repo_name
stringlengths 9
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stringclasses 30
values | issue_number
int64 1
203k
| title
stringlengths 1
976
| body
stringlengths 0
254k
| state
stringclasses 2
values | created_at
stringlengths 20
20
| updated_at
stringlengths 20
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stringlengths 38
105
| labels
listlengths 0
9
| user_login
stringlengths 1
39
| comments_count
int64 0
452
|
|---|---|---|---|---|---|---|---|---|---|---|---|
jmcnamara/XlsxWriter
|
pandas
| 997
|
question: Is it possible to create a pie-of-pie chart?
|
### Question
Is it possible to create a pie-of-pie or bar-of-pie chart with XlsWriter? If not, is there a work-around or alternative to represent the same information?
|
closed
|
2023-07-01T01:16:58Z
|
2023-07-01T15:06:05Z
|
https://github.com/jmcnamara/XlsxWriter/issues/997
|
[
"question"
] |
rmsapre
| 1
|
microsoft/unilm
|
nlp
| 1,361
|
How to load beit3 fine-tined weight such as beit3_base_patch16_224_nlvr2.pth?
|
timm.create_model doesn't support beit3 yet, so how to load the fine-tined weight such as beit3_base_patch16_224_nlvr2.pth?
|
open
|
2023-11-08T13:16:14Z
|
2024-02-22T03:01:34Z
|
https://github.com/microsoft/unilm/issues/1361
|
[] |
yangzhj53
| 6
|
pyeve/eve
|
flask
| 1,077
|
At time comparison, $gt is the same as $gte query results
|
First of all, thanks for this project！
I have a scene that is querying data larger than a certain time, using "_updated": {"$gt": "2017-08-26 09:12:59"}, but the query returns a data equal to this time The The effect is equivalent to "$gte"
|
closed
|
2017-10-19T07:50:29Z
|
2018-05-18T16:19:48Z
|
https://github.com/pyeve/eve/issues/1077
|
[
"stale"
] |
fantasykai
| 1
|
pallets/flask
|
python
| 4,594
|
Recommended link update for "Make the Project Installable" docs
|
The link to the "official packaging guide" in the "Make the Project Installable" tutorial section makes little reference to `setup.py` and no reference to the `MANIFEST.in` file.
I recommend updating the link from the ["Packing Python Projects"](https://packaging.python.org/en/latest/tutorials/packaging-projects/) tutorial to the ["Packaging and distributing projects"](https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/) guide which details usage of `setup.py` and the `MANIFEST.in` file.
I'd be happy to make a PR to update if this change is welcome.
|
closed
|
2022-05-12T19:18:01Z
|
2022-06-07T00:05:24Z
|
https://github.com/pallets/flask/issues/4594
|
[
"docs"
] |
nkabrown
| 1
|
piskvorky/gensim
|
machine-learning
| 2,657
|
Tweak placeholders
|
Continued from https://github.com/RaRe-Technologies/gensim/pull/2654
Point each placeholder to its corresponding tutorial.
|
open
|
2019-10-29T08:43:15Z
|
2019-10-29T08:43:22Z
|
https://github.com/piskvorky/gensim/issues/2657
|
[
"documentation"
] |
mpenkov
| 0
|
dask/dask
|
pandas
| 11,681
|
Unable to use scheduler 'external_address'
|
<!-- Please include a self-contained copy-pastable example that generates the issue if possible.
Please be concise with code posted. See guidelines below on how to provide a good bug report:
- Craft Minimal Bug Reports http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples https://stackoverflow.com/help/mcve
Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->
**Describe the issue**:
Seems unable to find in the doc how to specify the `external_address` for the scheduler when initiating the LocalCluster. By guesswork, I failed to "inject" the setting via `scheduler_kwargs` due to `RuntimeError(f"Cluster failed to start: {e}") from e`.
**Minimal Complete Verifiable Example**:
```python
cluster = LocalCluster(
host="0.0.0.0",
scheduler_kwargs={"external_address": "localhost"},
)
```
The error stack:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/deploy/spec.py:324, in SpecCluster._start(self)
323 cls = import_term(cls)
--> 324 self.scheduler = cls(**self.scheduler_spec.get("options", {}))
325 self.scheduler = await self.scheduler
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/scheduler.py:4019, in Scheduler.__init__(self, loop, services, service_kwargs, allowed_failures, extensions, validate, scheduler_file, security, worker_ttl, idle_timeout, interface, host, port, protocol, dashboard_address, dashboard, http_prefix, preload, preload_argv, plugins, contact_address, transition_counter_max, jupyter, **kwargs)
4005 SchedulerState.__init__(
4006 self,
4007 aliases=aliases,
(...)
4017 transition_counter_max=transition_counter_max,
4018 )
-> 4019 ServerNode.__init__(
4020 self,
4021 handlers=self.handlers,
4022 stream_handlers=merge(worker_handlers, client_handlers),
4023 connection_limit=connection_limit,
4024 deserialize=False,
4025 connection_args=self.connection_args,
4026 **kwargs,
4027 )
4029 if self.worker_ttl:
TypeError: Server.__init__() got an unexpected keyword argument 'external_address'
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
Cell In[2], line 1
----> 1 cluster = LocalCluster(
2 host="0.0.0.0",
3 scheduler_kwargs={"external_address": "localhost"},
4 )
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/deploy/local.py:256, in LocalCluster.__init__(self, name, n_workers, threads_per_worker, processes, loop, start, host, ip, scheduler_port, silence_logs, dashboard_address, worker_dashboard_address, diagnostics_port, services, worker_services, service_kwargs, asynchronous, security, protocol, blocked_handlers, interface, worker_class, scheduler_kwargs, scheduler_sync_interval, **worker_kwargs)
253 worker = {"cls": worker_class, "options": worker_kwargs}
254 workers = {i: worker for i in range(n_workers)}
--> 256 super().__init__(
257 name=name,
258 scheduler=scheduler,
259 workers=workers,
260 worker=worker,
261 loop=loop,
262 asynchronous=asynchronous,
263 silence_logs=silence_logs,
264 security=security,
265 scheduler_sync_interval=scheduler_sync_interval,
266 )
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/deploy/spec.py:284, in SpecCluster.__init__(self, workers, scheduler, worker, asynchronous, loop, security, silence_logs, name, shutdown_on_close, scheduler_sync_interval)
282 if not self.called_from_running_loop:
283 self._loop_runner.start()
--> 284 self.sync(self._start)
285 try:
286 self.sync(self._correct_state)
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/utils.py:363, in SyncMethodMixin.sync(self, func, asynchronous, callback_timeout, *args, **kwargs)
361 return future
362 else:
--> 363 return sync(
364 self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
365 )
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/utils.py:439, in sync(loop, func, callback_timeout, *args, **kwargs)
436 wait(10)
438 if error is not None:
--> 439 raise error
440 else:
441 return result
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/utils.py:413, in sync.<locals>.f()
411 awaitable = wait_for(awaitable, timeout)
412 future = asyncio.ensure_future(awaitable)
--> 413 result = yield future
414 except Exception as exception:
415 error = exception
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/tornado/gen.py:767, in Runner.run(self)
765 try:
766 try:
--> 767 value = future.result()
768 except Exception as e:
769 # Save the exception for later. It's important that
770 # gen.throw() not be called inside this try/except block
771 # because that makes sys.exc_info behave unexpectedly.
772 exc: Optional[Exception] = e
File ~/.pyenv/versions/3.12.2/envs/venv_3.12.2/lib/python3.12/site-packages/distributed/deploy/spec.py:335, in SpecCluster._start(self)
333 self.status = Status.failed
334 await self._close()
--> 335 raise RuntimeError(f"Cluster failed to start: {e}") from e
RuntimeError: Cluster failed to start: Server.__init__() got an unexpected keyword argument 'external_address'
```
**Anything else we need to know?**:
Nope
**Environment**:
- Dask version: 2024.12.1
- Python version:3.12.2
- Operating System:MacOS / Ubuntu 22.04
- Install method (conda, pip, source): pip
|
closed
|
2025-01-20T08:02:15Z
|
2025-02-07T11:41:57Z
|
https://github.com/dask/dask/issues/11681
|
[
"needs triage"
] |
carusyte
| 3
|
miguelgrinberg/flasky
|
flask
| 470
|
Cannot run docker container on VPS
|
I want to run the docker containers on my server. The services (database and app) can be started on my MacBook without problems, but when I start the same code on my server I get the following error message:
```
ERROR: for flasky-application Cannot start service app: OCI runtime create failed: container_linux.go:345: starting container process caused "process_linux.go:430: container init caused \"process_linux.go:413: running prestart hook 0 caused \\\"error running hook: exit status 2, stdout: , stderr: runtime/cgo: pthread_create failed: Resource temporarily unavailable
ERROR: for app Cannot start service app: OCI runtime create failed: container_linux.go:345: starting container process caused "process_linux.go:430: container init caused \"process_linux.go:413: running prestart hook 0 caused \\\"error running hook: exit status 2, stdout: , stderr: runtime/cgo: pthread_create failed: Resource temporarily unavailable
ERROR: Encountered errors while bringing up the project.
```
what can I do to make this work?
|
closed
|
2020-05-29T17:18:31Z
|
2020-05-29T19:02:19Z
|
https://github.com/miguelgrinberg/flasky/issues/470
|
[] |
mobeit00
| 0
|
coleifer/sqlite-web
|
flask
| 63
|
Rspberry pi installatiom
|
How to install it on RPi?
|
closed
|
2019-09-14T05:08:33Z
|
2019-09-14T15:00:50Z
|
https://github.com/coleifer/sqlite-web/issues/63
|
[] |
alanmilinovic
| 3
|
PokemonGoF/PokemonGo-Bot
|
automation
| 5,328
|
Starting bot error
|
Install By: ./setup.sh -i
Run By: ./run.sh
OS: Ubuntu 14.04.4 LTS
Branch: master
Python Version: Python 2.7.12
|
closed
|
2016-09-09T11:48:16Z
|
2016-09-22T05:58:04Z
|
https://github.com/PokemonGoF/PokemonGo-Bot/issues/5328
|
[] |
zZzWhoAmIzZz
| 1
|
gradio-app/gradio
|
python
| 10,084
|
tomlkit is out of date
|
### Describe the bug
The latest version of tomlkit is 0.13.2. This project currently requires `tomlkit==0.12.0`.
https://github.com/gradio-app/gradio/blob/main/requirements.txt#L24
I can work around it by creating a fork.
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Reproduction
requirements.txt
```requirements
gradio==5.7.1
tomlkit==0.13.2
```
```sh
pip install -r requirements.txt
```
### Screenshot
### Logs
```shell
INFO: pip is looking at multiple versions of gradio to determine which version is compatible with other requirements. This could take a while.
ERROR: Cannot install -r requirements.txt (line 13) and tomlkit==0.13.2 because these package versions have conflicting dependencies.
