Spaces:
Sleeping
Sleeping
File size: 39,407 Bytes
b2ba7d2 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 |
"""The main page for the Trackio UI."""
import os
import re
import secrets
import shutil
from dataclasses import dataclass
from typing import Any
import gradio as gr
import numpy as np
import pandas as pd
try:
import trackio.utils as utils
from trackio.file_storage import FileStorage
from trackio.media import TrackioImage, TrackioVideo
from trackio.sqlite_storage import SQLiteStorage
from trackio.table import Table
from trackio.typehints import LogEntry, UploadEntry
from trackio.ui import fns
from trackio.ui.helpers.run_selection import RunSelection
from trackio.ui.run_detail import run_detail_page
from trackio.ui.runs import run_page
except ImportError:
import utils
from file_storage import FileStorage
from media import TrackioImage, TrackioVideo
from sqlite_storage import SQLiteStorage
from table import Table
from typehints import LogEntry, UploadEntry
from ui import fns
from ui.helpers.run_selection import RunSelection
from ui.run_detail import run_detail_page
from ui.runs import run_page
INSTRUCTIONS_SPACES = """
## Start logging with Trackio 🤗
To start logging to this Trackio dashboard, first make sure you have the Trackio library installed. You can do this by running:
```bash
pip install trackio
```
Then, start logging to this Trackio dashboard by passing in the `space_id` to `trackio.init()`:
```python
import trackio
trackio.init(project="my-project", space_id="{}")
```
Then call `trackio.log()` to log metrics.
```python
for i in range(10):
trackio.log({{"loss": 1/(i+1)}})
```
Finally, call `trackio.finish()` to finish the run.
```python
trackio.finish()
```
"""
INSTRUCTIONS_LOCAL = """
## Start logging with Trackio 🤗
You can create a new project by calling `trackio.init()`:
```python
import trackio
trackio.init(project="my-project")
```
Then call `trackio.log()` to log metrics.
```python
for i in range(10):
trackio.log({"loss": 1/(i+1)})
```
Finally, call `trackio.finish()` to finish the run.
```python
trackio.finish()
```
Read the [Trackio documentation](https://huggingface.co/docs/trackio/en/index) for more examples.
"""
def get_runs(project) -> list[str]:
if not project:
return []
return SQLiteStorage.get_runs(project)
def get_available_metrics(project: str, runs: list[str]) -> list[str]:
"""Get all available metrics across all runs for x-axis selection."""
if not project or not runs:
return ["step", "time"]
all_metrics = set()
for run in runs:
metrics = SQLiteStorage.get_logs(project, run)
if metrics:
df = pd.DataFrame(metrics)
numeric_cols = df.select_dtypes(include="number").columns
numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]
all_metrics.update(numeric_cols)
all_metrics.add("step")
all_metrics.add("time")
sorted_metrics = utils.sort_metrics_by_prefix(list(all_metrics))
result = ["step", "time"]
for metric in sorted_metrics:
if metric not in result:
result.append(metric)
return result
@dataclass
class MediaData:
caption: str | None
file_path: str
def extract_media(logs: list[dict]) -> dict[str, list[MediaData]]:
media_by_key: dict[str, list[MediaData]] = {}
logs = sorted(logs, key=lambda x: x.get("step", 0))
for log in logs:
for key, value in log.items():
if isinstance(value, dict):
type = value.get("_type")
if type == TrackioImage.TYPE or type == TrackioVideo.TYPE:
if key not in media_by_key:
media_by_key[key] = []
try:
media_data = MediaData(
file_path=utils.MEDIA_DIR / value.get("file_path"),
caption=value.get("caption"),
)
media_by_key[key].append(media_data)
except Exception as e:
print(f"Media currently unavailable: {key}: {e}")
return media_by_key
def load_run_data(
project: str | None,
run: str | None,
smoothing_granularity: int,
x_axis: str,
log_scale: bool = False,
) -> tuple[pd.DataFrame, dict]:
if not project or not run:
