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b/doc/how_to/callbacks/async.md index 92156ed266..d43eb3bcfe 100644 --- a/doc/how_to/callbacks/async.md +++ b/doc/how_to/callbacks/async.md @@ -1,13 +1,22 @@ # Use Asynchronous Callbacks -This guide addresses how to leverage asynchronous callbacks to run I/O bound tasks in parallel. +This guide addresses how to leverage asynchronous callbacks to run I/O bound tasks in parallel. This technique is also beneficial for CPU bound tasks that release the GIL. -```{admonition} Prerequisites -1. Python has natively supported asynchronous functions since version 3.5, for a quick overview of some of the concepts involved see [the Python documentation](https://docs.python.org/3/library/asyncio-task.html). +You can use `async` function with event handlers like `on_click` as well as the reactive apis `.bind`, `.depends` and `.watch`. + +You can also schedule asynchronous periodic callbacks with `pn.state.add_periodic_callback` as well as run `async` functions directly with `pn.state.execute`. + +```{admonition} Asyncio +For a quick overview of the most important `asyncio` concepts see [the Python documentation](https://docs.python.org/3/library/asyncio-task.html). +``` + +```{admonition} Bokeh Models +It is important to note that asynchronous callbacks operate without locking the underlying Bokeh Document, which means Bokeh models cannot be safely modified by default. Usually this is not an issue because modifying Panel components appropriately schedules updates to underlying Bokeh models, however in cases where we want to modify a Bokeh model directly, e.g. when embedding and updating a Bokeh plot in a Panel application we explicitly have to decorate the asynchronous callback with `pn.io.with_lock` (see example below). ``` + --- -## `.param.watch` +## `on_click` One of the major benefits of leveraging async functions is that it is simple to write callbacks which will perform some longer running IO tasks in the background. Below we simulate this by creating a `Button` which will update some text when it starts and finishes running a long-running background task (here simulated using `asyncio.sleep`. If you are running this in the notebook you will note that you can start multiple tasks and it will update the text immediately but continue in the background: @@ -30,36 +39,7 @@ button.on_click(run_async) pn.Row(button, text) ``` -Note that `on_click` is simple one way of registering an asynchronous callback, but the more flexible `.param.watch` is also supported. Scheduling asynchronous periodic callbacks can be done with `pn.state.add_periodic_callback`. - -It is important to note that asynchronous callbacks operate without locking the underlying Bokeh Document, which means Bokeh models cannot be safely modified by default. Usually this is not an issue because modifying Panel components appropriately schedules updates to underlying Bokeh models, however in cases where we want to modify a Bokeh model directly, e.g. when embedding and updating a Bokeh plot in a Panel application we explicitly have to decorate the asynchronous callback with `pn.io.with_lock`. - -```{pyodide} -import numpy as np -from bokeh.plotting import figure -from bokeh.models import ColumnDataSource - -button = pn.widgets.Button(name='Click me!') - -p = figure(width=500, height=300) -cds = ColumnDataSource(data={'x': [0], 'y': [0]}) -p.line(x='x', y='y', source=cds) -pane = pn.pane.Bokeh(p) - -@pn.io.with_lock -async def stream(event): - await asyncio.sleep(1) - x, y = cds.data['x'][-1], cds.data['y'][-1] - cds.stream({'x': list(range(x+1, x+6)), 'y': y+np.random.randn(5).cumsum()}) - pane.param.trigger('object') - -# Equivalent to `.on_click` but shown -button.param.watch(stream, 'clicks') - -pn.Row(button, pane) -``` - -## `pn.bind` +## `.bind` ```{pyodide} widget = pn.widgets.IntSlider(start=0, end=10) @@ -80,7 +60,9 @@ pn.Column(widget, pn.bind(get_img, widget)) In this example Panel will invoke the function and update the output when the function returns while leaving the process unblocked for the duration of the `aiohttp` request. -The equivalent can be written using `.param.watch` as: +## `.watch` + +The app from the section above can be written using `.param.watch` as: ```{pyodide} widget = pn.widgets.IntSlider(start=0, end=10) @@ -107,6 +89,33 @@ pn.Column(widget, image) In this example Param will await the asynchronous function and the image will be updated when the request completes. +## Bokeh models with `pn.io.with_lock` + +```{pyodide} +import numpy as np +from bokeh.plotting import figure +from bokeh.models