The conflict is caused by:
The user requested tomlkit==0.13.2
gradio 5.7.1 depends on tomlkit==0.12.0
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip to attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
```
```
### System Info
```shell
➜ ✗ poetry run gradio environment
Launching in *reload mode* on: http://127.0.0.1:7860 (Press CTRL+C to quit)
Watching: '/home/brian/work/Presence-AI/presence/livekit/agent/.venv/lib/python3.12/site-packages/gradio', '/home/brian/work/Presence-AI/presence/livekit/agent'
ERROR: Error loading ASGI app. Could not import module "environment".
```
### Severity
I can work around it
|
closed
|
2024-11-29T23:45:01Z
|
2024-12-02T20:02:54Z
|
https://github.com/gradio-app/gradio/issues/10084
|
[
"bug"
] |
btakita
| 0
|
ray-project/ray
|
machine-learning
| 51,276
|
[core][gpu-objects] Support collective operations
|
### Description
Support collective operations of GPU objects such as gather / scatter / all-reduce.
### Use case
_No response_
|
open
|
2025-03-11T22:43:35Z
|
2025-03-11T22:43:51Z
|
https://github.com/ray-project/ray/issues/51276
|
[
"enhancement",
"P2",
"core",
"gpu-objects"
] |
kevin85421
| 0
|
s3rius/FastAPI-template
|
asyncio
| 65
|
Question: where to put Mangum handler?
|
How do I add the Mangum handler into this template produced code?
Intention:
https://dwisulfahnur.medium.com/fastapi-deployment-to-aws-lambda-with-serverless-framework-b637b455142c
```
from fastapi import FastAPI
from app.api.api_v1.api import router as api_router
from mangum import Mangum
app = FastAPI()
@app.get("/")
async def root():
return {"message": "Hello World!"}
app.include_router(api_router, prefix="api/v1")
handler = Mangum(app)
```
not sure how to wrap the handler to the app?
```
def main() -> None:
"""Entrypoint of the application."""
uvicorn.run(
"<project>.web.application:get_app",
workers=settings.workers_count,
host=settings.host,
port=settings.port,
reload=settings.reload,
factory=True,
)
```
```
def get_app() -> FastAPI:
"""
Get FastAPI application.
This is the main constructor of an application.
:return: application.
"""
app = FastAPI(
title="<project>",
description="<project>",
version=metadata.version("<project>"),
docs_url=None,
redoc_url=None,
openapi_url="/api/openapi.json",
default_response_class=UJSONResponse,
)
app.on_event("startup")(startup(app))
app.on_event("shutdown")(shutdown(app))
app.include_router(router=api_router, prefix="/api")
app.mount(
"/static",
StaticFiles(directory=APP_ROOT / "static"),
name="static",
)
return app
```
|
closed
|
2022-01-27T20:57:17Z
|
2023-12-12T23:51:25Z
|
https://github.com/s3rius/FastAPI-template/issues/65
|
[] |
am1ru1
| 6
|
recommenders-team/recommenders
|
deep-learning
| 2,154
|
[BUG] Ranking Evaluation Metrics Exceed 1 with "by_threshold" Relevancy Method
|
### Description
Hello!
I encountered an issue while evaluating the BPR (Bayesian Personalized Ranking) model with basically the same code provided in the example on a different dataset. Specifically, when using the "by_threshold" relevancy method with ranking metrics, the computed values for precision@k, ndcg@k, and map@k exceed 1, which seems incorrect. This issue does not occur when switching the relevancy method to "top_k."
### How do we replicate the issue?
I use the following parameter for BPR (all using the default seed):
```
bpr = cornac.models.BPR(
k=200,
max_iter=100,
learning_rate=0.01,
lambda_reg=0.001,
verbose=True
)
```
Using these evaluation
```
TOP_K = 10
threshold =50
eval_map = map_at_k(test, all_predictions, col_prediction="prediction",
relevancy_method='by_threshold', threshold=threshold, k=TOP_K)
eval_ndcg = ndcg_at_k(test, all_predictions, col_prediction="prediction",
relevancy_method='by_threshold', threshold=threshold, k=TOP_K)
eval_precision = precision_at_k(
test, all_predictions, col_prediction="prediction",
relevancy_method='by_threshold', threshold=threshold, k=TOP_K)
```
Here is the dataset I test on: https://github.com/mnhqut/rec_sys-dataset/blob/main/data.csv
My result:
MAP: 1.417529
NDCG: 1.359902
Precision@K: 2.256466
### Willingness to contribute
<!--- Go over all the following points, and put an `x` in the box that apply. -->
- [ ] Yes, I can contribute for this issue independently.
- [x ] Yes, I can contribute for this issue with guidance from Recommenders community.
- [ ] No, I cannot contribute at this time.
|
open
|
2024-08-23T15:01:27Z
|
2024-08-29T15:09:29Z
|
https://github.com/recommenders-team/recommenders/issues/2154
|
[
"bug"
] |
mnhqut
| 4
|
widgetti/solara
|
flask
| 156
|
No clear way to define dark theme
|
When I look at the solara output, I see that it is possible to define "theme--dark" in most widgets (see below). However, I couldn't find a programmatic way to set the theme in solara. Is it possible to set theme of widgets in a Page with a single statement from Python?
|
closed
|
2023-06-16T08:17:26Z
|
2023-06-16T14:40:46Z
|
https://github.com/widgetti/solara/issues/156
|
[
"documentation"
] |
hkayabilisim
| 3
|
CorentinJ/Real-Time-Voice-Cloning
|
pytorch
| 474
|
Should we create a Slack Channel to communicate?
|
@blue-fish should we create a Slack Channel to communicate?
|
closed
|
2020-08-06T16:03:47Z
|
2022-09-14T18:03:50Z
|
https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/474
|
[] |
mbdash
| 12
|
pytorch/vision
|
machine-learning
| 8,016
|
Scheduled workflow failed
|
Oh no, something went wrong in the scheduled workflow tests/download.
Please look into it:
https://github.com/pytorch/vision/actions/runs/6403841200
Feel free to close this if this was just a one-off error.
cc @pmeier
|
closed
|
2023-10-04T09:10:32Z
|
2023-10-12T07:19:39Z
|
https://github.com/pytorch/vision/issues/8016
|
[
"bug",
"module: datasets"
] |
github-actions[bot]
| 0
|
lucidrains/vit-pytorch
|
computer-vision
| 58
|
Error shows that ViT object has no attribute 'pool'
|
I am trying to load my saved model and then test it on some images.
This is my code snippet:
device = "cuda"
with torch.no_grad():
epoch_val_accuracy = 0
epoch_val_loss = 0
for data, label in val_loader:
data = data.to(device , dtype = torch.float)
label = label.to(device , dtype = torch.long)
val_output = model(data)
val_loss = criterion(val_output, label)
acc = (val_output.argmax(dim=1) == label).float().mean()
epoch_val_accuracy += acc / len(val_loader)
epoch_val_loss += val_loss / len(val_loader)
the screenshot is attached
|
closed
|
2021-01-08T14:15:24Z
|
2021-01-11T17:15:57Z
|
https://github.com/lucidrains/vit-pytorch/issues/58
|
[] |
Abhranta
| 4
|
autokey/autokey
|
automation
| 94
|
broken link in ABOUT
|
## Classification: none listed are relevant
## Summary
about file links to the autokey website:
https://code.google.com/archive/p/autokey/
that's 404.
and it seems like you sidestepped describing what the program does by pointing to the autokey website instead (which is reasonable when it works), so now there is none at all.
|
closed
|
2017-07-27T09:57:38Z
|
2017-07-27T14:52:10Z
|
https://github.com/autokey/autokey/issues/94
|
[] |
steanne
| 2
|
d2l-ai/d2l-en
|
data-science
| 2,107
|
Evaluation results of DenseNet TF look wrong
|
http://preview.d2l.ai.s3-website-us-west-2.amazonaws.com/d2l-en/master/chapter_convolutional-modern/densenet.html
l
|
closed
|
2022-04-21T00:58:48Z
|
2022-12-15T23:56:03Z
|
https://github.com/d2l-ai/d2l-en/issues/2107
|
[] |
astonzhang
| 2
|
pyeve/eve
|
flask
| 645
|
Restoring soft_delete using PATCH results in 422 unknown field “_deleted”
|
@nckpark [this](https://stackoverflow.com/questions/30632740/restoring-soft-delete-using-patch-results-in-422-unknown-field-deleted) soft-delete item was reported on Stack Overflow. Would you be willing to triage it? Might be a bug.
|
closed
|
2015-06-04T05:51:05Z
|
2015-06-09T06:55:50Z
|
https://github.com/pyeve/eve/issues/645
|
[] |
nicolaiarocci
| 2
|
custom-components/pyscript
|
jupyter
| 81
|
task.wait_until ignores state_check_now
|
The task.wait_until() function always checks state on entry, ignoring the documented state_check_now argument. In fact the state_check_now argument of wait_until() in trigger.py is declared but never used.
pyscript version: 0.32
|
closed
|
2020-11-05T08:12:52Z
|
2020-11-06T01:54:23Z
|
https://github.com/custom-components/pyscript/issues/81
|
[] |
dadler
| 2
|
exaloop/codon
|
numpy
| 432
|
Segmentation fault with -release flag
|
on current develop, works fine without -release flag, segfaults with it, even on small examples
--- SIGSEGV {si_signo=SIGSEGV, si_code=SEGV_MAPERR, si_addr=NULL} ---
+++ killed by SIGSEGV (core dumped) +++
Segmentation fault (core dumped)
|
open
|
2023-07-27T14:06:11Z
|
2024-11-10T06:25:39Z
|
https://github.com/exaloop/codon/issues/432
|
[] |
Miezhiko
| 4
|
mage-ai/mage-ai
|
data-science
| 5,460
|
Add Databend cloud data warehouse
|
**Is your feature request related to a problem? Please describe.**
[Databend](https://www.databend.com/) is an economical alternative to snowflake
**Describe the solution you'd like**
build intgration with databend for io and data integration pipelines
**Describe alternatives you've considered**
Snowflake Integration
|
open
|
2024-10-02T18:00:19Z
|
2024-10-09T18:49:26Z
|
https://github.com/mage-ai/mage-ai/issues/5460
|
[
"data integration",
"io"
] |
TalaatHasanin
| 0
|
pytest-dev/pytest-html
|
pytest
| 144
|
add custom test results to report
|
Hi,
I have a test function like this:
```
def test_score():
# load csv of one column
x = PrettyTable()
x.field_names = ['old','new']
for val in column:
new_val = some_func(val)
x.add_row([val, new_val])
assert val == new_val
output = x.get_string() # i want to print this in report when test success
```
and many other functions using this pattern, I can't figure out a way to retrieve output value inside pytest_html_results_table_html hook.
Do you have any suggestions ?
Thank you
|
closed
|
2018-01-04T17:18:04Z
|
2023-06-20T08:07:12Z
|
https://github.com/pytest-dev/pytest-html/issues/144
|
[] |
ohayak
| 5
|
qubvel-org/segmentation_models.pytorch
|
computer-vision
| 75
|
How to override input 3x3 param for Encoders
|
Hi,i see that it is defaulted to false for your encoders in param dict. How i override it while passing it to seg models ?
|
closed
|
2019-10-02T13:50:06Z
|
2022-02-09T01:53:31Z
|
https://github.com/qubvel-org/segmentation_models.pytorch/issues/75
|
[
"Stale"
] |
jaideep11061982
| 5
|
koxudaxi/datamodel-code-generator
|
pydantic
| 1,740
|
Invalid definition of graphql requirement
|
**Describe the bug**
Invalid definition of graphql requirement.