return None, None
logs = SQLiteStorage.get_logs(project, run)
if not logs:
return None, None
media = extract_media(logs)
df = pd.DataFrame(logs)
if "step" not in df.columns:
df["step"] = range(len(df))
if x_axis == "time" and "timestamp" in df.columns:
df["timestamp"] = pd.to_datetime(df["timestamp"])
first_timestamp = df["timestamp"].min()
df["time"] = (df["timestamp"] - first_timestamp).dt.total_seconds()
x_column = "time"
elif x_axis == "step":
x_column = "step"
else:
x_column = x_axis
if log_scale and x_column in df.columns:
x_vals = df[x_column]
if (x_vals <= 0).any():
df[x_column] = np.log10(np.maximum(x_vals, 0) + 1)
else:
df[x_column] = np.log10(x_vals)
if smoothing_granularity > 0:
numeric_cols = df.select_dtypes(include="number").columns
numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]
df_original = df.copy()
df_original["run"] = run
df_original["data_type"] = "original"
df_smoothed = df.copy()
window_size = max(3, min(smoothing_granularity, len(df)))
df_smoothed[numeric_cols] = (
df_smoothed[numeric_cols]
.rolling(window=window_size, center=True, min_periods=1)
.mean()
)
df_smoothed["run"] = f"{run}_smoothed"
df_smoothed["data_type"] = "smoothed"
combined_df = pd.concat([df_original, df_smoothed], ignore_index=True)
combined_df["x_axis"] = x_column
return combined_df, media
else:
df["run"] = run
df["data_type"] = "original"
df["x_axis"] = x_column
return df, media
def refresh_runs(
project: str | None,
filter_text: str | None,
selection: RunSelection,
selected_runs_from_url: list[str] | None = None,
):
if project is None:
runs: list[str] = []
else:
runs = get_runs(project)
if filter_text:
runs = [r for r in runs if filter_text in r]
preferred = None
if selected_runs_from_url:
preferred = [r for r in runs if r in selected_runs_from_url]
did_change = selection.update_choices(runs, preferred)
return (
fns.run_checkbox_update(selection) if did_change else gr.CheckboxGroup(),
gr.Textbox(label=f"Runs ({len(runs)})"),
selection,
)
def generate_embed(project: str, metrics: str, selection: RunSelection) -> str:
return utils.generate_embed_code(project, metrics, selection.selected)
def update_x_axis_choices(project, selection):
"""Update x-axis dropdown choices based on available metrics."""
runs = selection.selected
available_metrics = get_available_metrics(project, runs)
return gr.Dropdown(
label="X-axis",
choices=available_metrics,
value="step",
)
def toggle_timer(cb_value):
if cb_value:
return gr.Timer(active=True)
else:
return gr.Timer(active=False)
def upload_db_to_space(
project: str, uploaded_db: gr.FileData, hf_token: str | None
) -> None:
"""
Uploads the database of a local Trackio project to a Hugging Face Space.
"""
fns.check_hf_token_has_write_access(hf_token)
db_project_path = SQLiteStorage.get_project_db_path(project)
if os.path.exists(db_project_path):
raise gr.Error(
f"Trackio database file already exists for project {project}, cannot overwrite."
)
os.makedirs(os.path.dirname(db_project_path), exist_ok=True)
shutil.copy(uploaded_db["path"], db_project_path)
def bulk_upload_media(uploads: list[UploadEntry], hf_token: str | None) -> None:
"""
Uploads media files to a Trackio dashboard. Each entry in the list is a tuple of the project, run, and media file to be uploaded.
"""
fns.check_hf_token_has_write_access(hf_token)
for upload in uploads:
media_path = FileStorage.init_project_media_path(
upload["project"], upload["run"], upload["step"]
)
shutil.copy(upload["uploaded_file"]["path"], media_path)
def log(
project: str,
run: str,
metrics: dict[str, Any],
step: int | None,
hf_token: str | None,
) -> None:
"""
Note: this method is not used in the latest versions of Trackio (replaced by bulk_log) but
is kept for backwards compatibility for users who are connecting to a newer version of
a Trackio Spaces dashboard with an older version of Trackio installed locally.
