataframe("sample_amd.json", "amd") df_nvidia, _ = read_one_dataframe("sample_nvidia.json", "nvidia") # Join both dataframes joined = df_amd.join(df_nvidia, rsuffix="_nvidia", lsuffix="_amd", how="outer") joined = joined[KEYS_TO_KEEP] joined.index = joined.index.str.replace("^models_", "", regex=True) # Fitler out all but important models important_models_lower = [model.lower() for model in IMPORTANT_MODELS] filtered_joined = joined[joined.index.str.lower().isin(important_models_lower)] # Prefix all model names with "sample_" filtered_joined.index = "sample_" + filtered_joined.index return filtered_joined, "sample data was loaded" def safe_extract(row: pd.DataFrame, key: str) -> int: return int(row.get(key, 0)) if pd.notna(row.get(key, 0)) else 0 def extract_model_data(row: pd.Series) -> tuple[dict[str, int], dict[str, int], int, int, int, int]: """Extract and process model data from DataFrame row.""" # Handle missing values and get counts directly from dataframe success_nvidia = safe_extract(row, "success_nvidia") success_amd = safe_extract(row, "success_amd") skipped_nvidia = safe_extract(row, "skipped_nvidia") skipped_amd = safe_extract(row, "skipped_amd") failed_multi_amd = safe_extract(row, 'failed_multi_no_amd') failed_multi_nvidia = safe_extract(row, 'failed_multi_no_nvidia') failed_single_amd = safe_extract(row, 'failed_single_no_amd') failed_single_nvidia = safe_extract(row, 'failed_single_no_nvidia') # Calculate total failures total_failed_amd = failed_multi_amd + failed_single_amd total_failed_nvidia = failed_multi_nvidia + failed_single_nvidia # Create stats dictionaries directly from dataframe values amd_stats = { 'passed': success_amd, 'failed': total_failed_amd, 'skipped': skipped_amd, 'error': 0 # Not available in this dataset } nvidia_stats = { 'passed': success_nvidia, 'failed': total_failed_nvidia, 'skipped': skipped_nvidia, 'error': 0 # Not available in this dataset } return amd_stats, nvidia_stats, failed_multi_amd, failed_single_amd, failed_multi_nvidia, failed_single_nvidia class CIResults: def __init__(self): self.df = pd.DataFrame() self.available_models = [] self.latest_update_msg = "" def load_data(self) -> None: """Load data from the data source.""" # Try loading the distant data, and fall back on sample data for local tinkering try: logger.info("Loading distant data...") new_df, latest_update_msg = get_distant_data() self.latest_update_msg = latest_update_msg except Exception as e: error_msg = [ "Loading data failed:", "-" * 120, traceback.format_exc(), "-" * 120, "Falling back on sample data." ] logger.error("\n".join(error_msg)) new_df, latest_update_msg = get_sample_data() self.latest_update_msg = latest_update_msg # Update attributes self.df = new_df self.available_models = new_df.index.tolist() # Log and return distant load status logger.info(f"Data loaded successfully: {len(self.available_models)} models") logger.info(f"Models: {self.available_models[:5]}{'...' if len(self.available_models) > 5 else ''}") logger.info(f"Latest update message: {self.latest_update_msg}") # Log a preview of the df msg = {} for model in self.available_models[:3]: msg[model] = {} for col in self.df.columns: value = self.df.loc[model, col] if not isinstance(value, int): value = str(value) if len(value) > 10: value = value[:10] + "..." msg[model][col] = value logger.info(json.dumps(msg, indent=4)) def schedule_data_reload(self): """Schedule the next data reload.""" def reload_data(): self.load_data() # Schedule the next reload in 15 minutes (900 seconds) timer = threading.Timer(900.0, reload_data) timer.daemon = True # Dies when main thread dies timer.start() logger.info("Next data reload scheduled in 15 minutes") # Start the first reload timer timer = threading.Timer(900.0, reload_data) timer.daemon = True timer.start() logger.info("Data auto-reload scheduled every 15 minutes")