Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 5,580 Bytes
ce5c604 bba982c ce5c604 a507ee8 ce5c604 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 |
import json
import os
import pandas as pd
from huggingface_hub import Repository
from transformers import AutoConfig
from collections import defaultdict
from src.assets.hardcoded_evals import baseline
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
def get_all_requested_models(requested_models_dir: str) -> set[str]:
depth = 1
file_names = []
users_to_submission_dates = defaultdict(list)
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:
for file in files:
if not file.endswith(".json"): continue
with open(os.path.join(root, file), "r") as f:
info = json.load(f)
file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
# Select organisation
if info["model"].count("/") == 0 or "submitted_time" not in info:
continue
organisation, _ = info["model"].split("/")
users_to_submission_dates[organisation].append(info["submitted_time"])
return set(file_names), users_to_submission_dates
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, users_to_submission_dates = get_all_requested_models("eval-queue")
return eval_queue_repo, requested_models, eval_results_repo, users_to_submission_dates
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()
# 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!"
|