"""
fns.check_hf_token_has_write_access(hf_token)
SQLiteStorage.log(project=project, run=run, metrics=metrics, step=step)
def bulk_log(
logs: list[LogEntry],
hf_token: str | None,
) -> None:
"""
Logs a list of metrics to a Trackio dashboard. Each entry in the list is a dictionary of the project, run, a dictionary of metrics, and optionally, a step and config.
"""
fns.check_hf_token_has_write_access(hf_token)
logs_by_run = {}
for log_entry in logs:
key = (log_entry["project"], log_entry["run"])
if key not in logs_by_run:
logs_by_run[key] = {"metrics": [], "steps": [], "config": None}
logs_by_run[key]["metrics"].append(log_entry["metrics"])
logs_by_run[key]["steps"].append(log_entry.get("step"))
if log_entry.get("config") and logs_by_run[key]["config"] is None:
logs_by_run[key]["config"] = log_entry["config"]
for (project, run), data in logs_by_run.items():
SQLiteStorage.bulk_log(
project=project,
run=run,
metrics_list=data["metrics"],
steps=data["steps"],
config=data["config"],
)
def get_metric_values(
project: str,
run: str,
metric_name: str,
) -> list[dict]:
"""
Get all values for a specific metric in a project/run.
Returns a list of dictionaries with timestamp, step, and value.
"""
return SQLiteStorage.get_metric_values(project, run, metric_name)
def get_runs_for_project(
project: str,
) -> list[str]:
"""
Get all runs for a given project.
Returns a list of run names.
"""
return SQLiteStorage.get_runs(project)
def get_metrics_for_run(
project: str,
run: str,
) -> list[str]:
"""
Get all metrics for a given project and run.
Returns a list of metric names.
"""
return SQLiteStorage.get_all_metrics_for_run(project, run)
def filter_metrics_by_regex(metrics: list[str], filter_pattern: str) -> list[str]:
"""
Filter metrics using regex pattern.
Args:
metrics: List of metric names to filter
filter_pattern: Regex pattern to match against metric names
Returns:
List of metric names that match the pattern
"""
if not filter_pattern.strip():
return metrics
try:
pattern = re.compile(filter_pattern, re.IGNORECASE)
return [metric for metric in metrics if pattern.search(metric)]
except re.error:
return [
metric for metric in metrics if filter_pattern.lower() in metric.lower()
]
def get_all_projects() -> list[str]:
"""
Get all project names.
Returns a list of project names.
"""
return SQLiteStorage.get_projects()
def get_project_summary(project: str) -> dict:
"""
Get a summary of a project including number of runs and recent activity.
Args:
project: Project name
Returns:
Dictionary with project summary information
"""
runs = SQLiteStorage.get_runs(project)
if not runs:
return {"project": project, "num_runs": 0, "runs": [], "last_activity": None}
last_steps = SQLiteStorage.get_max_steps_for_runs(project)
return {
"project": project,
"num_runs": len(runs),
"runs": runs,
"last_activity": max(last_steps.values()) if last_steps else None,
}
def get_run_summary(project: str, run: str) -> dict:
"""
Get a summary of a specific run including metrics and configuration.