import ColumnDataSource + +button = pn.widgets.Button(name='Click me!') + +p = figure(width=500, height=300) +cds = ColumnDataSource(data={'x': [0], 'y': [0]}) +p.line(x='x', y='y', source=cds) +pane = pn.pane.Bokeh(p) + +@pn.io.with_lock +async def stream(event): + await asyncio.sleep(1) + x, y = cds.data['x'][-1], cds.data['y'][-1] + cds.stream({'x': list(range(x+1, x+6)), 'y': y+np.random.randn(5).cumsum()}) + pane.param.trigger('object') + +# Equivalent to `.on_click` but shown +button.param.watch(stream, 'clicks') + +pn.Row(button, pane) +``` + ## Related Resources - See the related [How-to > Link Parameters with Callbacks API](../links/index.md) guides, including [How to > Create Low-Level Python Links Using `.watch`](../links/watchers.md). diff --git a/doc/how_to/concurrency/async.md b/doc/how_to/concurrency/async.md deleted file mode 100644 index 7fb7494bab..0000000000 --- a/doc/how_to/concurrency/async.md +++ /dev/null @@ -1,51 +0,0 @@ -# Use Asynchronous Processing - -When using Python you can use async callbacks wherever you would ordinarily use a regular synchronous function. For instance you can use `pn.bind` on an async function: - -```{pyodide} -import panel as pn - -widget = pn.widgets.IntSlider(start=0, end=10) - -async def get_img(index): - url = f"https://picsum.photos/800/300?image={index}" - if pn.state._is_pyodide: - from pyodide.http import pyfetch - return pn.pane.JPG(await (await pyfetch(url)).bytes()) - - import aiohttp - async with aiohttp.ClientSession() as session: - async with session.get(url) as resp: - return pn.pane.JPG(await resp.read()) - -pn.Column(widget, pn.bind(get_img, widget)) -``` - -In this example Panel will invoke the function and update the output when the function returns while leaving the process unblocked for the duration of the `aiohttp` request. - -Similarly you can attach asynchronous callbacks using `.param.watch`: - -```{pyodide} -widget = pn.widgets.IntSlider(start=0, end=10) - -image = pn.pane.JPG() - -async def update_img(event): - url = f"https://picsum.photos/800/300?image={event.new}" - if pn.state._is_pyodide: - from pyodide.http import pyfetch - image.object = await (await pyfetch(url)).bytes() - return - - import aiohttp - async with aiohttp.ClientSession() as session: - async with session.get(url) as resp: - image.object = await resp.read() - -widget.param.watch(update_img, 'value') -widget.param.trigger('value') - -pn.Column(widget, image) -``` - -In this example Param will await the asynchronous function and the image will be updated when the request completes. diff --git a/doc/how_to/concurrency/dask.md b/doc/how_to/concurrency/dask.md new file mode 100644 index 0000000000..bd015acf00 --- /dev/null +++ b/doc/how_to/concurrency/dask.md @@ -0,0 +1,235 @@ +# Scaling with Dask + +This guide demonstrates how you can *offload tasks* to [Dask](https://www.dask.org/) to **scale your apps to bigger datasets, bigger calculations and more users**. + + + +Panel supports `async` and `await`. This means you can easily **offload large computations to your Dask cluster asynchronously and keep your app responsive while you `await` the results**. Please note that off loading the computations to the Dask cluster can add ~250msec of overhead and thus is not suitable for all kinds of use cases. + +## Installation + +Lets start by installing *Panel*, *hvPlot* and *Dask Distributed*. + +```bash +pip install panel hvplot dask[distributed] +``` + +## Start the Cluster + +For development, testing and many use cases a [`LocalCluster`](https://docs.dask.org/en/stable/deploying-python.html#localcluster) is more than fine and will allow you to leverage all the CPUs on your machine. When you want to scale out to an entire cluster will you can switch to a non-local cluster. To avoid any issues when combining Panel and Dask we recommend starting the `LocalCluster` +separately from the Dask `Client` and your Panel app. + +```python +# cluster.py +from dask.distributed import LocalCluster + +DASK_SCHEDULER_PORT = 64719 +DASK_SCHEDULER_ADDRESS = f"tcp://127.0.0.1:{DASK_SCHEDULER_PORT}" +N_WORKERS = 4 + +if __name__ == '__main__': + cluster = LocalCluster(scheduler_port=DASK_SCHEDULER_PORT, n_workers=N_WORKERS) + print(cluster.scheduler_address) + input() +``` + +and running + +```bash +$ python cluster.py +tcp://127.0.0.1:64719 +``` + +You can now open the [Dask Dashboard](https://docs.dask.org/en/stable/dashboard.html) at [http://localhost:8787/status](http://localhost:8787/status). + +So far there is not a lot to see here: + +![Empty Dask Dashboard](../../_static/images/dask-dashboard-empty.png) + +The