Advanced Topics#
Note
Because this documentation consists of static html, the live_plot and live_info widget is not live.
Download the notebook in order to see the real behaviour. 1
import adaptive
adaptive.notebook_extension()
import asyncio
from functools import partial
import random
offset = random.uniform(-0.5, 0.5)
def f(x, offset=offset):
a = 0.01
return x + a**2 / (a**2 + (x - offset) ** 2)
Saving and loading learners#
Every learner has a save and load method that can be used to save and load only the data of a learner.
Use the fname argument in learner.save(fname=...).
Or, when using a BalancingLearner one can use either a callable that takes the child learner and returns a filename or a list of filenames.
By default the resulting pickle files are compressed, to turn this off use learner.save(fname=..., compress=False)
# Let's create two learners and run only one.
learner = adaptive.Learner1D(f, bounds=(-1, 1))
control = adaptive.Learner1D(f, bounds=(-1, 1))
# Let's only run the learner
runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)
await runner.task # This is not needed in a notebook environment!
runner.live_info()
fname = "data/example_file.p"
learner.save(fname)
control.load(fname)
(learner.plot().relabel("saved learner") + control.plot().relabel("loaded learner"))
Or just (without saving):
control = adaptive.Learner1D(f, bounds=(-1, 1))
control.copy_from(learner)
One can also periodically save the learner while running in a Runner. Use it like:
def slow_f(x):
from time import sleep
sleep(5)
return x
learner = adaptive.Learner1D(slow_f, bounds=[0, 1])
runner = adaptive.Runner(learner, goal=lambda l: l.npoints > 100)
runner.start_periodic_saving(
save_kwargs=dict(fname="data/periodic_example.p"), interval=6
)
<Task pending name='Task-8' coro=<AsyncRunner.start_periodic_saving.<locals>._saver() running at /home/docs/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:709>>
await asyncio.sleep(6) # This is not needed in a notebook environment!
runner.cancel()
runner.live_info() # we cancelled it after 6 seconds
# See the data 6 later seconds with
#!ls -lah data # only works on macOS and Linux systems
A watched pot never boils!#
The adaptive.Runner does its work in an asyncio task that runs concurrently with the IPython kernel, when using adaptive from a Jupyter notebook.
This is advantageous because it allows us to do things like live-updating plots, however it can trip you up if you’re not careful.
Notably: if you block the IPython kernel, the runner will not do any work.
For example if you wanted to wait for a runner to complete, do not wait in a busy loop:
while not runner.task.done():
pass
If you do this then the runner will never finish.
What to do if you don’t care about live plotting, and just want to run something until its done?
The simplest way to accomplish this is to use adaptive.BlockingRunner:
learner = adaptive.Learner1D(f, bounds=(-1, 1))
adaptive.BlockingRunner(learner, goal=lambda l: l.loss() < 0.01)
# This will only get run after the runner has finished
learner.plot()
Reproducibility#
By default adaptive runners evaluate the learned function in parallel across several cores.
The runners are also opportunistic, in that as soon as a result is available they will feed it to the learner and request another point to replace the one that just finished.
Because the order in which computations complete is non-deterministic, this means that the runner behaves in a non-deterministic way. Adaptive makes this choice because in many cases the speedup from parallel execution is worth sacrificing the “purity” of exactly reproducible computations.
Nevertheless it is still possible to run a learner in a deterministic way with adaptive.
The simplest way is to use adaptive.runner.simple to run your learner:
learner = adaptive.Learner1D(f, bounds=(-1, 1))
# blocks until completion
adaptive.runner.simple(learner, goal=lambda l: l.loss() < 0.01)
learner.plot()
Note that unlike adaptive.Runner, adaptive.runner.simple blocks until it is finished.