Args:
project: Project name
run: Run name
Returns:
Dictionary with run summary information
"""
logs = SQLiteStorage.get_logs(project, run)
metrics = SQLiteStorage.get_all_metrics_for_run(project, run)
if not logs:
return {
"project": project,
"run": run,
"num_logs": 0,
"metrics": [],
"config": None,
"last_step": None,
}
df = pd.DataFrame(logs)
config = logs[0].get("config") if logs else None
last_step = df["step"].max() if "step" in df.columns else len(logs) - 1
return {
"project": project,
"run": run,
"num_logs": len(logs),
"metrics": metrics,
"config": config,
"last_step": last_step,
}
def configure(request: gr.Request):
sidebar_param = request.query_params.get("sidebar")
match sidebar_param:
case "collapsed":
sidebar = gr.Sidebar(open=False, visible=True)
case "hidden":
sidebar = gr.Sidebar(open=False, visible=False)
case _:
sidebar = gr.Sidebar(open=True, visible=True)
metrics_param = request.query_params.get("metrics", "")
runs_param = request.query_params.get("runs", "")
selected_runs = runs_param.split(",") if runs_param else []
navbar_param = request.query_params.get("navbar")
match navbar_param:
case "hidden":
navbar = gr.Navbar(visible=False)
case _:
navbar = gr.Navbar(visible=True)
return [], sidebar, metrics_param, selected_runs, navbar
def create_media_section(media_by_run: dict[str, dict[str, list[MediaData]]]):
with gr.Accordion(label="media"):
with gr.Group(elem_classes=("media-group")):
for run, media_by_key in media_by_run.items():
with gr.Tab(label=run, elem_classes=("media-tab")):
for key, media_item in media_by_key.items():
gr.Gallery(
[(item.file_path, item.caption) for item in media_item],
label=key,
columns=6,
elem_classes=("media-gallery"),
)
css = """
#run-cb .wrap { gap: 2px; }
#run-cb .wrap label {
line-height: 1;
padding: 6px;
}
.logo-light { display: block; }
.logo-dark { display: none; }
.dark .logo-light { display: none; }
.dark .logo-dark { display: block; }
.dark .caption-label { color: white; }
.info-container {
position: relative;
display: inline;
}
.info-checkbox {
position: absolute;
opacity: 0;
pointer-events: none;
}
.info-icon {
border-bottom: 1px dotted;
cursor: pointer;
user-select: none;
color: var(--color-accent);
}
.info-expandable {
display: none;
opacity: 0;
transition: opacity 0.2s ease-in-out;
}
.info-checkbox:checked ~ .info-expandable {
display: inline;
opacity: 1;
}
.info-icon:hover { opacity: 0.8; }
.accent-link { font-weight: bold; }
.media-gallery .fixed-height { min-height: 275px; }
.media-group, .media-group > div { background: none; }
.media-group .tabs { padding: 0.5em; }
.media-tab { max-height: 500px; overflow-y: scroll; }
"""
javascript = """
<script>
function setCookie(name, value, days) {
var expires = "";
if (days) {
var date = new Date();
date.setTime(date.getTime() + (days * 24 * 60 * 60 * 1000));
expires = "; expires=" + date.toUTCString();
}
document.cookie = name + "=" + (value || "") + expires + "; path=/; SameSite=Lax";
}
function getCookie(name) {
var nameEQ = name + "=";
var ca = document.cookie.split(';');
for(var i=0;i < ca.length;i++) {
var c = ca[i];
while (c.charAt(0)==' ') c = c.substring(1,c.length);
if (c.indexOf(nameEQ) == 0) return c.substring(nameEQ.length,c.length);
}
return null;
}
(function() {
const urlParams = new URLSearchParams(window.location.search);
const writeToken = urlParams.get('write_token');
if (writeToken) {
setCookie('trackio_write_token', writeToken, 7);
// Only remove write_token from URL if not in iframe
// In iframes, keep it in URL as cookies may be blocked
const inIframe = window.self !== window.top;
if (!inIframe) {
urlParams.delete('write_token');
const newUrl = window.location.pathname +
(urlParams.toString() ? '?' + urlParams.toString() : '') +
window.location.hash;
window.history.replaceState({}, document.title, newUrl);
}
}
})();
</script>
"""
gr.set_static_paths(paths=[utils.MEDIA_DIR])
with gr.Blocks(title="Trackio Dashboard", css=css, head=javascript) as demo:
with gr.Sidebar(open=False) as sidebar:
logo = gr.Markdown(
f"""
<img src='/gradio_api/file={utils.TRACKIO_LOGO_DIR}/trackio_logo_type_light_transparent.png' width='80%' class='logo-light'>
<img src='/gradio_api/file={utils.TRACKIO_LOGO_DIR}/trackio_logo_type_dark_transparent.png' width='80%' class='logo-dark'>
"""
)
project_dd = gr.Dropdown(label="Project", allow_custom_value=True)
embed_code = gr.Code(
label="Embed this view",
max_lines=2,
lines=2,
language="html",
visible=bool(os.environ.get("SPACE_HOST")),
)
with gr.Group():
run_tb = gr.Textbox(label="Runs", placeholder="Type to filter...")
