Spaces:
Running
Running
File size: 12,626 Bytes
9166535 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 |
import os
import logging
import time
import datetime
import gradio as gr
import datasets
from huggingface_hub import snapshot_download, WebhooksServer, WebhookPayload, RepoCard
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
from src.display.about import (
CITATION_BUTTON_LABEL,
CITATION_BUTTON_TEXT,
EVALUATION_QUEUE_TEXT,
FAQ_TEXT,
INTRODUCTION_TEXT,
LLM_BENCHMARKS_TEXT,
TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
BENCHMARK_COLS,
COLS,
EVAL_COLS,
EVAL_TYPES,
AutoEvalColumn,
ModelType,
Precision,
WeightType,
fields,
)
from src.envs import (
API,
EVAL_REQUESTS_PATH,
AGGREGATED_REPO,
HF_TOKEN,
QUEUE_REPO,
REPO_ID,
HF_HOME,
)
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
# Convert the environment variable "LEADERBOARD_FULL_INIT" to a boolean value, defaulting to True if the variable is not set.
# This controls whether a full initialization should be performed.
DO_FULL_INIT = os.getenv("LEADERBOARD_FULL_INIT", "True") == "True"
LAST_UPDATE_LEADERBOARD = datetime.datetime.now()
def restart_space():
API.restart_space(repo_id=REPO_ID, token=HF_TOKEN)
def time_diff_wrapper(func):
def wrapper(*args, **kwargs):
start_time = time.time()
result = func(*args, **kwargs)
end_time = time.time()
diff = end_time - start_time
logging.info(f"Time taken for {func.__name__}: {diff} seconds")
return result
return wrapper
@time_diff_wrapper
def download_dataset(repo_id, local_dir, repo_type="dataset", max_attempts=3, backoff_factor=1.5):
"""Download dataset with exponential backoff retries."""
attempt = 0
while attempt < max_attempts:
try:
logging.info(f"Downloading {repo_id} to {local_dir}")
snapshot_download(
repo_id=repo_id,
local_dir=local_dir,
repo_type=repo_type,
tqdm_class=None,
etag_timeout=30,
max_workers=8,
)
logging.info("Download successful")
return
except Exception as e:
wait_time = backoff_factor**attempt
logging.error(f"Error downloading {repo_id}: {e}, retrying in {wait_time}s")
time.sleep(wait_time)
attempt += 1
raise Exception(f"Failed to download {repo_id} after {max_attempts} attempts")
def get_latest_data_leaderboard(leaderboard_initial_df = None):
current_time = datetime.datetime.now()
global LAST_UPDATE_LEADERBOARD
if current_time - LAST_UPDATE_LEADERBOARD < datetime.timedelta(minutes=10) and leaderboard_initial_df is not None:
return leaderboard_initial_df
LAST_UPDATE_LEADERBOARD = current_time
leaderboard_dataset = datasets.load_dataset(
AGGREGATED_REPO,
"default",
split="train",
cache_dir=HF_HOME,
download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, # Uses the cached dataset
verification_mode="no_checks"
)
leaderboard_df = get_leaderboard_df(
leaderboard_dataset=leaderboard_dataset,
cols=COLS,
benchmark_cols=BENCHMARK_COLS,
)
return leaderboard_df
def get_latest_data_queue():
eval_queue_dfs = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
return eval_queue_dfs
def init_space():
"""Initializes the application space, loading only necessary data."""
if DO_FULL_INIT:
# These downloads only occur on full initialization
try:
download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
except Exception:
restart_space()
# Always redownload the leaderboard DataFrame
leaderboard_df = get_latest_data_leaderboard()
# Evaluation queue DataFrame retrieval is independent of initialization detail level
eval_queue_dfs = get_latest_data_queue()
return leaderboard_df, eval_queue_dfs
# Calls the init_space function with the `full_init` parameter determined by the `do_full_init` variable.
# This initializes various DataFrames used throughout the application, with the level of initialization detail controlled by the `do_full_init` flag.