If you want to enable determinism, want to continue using the non-blocking adaptive.Runner, you can use the adaptive.runner.SequentialExecutor:
from adaptive.runner import SequentialExecutor
learner = adaptive.Learner1D(f, bounds=(-1, 1))
runner = adaptive.Runner(
learner, executor=SequentialExecutor(), goal=lambda l: l.loss() < 0.01
)
await runner.task # This is not needed in a notebook environment!
runner.live_info()
runner.live_plot(update_interval=0.1)
Cancelling a runner#
Sometimes you want to interactively explore a parameter space, and want the function to be evaluated at finer and finer resolution and manually control when the calculation stops.
If no goal is provided to a runner then the runner will run until cancelled.
runner.live_info() will provide a button that can be clicked to stop the runner.
You can also stop the runner programatically using runner.cancel().
learner = adaptive.Learner1D(f, bounds=(-1, 1))
runner = adaptive.Runner(learner)
await asyncio.sleep(0.1) # This is not needed in the notebook!
runner.cancel() # Let's execute this after 0.1 seconds
runner.live_info()
runner.live_plot(update_interval=0.1)
print(runner.status())
cancelled
Debugging Problems#
Runners work in the background with respect to the IPython kernel, which makes it convenient, but also means that inspecting errors is more difficult because exceptions will not be raised directly in the notebook. Often the only indication you will have that something has gone wrong is that nothing will be happening.
Let’s look at the following example, where the function to be learned will raise an exception 10% of the time.
def will_raise(x):
from random import random
from time import sleep
sleep(random())
if random() < 0.1:
raise RuntimeError("something went wrong!")
return x**2
learner = adaptive.Learner1D(will_raise, (-1, 1))
runner = adaptive.Runner(
learner
) # without 'goal' the runner will run forever unless cancelled
await asyncio.sleep(4) # in 4 seconds it will surely have failed
runner.live_info()
runner.live_plot()
The above runner should continue forever, but we notice that it stops after a few points are evaluated.
First we should check that the runner has really finished:
runner.task.done()
True
If it has indeed finished then we should check the result of the runner.
This should be None if the runner stopped successfully.
If the runner stopped due to an exception then asking for the result will raise the exception with the stack trace:
runner.task.result()
---------------------------------------------------------------------------
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/adaptive/conda/v0.14.1/lib/python3.9/concurrent/futures/process.py", line 246, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File "/tmp/ipykernel_2899/1684974929.py", line 7, in will_raise
raise RuntimeError("something went wrong!")
RuntimeError: something went wrong!
"""
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:224, in BaseRunner._process_futures(self, done_futs)
223 try:
--> 224 y = fut.result()
225 t = time.time() - fut.start_time # total execution time
RuntimeError: something went wrong!
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
Cell In [28], line 1
----> 1 runner.task.result()
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:589, in AsyncRunner.status(self)
584 """Return the runner status as a string.
585
586 The possible statuses are: running, cancelled, failed, and finished.
587 """
588 try:
--> 589 self.task.result()
590 except asyncio.CancelledError:
591 return "cancelled"
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:589, in AsyncRunner.status(self)
584 """Return the runner status as a string.
585
586 The possible statuses are: running, cancelled, failed, and finished.
587 """
588 try:
--> 589 self.task.result()
590 except asyncio.CancelledError:
591 return "cancelled"
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:652, in AsyncRunner._run(self)
650 kw = {"loop": self.ioloop} if sys.version_info[:2] < (3, 10) else {}
651 done, _ = await asyncio.wait(futures, return_when=first_completed, **kw)
--> 652 self._process_futures(done)
653 finally:
654 remaining = self._remove_unfinished()
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:232, in BaseRunner._process_futures(self, done_futs)
230 self._to_retry.pop(pid)
231 if self.raise_if_retries_exceeded:
--> 232 self._do_raise(e, pid)
233 else:
234 self._elapsed_function_time += t / self._get_max_tasks()
File ~/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py:170, in BaseRunner._do_raise(self, e, i)
168 tb = self._tracebacks[i]
169 x = self._id_to_point[i]
--> 170 raise RuntimeError(
171 "An error occured while evaluating "
172 f'"learner.function({x})". '
173 f"See the traceback for details.:\n\n{tb}"
174 ) from e
RuntimeError: An error occured while evaluating "learner.function(1)". See the traceback for details.:
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/adaptive/conda/v0.14.1/lib/python3.9/concurrent/futures/process.py", line 246, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File "/tmp/ipykernel_2899/1684974929.py", line 7, in will_raise
raise RuntimeError("something went wrong!")
