Friendly fork ? #65
Comments
|
@bersace have a look at: https://github.com/jd/tenacity |
|
For the record, yeah, I tried to work with @rholder for a few months but I guess he's very busy. Since we did not manage to go anywhere with retrying for a long time, so I ended up forking it. |
|
Can we keep a fork with the original name please? I really don't want to go
back and change a bunch of code. Name change are a big deal.
Also, i dont mind a fork, but it's much more polite if you actually forked
something on GitHub to actually fork it on GitHub so that people can see
the lineage and that it is a fork of an original repo. This looks - at best
- either inept or discourteous.
I'm seriously not trying to ruffle feathers here - i just think these
things should be considered and we should treat people with respect even
when they get busy or whatever.
This is, after all, a thread about a friendly fork. Let's be friendly.
|
|
I switched to tenacity. It has been nicely cleaned. @underrun you may provide a open a PR with compatibility module in https://github.com/jd/tenacity ? |
|
While I understand your concern @underrun, I imagine the lack of context and information makes your comment a little bit offensive. :) So for the record, the API is not compatible, so there's no point keeping any name. And @rholder never replied to any of my request to help maintaining this library and I've no PyPI access to upload it. |
|
That's the sad story of many nice Python libraries that become abandonware because they had only one person behind them. For this reason I published all Python libraries under pycontribs github team where I assured there are others that can continue improving them even without me. Some people do think that forks are good but my view is a little bit different: they are usually a sign of something not working in the forked project. Forked projects to confuse people, not adding much to the overall user/consumer/developer experience. It is much better when there is no need to fork, everyone working for the same goal: of having one library that really works (as opposed to having 10 blends which all partially work). @rholder If you read this and like the idea of sharing maintenance with few other skilled Python developers, feel free to ping me. For the moment I am going to have a look at https://github.com/jd/tenacity as it seems to be better bet (actively maintained) ;) |
|
@ssbarnea Another, more popular Python join effort is https://jazzband.co/ |
lutostag
added a commit
to lutostag/awesome-python-decorator
that referenced
this issue
Oct 17, 2017
Retrying is kind of abandoned, as per rholder/retrying#65 and jd/tenacity#73. A suggestion to move to the more actively maintained fork, while still giving a mention to the parent project. Thanks for a useful curated list!
lord63
pushed a commit
to lord63/awesome-python-decorator
that referenced
this issue
Oct 22, 2017
Retrying is kind of abandoned, as per rholder/retrying#65 and jd/tenacity#73. A suggestion to move to the more actively maintained fork, while still giving a mention to the parent project. Thanks for a useful curated list!
This was referenced May 2, 2018
|
@rholder Hi, can you state the project is unmaintained either by archiving it or adding a tag in the description ? What do you think of it ? |
aidgent
added a commit
to aidgent/magentic
that referenced
this issue
Jun 10, 2024
# Aider chat conversation:
USER: 'do what is needed to solve this issue without further instruction. i am counting on you. i trust you. follow the process: 1. start by reviewing the issue below, and think step-by-step to first add the necessary files. 2. make the minimum changes needed to fix the issue. 3. commit the changes. always follow the process. here is the issue you are assigned to solve: ---[{\'id\': 2209612498, \'title\': \'Retry on failure to parse LLM output\', \'body\': "When using `\\@prompt`, if the model returns an output that cannot be parsed into the return type or function arguments, or a string output when this is not accepted, the error message should be added as a new message in the chat and the query should be tried again within the `\\@prompt`-decorated function\'s invocation. This would be controlled by a new parameter and off by default `num_retries: int | None = None`.\\r\\n\\r\\nThis should just retry magentic exceptions for parsing responses / errors due to the LLM failing to generate valid output. OpenAI rate limiting errors, internet connection errors etc. should not be handled by this and instead users should use https://github.com/jd/tenacity or https://github.com/hynek/stamina to deal with those."}, {\'id\': 2033081473, \'body\': "This would be a great addition. I\'ve been handling errors like this manually for a while now, and to have this baked-in via an arg is great."}, {\'id\': 2159460338, \'body\': \'\\@Aidgent - please try to solve this issue. Make sure to think step by step to add the new retry parameter and handle passing the error messages back into the LLM chat \'}, {\'id\': 2159460705, \'body\': "Aidgent reporting for duty! Thank you for giving me the opportunity to solve this issue. I\'m getting to work on it now and I will reply soon with my solution."}]---'
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## Repository files navigation
- [README](#)
- [License](#)
# Tenacity
[](#tenacity)
[](https://pypi.python.org/pypi/tenacity)
**Please refer to the** [tenacity
documentation](https://tenacity.readthedocs.io/en/latest/) **for a
better experience.**
Tenacity is an Apache 2.0 licensed general-purpose retrying library,
written in Python, to simplify the task of adding retry behavior to just
about anything. It originates from [a fork of
retrying](https://github.com/rholder/retrying/issues/65) which is sadly
no longer [maintained](https://julien.danjou.info/python-tenacity/).