**To Reproduce**
For a graphql schema we have
Used commandline:
```
$ datamodel-codegen --input schema.graphql --output model.py --input-file-type graphql
```
we have
```bash
Exception: Please run `$pip install 'datamodel-code-generator[graphql]`' to generate data-model from a GraphQL schema.
```
**Expected behavior**
We get this behavior because we have
**pyproject.toml**
```toml
...
[tool.poetry.group.graphql.dependencies]
graphql-core = "^3.2.3"
...
```
It must be like this
**pyproject.toml**
```toml
...
[tool.poetry.dependencies]
...
graphql-core = { version = "^3.2.3", optional = true }
...
```
**Version:**
- OS: [e.g. iOS]
- Python version: 3.7-3.11 (i tested in 3.11)
- datamodel-code-generator version: 0.25.0
I will create a fix.
|
closed
|
2023-11-26T13:32:41Z
|
2023-11-26T19:08:12Z
|
https://github.com/koxudaxi/datamodel-code-generator/issues/1740
|
[] |
denisart
| 1
|
ets-labs/python-dependency-injector
|
flask
| 873
|
Making injections into class attributes can't work with `Resource` in nested container.
|
Reproducible project: https://github.com/YogiLiu/pdi_issue
When I run `python -m pdi_issue.main`, an error was raised:
```
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/home/yogiliu/Workspace/YogiLiu/pdi_issue/src/pdi_issue/main.py", line 4, in <module>
container = SrvContainer()
^^^^^^^^^^^^^^
File "src/dependency_injector/containers.pyx", line 727, in dependency_injector.containers.DeclarativeContainer.__new__
File "src/dependency_injector/providers.pyx", line 4916, in dependency_injector.providers.deepcopy
File "src/dependency_injector/providers.pyx", line 4923, in dependency_injector.providers.deepcopy
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 153, in deepcopy
y = copier(memo)
^^^^^^^^^^^^
File "src/dependency_injector/providers.pyx", line 4024, in dependency_injector.providers.Container.__deepcopy__
File "src/dependency_injector/providers.pyx", line 4923, in dependency_injector.providers.deepcopy
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 153, in deepcopy
y = copier(memo)
^^^^^^^^^^^^
File "src/dependency_injector/containers.pyx", line 125, in dependency_injector.containers.DynamicContainer.__deepcopy__
File "src/dependency_injector/providers.pyx", line 4916, in dependency_injector.providers.deepcopy
File "src/dependency_injector/providers.pyx", line 4923, in dependency_injector.providers.deepcopy
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 146, in deepcopy
y = copier(x, memo)
^^^^^^^^^^^^^^^
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 231, in _deepcopy_dict
y[deepcopy(key, memo)] = deepcopy(value, memo)
^^^^^^^^^^^^^^^^^^^^^
File "/home/yogiliu/.local/share/uv/python/cpython-3.11.10-linux-x86_64-gnu/lib/python3.11/copy.py", line 153, in deepcopy
y = copier(memo)
^^^^^^^^^^^^
File "src/dependency_injector/providers.pyx", line 3669, in dependency_injector.providers.Resource.__deepcopy__
dependency_injector.errors.Error: Can not copy initialized resource
```
|
open
|
2025-03-23T12:36:43Z
|
2025-03-23T13:33:14Z
|
https://github.com/ets-labs/python-dependency-injector/issues/873
|
[] |
YogiLiu
| 2
|
huggingface/datasets
|
numpy
| 7,433
|
`Dataset.map` ignores existing caches and remaps when ran with different `num_proc`
|
### Describe the bug
If you `map` a dataset and save it to a specific `cache_file_name` with a specific `num_proc`, and then call map again with that same existing `cache_file_name` but a different `num_proc`, the dataset will be re-mapped.
### Steps to reproduce the bug
1. Download a dataset
```python
import datasets
dataset = datasets.load_dataset("ylecun/mnist")
```
```
Generating train split: 100%|██████████| 60000/60000 [00:00<00:00, 116429.85 examples/s]
Generating test split: 100%|██████████| 10000/10000 [00:00<00:00, 103310.27 examples/s]
```
2. `map` and cache it with a specific `num_proc`
```python
cache_file_name="./cache/train.map"
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=2)
```
```
Map (num_proc=2): 100%|██████████| 60000/60000 [00:01<00:00, 53764.03 examples/s]
```
3. `map` it with a different `num_proc` and the same `cache_file_name` as before
```python
dataset["train"].map(lambda x: x, cache_file_name=cache_file_name, num_proc=3)
```
```
Map (num_proc=3): 100%|██████████| 60000/60000 [00:00<00:00, 65377.12 examples/s]
```
### Expected behavior
If I specify an existing `cache_file_name`, I don't expect using a different `num_proc` than the one that was used to generate it to cause the dataset to have be be re-mapped.
### Environment info
```console
$ datasets-cli env
- `datasets` version: 3.3.2
- Platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
- Python version: 3.10.16
- `huggingface_hub` version: 0.29.1
- PyArrow version: 19.0.1
- Pandas version: 2.2.3
- `fsspec` version: 2024.12.0
```
|
open
|
2025-03-03T05:51:26Z
|
2025-03-04T05:55:08Z
|
https://github.com/huggingface/datasets/issues/7433
|
[] |
ringohoffman
| 2
|
nolar/kopf
|
asyncio
| 631
|
Defining a pydantic model in a kopf handler file seems to break kopf startup
|
## Long story short
If I define a pydantic class, kopf refuses to start
## Description
Steps to repro:
start out with a super-simple kopf handler file, such as:
```python
import kopf
@kopf.on.create('what.com', 'v1', 'stuffs')
def create_fn(body, **kwargs):
print(body)
@kopf.on.update('what.com', 'v1', 'stuffs')
def update_fn(body, **kwargs):
print(body)
```
I'm using pipenv so I'd run this with: `pipenv run kopf run "src/crawler_manager/handler.py"`. All good.
Now, expand the file with a simple pydantic model definition:
```python
import kopf
from pydantic import BaseModel, Field
from typing import Dict
class TestModel(BaseModel):
name: str = Field(...)
size: int
size2: int
@kopf.on.create('mohawkanalytics.com', 'v1', 'stuffs')
def create_fn(body, **kwargs):
print(json.dumps(body, indent=4, sort_keys=True))
@kopf.on.update('mohawkanalytics.com', 'v1', 'stuffs')
def update_fn(body, **kwargs):
print(json.dumps(body, indent=4, sort_keys=True))
```
This code now throws:
```
File "<frozen importlib._bootstrap_external>", line 790, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "src/funnel_crawler_manager/handler.py", line 9, in <module>
class TestModel(BaseModel):
File "pydantic/main.py", line 247, in pydantic.main.ModelMetaclass.__new__
File "pydantic/typing.py", line 207, in pydantic.typing.resolve_annotations
for p in parameters:
KeyError: 'handler'
```
I have no idea why. The idea was to use a pydantic-defined model to define the CRD at if needed configure it during kopf startup (pydantic can generate openapi-compatible schemas, so this way we were hoping to maintain the CRD in code instead of in yaml).
Testing done using `pydantic==1.7.3`
I'm afraid I don't know more than this - I also don't know if the actual bug is in Pydantic or kopf.
## Environment
* Kopf version: 0.28.3
* Kubernetes version: 3.9
* Python version:
* OS/platform:
|
closed
|
2021-01-05T10:25:49Z
|
2021-01-22T20:04:59Z
|
https://github.com/nolar/kopf/issues/631
|
[
"bug"
] |
trondhindenes
| 7
|
deepspeedai/DeepSpeed
|
deep-learning
| 7,037
|
[REQUEST] Why is the column linear layer with all-gather not implemented in DeepSpeed Inference?
|
**Is your feature request related to a problem? Please describe.**
if there is no column linear layer with all-gather, we can't deal with single linear layer
i can see the rowlinear with allreduce aka [LinearAllreduce](https://github.com/deepspeedai/DeepSpeed/blob/e637677766e0a2063adc61ddd67b58abef74753e/deepspeed/module_inject/layers.py#L300). but there is no any implementations about column linear layer with all gather.
how could i set the linear type when running dit models:
```python
(transformer_blocks): ModuleList(
(0-18): 19 x FluxTransformerBlock(
(norm1): AdaLayerNormZero(
(silu): SiLU()
(linear): LinearLayer(in_features=3072, out_features=9216, bias=True, dtype=torch.bfloat16)
(norm): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
)
(norm1_context): AdaLayerNormZero(
(silu): SiLU()
(linear): LinearLayer(in_features=3072, out_features=9216, bias=True, dtype=torch.bfloat16)
(norm): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
)
(attn): Attention(
(norm_q): RMSNorm()
(norm_k): RMSNorm()
(to_q): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(to_k): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(to_v): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(add_k_proj): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(add_v_proj): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(add_q_proj): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(to_out): ModuleList(
(0): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(1): Dropout(p=0.0, inplace=False)
)
(to_add_out): LinearLayer(in_features=3072, out_features=1536, bias=True, dtype=torch.bfloat16)
(norm_added_q): RMSNorm()
(norm_added_k): RMSNorm()
)
(norm2): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
(ff): FeedForward(
(net): ModuleList(
(0): GELU(
(proj): LinearLayer(in_features=3072, out_features=6144, bias=True, dtype=torch.bfloat16)
)
(1): Dropout(p=0.0, inplace=False)
(2): LinearLayer(in_features=12288, out_features=1536, bias=True, dtype=torch.bfloat16)
)
)
(norm2_context): LayerNorm((3072,), eps=1e-06, elementwise_affine=False)
(ff_context): FeedForward(
(net): ModuleList(
(0): GELU(
(proj): LinearLayer(in_features=3072, out_features=6144, bias=True, dtype=torch.bfloat16)
)
(1): Dropout(p=0.0, inplace=False)
(2): LinearLayer(in_features=12288, out_features=1536, bias=True, dtype=torch.bfloat16)
)
)
)
)
```
i can set the ``` attn.to_out.0 attn.to_add_out ff.net.2 ff_context.net.2 ``` to LinearAllreduce, but how to deal with ``` norm1.linear ``` and ``` norm1_context.linear```. i need all gather the results of a single linear layer or it will cause error because the inputs of both norm1 and norm1_context are a whole hidden_states
|
open
|
2025-02-14T07:09:38Z
|
2025-02-14T07:09:38Z
|
https://github.com/deepspeedai/DeepSpeed/issues/7037
|
[
"enhancement"
] |
zhangvia
| 0
|
ARM-DOE/pyart
|
data-visualization
| 810
|
Very outdated gcc in conda-forge causing CyLP to fail building.
|
When I try and build @jjhelmus's branch I get the following error:
gcc -pthread -B /home/rjackson/anaconda3/envs/adi_env3/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I./cylp/cpp -I./cylp/cy -I/home/rjackson/anaconda3/envs/adi_env3/include/coin -I/home/rjackson/anaconda3/envs/adi_env3/lib/python3.6/site-packages/numpy/core/include -I. -I/home/rjackson/anaconda3/envs/adi_env3/include/python3.6m -c cylp/cpp/IClpPrimalColumnPivotBase.cpp -o build/temp.linux-x86_64-3.6/cylp/cpp/IClpPrimalColumnPivotBase.o -w
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ [enabled by default]
In file included from /home/rjackson/anaconda3/envs/adi_env3/gcc/include/c++/cstdint:35:0,
from /home/rjackson/anaconda3/envs/adi_env3/include/coin/CoinTypes.hpp:15,
from /home/rjackson/anaconda3/envs/adi_env3/include/coin/CoinHelperFunctions.hpp:24,
from /home/rjackson/anaconda3/envs/adi_env3/include/coin/CoinIndexedVector.hpp:20,
from cylp/cpp/IClpPrimalColumnPivotBase.h:6,
from cylp/cpp/IClpPrimalColumnPivotBase.cpp:1:
/home/rjackson/anaconda3/envs/adi_env3/gcc/include/c++/bits/c++0x_warning.h:32:2: error: #error This file requires compiler and library support for the ISO C++ 2011 standard. This support is currently experimental, and must be enabled with the -std=c++11 or -std=gnu++11 compiler options.