leaderboard_df, eval_queue_dfs = init_space()
finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = eval_queue_dfs
# Data processing for plots now only on demand in the respective Gradio tab
def load_and_create_plots():
plot_df = create_plot_df(create_scores_df(leaderboard_df))
return plot_df
def init_leaderboard(dataframe):
return Leaderboard(
value = dataframe,
datatype=[c.type for c in fields(AutoEvalColumn)],
select_columns=SelectColumns(
default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden or c.dummy],
label="Select Columns to Display:",
),
search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.fullname.name, AutoEvalColumn.license.name],
hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
filter_columns=[
ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
ColumnFilter(
AutoEvalColumn.params.name,
type="slider",
min=0.01,
max=150,
label="Select the number of parameters (B)",
),
ColumnFilter(
AutoEvalColumn.still_on_hub.name, type="boolean", label="Private or deleted", default=True
),
ColumnFilter(
AutoEvalColumn.merged.name, type="boolean", label="Contains a merge/moerge", default=True
),
ColumnFilter(AutoEvalColumn.moe.name, type="boolean", label="MoE", default=False),
ColumnFilter(AutoEvalColumn.not_flagged.name, type="boolean", label="Flagged", default=True),
],
bool_checkboxgroup_label="Hide models",
interactive=False,
)
demo = gr.Blocks(css=custom_css)
with demo:
gr.HTML(TITLE)
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
with gr.Tabs(elem_classes="tab-buttons") as tabs:
with gr.TabItem("π
LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
leaderboard = init_leaderboard(leaderboard_df)
with gr.TabItem("π Metrics through time", elem_id="llm-benchmark-tab-table", id=2):
with gr.Row():
with gr.Column():
plot_df = load_and_create_plots()
chart = create_metric_plot_obj(
plot_df,
[AutoEvalColumn.average.name],
title="Average of Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.Column():
plot_df = load_and_create_plots()
chart = create_metric_plot_obj(
plot_df,
BENCHMARK_COLS,
title="Top Scores and Human Baseline Over Time (from last update)",
)
gr.Plot(value=chart, min_width=500)
with gr.TabItem("π About", elem_id="llm-benchmark-tab-table", id=3):
gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
with gr.TabItem("βFAQ", elem_id="llm-benchmark-tab-table", id=4):
gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")
with gr.TabItem("π Submit? ", elem_id="llm-benchmark-tab-table", id=5):
countdown = gr.HTML(
"""<div align="center">
<div position: relative>
<img
src="https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard/resolve/main/gif.gif"
allowtransparency="true"
style="display:block;width:100%;height:auto;"
/>
<iframe
src="https://logwork.com/widget/countdown/?text=Surprise%20loading...&timezone=Europe%2FParis&width=&style=circles&uid=815898&loc=https://logwork.com/countdown-fxmc&language=en&textcolor=&background=%23ffd21e&date=2024-06-26%2015%3A00%3A00&digitscolor=%23ff9d00&unitscolor=&"
style="position: absolute; top:0; left: 0; border: medium; width:100%; height:100%; margin: 0px; visibility: visible;"
scrolling="no"
allowtransparency="true"
frameborder="0"
allowfullscreen
/>
</div>
</div>"""
)
#gif = gr.Image(value="./gif.gif", interactive=False)
gr.Markdown("*Countdown by Logwork.com, gif art by Chun Te Lee*")
with gr.Row():
with gr.Accordion("π Citation", open=False):
citation_button = gr.Textbox(
value=CITATION_BUTTON_TEXT,
label=CITATION_BUTTON_LABEL,
lines=20,
elem_id="citation-button",
show_copy_button=True,
)
demo.load(fn=get_latest_data_leaderboard, inputs=[leaderboard], outputs=[leaderboard])
demo.queue(default_concurrency_limit=40)
# Start ephemeral Spaces on PRs (see config in README.md)
from gradio_space_ci.webhook import IS_EPHEMERAL_SPACE, SPACE_ID, configure_space_ci
def enable_space_ci_and_return_server(ui: gr.Blocks) -> WebhooksServer:
# Taken from https://huggingface.co/spaces/Wauplin/gradio-space-ci/blob/075119aee75ab5e7150bf0814eec91c83482e790/src/gradio_space_ci/webhook.py#L61
# Compared to original, this one do not monkeypatch Gradio which allows us to define more webhooks.
# ht to Lucain!
if SPACE_ID is None:
print("Not in a Space: Space CI disabled.")
return WebhooksServer(ui=demo)
if IS_EPHEMERAL_SPACE:
print("In an ephemeral Space: Space CI disabled.")
return WebhooksServer(ui=demo)
card = RepoCard.load(repo_id_or_path=SPACE_ID, repo_type="space")
config = card.data.get("space_ci", {})
print(f"Enabling Space CI with config from README: {config}")
return configure_space_ci(
blocks=ui,
trusted_authors=config.get("trusted_authors"),
private=config.get("private", "auto"),
variables=config.get("variables", "auto"),
secrets=config.get("secrets"),
hardware=config.get("hardware"),
storage=config.get("storage"),
)
# Create webhooks server (with CI url if in Space and not ephemeral)
webhooks_server = enable_space_ci_and_return_server(ui=demo)
# Add webhooks
@webhooks_server.add_webhook
def update_leaderboard(payload: WebhookPayload) -> None:
"""Redownloads the leaderboard dataset each time it updates"""
if payload.repo.type == "dataset" and payload.event.action == "update":
datasets.load_dataset(
AGGREGATED_REPO,
"default",
split="train",
cache_dir=HF_HOME,
download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD,
verification_mode="no_checks"
)
# The below code is not used at the moment, as we can manage the queue file locally
LAST_UPDATE_QUEUE = datetime.datetime.now()
@webhooks_server.add_webhook
def update_queue(payload: WebhookPayload) -> None:
"""Redownloads the queue dataset each time it updates"""
if payload.repo.type == "dataset" and payload.event.action == "update":
current_time = datetime.datetime.now()
global LAST_UPDATE_QUEUE
if current_time - LAST_UPDATE_QUEUE > datetime.timedelta(minutes=10):
print("Would have updated the queue")
# We only redownload is last update was more than 10 minutes ago, as the queue is
# updated regularly and heavy to download
#download_dataset(QUEUE_REPO, EVAL_REQUESTS_PATH)
LAST_UPDATE_QUEUE = datetime.datetime.now()
webhooks_server.launch()
|