Tenacity isn\'t api compatible with retrying but adds significant new
functionality and fixes a number of longstanding bugs.
The simplest use case is retrying a flaky function whenever an Exception
occurs until a value is returned.
.. testcode::
import random
from tenacity import retry
@retry
def do_something_unreliable():
if random.randint(0, 10) > 1:
raise IOError("Broken sauce, everything is hosed!!!111one")
else:
return "Awesome sauce!"
print(do_something_unreliable())
.. testoutput::
:hide:
Awesome sauce!
.. toctree::
:hidden:
:maxdepth: 2
changelog
api
## Features
[](#features)
- Generic Decorator API
- Specify stop condition (i.e. limit by number of attempts)
- Specify wait condition (i.e. exponential backoff sleeping between
attempts)
- Customize retrying on Exceptions
- Customize retrying on expected returned result
- Retry on coroutines
- Retry code block with context manager
## Installation
[](#installation)
To install *tenacity*, simply:
$ pip install tenacity
## Examples
[](#examples)
### Basic Retry
[](#basic-retry)
.. testsetup:: *
import logging
#
# Note the following import is used for demonstration convenience only.
# Production code should always explicitly import the names it needs.
#
from tenacity import *
class MyException(Exception):
pass
As you saw above, the default behavior is to retry forever without
waiting when an exception is raised.
.. testcode::
@retry
def never_gonna_give_you_up():
print("Retry forever ignoring Exceptions, don't wait between retries")
raise Exception
### Stopping
[](#stopping)
Let\'s be a little less persistent and set some boundaries, such as the
number of attempts before giving up.
.. testcode::
@retry(stop=stop_after_attempt(7))
def stop_after_7_attempts():
print("Stopping after 7 attempts")
raise Exception
We don\'t have all day, so let\'s set a boundary for how long we should
be retrying stuff.
.. testcode::
@retry(stop=stop_after_delay(10))
def stop_after_10_s():
print("Stopping after 10 seconds")
raise Exception
If you\'re on a tight deadline, and exceeding your delay time isn\'t ok,
then you can give up on retries one attempt before you would exceed the
delay.
.. testcode::
@retry(stop=stop_before_delay(10))
def stop_before_10_s():
print("Stopping 1 attempt before 10 seconds")
raise Exception
You can combine several stop conditions by using the \| operator:
.. testcode::
@retry(stop=(stop_after_delay(10) | stop_after_attempt(5)))
def stop_after_10_s_or_5_retries():
print("Stopping after 10 seconds or 5 retries")
raise Exception
### Waiting before retrying
[](#waiting-before-retrying)
Most things don\'t like to be polled as fast as possible, so let\'s just
wait 2 seconds between retries.
.. testcode::
@retry(wait=wait_fixed(2))
def wait_2_s():
print("Wait 2 second between retries")
raise Exception
Some things perform best with a bit of randomness injected.