run_group_by_dd = gr.Dropdown(label="Group by...", choices=[], value=None)
grouped_runs_panel = gr.Group(visible=False)
run_cb = gr.CheckboxGroup(
label="Runs",
choices=[],
interactive=True,
elem_id="run-cb",
show_select_all=True,
)
gr.HTML("<hr>")
realtime_cb = gr.Checkbox(label="Refresh metrics realtime", value=True)
smoothing_slider = gr.Slider(
label="Smoothing Factor",
minimum=0,
maximum=20,
value=10,
step=1,
info="0 = no smoothing",
)
x_axis_dd = gr.Dropdown(
label="X-axis",
choices=["step", "time"],
value="step",
)
log_scale_cb = gr.Checkbox(label="Log scale X-axis", value=False)
metric_filter_tb = gr.Textbox(
label="Metric Filter (regex)",
placeholder="e.g., loss|ndcg@10|gpu",
value="",
info="Filter metrics using regex patterns. Leave empty to show all metrics.",
)
navbar = gr.Navbar(value=[("Metrics", ""), ("Runs", "/runs")], main_page_name=False)
timer = gr.Timer(value=1)
metrics_subset = gr.State([])
selected_runs_from_url = gr.State([])
run_selection_state = gr.State(RunSelection())
gr.on(
[demo.load],
fn=configure,
outputs=[
metrics_subset,
sidebar,
metric_filter_tb,
selected_runs_from_url,
navbar,
],
queue=False,
api_name=False,
)
gr.on(
[demo.load],
fn=fns.get_projects,
outputs=project_dd,
show_progress="hidden",
queue=False,
api_name=False,
)
gr.on(
[timer.tick],
fn=refresh_runs,
inputs=[project_dd, run_tb, run_selection_state, selected_runs_from_url],
outputs=[run_cb, run_tb, run_selection_state],
show_progress="hidden",
api_name=False,
)
gr.on(
[timer.tick],
fn=lambda: gr.Dropdown(info=fns.get_project_info()),
outputs=[project_dd],
show_progress="hidden",
api_name=False,
)
gr.on(
[demo.load, project_dd.change],
fn=refresh_runs,
inputs=[project_dd, run_tb, run_selection_state, selected_runs_from_url],
outputs=[run_cb, run_tb, run_selection_state],
show_progress="hidden",
queue=False,
api_name=False,
).then(
fn=update_x_axis_choices,
inputs=[project_dd, run_selection_state],
outputs=x_axis_dd,
show_progress="hidden",
queue=False,
api_name=False,
).then(
fn=generate_embed,
inputs=[project_dd, metric_filter_tb, run_selection_state],
outputs=[embed_code],
show_progress="hidden",
api_name=False,
queue=False,
).then(
fns.update_navbar_value,
inputs=[project_dd],
outputs=[navbar],
show_progress="hidden",
api_name=False,
queue=False,
).then(
fn=fns.get_group_by_fields,
inputs=[project_dd],
outputs=[run_group_by_dd],
show_progress="hidden",
api_name=False,
queue=False,
)
gr.on(
[run_cb.input],
fn=update_x_axis_choices,
inputs=[project_dd, run_selection_state],
outputs=x_axis_dd,
show_progress="hidden",
queue=False,
api_name=False,
)
gr.on(
[metric_filter_tb.change, run_cb.change],
fn=generate_embed,
inputs=[project_dd, metric_filter_tb, run_selection_state],
outputs=embed_code,
show_progress="hidden",
api_name=False,
queue=False,
)
def toggle_group_view(group_by_dd):
return (
gr.CheckboxGroup(visible=not bool(group_by_dd)),
gr.Group(visible=bool(group_by_dd)),
)
gr.on(
[run_group_by_dd.change],
fn=toggle_group_view,
inputs=[run_group_by_dd],
outputs=[run_cb, grouped_runs_panel],
show_progress="hidden",
api_name=False,
queue=False,
)
