mirror of
https://github.com/gradio-app/gradio.git
synced 2025-01-24 10:54:04 +08:00
Adds a monitoring dashboard to Gradio apps that can be used to view usage (#8478)
* changes * add changeset * changes * changes * changes * add changeset * changes --------- Co-authored-by: Ali Abid <aliabid94@gmail.com> Co-authored-by: gradio-pr-bot <gradio-pr-bot@users.noreply.github.com> Co-authored-by: Abubakar Abid <abubakar@huggingface.co>
This commit is contained in:
aliabid94
GitHub
parent
f9406b46e3
commit
73e11087a0
5
.changeset/common-paws-grab.md
Normal file
5
.changeset/common-paws-grab.md
Normal file
@ -0,0 +1,5 @@
|
||||
---
|
||||
"gradio": minor
|
||||
---
|
||||
|
||||
feat:Adds a monitoring dashboard to Gradio apps that can be used to view usage
|
||||
91
gradio/analytics_dashboard.py
Normal file
91
gradio/analytics_dashboard.py
Normal file
@ -0,0 +1,91 @@
|
||||
import random
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
|
||||
import gradio as gr
|
||||
|
||||
data = {"data": {}}
|
||||
|
||||
with gr.Blocks() as demo:
|
||||
with gr.Row():
|
||||
selected_function = gr.Dropdown(
|
||||
["All"],
|
||||
value="All",
|
||||
label="Endpoint",
|
||||
info="Select the function to see analytics for, or 'All' for aggregate.",
|
||||
scale=2,
|
||||
)
|
||||
demo.load(
|
||||
lambda: gr.Dropdown(
|
||||
choices=["All"]
|
||||
+ list({row["function"] for row in data["data"].values()}) # type: ignore
|
||||
),
|
||||
None,
|
||||
selected_function,
|
||||
)
|
||||
timespan = gr.Dropdown(
|
||||
["All Time", "24 hours", "1 hours", "10 minutes"],
|
||||
value="All Time",
|
||||
label="Timespan",
|
||||
info="Duration to see data for.",
|
||||
)
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
unique_users = gr.Label(label="Unique Users")
|
||||
unique_requests = gr.Label(label="Unique Requests")
|
||||
process_time = gr.Label(label="Avg Process Time")
|
||||
plot = gr.BarPlot(
|
||||
x="time",
|
||||
y="count",
|
||||
color="status",
|
||||
title="Requests over Time",
|
||||
y_title="Requests",
|
||||
width=600,
|
||||
)
|
||||
|
||||
@gr.on(
|
||||
[demo.load, selected_function.change, timespan.change],
|
||||
inputs=[selected_function, timespan],
|
||||
outputs=[unique_users, unique_requests, process_time, plot],
|
||||
)
|
||||
def load_dfs(function, timespan):
|
||||
df = pd.DataFrame(data["data"].values())
|
||||
if df.empty:
|
||||
return 0, 0, 0, gr.skip()
|
||||
df["time"] = pd.to_datetime(df["time"], unit="s")
|
||||
df_filtered = df if function == "All" else df[df["function"] == function]
|
||||
if timespan != "All Time":
|
||||
df_filtered = df_filtered[
|
||||
df_filtered["time"] > pd.Timestamp.now() - pd.Timedelta(timespan)
|
||||
]
|
||||
|
||||
df_filtered["time"] = df_filtered["time"].dt.floor("min")
|
||||
plot = df_filtered.groupby(["time", "status"]).size().reset_index(name="count") # type: ignore
|
||||
mean_process_time_for_success = df_filtered[df_filtered["status"] == "success"][
|
||||
"process_time"
|
||||
].mean()
|
||||
|
||||
return (
|
||||
df_filtered["session_hash"].nunique(),
|
||||
df_filtered.shape[0],
|
||||
round(mean_process_time_for_success, 2),
|
||||
plot,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
data["data"] = {
|
||||
random.randint(0, 1000000): {
|
||||
"time": time.time() - random.randint(0, 60 * 60 * 24 * 3),
|
||||
"status": random.choice(
|
||||
["success", "success", "failure", "pending", "queued"]
|
||||
),
|
||||
"function": random.choice(["predict", "chat", "chat"]),
|
||||
"process_time": random.randint(0, 10),
|
||||
"session_hash": str(random.randint(0, 4)),
|
||||
}
|
||||
for r in range(random.randint(100, 200))
|
||||
}
|
||||
|
||||
demo.launch()
|
||||
@ -2178,6 +2178,7 @@ Received outputs:
|
||||
auth_dependency: Callable[[fastapi.Request], str | None] | None = None,
|
||||
max_file_size: str | int | None = None,
|
||||
_frontend: bool = True,
|
||||
enable_monitoring: bool = False,
|
||||
) -> tuple[FastAPI, str, str]:
|
||||
"""
|
||||
Launches a simple web server that serves the demo. Can also be used to create a
|
||||
@ -2397,6 +2398,11 @@ Received outputs:
|
||||
else:
|
||||
self.share = share
|
||||
|
||||
if enable_monitoring:
|
||||
print(
|
||||
f"Monitoring URL: {self.local_url}monitoring/{self.app.analytics_key}"
|
||||
)
|
||||
|
||||
# If running in a colab or not able to access localhost,
|
||||
# a shareable link must be created.
|
||||
if (
|
||||
|
||||
@ -114,6 +114,7 @@ class Queue:
|
||||
self.default_concurrency_limit = self._resolve_concurrency_limit(
|
||||
default_concurrency_limit
|
||||
)
|
||||
self.event_analytics: dict[str, dict[str, float | str | None]] = {}
|
||||
|
||||
def start(self):
|
||||
self.active_jobs = [None] * self.max_thread_count
|
||||
@ -227,6 +228,13 @@ class Queue:
|
||||
"Event not found in queue. If you are deploying this Gradio app with multiple replicas, please enable stickiness to ensure that all requests from the same user are routed to the same instance."
