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[docs] slurm + progress_bar example (#10782)
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Barak Michener
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"""
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Progress Bar for Ray Actors (tqdm)
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==================================
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Tracking progress of distributed tasks can be tricky.
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This script will demonstrate how to implement a simple
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progress bar for a Ray actor to track progress across various
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different distributed components.
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Setup: Dependencies
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-------------------
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First, import some dependencies.
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"""
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# Inspiration: https://github.com/honnibal/spacy-ray/pull/
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# 1/files#diff-7ede881ddc3e8456b320afb958362b2aR12-R45
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from asyncio import Event
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from typing import Tuple
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from time import sleep
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import ray
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# For typing purposes
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from ray.actor import ActorHandle
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from tqdm import tqdm
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############################################################
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# This is the Ray "actor" that can be called from anywhere to update
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# our progress. You'll be using the `update` method. Don't
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# instantiate this class yourself. Instead,
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# it's something that you'll get from a `ProgressBar`.
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@ray.remote
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class ProgressBarActor:
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counter: int
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delta: int
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event: Event
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def __init__(self) -> None:
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self.counter = 0
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self.delta = 0
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self.event = Event()
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def update(self, num_items_completed: int) -> None:
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"""Updates the ProgressBar with the incremental
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number of items that were just completed.
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"""
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self.counter += num_items_completed
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self.delta += num_items_completed
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self.event.set()
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async def wait_for_update(self) -> Tuple[int, int]:
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"""Blocking call.
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Waits until somebody calls `update`, then returns a tuple of
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the number of updates since the last call to
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`wait_for_update`, and the total number of completed items.
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"""
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await self.event.wait()
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self.event.clear()
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saved_delta = self.delta
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self.delta = 0
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return saved_delta, self.counter
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def get_counter(self) -> int:
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"""
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Returns the total number of complete items.
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"""
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return self.counter
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######################################################################
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# This is where the progress bar starts. You create one of these
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# on the head node, passing in the expected total number of items,
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# and an optional string description.
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# Pass along the `actor` reference to any remote task,
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# and if they complete ten
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# tasks, they'll call `actor.update.remote(10)`.
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# Back on the local node, once you launch your remote Ray tasks, call
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# `print_until_done`, which will feed everything back into a `tqdm` counter.
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class ProgressBar:
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progress_actor: ActorHandle
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total: int
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description: str
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pbar: tqdm
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def __init__(self, total: int, description: str = ""):
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# Ray actors don't seem to play nice with mypy, generating
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# a spurious warning for the following line,
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# which we need to suppress. The code is fine.
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self.progress_actor = ProgressBarActor.remote() # type: ignore
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self.total = total
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self.description = description
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@property
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def actor(self) -> ActorHandle:
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"""Returns a reference to the remote `ProgressBarActor`.
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When you complete tasks, call `update` on the actor.
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"""
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return self.progress_actor
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def print_until_done(self) -> None:
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"""Blocking call.
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Do this after starting a series of remote Ray tasks, to which you've
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passed the actor handle. Each of them calls `update` on the actor.
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When the progress meter reaches 100%, this method returns.
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"""
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pbar = tqdm(desc=self.description, total=self.total)
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while True:
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delta, counter = ray.get(self.actor.wait_for_update.remote())
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pbar.update(delta)
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if counter >= self.total:
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pbar.close()
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return
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#################################################################
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# This is an example of a task that increments the progress bar.
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# Note that this is a Ray Task, but it could very well
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# be any generic Ray Actor.
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#
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@ray.remote
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def sleep_then_increment(i: int, pba: ActorHandle) -> int:
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sleep(i / 2.0)
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pba.update.remote(1)
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return i
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#################################################################
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# Now you can run it and see what happens!
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#
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def run():
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ray.init()
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num_ticks = 6
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pb = ProgressBar(num_ticks)
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actor = pb.actor
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# You can replace this with any arbitrary Ray task/actor.
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tasks_pre_launch = [
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sleep_then_increment.remote(i, actor) for i in range(0, num_ticks)
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]
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pb.print_until_done()
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tasks = ray.get(tasks_pre_launch)
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tasks == list(range(num_ticks))
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num_ticks == ray.get(actor.get_counter.remote())
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run()
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