realtime_cb.change(
fn=toggle_timer,
inputs=realtime_cb,
outputs=timer,
api_name=False,
queue=False,
)
run_cb.input(
fn=fns.handle_run_checkbox_change,
inputs=[run_cb, run_selection_state],
outputs=run_selection_state,
api_name=False,
queue=False,
).then(
fn=generate_embed,
inputs=[project_dd, metric_filter_tb, run_selection_state],
outputs=embed_code,
show_progress="hidden",
api_name=False,
queue=False,
)
run_tb.input(
fn=refresh_runs,
inputs=[project_dd, run_tb, run_selection_state],
outputs=[run_cb, run_tb, run_selection_state],
api_name=False,
queue=False,
show_progress="hidden",
)
gr.api(
fn=upload_db_to_space,
api_name="upload_db_to_space",
)
gr.api(
fn=bulk_upload_media,
api_name="bulk_upload_media",
)
gr.api(
fn=log,
api_name="log",
)
gr.api(
fn=bulk_log,
api_name="bulk_log",
)
gr.api(
fn=get_metric_values,
api_name="get_metric_values",
)
gr.api(
fn=get_runs_for_project,
api_name="get_runs_for_project",
)
gr.api(
fn=get_metrics_for_run,
api_name="get_metrics_for_run",
)
gr.api(
fn=get_all_projects,
api_name="get_all_projects",
)
gr.api(
fn=get_project_summary,
api_name="get_project_summary",
)
gr.api(
fn=get_run_summary,
api_name="get_run_summary",
)
x_lim = gr.State(None)
last_steps = gr.State({})
def update_x_lim(select_data: gr.SelectData):
return select_data.index
def update_last_steps(project):
"""Check the last step for each run to detect when new data is available."""
if not project:
return {}
return SQLiteStorage.get_max_steps_for_runs(project)
timer.tick(
fn=update_last_steps,
inputs=[project_dd],
outputs=last_steps,
show_progress="hidden",
api_name=False,
)
@gr.render(
triggers=[
demo.load,
run_cb.change,
last_steps.change,
smoothing_slider.change,
x_lim.change,
x_axis_dd.change,
log_scale_cb.change,
metric_filter_tb.change,
],
inputs=[
project_dd,
run_cb,
smoothing_slider,
metrics_subset,
x_lim,
x_axis_dd,
log_scale_cb,
metric_filter_tb,
],
show_progress="hidden",
queue=False,
)
def update_dashboard(
project,
runs,
smoothing_granularity,
metrics_subset,
x_lim_value,
x_axis,
log_scale,
metric_filter,
):
dfs = []
images_by_run = {}
original_runs = runs.copy()
for run in runs:
df, images_by_key = load_run_data(
project, run, smoothing_granularity, x_axis, log_scale
)
if df is not None:
dfs.append(df)
images_by_run[run] = images_by_key
if dfs:
if smoothing_granularity > 0:
original_dfs = []
smoothed_dfs = []
for df in dfs:
original_data = df[df["data_type"] == "original"]
smoothed_data = df[df["data_type"] == "smoothed"]
if not original_data.empty:
original_dfs.append(original_data)
if not smoothed_data.empty:
smoothed_dfs.append(smoothed_data)
all_dfs = original_dfs + smoothed_dfs
master_df = (
pd.concat(all_dfs, ignore_index=True) if all_dfs else pd.DataFrame()
)
else:
master_df = pd.concat(dfs, ignore_index=True)
else:
master_df = pd.DataFrame()
if master_df.empty:
if space_id := utils.get_space():
gr.Markdown(INSTRUCTIONS_SPACES.format(space_id))
else:
gr.Markdown(INSTRUCTIONS_LOCAL)
return
x_column = "step"
if dfs and not dfs[0].empty and "x_axis" in dfs[0].columns:
x_column = dfs[0]["x_axis"].iloc[0]
numeric_cols = master_df.select_dtypes(include="number").columns