Dask `Client` will serialize any *tasks* and send them to the Dask `Cluster` for execution. This means that the `Client` and `Cluster` must able to import the same versions of all *tasks* and python package dependencies. + +## Dask Distributed + +## Fibonacci Task Queue + +In this section we will define a Panel app to *submit* and *monitor* Fibonacci tasks. + +Let's start by defining the *fibonacci* tasks in a `tasks.py` file: + +```python +# tasks.py +from datetime import datetime as dt + +import numpy as np + + +def _fib(n): + if n < 2: + return n + else: + return _fib(n - 1) + _fib(n - 2) + + +def fibonacci(n): + start = dt.now() + print(start, "start", n) + result = _fib(n) + end = dt.now() + print(end, "end", (end-start).seconds, n, result) + return result +``` + +Lets now define the full `app.py` file. + +```python +# app.py +from datetime import datetime as dt + +from dask.distributed import Client + +import panel as pn + +from cluster import DASK_SCHEDULER_ADDRESS +from tasks import fibonacci + +QUEUE = [] + +pn.extension("terminal", design="material", sizing_mode="stretch_width") + +@pn.cache # We use caching to share the client across all users and sessions +async def get_client(): + return await Client( + DASK_SCHEDULER_ADDRESS, asynchronous=True + ) + +n_input = pn.widgets.IntInput(value=0, width=100, sizing_mode="fixed", name="n") +submit_button = pn.widgets.Button(name="SUBMIT", button_type="primary", align="end") +terminal_widget = pn.widgets.Terminal( + height=200, +) + +queue = pn.rx(QUEUE) + +@pn.depends(submit_button, watch=True) +async def _handle_click(_): + n = n_input.value + n_input.value += 1 + + start = dt.now() + QUEUE.append(n) + queue.rx.value = QUEUE + + client = await get_client() + fib_n = await client.submit(fibonacci, n) + + end = dt.now() + + QUEUE.pop(QUEUE.index(n)) + queue.rx.value = QUEUE + + duration = (end - start).seconds + terminal_widget.write(f"fibonacci({n})={fib_n} in {duration}sec\n") + + +pn.Column( + "# Fibonacci Tasks", + pn.Row(n_input, submit_button), + pn.rx("## Task queue: {}").format(queue), + "## Results", + terminal_widget, +).servable() +``` + +You can now run `panel serve app.py` and the app will look like + + + +## Dask Dashboard Components + +It can be very useful to include some of the live [Dask endpoints](https://distributed.dask.org/en/stable/http_services.html) in your app. Its easy to do by embedding the specific urls in an *iframe*. + +In the `dashboard.py` file we define the `DaskViewer` component that can be used to explore the *individual dask plots*. + +```python +# dashboard.py +import os + +import param + +import panel as pn + +DASK_DASHBOARD_ADDRESS = os.getenv("DASK_DASHBOARD", "http://localhost:8787/status") + +VIEWS = { + "aggregate-time-per-action": "individual-aggregate-time-per-action", + "bandwidth-types": "individual-bandwidth-types", + "bandwidth-workers": "individual-bandwidth-workers", + "cluster-memory": "individual-cluster-memory", + "compute-time-per-key": "individual-compute-time-per-key", + "cpu": "individual-cpu", + "exceptions": "individual-exceptions", + "gpu-memory": "individual-gpu-memory", + "gpu-utilization": "individual-gpu-utilization", + "graph": "individual-graph", + "groups": "individual-groups", + "memory-by-key": "individual-memory-by-key", + "nprocessing": "individual-nprocessing", + "occupancy": "individual-occupancy", + "profile-server": "individual-profile-server", + "profile": "individual-profile", + "progress": "individual-progress", + "scheduler-system": "individual-scheduler-system", + "task-stream": "individual-task-stream", + "workers-cpu-timeseries": "individual-workers-cpu-timeseries", + "workers-disk-timeseries": "individual-workers-disk-timeseries", + "workers-disk": "individual-workers-disk", + "workers-memory-timeseries": "individual-workers-memory-timeseries", + "workers-memory": "individual-workers-memory", + "workers-network-timeseries": "individual-workers-network-timeseries", + "workers-network": "individual-workers-network", + "workers": "individual-workers", +} + +VIEWER_PARAMETERS = ["url", "path"] + +def dask_dashboard_view(path="individual-cpu", url=DASK_DASHBOARD_ADDRESS): + url = url.replace("/status", "/") + path + return f"""""" + +class DaskViewer(pn.viewable.Viewer): + url = param.String(DASK_DASHBOARD_ADDRESS, doc="The url of the Dask status dashboard") + path = param.Selector(default="individual-cpu", objects=VIEWS, doc="the endpoint", label="View") + + def __init__(self, size=20, **params): + viewer_params = {k:v for k, v in params.items() if k in VIEWER_PARAMETERS} + layout_params = {k:v for k, v in params.items() if k not in VIEWER_PARAMETERS} + + super().