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CPU Upgrade
Clémentine
Cleaned and refactored the code, improved filtering, added selection of deleted models
8c49cb6
| import json | |
| import os | |
| import pandas as pd | |
| from huggingface_hub import Repository | |
| from transformers import AutoConfig | |
| from src.assets.hardcoded_evals import baseline, gpt4_values, gpt35_values | |
| from src.display_models.get_model_metadata import apply_metadata | |
| from src.display_models.read_results import get_eval_results_dicts, make_clickable_model | |
| from src.display_models.utils import AutoEvalColumn, EvalQueueColumn, has_no_nan_values | |
| IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True)) | |
| def get_all_requested_models(requested_models_dir: str) -> set[str]: | |
| depth = 1 | |
| file_names = [] | |
| for root, _, files in os.walk(requested_models_dir): | |
| current_depth = root.count(os.sep) - requested_models_dir.count(os.sep) | |
| if current_depth == depth: | |
| file_names.extend([os.path.join(root, file) for file in files]) | |
| return set([file_name.lower().split("eval-queue/")[1] for file_name in file_names]) | |
| def load_all_info_from_hub(QUEUE_REPO: str, RESULTS_REPO: str, QUEUE_PATH: str, RESULTS_PATH: str) -> list[Repository]: | |
| eval_queue_repo = None | |
| eval_results_repo = None | |
| requested_models = None | |
| print("Pulling evaluation requests and results.") | |
| eval_queue_repo = Repository( | |
| local_dir=QUEUE_PATH, | |
| clone_from=QUEUE_REPO, | |
| repo_type="dataset", | |
| ) | |
| eval_queue_repo.git_pull() | |
| eval_results_repo = Repository( | |
| local_dir=RESULTS_PATH, | |
| clone_from=RESULTS_REPO, | |
| repo_type="dataset", | |
| ) | |
| eval_results_repo.git_pull() | |
| requested_models = get_all_requested_models("eval-queue") | |
| return eval_queue_repo, requested_models, eval_results_repo | |
| def get_leaderboard_df( | |
| eval_results: Repository, eval_results_private: Repository, cols: list, benchmark_cols: list | |
| ) -> pd.DataFrame: | |
| if eval_results: | |
| print("Pulling evaluation results for the leaderboard.") | |
| eval_results.git_pull() | |
| if eval_results_private: | |
| print("Pulling evaluation results for the leaderboard.") | |
| eval_results_private.git_pull() | |
| all_data = get_eval_results_dicts() | |
| if not IS_PUBLIC: | |
| all_data.append(gpt4_values) | |
| all_data.append(gpt35_values) | |
| all_data.append(baseline) | |
| apply_metadata(all_data) # Populate model type based on known hardcoded values in `metadata.py` | |
| df = pd.DataFrame.from_records(all_data) | |
| df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False) | |
| df = df[cols].round(decimals=2) | |
| # filter out if any of the benchmarks have not been produced | |
| df = df[has_no_nan_values(df, benchmark_cols)] | |
| return df | |
| def get_evaluation_queue_df( | |
| eval_queue: Repository, eval_queue_private: Repository, save_path: str, cols: list | |
| ) -> list[pd.DataFrame]: | |
| if eval_queue: | |
| print("Pulling changes for the evaluation queue.") | |
| eval_queue.git_pull() | |
| if eval_queue_private: | |
| print("Pulling changes for the evaluation queue.") | |
| eval_queue_private.git_pull() | |
| entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")] | |
| all_evals = [] | |
| for entry in entries: | |
| if ".json" in entry: | |
| file_path = os.path.join(save_path, entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| all_evals.append(data) | |
| elif ".md" not in entry: | |
| # this is a folder | |
| sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")] | |
| for sub_entry in sub_entries: | |
| file_path = os.path.join(save_path, entry, sub_entry) | |
| with open(file_path) as fp: | |
| data = json.load(fp) | |
| data[EvalQueueColumn.model.name] = make_clickable_model(data["model"]) | |
| data[EvalQueueColumn.revision.name] = data.get("revision", "main") | |
| all_evals.append(data) | |
| pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]] | |
| running_list = [e for e in all_evals if e["status"] == "RUNNING"] | |
| finished_list = [e for e in all_evals if e["status"].startswith("FINISHED")] | |
| df_pending = pd.DataFrame.from_records(pending_list, columns=cols) | |
| df_running = pd.DataFrame.from_records(running_list, columns=cols) | |
| df_finished = pd.DataFrame.from_records(finished_list, columns=cols) | |
| return df_finished[cols], df_running[cols], df_pending[cols] | |
| def is_model_on_hub(model_name: str, revision: str) -> bool: | |
| try: | |
| AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=False) | |
| return True, None | |
| except ValueError: | |
| return ( | |
| False, | |
| "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.", | |
| ) | |
| except Exception as e: | |
| print(f"Could not get the model config from the hub.: {e}") | |
| return False, "was not found on hub!" | |