numeric_cols = [c for c in numeric_cols if c not in utils.RESERVED_KEYS]
if x_column and x_column in numeric_cols:
numeric_cols.remove(x_column)
if metrics_subset:
numeric_cols = [c for c in numeric_cols if c in metrics_subset]
if metric_filter and metric_filter.strip():
numeric_cols = filter_metrics_by_regex(list(numeric_cols), metric_filter)
nested_metric_groups = utils.group_metrics_with_subprefixes(list(numeric_cols))
color_map = utils.get_color_mapping(original_runs, smoothing_granularity > 0)
metric_idx = 0
for group_name in sorted(nested_metric_groups.keys()):
group_data = nested_metric_groups[group_name]
total_plot_count = sum(
1
for m in group_data["direct_metrics"]
if not master_df.dropna(subset=[m]).empty
) + sum(
sum(1 for m in metrics if not master_df.dropna(subset=[m]).empty)
for metrics in group_data["subgroups"].values()
)
group_label = (
f"{group_name} ({total_plot_count})"
if total_plot_count > 0
else group_name
)
with gr.Accordion(
label=group_label,
open=True,
key=f"accordion-{group_name}",
preserved_by_key=["value", "open"],
):
if group_data["direct_metrics"]:
with gr.Draggable(
key=f"row-{group_name}-direct", orientation="row"
):
for metric_name in group_data["direct_metrics"]:
metric_df = master_df.dropna(subset=[metric_name])
color = "run" if "run" in metric_df.columns else None
if not metric_df.empty:
plot = gr.LinePlot(
utils.downsample(
metric_df,
x_column,
metric_name,
color,
x_lim_value,
),
x=x_column,
y=metric_name,
y_title=metric_name.split("/")[-1],
color=color,
color_map=color_map,
title=metric_name,
key=f"plot-{metric_idx}",
preserved_by_key=None,
x_lim=x_lim_value,
show_fullscreen_button=True,
min_width=400,
show_export_button=True,
)
plot.select(
update_x_lim,
outputs=x_lim,
key=f"select-{metric_idx}",
)
plot.double_click(
lambda: None,
outputs=x_lim,
key=f"double-{metric_idx}",
)
metric_idx += 1
if group_data["subgroups"]:
for subgroup_name in sorted(group_data["subgroups"].keys()):
subgroup_metrics = group_data["subgroups"][subgroup_name]
subgroup_plot_count = sum(
1
for m in subgroup_metrics
if not master_df.dropna(subset=[m]).empty
)
subgroup_label = (
f"{subgroup_name} ({subgroup_plot_count})"
if subgroup_plot_count > 0
else subgroup_name
)
with gr.Accordion(
label=subgroup_label,
open=True,
key=f"accordion-{group_name}-{subgroup_name}",
preserved_by_key=["value", "open"],
):
with gr.Draggable(key=f"row-{group_name}-{subgroup_name}"):
for metric_name in subgroup_metrics:
metric_df = master_df.dropna(subset=[metric_name])
color = (
"run" if "run" in metric_df.columns else None
)
if not metric_df.empty:
plot = gr.LinePlot(
utils.downsample(
metric_df,
x_column,
metric_name,
color,
x_lim_value,
),
x=x_column,
y=metric_name,
y_title=metric_name.split("/")[-1],
color=color,
color_map=color_map,
title=metric_name,
key=f"plot-{metric_idx}",
preserved_by_key=None,
x_lim=x_lim_value,
show_fullscreen_button=True,
min_width=400,
show_export_button=True,
)
plot.select(
update_x_lim,
outputs=x_lim,
key=f"select-{metric_idx}",
)
plot.double_click(
lambda: None,
outputs=x_lim,
key=f"double-{metric_idx}",