.. testcode::
@retry(wait=wait_random(min=1, max=2))
def wait_random_1_to_2_s():
print("Randomly wait 1 to 2 seconds between retries")
raise Exception
Then again, it\'s hard to beat exponential backoff when retrying
distributed services and other remote endpoints.
.. testcode::
@retry(wait=wait_exponential(multiplier=1, min=4, max=10))
def wait_exponential_1():
print("Wait 2^x * 1 second between each retry starting with 4 seconds, then up to 10 seconds, then 10 seconds afterwards")
raise Exception
Then again, it\'s also hard to beat combining fixed waits and jitter (to
help avoid thundering herds) when retrying distributed services and
other remote endpoints.
.. testcode::
@retry(wait=wait_fixed(3) + wait_random(0, 2))
def wait_fixed_jitter():
print("Wait at least 3 seconds, and add up to 2 seconds of random delay")
raise Exception
When multiple processes are in contention for a shared resource,
exponentially increasing jitter helps minimise collisions.
.. testcode::
@retry(wait=wait_random_exponential(multiplier=1, max=60))
def wait_exponential_jitter():
print("Randomly wait up to 2^x * 1 seconds between each retry until the range reaches 60 seconds, then randomly up to 60 seconds afterwards")
raise Exception
Sometimes it\'s necessary to build a chain of backoffs.
.. testcode::
@retry(wait=wait_chain(*[wait_fixed(3) for i in range(3)] +
[wait_fixed(7) for i in range(2)] +
[wait_fixed(9)]))
def wait_fixed_chained():
print("Wait 3s for 3 attempts, 7s for the next 2 attempts and 9s for all attempts thereafter")
raise Exception
### Whether to retry
[](#whether-to-retry)
We have a few options for dealing with retries that raise specific or
general exceptions, as in the cases here.
.. testcode::
class ClientError(Exception):
"""Some type of client error."""
@retry(retry=retry_if_exception_type(IOError))
def might_io_error():
print("Retry forever with no wait if an IOError occurs, raise any other errors")
raise Exception
@retry(retry=retry_if_not_exception_type(ClientError))
def might_client_error():
print("Retry forever with no wait if any error other than ClientError occurs. Immediately raise ClientError.")
raise Exception
We can also use the result of the function to alter the behavior of
retrying.
.. testcode::
def is_none_p(value):
"""Return True if value is None"""
return value is None
@retry(retry=retry_if_result(is_none_p))
def might_return_none():
print("Retry with no wait if return value is None")
See also these methods:
.. testcode::
retry_if_exception
retry_if_exception_type
retry_if_not_exception_type
retry_unless_exception_type
retry_if_result
retry_if_not_result
retry_if_exception_message
retry_if_not_exception_message
retry_any
retry_all
We can also combine several conditions:
.. testcode::
def is_none_p(value):
"""Return True if value is None"""
return value is None
@retry(retry=(retry_if_result(is_none_p) | retry_if_exception_type()))
def might_return_none():
print("Retry forever ignoring Exceptions with no wait if return value is None")
Any combination of stop, wait, etc. is also supported to give you the
freedom to mix and match.
It\'s also possible to retry explicitly at any time by raising the
TryAgain exception:
.. testcode::
@retry
def do_something():
result = something_else()
if result == 23:
raise TryAgain
### Error Handling
[](#error-handling)
Normally when your function fails its final time (and will not be
retried again based on your settings), a RetryError is raised. The
exception your code encountered will be shown somewhere in the *middle*
of the stack trace.
If you would rather see the exception your code encountered at the *end*
of the stack trace (where it is most visible), you can set reraise=True.