#error This file requires compiler and library support for the \
I tracked this down to the very, very old version of gcc that is in conda-forge (4.8.5). I got it to build with gcc 6.3.0, so I would *not* use the conda-forge gcc to try and build CyLP.
|
closed
|
2019-02-06T19:56:11Z
|
2019-10-01T21:21:07Z
|
https://github.com/ARM-DOE/pyart/issues/810
|
[] |
rcjackson
| 7
|
scrapy/scrapy
|
web-scraping
| 6,186
|
lxml parser gives back wrong parsing results, messes up html
|
<!--
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If you have a question that starts with "How to...", please see the Scrapy Community page: https://scrapy.org/community/.
The GitHub issue tracker's purpose is to deal with bug reports and feature requests for the project itself.
Keep in mind that by filing an issue, you are expected to comply with Scrapy's Code of Conduct, including treating everyone with respect: https://github.com/scrapy/scrapy/blob/master/CODE_OF_CONDUCT.md
The following is a suggested template to structure your issue, you can find more guidelines at https://doc.scrapy.org/en/latest/contributing.html#reporting-bugs
-->
### Description
parsing with lxml gives wrong result
### Steps to Reproduce
1. fetch this html code: https://pastebin.com/durYf56c
2. select section `section = response.css('main section section')[0]`
**Expected behavior:** [What you expect to happen]
section to be this:
https://pastebin.com/3wxdHFqY
**Actual behavior:** [What actually happens]
section is this:
https://pastebin.com/jzXZ0BRD
**Reproduces how often:** [What percentage of the time does it reproduce?]
100%
### Versions
Scrapy : 2.11.0
lxml : 4.9.4.0
libxml2 : 2.10.3
cssselect : 1.2.0
parsel : 1.8.1
w3lib : 2.1.2
Twisted : 22.10.0
Python : 3.10.4 (tags/v3.10.4:9d38120, Mar 23 2022, 23:13:41) [MSC v.1929 64 bit (AMD64)]
pyOpenSSL : 23.3.0 (OpenSSL 3.1.4 24 Oct 2023)
cryptography : 41.0.7
Platform : Windows-10-10.0.19045-SP0
### Additional context
Same error happens with Beautiful soup using `lxml` parser. Using regular `html.parser` there is no error.
So it must be lxml.
|
closed
|
2023-12-24T03:43:57Z
|
2023-12-24T15:47:30Z
|
https://github.com/scrapy/scrapy/issues/6186
|
[] |
tombohub
| 3
|
vllm-project/vllm
|
pytorch
| 15,207
|
[Bug]: msgspec.DecodeError: MessagePack data is malformed: trailing characters (byte 13)
|
### Your current environment
<details>
<summary>vllm==0.8.0, python=3.11</summary>
</details>
### 🐛 Describe the bug
I keep having this decode error when using vllm=0.8.0 for qwen-2.5 inference, I have the same error on both 1.5B and 7B model once I use apply-chat-template.
`msgspec.DecodeError: MessagePack data is malformed: trailing characters (byte 13)`
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
open
|
2025-03-20T10:48:12Z
|
2025-03-24T14:47:15Z
|
https://github.com/vllm-project/vllm/issues/15207
|
[
"bug"
] |
fangru-lin
| 1
|
KaiyangZhou/deep-person-reid
|
computer-vision
| 375
|
RuntimeError: cublas runtime error
|
I am getting this error
```
(torchreid) $ python scripts/main.py \
> --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
> --transforms random_flip random_erase \
> --root $PATH_TO_DATA
Show configuration
adam:
beta1: 0.9
beta2: 0.999
cuhk03:
classic_split: False
labeled_images: False
use_metric_cuhk03: False
data:
combineall: False
height: 256
k_tfm: 1
load_train_targets: False
norm_mean: [0.485, 0.456, 0.406]
norm_std: [0.229, 0.224, 0.225]
root: /home/me/code/tmp/deep-person-reid/data
save_dir: log/osnet_x1_0_market1501_softmax_cosinelr
sources: ['market1501']
split_id: 0
targets: ['market1501']
transforms: ['random_flip', 'random_erase']
type: image
width: 128
workers: 4
loss:
name: softmax
softmax:
label_smooth: True
triplet:
margin: 0.3
weight_t: 1.0
weight_x: 0.0
market1501:
use_500k_distractors: False
model:
load_weights:
name: osnet_x1_0
pretrained: True
resume:
rmsprop:
alpha: 0.99
sampler:
num_cams: 1
num_datasets: 1
num_instances: 4
train_sampler: RandomSampler
train_sampler_t: RandomSampler
sgd:
dampening: 0.0
momentum: 0.9
nesterov: False
test:
batch_size: 300
dist_metric: euclidean
eval_freq: -1
evaluate: False
normalize_feature: False
ranks: [1, 5, 10, 20]
rerank: False
start_eval: 0
visrank: False
visrank_topk: 10
train:
base_lr_mult: 0.1
batch_size: 64
fixbase_epoch: 10
gamma: 0.1
lr: 0.0015
lr_scheduler: cosine
max_epoch: 250
new_layers: ['classifier']
open_layers: ['classifier']
optim: amsgrad
print_freq: 20
seed: 1
staged_lr: False
start_epoch: 0
stepsize: [20]
weight_decay: 0.0005
use_gpu: True
video:
pooling_method: avg
sample_method: evenly
seq_len: 15
Collecting env info ...
** System info **
PyTorch version: 1.1.0
Is debug build: No
CUDA used to build PyTorch: 9.0.176
OS: Ubuntu 18.04.5 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: version 3.10.2
Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce RTX 2080 Ti
Nvidia driver version: 440.33.01
cuDNN version: Could not collect
Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] torch==1.1.0
[pip3] torchreid==1.3.3
[pip3] torchvision==0.3.0
[conda] blas 1.0 mkl
[conda] mkl 2020.2 256
[conda] mkl-service 2.3.0 py37he904b0f_0
[conda] mkl_fft 1.2.0 py37h23d657b_0
[conda] mkl_random 1.1.1 py37h0573a6f_0
[conda] pytorch 1.1.0 py3.7_cuda9.0.176_cudnn7.5.1_0 pytorch
[conda] torchreid 1.3.3 dev_0 <develop>
[conda] torchvision 0.3.0 py37_cu9.0.176_1 pytorch
Pillow (7.2.0)
Building train transforms ...
+ resize to 256x128
+ random flip
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+ random erase
Building test transforms ...
+ resize to 256x128
+ to torch tensor of range [0, 1]
+ normalization (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
=> Loading train (source) dataset
=> Loaded Market1501
----------------------------------------
subset | # ids | # images | # cameras
----------------------------------------
train | 751 | 12936 | 6
query | 750 | 3368 | 6
gallery | 751 | 15913 | 6
----------------------------------------
=> Loading test (target) dataset
=> Loaded Market1501
----------------------------------------
subset | # ids | # images | # cameras
----------------------------------------
train | 751 | 12936 | 6
query | 750 | 3368 | 6
gallery | 751 | 15913 | 6
----------------------------------------
**************** Summary ****************
source : ['market1501']
# source datasets : 1
# source ids : 751
# source images : 12936
# source cameras : 6
target : ['market1501']
*****************************************
Building model: osnet_x1_0
Successfully loaded imagenet pretrained weights from "/home/me/.cache/torch/checkpoints/osnet_x1_0_imagenet.pth"
** The following layers are discarded due to unmatched keys or layer size: ['classifier.weight', 'classifier.bias']
Model complexity: params=2,193,616 flops=978,878,352
Building softmax-engine for image-reid
=> Start training
* Only train ['classifier'] (epoch: 1/10)
Traceback (most recent call last):
File "scripts/main.py", line 191, in <module>
main()
File "scripts/main.py", line 187, in main
engine.run(**engine_run_kwargs(cfg))
File "/home/me/code/deep-person-reid/torchreid/engine/engine.py", line 193, in run
open_layers=open_layers
File "/home/me/code/deep-person-reid/torchreid/engine/engine.py", line 245, in train
loss_summary = self.forward_backward(data)
File "/home/me/code/deep-person-reid/torchreid/engine/image/softmax.py", line 85, in forward_backward
outputs = self.model(imgs)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
return self.module(*inputs[0], **kwargs[0])
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/me/code/deep-person-reid/torchreid/models/osnet.py", line 429, in forward
v = self.fc(v)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/module.py", line 493, in __call__
result = self.forward(*input, **kwargs)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/modules/linear.py", line 92, in forward
return F.linear(input, self.weight, self.bias)
File "/home/me/.conda/envs/torchreid/lib/python3.7/site-packages/torch/nn/functional.py", line 1406, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653215914/work/aten/src/THC/THCBlas.cu:259
```
What worries me is that I did not install conda in `/opt/conda` but in `/usr/local/miniconda3/condabin/conda`. However conda works perfectly.
