from __future__ import annotations
import logging
from dask.core import validate_key
from distributed.client import Future, futures_of, wait
from distributed.protocol.serialize import ToPickle
from distributed.utils import sync
from distributed.utils_comm import pack_data
logger = logging.getLogger(__name__)
class ReplayTaskScheduler:
"""A plugin for the scheduler to recreate tasks locally
This adds the following routes to the scheduler
* get_runspec
* get_error_cause
"""
def __init__(self, scheduler):
self.scheduler = scheduler
self.scheduler.handlers["get_runspec"] = self.get_runspec
self.scheduler.handlers["get_error_cause"] = self.get_error_cause
def _process_key(self, key):
if isinstance(key, list):
key = tuple(key) # ensure not a list from msgpack
return key
def get_error_cause(self, *args, keys=(), **kwargs):
for key in keys:
key = self._process_key(key)
ts = self.scheduler.tasks.get(key)
if ts is not None and ts.exception_blame is not None:
return ts.exception_blame.key
def get_runspec(self, *args, key=None, **kwargs):
key = self._process_key(key)
ts = self.scheduler.tasks.get(key)
return {
"task": ToPickle(ts.run_spec),
"deps": [dts.key for dts in ts.dependencies],
}
[docs]class ReplayTaskClient:
"""
A plugin for the client allowing replay of remote tasks locally
Adds the following methods to the given client:
- ``recreate_error_locally``: main user method for replaying failed tasks
- ``recreate_task_locally``: main user method for replaying any task
"""
def __init__(self, client):
self.client = client
self.client.extensions["replay-tasks"] = self
# monkey patch
self.client._get_raw_components_from_future = (
self._get_raw_components_from_future
)
self.client._prepare_raw_components = self._prepare_raw_components
self.client._get_components_from_future = self._get_components_from_future
self.client._get_errored_future = self._get_errored_future
self.client.recreate_task_locally = self.recreate_task_locally
self.client.recreate_error_locally = self.recreate_error_locally
@property
def scheduler(self):
return self.client.scheduler
async def _get_raw_components_from_future(self, future):
"""
For a given future return the func, args and kwargs and future
deps that would be executed remotely.
"""
if isinstance(future, Future):
await wait(future)
key = future.key
else:
validate_key(future)
key = future
spec = await self.scheduler.get_runspec(key=key)
return (*spec["task"], spec["deps"])
async def _prepare_raw_components(self, raw_components):
"""
Take raw components and resolve future dependencies.
"""
function, args, kwargs, deps = raw_components
futures = self.client._graph_to_futures({}, deps)
data = await self.client._gather(futures)
args = pack_data(args, data)
kwargs = pack_data(kwargs, data)
return (function, args, kwargs)
async def _get_components_from_future(self, future):
"""
For a given future return the func, args and kwargs that would be
executed remotely. Any args/kwargs that are themselves futures will
be resolved to the return value of those futures.
"""
raw_components = await self._get_raw_components_from_future(future)
return await self._prepare_raw_components(raw_components)
[docs] def recreate_task_locally(self, future):
"""
For any calculation, whether it succeeded or failed, perform the task
locally for debugging.
This operation should be performed after a future (result of ``gather``,
``compute``, etc) comes back with a status other than "pending". Cases
where you might want to debug a successfully completed future could
include a calculation that returns an unexpected results. A common
debugging process might include running the task locally in debug mode,
with `pdb.runcall`.
Examples
--------
>>> import pdb # doctest: +SKIP
>>> future = c.submit(div, 1, 1) # doctest: +SKIP
>>> future.status # doctest: +SKIP
'finished'
>>> pdb.runcall(c.recreate_task_locally, future) # doctest: +SKIP
Parameters
----------
future : future
The same thing as was given to ``gather``.
Returns
-------
Any; will return the result of the task future.
"""
func, args, kwargs = sync(
self.client.loop, self._get_components_from_future, future
)
return func(*args, **kwargs)
async def _get_errored_future(self, future):
"""
For a given future collection, return the first future that raised
an error.
"""
await wait(future)
futures = [f.key for f in futures_of(future) if f.status == "error"]
if not futures:
raise ValueError("No errored futures passed")
cause_key = await self.scheduler.get_error_cause(keys=futures)
return cause_key
[docs] def recreate_error_locally(self, future):
"""
For a failed calculation, perform the blamed task locally for debugging.
This operation should be performed after a future (result of ``gather``,
``compute``, etc) comes back with a status of "error", if the stack-
trace is not informative enough to diagnose the problem. The specific
task (part of the graph pointing to the future) responsible for the
error will be fetched from the scheduler, together with the values of
its inputs. The function will then be executed, so that ``pdb`` can
be used for debugging.
Examples
--------
>>> future = c.submit(div, 1, 0) # doctest: +SKIP
>>> future.status # doctest: +SKIP
'error'
>>> c.recreate_error_locally(future) # doctest: +SKIP
ZeroDivisionError: division by zero
If you're in IPython you might take this opportunity to use pdb
>>> %pdb # doctest: +SKIP
Automatic pdb calling has been turned ON
>>> c.recreate_error_locally(future) # doctest: +SKIP
ZeroDivisionError: division by zero
1 def div(x, y):
----> 2 return x / y
ipdb>
Parameters
----------
future : future or collection that failed
The same thing as was given to ``gather``, but came back with
an exception/stack-trace. Can also be a (persisted) dask collection
containing any errored futures.
Returns
-------
Nothing; the function runs and should raise an exception, allowing
the debugger to run.
"""
errored_future_key = sync(self.client.loop, self._get_errored_future, future)
return self.recreate_task_locally(errored_future_key)