.. testcode::
@retry(reraise=True, stop=stop_after_attempt(3))
def raise_my_exception():
raise MyException("Fail")
try:
raise_my_exception()
except MyException:
# timed out retrying
pass
### Before and After Retry, and Logging
[](#before-and-after-retry-and-logging)
It\'s possible to execute an action before any attempt of calling the
function by using the before callback function:
.. testcode::
import logging
import sys
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
@retry(stop=stop_after_attempt(3), before=before_log(logger, logging.DEBUG))
def raise_my_exception():
raise MyException("Fail")
In the same spirit, It\'s possible to execute after a call that failed:
.. testcode::
import logging
import sys
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
@retry(stop=stop_after_attempt(3), after=after_log(logger, logging.DEBUG))
def raise_my_exception():
raise MyException("Fail")
It\'s also possible to only log failures that are going to be retried.
Normally retries happen after a wait interval, so the keyword argument
is called `before_sleep`:
.. testcode::
import logging
import sys
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
@retry(stop=stop_after_attempt(3),
before_sleep=before_sleep_log(logger, logging.DEBUG))
def raise_my_exception():
raise MyException("Fail")
### Statistics
[](#statistics)
You can access the statistics about the retry made over a function by
using the retry attribute attached to the function and its statistics
attribute:
.. testcode::
@retry(stop=stop_after_attempt(3))
def raise_my_exception():
raise MyException("Fail")
try:
raise_my_exception()
except Exception:
pass
print(raise_my_exception.retry.statistics)
.. testoutput::
:hide:
...
### Custom Callbacks
[](#custom-callbacks)
You can also define your own callbacks. The callback should accept one
parameter called `retry_state` that contains all information about
current retry invocation.
For example, you can call a custom callback function after all retries
failed, without raising an exception (or you can re-raise or do anything
really)
.. testcode::
def return_last_value(retry_state):
"""return the result of the last call attempt"""
return retry_state.outcome.result()
def is_false(value):
"""Return True if value is False"""
return value is False
# will return False after trying 3 times to get a different result
@retry(stop=stop_after_attempt(3),
retry_error_callback=return_last_value,
retry=retry_if_result(is_false))
def eventually_return_false():
return False
### RetryCallState
[](#retrycallstate)
`retry_state` argument is an object of
[:class:\`\~tenacity.RetryCallState\`](#id1) class.
### Other Custom Callbacks
[](#other-custom-callbacks)
It\'s also possible to define custom callbacks for other keyword
arguments.
.. function:: my_stop(retry_state)
:param RetryCallState retry_state: info about current retry invocation
:return: whether or not retrying should stop
:rtype: bool
.. function:: my_wait(retry_state)
:param RetryCallState retry_state: info about current retry invocation
:return: number of seconds to wait before next retry
:rtype: float
.. function:: my_retry(retry_state)
:param RetryCallState retry_state: info about current retry invocation
:return: whether or not retrying should continue
:rtype: bool
.. function:: my_before(retry_state)
:param RetryCallState retry_state: info about current retry invocation
.. function:: my_after(retry_state)
:param RetryCallState retry_state: info about current retry invocation
.. function:: my_before_sleep(retry_state)
:param RetryCallState retry_state: info about current retry invocation
Here\'s an example with a custom `before_sleep` function:
.. testcode::
import logging
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)
logger = logging.getLogger(__name__)
def my_before_sleep(retry_state):
if retry_state.attempt_number < 1:
loglevel = logging.INFO
else:
loglevel = logging.WARNING
logger.log(
loglevel, 'Retrying %s: attempt %s ended with: %s',
retry_state.fn, retry_state.attempt_number, retry_state.outcome)
@retry(stop=stop_after_attempt(3), before_sleep=my_before_sleep)
def raise_my_exception():
raise MyException("Fail")
try:
raise_my_exception()
except RetryError:
pass
### Changing Arguments at Run Time
[](#changing-arguments-at-run-time)
You can change the arguments of a retry decorator as needed when calling
it by using the retry_with function attached to the wrapped function:
.. testcode::
@retry(stop=stop_after_attempt(3))
def raise_my_exception():
raise MyException("Fail")
try:
raise_my_exception.retry_with(stop=stop_after_attempt(4))()
except Exception:
pass
print(raise_my_exception.retry.statistics)
.. testoutput::
:hide:
...