RuntimeError: something went wrong!
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py", line 224, in _process_futures
y = fut.result()
RuntimeError: something went wrong!
You can also check runner.tracebacks which is a list of tuples with (point, traceback).
for point, tb in runner.tracebacks:
print(f"point: {point}:\n {tb}")
point: 1:
concurrent.futures.process._RemoteTraceback:
"""
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/adaptive/conda/v0.14.1/lib/python3.9/concurrent/futures/process.py", line 246, in _process_worker
r = call_item.fn(*call_item.args, **call_item.kwargs)
File "/tmp/ipykernel_2899/1684974929.py", line 7, in will_raise
raise RuntimeError("something went wrong!")
RuntimeError: something went wrong!
"""
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/docs/checkouts/readthedocs.org/user_builds/adaptive/checkouts/v0.14.1/adaptive/runner.py", line 224, in _process_futures
y = fut.result()
RuntimeError: something went wrong!
Logging runners#
Runners do their job in the background, which makes introspection quite cumbersome.
One way to inspect runners is to instantiate one with log=True:
learner = adaptive.Learner1D(f, bounds=(-1, 1))
runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01, log=True)
await runner.task # This is not needed in a notebook environment!
runner.live_info()
This gives a the runner a log attribute, which is a list of the learner methods that were called, as well as their arguments.
This is useful because executors typically execute their tasks in a non-deterministic order.
This can be used with adaptive.runner.replay_log to perfom the same set of operations on another runner:
reconstructed_learner = adaptive.Learner1D(f, bounds=learner.bounds)
adaptive.runner.replay_log(reconstructed_learner, runner.log)
learner.plot().Scatter.I.opts(style=dict(size=6)) * reconstructed_learner.plot()
Adding coroutines#
In the following example we’ll add a Task that times the runner.
This is only for demonstration purposes because one can simply check runner.elapsed_time() or use the runner.live_info() widget to see the time since the runner has started.
So let’s get on with the example. To time the runner you cannot simply use
now = datetime.now()
runner = adaptive.Runner(...)
print(datetime.now() - now)
because this will be done immediately. Also blocking the kernel with while not runner.task.done() will not work because the runner will not do anything when the kernel is blocked.
Therefore you need to create an async function and hook it into the ioloop like so:
import asyncio
async def time(runner):
from datetime import datetime
now = datetime.now()
await runner.task
return datetime.now() - now
ioloop = asyncio.get_event_loop()
learner = adaptive.Learner1D(f, bounds=(-1, 1))
runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)
timer = ioloop.create_task(time(runner))
await runner.task # This is not needed in a notebook environment!
# The result will only be set when the runner is done.
timer.result()
datetime.timedelta(microseconds=347661)
Using Runners from a script#
Runners can also be used from a Python script independently of the notebook.
The simplest way to accomplish this is simply to use the BlockingRunner:
import adaptive
def f(x):
return x
learner = adaptive.Learner1D(f, (-1, 1))
adaptive.BlockingRunner(learner, goal=lambda l: l.loss() < 0.1)
If you use asyncio already in your script and want to integrate adaptive into it, then you can use the default Runner as you would from a notebook.
If you want to wait for the runner to finish, then you can simply
await runner.task
from within a coroutine.
- 1
This notebook can be downloaded as
tutorial.advanced-topics.ipynbandtutorial.advanced-topics.md.