To reproduce it, one just needs to install it following the README's instructions:
```
# cd to your preferred directory and clone this repo
git clone https://github.com/KaiyangZhou/deep-person-reid.git
# create environment
cd deep-person-reid/
conda create --name torchreid python=3.7
conda activate torchreid
# install dependencies
# make sure `which python` and `which pip` point to the correct path
pip install -r requirements.txt
# install torch and torchvision (select the proper cuda version to suit your machine)
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
# install torchreid (don't need to re-build it if you modify the source code)
python setup.py develop
# added by me
PATH_TO_DATA="/somewhere_accessible"
mkdir -p $PATH_TO_DATA
```
Here's my conda environment
```
(torchreid) $ conda list
# packages in environment at /home/me/.conda/envs/torchreid:
#
# Name Version Build Channel
_libgcc_mutex 0.1 main
absl-py 0.10.0 pypi_0 pypi
blas 1.0 mkl
ca-certificates 2020.7.22 0
cachetools 4.1.1 pypi_0 pypi
certifi 2020.6.20 py37_0
cffi 1.14.3 py37he30daa8_0
chardet 3.0.4 pypi_0 pypi
cudatoolkit 9.0 h13b8566_0
cycler 0.10.0 pypi_0 pypi
cython 0.29.21 pypi_0 pypi
filelock 3.0.12 pypi_0 pypi
flake8 3.8.3 pypi_0 pypi
freetype 2.10.2 h5ab3b9f_0
future 0.18.2 pypi_0 pypi
gdown 3.12.2 pypi_0 pypi
google-auth 1.21.3 pypi_0 pypi
google-auth-oauthlib 0.4.1 pypi_0 pypi
grpcio 1.32.0 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
idna 2.10 pypi_0 pypi
imageio 2.9.0 pypi_0 pypi
importlib-metadata 2.0.0 pypi_0 pypi
intel-openmp 2020.2 254
jpeg 9b h024ee3a_2
kiwisolver 1.2.0 pypi_0 pypi
lcms2 2.11 h396b838_0
ld_impl_linux-64 2.33.1 h53a641e_7
libedit 3.1.20191231 h14c3975_1
libffi 3.3 he6710b0_2
libgcc-ng 9.1.0 hdf63c60_0
libpng 1.6.37 hbc83047_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.1.0 h2733197_1
lz4-c 1.9.2 he6710b0_1
markdown 3.2.2 pypi_0 pypi
matplotlib 3.3.2 pypi_0 pypi
mkl 2020.2 256
mkl-service 2.3.0 py37he904b0f_0
mkl_fft 1.2.0 py37h23d657b_0
mkl_random 1.1.1 py37h0573a6f_0
ncurses 6.2 he6710b0_1
ninja 1.10.1 py37hfd86e86_0
numpy 1.19.2 pypi_0 pypi
numpy-base 1.19.1 py37hfa32c7d_0
oauthlib 3.1.0 pypi_0 pypi
olefile 0.46 py37_0
opencv-python 4.4.0.44 pypi_0 pypi
openssl 1.1.1h h7b6447c_0
pillow 7.2.0 py37hb39fc2d_0
pip 20.2.2 py37_0
protobuf 3.13.0 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pycparser 2.20 py_2
pyflakes 2.2.0 pypi_0 pypi
pyparsing 2.4.7 pypi_0 pypi
pysocks 1.7.1 pypi_0 pypi
python 3.7.9 h7579374_0
python-dateutil 2.8.1 pypi_0 pypi
pytorch 1.1.0 py3.7_cuda9.0.176_cudnn7.5.1_0 pytorch
pyyaml 5.3.1 pypi_0 pypi
readline 8.0 h7b6447c_0
requests 2.24.0 pypi_0 pypi
requests-oauthlib 1.3.0 pypi_0 pypi
rsa 4.6 pypi_0 pypi
scipy 1.5.2 pypi_0 pypi
setuptools 49.6.0 py37_0
six 1.15.0 py_0
sqlite 3.33.0 h62c20be_0
tb-nightly 2.4.0a20200921 pypi_0 pypi
tensorboard-plugin-wit 1.7.0 pypi_0 pypi
tk 8.6.10 hbc83047_0
torchreid 1.3.3 dev_0 <develop>
torchvision 0.3.0 py37_cu9.0.176_1 pytorch
tqdm 4.49.0 pypi_0 pypi
urllib3 1.25.10 pypi_0 pypi
werkzeug 1.0.1 pypi_0 pypi
wheel 0.35.1 pypi_0 pypi
xz 5.2.5 h7b6447c_0
yacs 0.1.8 pypi_0 pypi
yapf 0.30.0 pypi_0 pypi
zipp 3.2.0 pypi_0 pypi
zlib 1.2.11 h7b6447c_3
zstd 1.4.5 h9ceee32_0
```
|
closed
|
2020-09-24T15:40:36Z
|
2020-09-24T21:56:23Z
|
https://github.com/KaiyangZhou/deep-person-reid/issues/375
|
[] |
sopsos
| 2
|
pytorch/vision
|
machine-learning
| 8,845
|
`CocoDetection()` doesn't work using some train and validation images with some annotations
|
### 🚀 The feature
[CocoDetection()](https://pytorch.org/vision/stable/generated/torchvision.datasets.CocoDetection.html) doesn't work using `stuff_train2017_pixelmaps` with `stuff_train2017.json` and using `stuff_val2017_pixelmaps` with `stuff_val2017.json` as shown below:
```python
from torchvision.datasets import CocoDetection
pms_stf_train2017_data = CocoDetection(
root="data/coco/anns/stuff_trainval2017/stuff_train2017_pixelmaps",
annFile="data/coco/anns/stuff_trainval2017/stuff_train2017.json"
)
pms_stf_val2017_data = CocoDetection(
root="data/coco/anns/stuff_trainval2017/stuff_val2017_pixelmaps",
annFile="data/coco/anns/stuff_trainval2017/stuff_val2017.json"
)
pms_stf_train2017_data[0] # Error
pms_stf_val2017_data[0] # Error
```
> FileNotFoundError: [Errno 2] No such file or directory: '/.../data/coco/anns/stuff_trainval2017/stuff_train2017_pixelmaps/000000000009.jpg'
> FileNotFoundError: [Errno 2] No such file or directory: '/../data/coco/anns/stuff_trainval2017/stuff_val2017_pixelmaps/000000000139.jpg'
And, `CocoDetection()` doesn't work using `panoptic_train2017` with `panoptic_train2017.json` and using `panoptic_val2017` and `panoptic_val2017.json` as shown below:
```python
from torchvision.datasets import CocoDetection
pan_train2017_data = CocoDetection(
root="data/coco/anns/panoptic_trainval2017/panoptic_train2017",
annFile="data/coco/anns/panoptic_trainval2017/panoptic_train2017.json"
) # Error
pan_val2017_data = CocoDetection(
root="data/coco/anns/panoptic_trainval2017/panoptic_val2017",
annFile="data/coco/anns/panoptic_trainval2017/panoptic_val2017.json"
) # Error
```
> KeyError: 'id'
### Motivation, pitch
So, `CocoDetection()` should support for them.
### Alternatives
_No response_
### Additional context
_No response_
|
open
|
2025-01-10T15:36:41Z
|
2025-01-13T10:53:20Z
|
https://github.com/pytorch/vision/issues/8845
|
[] |
hyperkai
| 1
|
roboflow/supervision
|
computer-vision
| 1,709
|
Object detection precision-ap
|
### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
### Question
Suppose we have a single class object detection problem. Shouldn't the average precision and the precision metrics across the different iou thresholds be the same? This does not seem to be the case here.
### Additional
_No response_
|
open
|
2024-12-03T12:55:58Z
|
2024-12-04T10:16:47Z
|
https://github.com/roboflow/supervision/issues/1709
|
[
"question"
] |
GiannisApost
| 1
|
mlfoundations/open_clip
|
computer-vision
| 549
|
build_cls_mask() in CoCa TextTransfotmer
|
TL, DR: current implementation of `build_cls_mask()` produces `cls_mask` for [CLS] being as the first token. But in CoCa, [CLS] is the end token.
In [Issue 312](https://github.com/mlfoundations/open_clip/pull/312), `build_cls_mask()` was introduced by @gpucce in `TextTransformer` in CoCa to "preventing the CLS token at the end of the sequence from attending to padded tokens".
```python
# https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py#L587
def build_cls_mask(self, text, cast_dtype: torch.dtype):
cls_mask = (text != self.pad_id).unsqueeze(1)
cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)
additive_mask = torch.empty(cls_mask.shape, dtype=cast_dtype, device=cls_mask.device)
additive_mask.fill_(0)
additive_mask.masked_fill_(~cls_mask, float("-inf"))
additive_mask = torch.repeat_interleave(additive_mask, self.heads, 0)
return additive_mask
```
Taking `text = torch.tensor([[1,2,3,4,0,0,0]])` as an example,
```python
import torch
import torch.nn.functional as F
text = torch.tensor([[1,2,3,4,0,0,0]]) ### batch size 1, sequence 4 with 3 padding (pad_id=0)
pad_id = 0
cls_mask = (text != pad_id).unsqueeze(1)
cls_mask = F.pad(cls_mask, (1, 0, cls_mask.shape[2], 0), value=1.0)
additive_mask = torch.empty(cls_mask.shape)
additive_mask.fill_(0)
additive_mask.masked_fill_(~cls_mask, float("-inf"))
print(additive_mask)
```
This output
```
tensor([[[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., -inf, -inf, -inf]]])
```
In @lucidrains [implementation](https://github.com/lucidrains/CoCa-pytorch/blob/main/coca_pytorch/coca_pytorch.py#L384-L385)
```python
# https://github.com/lucidrains/CoCa-pytorch/blob/main/coca_pytorch/coca_pytorch.py#L384-L385
cls_mask = rearrange(text!=self.pad_id, 'b j -> b 1 j')
attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True)
```
taking the same `text` as the example
```python
import einops
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
text = torch.tensor([[1,2,3,4,0,0,0]])
pad_id = 0
seq = text.shape[1]
cls_mask = rearrange(text!=pad_id, 'b j -> b 1 j')
attn_mask = F.pad(cls_mask, (0, 1, seq, 0), value=True)
print(attn_mask)
```
it produces (which I believe should be the desired outcome)
```
tensor([[[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, True, True, True, True],
[ True, True, True, True, False, False, False, True]]])
```
Since [CLS] token is appended at the end of a sequence,
```python
# https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/transformer.py#L607
x = torch.cat([x, self._repeat(self.cls_emb, x.shape[0])], dim=1)
```
I feel that the current implementation in `open_clip` is wrong? Do I miss anything?
|
open
|
2023-06-24T04:05:42Z
|
2023-09-20T17:31:30Z
|
https://github.com/mlfoundations/open_clip/issues/549
|
[] |
yiren-jian
| 2
|
CatchTheTornado/text-extract-api
|
api
| 55
|
[feat] Add `markitdown` support
|
https://github.com/microsoft/markitdown
It can be added as another OCR strategy
|
open
|
2025-01-08T10:32:31Z
|
2025-01-19T16:55:21Z
|
https://github.com/CatchTheTornado/text-extract-api/issues/55
|
[
"feature"
] |
pkarw
| 0
|
deezer/spleeter
|
deep-learning
| 775
|
[Discussion] How can I make sure separate running on GPU?
|
The separate worked, I use --verbose and it shows some info, but i'm not sure it run on GPU. how can i make sure it?
```
C:\Users\Administrator\Desktop\testSound>python -m spleeter separate -p spleeter:5stems -o output --verbose audio_example.mp3
INFO:tensorflow:Using config: {'_model_dir': 'pretrained_models\\5stems', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.7
}
, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}
WARNING:tensorflow:From C:\Users\Administrator\AppData\Local\Programs\Python\Python310\lib\site-packages\spleeter\separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_types is deprecated and will be removed in a future version.