If you want to use variables to set up the retry parameters, you don\'t
have to use the retry decorator - you can instead use Retrying directly:
.. testcode::
def never_good_enough(arg1):
raise Exception('Invalid argument: {}'.format(arg1))
def try_never_good_enough(max_attempts=3):
retryer = Retrying(stop=stop_after_attempt(max_attempts), reraise=True)
retryer(never_good_enough, 'I really do try')
### Retrying code block
[](#retrying-code-block)
Tenacity allows you to retry a code block without the need to wraps it
in an isolated function. This makes it easy to isolate failing block
while sharing context. The trick is to combine a for loop and a context
manager.
.. testcode::
from tenacity import Retrying, RetryError, stop_after_attempt
try:
for attempt in Retrying(stop=stop_after_attempt(3)):
with attempt:
raise Exception('My code is failing!')
except RetryError:
pass
You can configure every details of retry policy by configuring the
Retrying object.
With async code you can use AsyncRetrying.
.. testcode::
from tenacity import AsyncRetrying, RetryError, stop_after_attempt
async def function():
try:
async for attempt in AsyncRetrying(stop=stop_after_attempt(3)):
with attempt:
raise Exception('My code is failing!')
except RetryError:
pass
In both cases, you may want to set the result to the attempt so it\'s
available in retry strategies like `retry_if_result`. This can be done
accessing the `retry_state` property:
.. testcode::
from tenacity import AsyncRetrying, retry_if_result
async def function():
async for attempt in AsyncRetrying(retry=retry_if_result(lambda x: x < 3)):
with attempt:
result = 1 # Some complex calculation, function call, etc.
if not attempt.retry_state.outcome.failed:
attempt.retry_state.set_result(result)
return result
### Async and retry
[](#async-and-retry)
Finally, `retry` works also on asyncio and Tornado (\>= 4.5) coroutines.
Sleeps are done asynchronously too.
@retry
async def my_async_function(loop):
await loop.getaddrinfo('8.8.8.8', 53)
@retry
@tornado.gen.coroutine
def my_async_function(http_client, url):
yield http_client.fetch(url)
You can even use alternative event loops such as curio or Trio by
passing the correct sleep function:
@retry(sleep=trio.sleep)
async def my_async_function(loop):
await asks.get('https://example.org')
## Contribute
[](#contribute)
1. Check for open issues or open a fresh issue to start a discussion
around a feature idea or a bug.
2. Fork [the repository](https://github.com/jd/tenacity) on GitHub to
start making your changes to the **main** branch (or branch off of
it).
3. Write a test which shows that the bug was fixed or that the feature
works as expected.
4. Add a [changelog](#Changelogs)
5. Make the docs better (or more detailed, or more easier to read, or
\...)
### Changelogs
[](#changelogs)
[reno](https://docs.openstack.org/reno/latest/user/usage.html) is used
for managing changelogs. Take a look at their usage docs.
The doc generation will automatically compile the changelogs. You just
need to add them.
# Opens a template file in an editor
tox -e reno -- new some-slug-for-my-change --edit
## About
Retrying library for Python
[tenacity.readthedocs.io](http://tenacity.readthedocs.io)
### Topics
[python](/topics/python) [retry](/topics/retry)
[failure](/topics/failure) [retry-library](/topics/retry-library)
[hacktoberfest](/topics/hacktoberfest)
### Resources
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Production-grade retries for Python
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+-----------------+-----------------+-----------------+-----------------+
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TL;DR Go use https://github.com/jd/tenacity ! :)
Hi,
retryingis a nice piece of code, thanks to @rholder and contributors !Sadly there is no activity since june 2016. Several contributors suggest to create a friendly fork to keep
retryingalive. I'm one of them.What do you think of this ?
cc @RyPeck @charlescbeebe @reversefold @harlowja @bodenr @robin81 @jpic @doubleloop @davidyen1124 @underrun
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