)
metric_idx += 1
if images_by_run and any(any(images) for images in images_by_run.values()):
create_media_section(images_by_run)
table_cols = master_df.select_dtypes(include="object").columns
table_cols = [c for c in table_cols if c not in utils.RESERVED_KEYS]
if metrics_subset:
table_cols = [c for c in table_cols if c in metrics_subset]
if metric_filter and metric_filter.strip():
table_cols = filter_metrics_by_regex(list(table_cols), metric_filter)
actual_table_count = sum(
1
for metric_name in table_cols
if not (metric_df := master_df.dropna(subset=[metric_name])).empty
and isinstance(value := metric_df[metric_name].iloc[-1], dict)
and value.get("_type") == Table.TYPE
)
if actual_table_count > 0:
with gr.Accordion(f"tables ({actual_table_count})", open=True):
with gr.Row(key="row"):
for metric_idx, metric_name in enumerate(table_cols):
metric_df = master_df.dropna(subset=[metric_name])
if not metric_df.empty:
value = metric_df[metric_name].iloc[-1]
if (
isinstance(value, dict)
and "_type" in value
and value["_type"] == Table.TYPE
):
try:
df = pd.DataFrame(value["_value"])
gr.DataFrame(
df,
label=f"{metric_name} (latest)",
key=f"table-{metric_idx}",
wrap=True,
)
except Exception as e:
gr.Warning(
f"Column {metric_name} failed to render as a table: {e}"
)
with grouped_runs_panel:
@gr.render(
triggers=[
demo.load,
project_dd.change,
run_group_by_dd.change,
run_tb.input,
run_selection_state.change,
],
inputs=[project_dd, run_group_by_dd, run_tb, run_selection_state],
show_progress="hidden",
queue=False,
)
def render_grouped_runs(project, group_key, filter_text, selection):
if not group_key:
return
selection = selection or RunSelection()
groups = fns.group_runs_by_config(project, group_key, filter_text)
for label, runs in groups.items():
ordered_current = utils.ordered_subset(runs, selection.selected)
with gr.Group():
show_group_cb = gr.Checkbox(
label="Show/Hide",
value=bool(ordered_current),
key=f"show-cb-{group_key}-{label}",
preserved_by_key=["value"],
)
with gr.Accordion(
f"{label} ({len(runs)})",
open=False,
key=f"accordion-{group_key}-{label}",
preserved_by_key=["open"],
):
group_cb = gr.CheckboxGroup(
choices=runs,
value=ordered_current,
show_label=False,
key=f"group-cb-{group_key}-{label}",
)
gr.on(
[group_cb.change],
fn=fns.handle_group_checkbox_change,
inputs=[
group_cb,
run_selection_state,
gr.State(runs),
],
outputs=[
run_selection_state,
group_cb,
run_cb,
],
show_progress="hidden",
api_name=False,
queue=False,
)
gr.on(
[show_group_cb.change],
fn=fns.handle_group_toggle,
inputs=[
show_group_cb,
run_selection_state,
gr.State(runs),
],
outputs=[run_selection_state, group_cb, run_cb],
show_progress="hidden",
api_name=False,
queue=False,
)
with demo.route("Runs", show_in_navbar=False):
run_page.render()
with demo.route("Run", show_in_navbar=False):
run_detail_page.render()
write_token = secrets.token_urlsafe(32)
demo.write_token = write_token
run_page.write_token = write_token
run_detail_page.write_token = write_token
if __name__ == "__main__":
demo.launch(allowed_paths=[utils.TRACKIO_LOGO_DIR], show_api=False, show_error=True)
|