Instructions for updating:
Use output_signature instead
WARNING:tensorflow:From C:\Users\Administrator\AppData\Local\Programs\Python\Python310\lib\site-packages\spleeter\separator.py:146: calling DatasetV2.from_generator (from tensorflow.python.data.ops.dataset_ops) with output_shapes is deprecated and will be removed in a future version.
Instructions for updating:
Use output_signature instead
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Apply unet for vocals_spectrogram
WARNING:tensorflow:From C:\Users\Administrator\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\layers\normalization\batch_normalization.py:532: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
INFO:tensorflow:Apply unet for piano_spectrogram
INFO:tensorflow:Apply unet for drums_spectrogram
INFO:tensorflow:Apply unet for bass_spectrogram
INFO:tensorflow:Apply unet for other_spectrogram
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from pretrained_models\5stems\model
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:spleeter:File output\audio_example/piano.wav written succesfully
INFO:spleeter:File output\audio_example/other.wav written succesfully
INFO:spleeter:File output\audio_example/vocals.wav written succesfully
INFO:spleeter:File output\audio_example/drums.wav written succesfully
INFO:spleeter:File output\audio_example/bass.wav written succesfully
```
I don't think it worked on GPU, because it run 28s. I don't think this is the right speed. Am I right?
|
open
|
2022-06-24T04:23:56Z
|
2024-02-04T05:38:42Z
|
https://github.com/deezer/spleeter/issues/775
|
[
"question"
] |
limengqilove
| 2
|
plotly/dash-core-components
|
dash
| 637
|
soft link to LICENSE.txt makes the github license invalid
|
@rpkyle your change of soft link makes the license tab on github invalid. Can you see how to fix that?
|
closed
|
2019-09-04T21:04:48Z
|
2019-09-05T11:29:37Z
|
https://github.com/plotly/dash-core-components/issues/637
|
[] |
byronz
| 1
|
BMW-InnovationLab/BMW-YOLOv4-Training-Automation
|
rest-api
| 10
|
Inference after training the model
|
Are there any ways to do inference/predictions using the latest weight after the model is trained?
I am able to do predictions during the training process using the Custom API at port 8099. However, the port is also closed after the training is finished.
Thanks!
|
closed
|
2020-07-27T19:46:10Z
|
2021-03-12T18:38:05Z
|
https://github.com/BMW-InnovationLab/BMW-YOLOv4-Training-Automation/issues/10
|
[] |
LSQI15
| 4
|
mckinsey/vizro
|
plotly
| 460
|
Multiple Series Line Chart Updates
|
### Description
When creating a line chart with multiple series, I've noticed it's not possible to update the legend and y_axis titles (potentially more). If specifying a single series, the titles are updated and represented accordingly.
### Expected behavior
I'd expect to be able to update the y_axis title and potentially also the legend titles when passing multiple column names (as a python list) to a px.line call, as can be done with dash.
### Which package?
vizro
### Package version
0.1.16
### Python version
3.10.14
### OS
Ubuntu 20.04
### How to Reproduce
Code to reproduce the behavior I'm observing
```
from vizro import Vizro
import vizro.plotly.express as px
import vizro.models as vm
df = px.data.stocks()
page = vm.Page(
title="My first dashboard",
components=[
vm.Graph(id="scatter_chart", figure=px.line(df, x="date", y=["GOOG", 'AAPL']).update_layout(yaxis_title='New Y-axis', legend_title='new legend')),
]
)
dashboard = vm.Dashboard(pages=[page])
Vizro().build(dashboard).run()
```
### Output
Sharing here the resulting dashboard view, titles are not being updated even though updates are specified.
### Code of Conduct
- [X] I agree to follow the [Code of Conduct](https://github.com/mckinsey/vizro/blob/main/CODE_OF_CONDUCT.md).
|
closed
|
2024-05-07T01:22:16Z
|
2024-05-08T01:26:01Z
|
https://github.com/mckinsey/vizro/issues/460
|
[
"Docs :spiral_notepad:",
"General Question :question:"
] |
mkretsch327
| 2
|
jstrieb/github-stats
|
asyncio
| 15
|
Update cronjob schedule
|
Hi!
Nice work, but I believe running the github action every hour is a bit overkill. (Over 3000 commits on just generated image)
Maybe everyday or every month could be a bit more reasonable.
What do you think?
|
closed
|
2021-01-14T14:30:23Z
|
2021-01-19T22:46:07Z
|
https://github.com/jstrieb/github-stats/issues/15
|
[] |
sylhare
| 1
|
zappa/Zappa
|
flask
| 842
|
[Migrated] 502 error while deploying
|
Originally from: https://github.com/Miserlou/Zappa/issues/2084 by [tomekbuszewski](https://github.com/tomekbuszewski)
I am getting 502 error while trying to deploy the app.
## Context
I have an app written with Django (3.0.4), works great locally. For dev purposes, I use sqlite only. I wanted to deploy it to AWS today, but I am getting 502 errors.
Sorry, I cannot post an app here, since it's private.
## Expected Behavior
It should be deployed without problems.
## Actual Behavior
Results in 502 error.
## Your Environment
Zappa version: 0.51.0
Django version: 3.0.4
Python: 3.7
Zappa config:
```
{
"dev": {
"aws_region": "eu-central-1",
"django_settings": "hay.hay.settings",
"profile_name": "default",
"project_name": "hundred-a-year",
"runtime": "python3.7",
"s3_bucket": "hay-dev",
"slim_handler": false
}
}
```
Error on AWS:
```
"{'message': 'An uncaught exception happened while servicing this request. You can investigate this with the `zappa tail` command.', 'traceback': ['Traceback (most recent call last):\\n', ' File \"/var/task/handler.py\", line 540, in handler\\n with Response.from_app(self.wsgi_app, environ) as response:\\n', ' File \"/var/task/werkzeug/wrappers/base_response.py\", line 287, in from_app\\n return cls(*_run_wsgi_app(app, environ, buffered))\\n', ' File \"/var/task/werkzeug/wrappers/base_response.py\", line 26, in _run_wsgi_app\\n return _run_wsgi_app(*args)\\n', ' File \"/var/task/werkzeug/test.py\", line 1119, in run_wsgi_app\\n app_rv = app(environ, start_response)\\n', \"TypeError: 'NoneType' object is not callable\\n\"]}"
```
Results of `zappa tail`:
```
[1587228323192] [DEBUG] 2020-04-18T16:45:23.192Z f41f3ef4-dfa2-46dc-95c2-99d6fd9f0852 Zappa Event: {'resource': '/', 'path': '/', 'httpMethod': 'GET', 'headers': {'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9', 'Accept-Encoding': 'gzip, deflate, br', 'Accept-Language': 'pl-PL,pl;q=0.9,en-US;q=0.8,en;q=0.7,ru;q=0.6,so;q=0.5', 'cache-control': 'max-age=0', 'CloudFront-Forwarded-Proto': 'https', 'CloudFront-Is-Desktop-Viewer': 'true', 'CloudFront-Is-Mobile-Viewer': 'false', 'CloudFront-Is-SmartTV-Viewer': 'false', 'CloudFront-Is-Tablet-Viewer': 'false', 'CloudFront-Viewer-Country': 'PL', 'Host': 'yiudwi21ux.execute-api.eu-central-1.amazonaws.com', 'Referer': 'https://eu-central-1.console.aws.amazon.com/apigateway/home?region=eu-central-1', 'sec-fetch-dest': 'document', 'sec-fetch-mode': 'navigate', 'sec-fetch-site': 'cross-site', 'sec-fetch-user': '?1', 'upgrade-insecure-requests': '1', 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36', 'Via': '2.0 70d111e01220d4724cfea727fa9dfb91.cloudfront.net (CloudFront)', 'X-Amz-Cf-Id': 'VMUK9rPoyIcqWqOjowgpHrWsI-qW0OTnW1YAUIJ0BZHH-Vr7Z3rWCw==', 'X-Amzn-Trace-Id': 'Root=1-5e9b2ea3-5181626ee6404a71bf9a1db9', 'X-Forwarded-For': '89.64.74.188, 54.239.171.153', 'X-Forwarded-Port': '443', 'X-Forwarded-Proto': 'https'}, 'multiValueHeaders': {'Accept': ['text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9'], 'Accept-Encoding': ['gzip, deflate, br'], 'Accept-Language': ['pl-PL,pl;q=0.9,en-US;q=0.8,en;q=0.7,ru;q=0.6,so;q=0.5'], 'cache-control': ['max-age=0'], 'CloudFront-Forwarded-Proto': ['https'], 'CloudFront-Is-Desktop-Viewer': ['true'], 'CloudFront-Is-Mobile-Viewer': ['false'], 'CloudFront-Is-SmartTV-Viewer': ['false'], 'CloudFront-Is-Tablet-Viewer': ['false'], 'CloudFront-Viewer-Country': ['PL'], 'Host': ['yiudwi21ux.execute-api.eu-central-1.amazonaws.com'], 'Referer': ['https://eu-central-1.console.aws.amazon.com/apigateway/home?region=eu-central-1'], 'sec-fetch-dest': ['document'], 'sec-fetch-mode': ['navigate'], 'sec-fetch-site': ['cross-site'], 'sec-fetch-user': ['?1'], 'upgrade-insecure-requests': ['1'], 'User-Agent': ['Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36'], 'Via': ['2.0 70d111e01220d4724cfea727fa9dfb91.cloudfront.net (CloudFront)'], 'X-Amz-Cf-Id': ['VMUK9rPoyIcqWqOjowgpHrWsI-qW0OTnW1YAUIJ0BZHH-Vr7Z3rWCw=='], 'X-Amzn-Trace-Id': ['Root=1-5e9b2ea3-5181626ee6404a71bf9a1db9'], 'X-Forwarded-For': ['89.64.74.188, 54.239.171.153'], 'X-Forwarded-Port': ['443'], 'X-Forwarded-Proto': ['https']}, 'queryStringParameters': None, 'multiValueQueryStringParameters': None, 'pathParameters': None, 'stageVariables': None, 'requestContext': {'resourceId': '3achqb2vbg', 'resourcePath': '/', 'httpMethod': 'GET', 'extendedRequestId': 'LMQ5gG_WliAFbBw=', 'requestTime': '18/Apr/2020:16:45:23 +0000', 'path': '/dev', 'accountId': '536687225340', 'protocol': 'HTTP/1.1', 'stage': 'dev', 'domainPrefix': 'yiudwi21ux', 'requestTimeEpoch': 1587228323137, 'requestId': 'd3fd55d0-bec3-4424-877b-f6cf00c7affb', 'identity': {'cognitoIdentityPoolId': None, 'accountId': None, 'cognitoIdentityId': None, 'caller': None, 'sourceIp': '89.64.74.188', 'principalOrgId': None, 'accessKey': None, 'cognitoAuthenticationType': None, 'cognitoAuthenticationProvider': None, 'userArn': None, 'userAgent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.163 Safari/537.36', 'user': None}, 'domainName': 'yiudwi21ux.execute-api.eu-central-1.amazonaws.com', 'apiId': 'yiudwi21ux'}, 'body': None, 'isBase64Encoded': False}
[1587228323192] [DEBUG] 2020-04-18T16:45:23.192Z f41f3ef4-dfa2-46dc-95c2-99d6fd9f0852 host found: [yiudwi21ux.execute-api.eu-central-1.amazonaws.com]
[1587228323192] [DEBUG] 2020-04-18T16:45:23.192Z f41f3ef4-dfa2-46dc-95c2-99d6fd9f0852 amazonaws found in host
[1587228323192] 'NoneType' object is not callable
```
I've tried various solutions found on the internet, disabling `slim_handler`, removing `pyc` files, reinstalling venv. Nothing helped.