|
||||
) from e
|
||||
event_queue.queue.append(event)
|
||||
self.event_analytics[event._id] = {
|
||||
"time": time.time(),
|
||||
"status": "queued",
|
||||
"process_time": None,
|
||||
"function": fn.api_name,
|
||||
"session_hash": body.session_hash,
|
||||
}
|
||||
|
||||
self.broadcast_estimations(event.concurrency_id, len(event_queue.queue) - 1)
|
||||
|
||||
@ -294,6 +302,8 @@ class Queue:
|
||||
event_queue.current_concurrency += 1
|
||||
start_time = time.time()
|
||||
event_queue.start_times_per_fn[events[0].fn].add(start_time)
|
||||
for event in events:
|
||||
self.event_analytics[event._id]["status"] = "processing"
|
||||
process_event_task = run_coro_in_background(
|
||||
self.process_events, events, batch, start_time
|
||||
)
|
||||
@ -470,6 +480,7 @@ class Queue:
|
||||
) -> None:
|
||||
awake_events: list[Event] = []
|
||||
fn = events[0].fn
|
||||
success = False
|
||||
try:
|
||||
for event in events:
|
||||
if event.alive:
|
||||
@ -587,16 +598,20 @@ class Queue:
|
||||
for e, event in enumerate(awake_events):
|
||||
if batch and "data" in output:
|
||||
output["data"] = list(zip(*response.get("data")))[e]
|
||||
success = response is not None
|
||||
self.send_message(
|
||||
event,
|
||||
ProcessCompletedMessage(
|
||||
output=output,
|
||||
success=response is not None,
|
||||
success=success,
|
||||
),
|
||||
)
|
||||
end_time = time.time()
|
||||
if response is not None:
|
||||
self.process_time_per_fn[events[0].fn].add(end_time - begin_time)
|
||||
duration = end_time - begin_time
|
||||
self.process_time_per_fn[events[0].fn].add(duration)
|
||||
for event in events:
|
||||
self.event_analytics[event._id]["process_time"] = duration
|
||||
except Exception as e:
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
@ -620,6 +635,13 @@ class Queue:
|
||||
# to start "from scratch"
|
||||
await self.reset_iterators(event._id)
|
||||
|
||||
if event in awake_events:
|
||||
self.event_analytics[event._id]["status"] = (
|
||||
"success" if success else "failed"
|
||||
)
|
||||
else:
|
||||
self.event_analytics[event._id]["status"] = "cancelled"
|
||||
|
||||
async def reset_iterators(self, event_id: str):
|
||||
# Do the same thing as the /reset route
|
||||
app = self.server_app
|
||||
|
||||
@ -164,6 +164,8 @@ class App(FastAPI):
|
||||
):
|
||||
self.tokens = {}
|
||||
self.auth = None
|
||||
self.analytics_key = secrets.token_urlsafe(16)
|
||||
self.analytics_enabled = False
|
||||
self.blocks: gradio.Blocks | None = None
|
||||
self.state_holder = StateHolder()
|
||||
self.iterators: dict[str, AsyncIterator] = {}
|
||||
@ -1165,6 +1167,32 @@ class App(FastAPI):
|
||||
else:
|
||||
return "User-agent: *\nDisallow: "
|
||||
|
||||
@app.get("/monitoring")
|
||||
async def analytics_login():
|
||||
print(
|
||||
f"Monitoring URL: {app.get_blocks().local_url}monitoring/{app.analytics_key}"
|
||||
)
|
||||
return HTMLResponse("See console for monitoring URL.")
|
||||
|
||||
@app.get("/monitoring/{key}")
|
||||
async def analytics_dashboard(key: str):
|
||||
if key == app.analytics_key:
|
||||
analytics_url = f"/monitoring/{app.analytics_key}/dashboard"
|
||||
if not app.analytics_enabled:
|
||||
from gradio.analytics_dashboard import data
|
||||
from gradio.analytics_dashboard import demo as dashboard
|
||||
|
||||
mount_gradio_app(app, dashboard, path=analytics_url)
|
||||
dashboard._queue.start()
|
||||
analytics = app.get_blocks()._queue.event_analytics
|
||||
data["data"] = analytics
|
||||
app.analytics_enabled = True
|
||||
return RedirectResponse(
|
||||
url=analytics_url, status_code=status.HTTP_302_FOUND
|
||||
)
|
||||
else:
|
||||
raise HTTPException(status_code=403, detail="Invalid key.")
|
||||
|
||||
return app
|
||||
|
||||
|
||||
|
||||
@ -13,3 +13,4 @@ This limits the number of requests processed for this event listener at a single
|
||||
|
||||
See the [docs on queueing](/docs/gradio/interface#interface-queue) for more details on configuring the queuing parameters.
|
||||
|
||||
You can see analytics on the number and status of all requests processed by the queue by visiting the `/monitoring` endpoint of your app. This endpoint will print a secret URL to your console that links to the full analytics dashboard.
|
||||
Loading…
Reference in New Issue
Block a user