__init__(**viewer_params) + + view = pn.bind(dask_dashboard_view, self.param.path, self.param.url) + self._iframe = pn.pane.HTML(view, sizing_mode="stretch_both") + self._select = pn.widgets.Select.from_param(self.param.path, size=size, width=300, sizing_mode="fixed", margin=(20,5,10,5)) + self._link = pn.panel(f"""Dask Dashboard""", height=50, margin=(0,20)) + self._panel = pn.Column(pn.Row(self._iframe, self._select, sizing_mode="stretch_both"), self._link, **layout_params) + + def __panel__(self): + return self._panel + +if __name__.startswith("bokeh"): + pn.extension(sizing_mode="stretch_width") + + DaskViewer(height=500, size=25).servable() +``` + +Try running `panel serve dashboard.py`. If your Dask cluster is working, you will see something like + +![Dask Viewer](https://assets.holoviz.org/panel/how_to/concurrency/dask-dashboard.gif) + +## Additional Resources + +- [Panel - Use Async Callbacks](../callbacks/async.md) +- [Dask - Async/Await and Non-Blocking Execution Documentation](https://examples.dask.org/applications/async-await.html#Async/Await-and-Non-Blocking-Execution) +- [Dask - Async Web Server](https://examples.dask.org/applications/async-web-server.html) diff --git a/doc/how_to/concurrency/index.md b/doc/how_to/concurrency/index.md index 03be0a31c6..7e965bb0ef 100644 --- a/doc/how_to/concurrency/index.md +++ b/doc/how_to/concurrency/index.md @@ -54,10 +54,33 @@ Discover how to manually set up a Thread to process an event queue. ::: :::{grid-item-card} {octicon}`arrow-switch;2.5em;sd-mr-1 sd-animate-grow50` Use Asynchronous Processing -:link: async +:link: ../callbacks/async :link-type: doc -Discover how to make use of asynchronous callbacks to handle I/O bound operations concurrently. +Discover how to make use of asynchronous callbacks to handle I/O and cpu bound operations concurrently. +::: + +:::{grid-item-card} {octicon}`paper-airplane;2.5em;sd-mr-1 sd-animate-grow50` Sync to Async +:link: sync_to_async +:link-type: doc + +Discover how to run your sync callbacks asynchronously to handle I/O and cpu bound operations concurrently. +::: + +:::: + +## Scaling via an external compute engine + +You can also scale your application by offloading your compute heavy tasks to an external compute engine like [Dask](https://www.dask.org/). Please note that this may add additional overhead of several 100ms to your tasks. + +::::{grid} 1 2 2 2 +:gutter: 1 1 1 2 + +:::{grid-item-card} {octicon}`versions;2.5em;sd-mr-1 sd-animate-grow50` Dask +:link: dask +:link-type: doc + +Discover how-to configure and use Dask to scale your Panel application ::: :::: @@ -70,5 +93,8 @@ Discover how to make use of asynchronous callbacks to handle I/O bound operation load_balancing processes threading -async +manual_threading +../callbacks/async +sync_to_async +dask ``` diff --git a/doc/how_to/concurrency/sync_to_async.md b/doc/how_to/concurrency/sync_to_async.md new file mode 100644 index 0000000000..cd80c3da17 --- /dev/null +++ b/doc/how_to/concurrency/sync_to_async.md @@ -0,0 +1,40 @@ +# Run synchronous functions asynchronously + +Running your bound, synchronous functions asynchronously can be an easy way to make your application responsive and scale to more users. + +## Asyncify + +This example will show how to make your app responsive by running a sync, cpu bound function asynchronously. We will be using [asyncer](https://asyncer.tiangolo.com) by Tiangolo. You can install the package via `pip install asyncer`. + +```python +import numpy as np +import pandas as pd + +from asyncer import asyncify +import panel as pn + +widget = pn.widgets.IntSlider(value=5, start=0, end=10) + +def do_sync_work(it, n): + return sum(pd.DataFrame(np.random.rand(n,n)).sum().sum() for _ in range(it)) + +async def create_result(): + yield pn.indicators.LoadingSpinner(value=True, width=25, height=25) + result = await asyncify(do_sync_work)(it=5, n=10000) + yield f"Wow. That was slow.\n\nThe sum is **{result:.2f}**" + +pn.Column(widget.rx() + 1, create_result).servable() +``` + + + +Without [`asyncify`](https://asyncer.tiangolo.com/tutorial/asyncify/) the app would have been unresponsive for 5-10 seconds while loading. + +[`asyncify`](https://asyncer.tiangolo.com/tutorial/asyncify/) works well for IO bound functions as well as for CPU bound functions that releases the GIL. + +## Dask + +If you run many cpu bound functions you may consider offloading your functions asynchronously to an external compute engine like [Dask](https://www.dask.org/). See our [Dask how-to Guide](../performance/dask.md).