|
closed
|
2021-02-20T12:52:22Z
|
2022-07-16T05:45:41Z
|
https://github.com/zappa/Zappa/issues/842
|
[] |
jneves
| 3
|
autogluon/autogluon
|
computer-vision
| 4,441
|
[BUG] feature_prune_kwargs={"force_prune": True} does not work when tuning_data is on for presets="medium_quality",
|
```
Verbosity: 4 (Maximum Logging)
=================== System Info ===================
AutoGluon Version: 1.1.1
Python Version: 3.11.9
Operating System: Linux
Platform Machine: x86_64
Platform Version: #1 SMP Fri Mar 29 23:14:13 UTC 2024
CPU Count: 8
GPU Count: 1
Memory Avail: 8.36 GB / 23.47 GB (35.6%)
Disk Space Avail: 326.38 GB / 911.84 GB (35.8%)
===================================================
Presets specified: ['medium_quality']
============ fit kwarg info ============
User Specified kwargs:
{'auto_stack': False,
'feature_prune_kwargs': {'force_prune': True},
'verbosity': 4}
Full kwargs:
{'_feature_generator_kwargs': None,
'_save_bag_folds': None,
'ag_args': None,
'ag_args_ensemble': None,
'ag_args_fit': None,
'auto_stack': False,
'calibrate': 'auto',
'ds_args': {'clean_up_fits': True,
'detection_time_frac': 0.25,
'enable_ray_logging': True,
'holdout_data': None,
'holdout_frac': 0.1111111111111111,
'memory_safe_fits': True,
'n_folds': 2,
'n_repeats': 1,
'validation_procedure': 'holdout'},
'excluded_model_types': None,
'feature_generator': 'auto',
'feature_prune_kwargs': {'force_prune': True},
'holdout_frac': None,
'hyperparameter_tune_kwargs': None,
'included_model_types': None,
'keep_only_best': False,
'name_suffix': None,
'num_bag_folds': None,
'num_bag_sets': None,
'num_stack_levels': None,
'pseudo_data': None,
'refit_full': False,
'save_bag_folds': None,
'save_space': False,
'set_best_to_refit_full': False,
'unlabeled_data': None,
'use_bag_holdout': False,
'verbosity': 4}
========================================
Warning: Training may take a very long time because `time_limit` was not specified and `train_data` is large (500000 samples, 88.0 MB).
Consider setting `time_limit` to ensure training finishes within an expected duration or experiment with a small portion of `train_data` to identify an ideal `presets` and `hyperparameters` configuration.
Saving /mnt/d/python_directory_2/models_temp/learner.pkl
Saving /mnt/d/python_directory_2/models_temp/predictor.pkl
Beginning AutoGluon training ...
AutoGluon will save models to "/mnt/d/python_directory_2/models_temp"
Train Data Rows: 500000
Train Data Columns: 41
Tuning Data Rows: 75000
Tuning Data Columns: 41
Label Column: target
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
2 unique label values: [0, 1]
If 'binary' is not the correct problem_type, please manually specify the problem_type parameter during Predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression', 'quantile'])
Problem Type: binary
Preprocessing data ...
Selected class <--> label mapping: class 1 = 1, class 0 = 0
Using Feature Generators to preprocess the data ...
Performing general data preprocessing with merged train & validation data, so validation performance may not accurately reflect performance on new test data
Fitting AutoMLPipelineFeatureGenerator...
Available Memory: 8580.40 MB
Train Data (Original) Memory Usage: 92.13 MB (1.1% of available memory)
Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
Stage 1 Generators:
Fitting AsTypeFeatureGenerator...
Note: Converting 2 features to boolean dtype as they only contain 2 unique values.
Original Features (exact raw dtype, raw dtype):
('datetime64[ns]', 'datetime') : 1 | ['time_date']
('float32', 'float') : 37 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', ...]
Types of features in original data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
('float', []) : 37 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', ...]
Types of features in processed data (exact raw dtype, raw dtype):
('datetime64[ns]', 'datetime') : 1 | ['time_date']
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
0.2s = Fit runtime
38 features in original data used to generate 38 features in processed data.
Stage 2 Generators:
Fitting FillNaFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (exact raw dtype, raw dtype):
('datetime64[ns]', 'datetime') : 1 | ['time_date']
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
0.1s = Fit runtime
38 features in original data used to generate 38 features in processed data.
Stage 3 Generators:
Fitting IdentityFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (exact raw dtype, raw dtype):
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
0.1s = Fit runtime
37 features in original data used to generate 37 features in processed data.
Skipping CategoryFeatureGenerator: No input feature with required dtypes.
Fitting DatetimeFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
Types of features in processed data (exact raw dtype, raw dtype):
('int64', 'int') : 5 | ['time_date', 'time_date.year', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
Types of features in processed data (raw dtype, special dtypes):
('int', ['datetime_as_int']) : 5 | ['time_date', 'time_date.year', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
0.1s = Fit runtime
1 features in original data used to generate 5 features in processed data.
Skipping TextSpecialFeatureGenerator: No input feature with required dtypes.
Skipping TextNgramFeatureGenerator: No input feature with required dtypes.
Skipping IdentityFeatureGenerator: No input feature with required dtypes.
Skipping IsNanFeatureGenerator: No input feature with required dtypes.
Stage 4 Generators:
Fitting DropUniqueFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
('int', ['datetime_as_int']) : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
Types of features in processed data (exact raw dtype, raw dtype):
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int64', 'int') : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
('int', ['datetime_as_int']) : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
0.2s = Fit runtime
41 features in original data used to generate 41 features in processed data.
Stage 5 Generators:
Fitting DropDuplicatesFeatureGenerator...
Types of features in original data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
('int', ['datetime_as_int']) : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
Types of features in processed data (exact raw dtype, raw dtype):
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int64', 'int') : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
('int', ['datetime_as_int']) : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
0.1s = Fit runtime
41 features in original data used to generate 41 features in processed data.
Useless Original Features (Count: 3): ['STDDEV_30_n_03s__SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1', 'STOCHRSI_fastk_3_n_03m__CO_HistogramAMI_even_2_5', 'TEMA_3_n_15m__SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1']
These features carry no predictive signal and should be manually investigated.
This is typically a feature which has the same value for all rows.
These features do not need to be present at inference time.
Types of features in original data (exact raw dtype, raw dtype):
('datetime64[ns]', 'datetime') : 1 | ['time_date']
('float32', 'float') : 37 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', ...]
Types of features in original data (raw dtype, special dtypes):
('datetime', []) : 1 | ['time_date']
('float', []) : 37 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', ...]
Types of features in processed data (exact raw dtype, raw dtype):
('float32', 'float') : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int64', 'int') : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
('int8', 'int') : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
Types of features in processed data (raw dtype, special dtypes):
('float', []) : 35 | ['DIV_5_n_01s__DN_Mean', 'KAMA_10_n_01s__CO_FirstMin_ac', 'MACDEXT_macd_10_n_03s__SP_Summaries_welch_rect_centroid', 'MIN_15_n_05s__DN_Spread_Std', 'CCI_3_n_10s__SP_Summaries_welch_rect_centroid', ...]
('int', ['bool']) : 2 | ['STOCH_slowk_3_n_01s__SB_BinaryStats_diff_longstretch0', 'MAX_3_n_30s__FC_LocalSimple_mean1_tauresrat']
('int', ['datetime_as_int']) : 4 | ['time_date', 'time_date.month', 'time_date.day', 'time_date.dayofweek']
1.0s = Fit runtime
38 features in original data used to generate 41 features in processed data.
Train Data (Processed) Memory Usage: 95.42 MB (1.1% of available memory)
Data preprocessing and feature engineering runtime = 1.11s ...
AutoGluon will gauge predictive performance using evaluation metric: 'roc_auc'
This metric expects predicted probabilities rather than predicted class labels, so you'll need to use predict_proba() instead of predict()
To change this, specify the eval_metric parameter of Predictor()
Saving /mnt/d/python_directory_2/models_temp/learner.pkl
User-specified model hyperparameters to be fit:
{
'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}, 'learning_rate': 0.45}, {'learning_rate': 0.45}],
'CAT': {'iterations': 10000, 'learning_rate': 0.75, 'allow_writing_files': False, 'eval_metric': 'Logloss', 'thread_count': 8},
}
Saving /mnt/d/python_directory_2/models_temp/utils/data/X.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/data/y.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/data/X_val.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/data/y_val.pkl
Model configs that will be trained (in order):
LightGBMXT: {'extra_trees': True, 'ag_args': {'name_suffix': 'XT', 'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}, 'learning_rate': 0.45}
LightGBM: {'learning_rate': 0.45, 'ag_args': {'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}}
CatBoost: {'iterations': 10000, 'learning_rate': 0.75, 'allow_writing_files': False, 'eval_metric': 'Logloss', 'thread_count': 8, 'ag_args': {'model_type': <class 'autogluon.tabular.models.catboost.catboost_model.CatBoostModel'>, 'priority': 70}}
Fitting 3 L1 models ...
Fitting model: LightGBMXT ...
Dropped 0 of 41 features.
Fitting LightGBMXT with 'num_gpus': 0, 'num_cpus': 8
Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.45, 'extra_trees': True}
[1] valid_set's binary_logloss: 0.690857
[2] valid_set's binary_logloss: 0.68494
[3] valid_set's binary_logloss: 0.684353
[4] valid_set's binary_logloss: 0.686686
[5] valid_set's binary_logloss: 0.691233
[6] valid_set's binary_logloss: 0.691459
[7] valid_set's binary_logloss: 0.701168
[8] valid_set's binary_logloss: 0.712405
[9] valid_set's binary_logloss: 0.716977
[10] valid_set's binary_logloss: 0.717592
[11] valid_set's binary_logloss: 0.715944
[12] valid_set's binary_logloss: 0.700291
[13] valid_set's binary_logloss: 0.702016
[14] valid_set's binary_logloss: 0.706436
[15] valid_set's binary_logloss: 0.703444
[16] valid_set's binary_logloss: 0.702883
[17] valid_set's binary_logloss: 0.702753
[18] valid_set's binary_logloss: 0.70785
[19] valid_set's binary_logloss: 0.70979
[20] valid_set's binary_logloss: 0.711334
[21] valid_set's binary_logloss: 0.710002
[22] valid_set's binary_logloss: 0.709985
[23] valid_set's binary_logloss: 0.710674
[24] valid_set's binary_logloss: 0.711116
Saving /mnt/d/python_directory_2/models_temp/models/LightGBMXT/model.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/attr/LightGBMXT/y_pred_proba_val.pkl
0.541 = Validation score (roc_auc)
1.13s = Training runtime
0.02s = Validation runtime
3801989.4 = Inference throughput (rows/s | 75000 batch size)
Saving /mnt/d/python_directory_2/models_temp/models/trainer.pkl
Fitting model: LightGBM ...
Dropped 0 of 41 features.
Fitting LightGBM with 'num_gpus': 0, 'num_cpus': 8
Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.45}
[1] valid_set's binary_logloss: 0.726226
[2] valid_set's binary_logloss: 0.741846
[3] valid_set's binary_logloss: 0.737792
[4] valid_set's binary_logloss: 0.791165
[5] valid_set's binary_logloss: 0.838001
[6] valid_set's binary_logloss: 0.838667
[7] valid_set's binary_logloss: 0.839008
[8] valid_set's binary_logloss: 0.861499
[9] valid_set's binary_logloss: 0.85956
[10] valid_set's binary_logloss: 0.847175
[11] valid_set's binary_logloss: 0.84703
[12] valid_set's binary_logloss: 0.846104
[13] valid_set's binary_logloss: 0.832776
[14] valid_set's binary_logloss: 0.833003
[15] valid_set's binary_logloss: 0.831728
[16] valid_set's binary_logloss: 0.834595
[17] valid_set's binary_logloss: 0.833374
[18] valid_set's binary_logloss: 0.833442
[19] valid_set's binary_logloss: 0.833311
[20] valid_set's binary_logloss: 0.824875
[21] valid_set's binary_logloss: 0.822567
Saving /mnt/d/python_directory_2/models_temp/models/LightGBM/model.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/attr/LightGBM/y_pred_proba_val.pkl
0.5 = Validation score (roc_auc)
1.01s = Training runtime
0.02s = Validation runtime
3795109.1 = Inference throughput (rows/s | 75000 batch size)
Saving /mnt/d/python_directory_2/models_temp/models/trainer.pkl
Fitting model: CatBoost ...
Dropped 0 of 41 features.
Fitting CatBoost with 'num_gpus': 0, 'num_cpus': 8
Catboost model hyperparameters: {'iterations': 10000, 'learning_rate': 0.75, 'random_seed': 0, 'allow_writing_files': False, 'eval_metric': 'Logloss', 'thread_count': 8}
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bestTest = 0.6821776997
bestIteration = 22
Shrink model to first 23 iterations.
Saving /mnt/d/python_directory_2/models_temp/models/CatBoost/model.pkl
Saving /mnt/d/python_directory_2/models_temp/utils/attr/CatBoost/y_pred_proba_val.pkl
0.6525 = Validation score (roc_auc)
2.64s = Training runtime
0.01s = Validation runtime
7341255.5 = Inference throughput (rows/s | 75000 batch size)
Saving /mnt/d/python_directory_2/models_temp/models/trainer.pkl
Loading: /mnt/d/python_directory_2/models_temp/models/LightGBMXT/model.pkl
Performing feature pruning with model: FeatureSelector_LightGBMXT, total time limit: 300s, stop threshold: 10, prune ratio: 0.05, prune threshold: noise.
Number of training samples 500000 is greater than 50000. Using 50000 samples as training data.
Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/utils/decorators.py", line 31, in _call
return f(*gargs, **gkwargs)
^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/tabular/predictor/predictor.py", line 1167, in fit
self._fit(ag_fit_kwargs=ag_fit_kwargs, ag_post_fit_kwargs=ag_post_fit_kwargs)
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/tabular/predictor/predictor.py", line 1173, in _fit
self._learner.fit(**ag_fit_kwargs)
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/tabular/learner/abstract_learner.py", line 159, in fit
return self._fit(X=X, X_val=X_val, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/tabular/learner/default_learner.py", line 122, in _fit
trainer.fit(
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/tabular/trainer/auto_trainer.py", line 125, in fit
self._train_multi_and_ensemble(
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 2589, in _train_multi_and_ensemble
model_names_fit = self.train_multi_levels(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 452, in train_multi_levels
base_model_names, aux_models = self.stack_new_level(
^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 600, in stack_new_level
core_models = self.stack_new_level_core(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 730, in stack_new_level_core
return self._train_multi(
^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 2539, in _train_multi
model_names_trained = self._train_multi_initial(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 2422, in _train_multi_initial
candidate_features = self._proxy_model_feature_prune(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/trainer/abstract_trainer.py", line 2669, in _proxy_model_feature_prune
candidate_features = selector.select_features(**feature_prune_kwargs, **model_fit_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/utils/feature_selection.py", line 225, in select_features
X, y, X_val, y_val, X_fi, y_fi, prune_threshold, noise_columns, feature_metadata = self.setup(
^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/utils/feature_selection.py", line 549, in setup
X_train, _, y_train, _ = generate_train_test_split(X=X, y=y, problem_type=self.problem_type, random_state=random_state, test_size=drop_ratio)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/site-packages/autogluon/core/utils/utils.py", line 513, in generate_train_test_split
random.seed(random_state)
File "/home/artur/miniforge3/envs/py311_ag/lib/python3.11/random.py", line 160, in seed
raise TypeError('The only supported seed types are: None,\n'
TypeError: The only supported seed types are: None,
int, float, str, bytes, and bytearray.
```
|
open
|
2024-08-28T19:05:37Z
|
2024-11-25T22:47:13Z
|
https://github.com/autogluon/autogluon/issues/4441
|
[
"bug",
"module: tabular",
"Needs Triage"
] |
arturdaraujo
| 1
|
neuml/txtai
|
nlp
| 295
|
Labels pipeline outputs changed with transformers 4.20.0
|
Unit tests are now failing with transformers 4.20.0. The output of the text-classification pipeline changed when passing a single text element in. Previously, a list of lists was produced, now only a single list with a dict of label outputs is produced.
https://github.com/huggingface/transformers/issues/17754 filed upstream to determine if this change was intentional (need to fix in txtai) or a bug that would be addressed in transformers.
|
closed
|
2022-06-17T13:19:11Z
|
2022-07-25T19:03:41Z
|
https://github.com/neuml/txtai/issues/295
|
[
"bug"
] |
davidmezzetti
| 2
|
scrapy/scrapy
|
python
| 6,552
|
Document @inthread
|
I’ve seen @VMRuiz recently use [`@inthread`](https://github.com/scrapy/scrapy/blob/efb53aafdcaae058962c6189ddecb3dc62b02c31/scrapy/utils/decorators.py#L58). I had not seen that before, and it seems like something we should cover in documentation about asynchronous code. Not sure where in the docs it fits best, but I think we need to point people to that for scenarios where they need to use some expensive non-async code, like some web service client without async support.
|
open
|
2024-11-19T08:57:30Z
|
2025-01-10T13:10:02Z
|
https://github.com/scrapy/scrapy/issues/6552
|
[
"enhancement",
"docs"
] |
Gallaecio
| 2
|
ultralytics/yolov5
|
machine-learning
| 13,357
|
How to train with new pictures?
|
### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
Hi, I have to train as code YOLOV5-6.1 and get good result.Now , I add some new images to the dataset.I want to know how to continue training on the best.pt before.
And please tell me how to call this operation,thanks!
### Additional
_No response_
|
open
|
2024-10-14T14:44:04Z
|
2024-11-09T06:29:27Z
|
https://github.com/ultralytics/yolov5/issues/13357
|
[
"question"
] |
PengPeng-JunJun
| 2
|
mouredev/Hello-Python
|
fastapi
| 205
|
网上网赌被黑平台赢钱提不了款怎么解决
|
被黑平台赢钱提不了款的解决办法：
当你遇到这种情况千万不要跟客服发生争执，如果你谩就会骂客服那你的账号就被冻结了，同时你的分也被清空了，薇：lcly479飞机@ly2088先出款，后收费！
一、与平台沟通相关
保持冷静沟通
首先不要和客服理论，尤其是在盈利款额巨大的情况下，因为一直纠缠可能会导致被封号，而且客服会找各种理由拖延时间、推脱责任。被黑时如果平台出现提款不到账、系统维护、正在审核等情况，很可能是平台不想让你提款。此时可以不动声色地与平台客服沟通，称系统有问题，如果解决得好可以追加大量投资，但由于前期投的钱出不了金，心中有顾虑而不敢投入更多的钱，看是否能诱导平台给你出款。不过要注意这只是一种尝试的策略，并不一定能成功。
二、收集证据相关
证据材料收集
及时收集和整理好银行卡流水、提款记录等证据材料，并且保留好所有提交给客服的聊天记录、电话录音等资料，以便后续维权时使用。这些证据可能在向相关机构投诉或者寻求法律帮助时起到关键作用。
三、寻求外部帮助相关
联系客服或监管部门
如果是正规的银行或支付平台方面提示取款通道维护不能提款时，可以及时联系银行或支付平台客服解决。可在银行或支付平台官网上查看是否有通知或公告说明维护时间和具体处理方式，联系方式通常可以在官网上找到，也可以通过支付平台或银行APP内的在线客服咨询或拨打客服热线联系。如果是网赌平台不给出款，可以向平台网站留言，在论坛、贴吧发帖等形式反映平台的错误做法，要求平台及时更正；也可以向网信办投诉网站进行投诉（如果平台存在违规行为的话）。如果怀疑遇到诈骗，还可以向公安机关报案，但需要注意网赌本身是涉嫌赌博罪的行为，赌资不受法律保护。
咨询律师
可以在线咨询律师或者寻求专业法律人士的帮助，例如向擅长婚姻家庭、债权债务、经济纠纷等相关领域的律师咨询这种情况下自己可能采取的措施以及面临的风险等情况。
|
closed
|
2024-11-10T12:16:32Z
|
2024-11-28T13:34:03Z
|
https://github.com/mouredev/Hello-Python/issues/205
|
[] |
lcly479
| 0
|
serengil/deepface
|
deep-learning
| 712
|
How change min_face_size?
|
### How to change min_face_size parameter using detector_backend and MTCNN parameter?
def build_model():
from mtcnn import MTCNN
face_detector = MTCNN()
return face_detector
_Here you can not change and add the item min_face_size?_
|
closed
|
2023-04-07T15:56:08Z
|
2023-04-07T20:04:11Z
|
https://github.com/serengil/deepface/issues/712
|
[
"question"
] |
PrincePepper
| 1
|
localstack/localstack
|
python
| 12,321
|
bug: Apigatewaymanagementapi for Websocket API stack trace on GetConnection
|
### Is there an existing issue for this?
- [x] I have searched the existing issues
### Current Behavior
My API Gateway is setup properly and I can use almost all features including lambda authorisation, request integrations and responses from backend etc. I can also SendMessage to connected websocket clients via the API and CLI. But there is an issue using the GetConnection endpoint via the CLI or API. There seems to be a bug in the Localstack code when looking for a User-Agent header that doesnt exist. See below Debug Stack trace. Even though the connection is active and I can Post a message to the connection I cant Get the connection ever (always stack trace).
```
2025-03-02 01:06:31,042 - MainThread - awscli.clidriver - DEBUG - CLI version: aws-cli/2.17.25 Python/3.11.9 Darwin/24.1.0 exe/x86_64
2025-03-02 01:06:31,044 - MainThread - awscli.clidriver - DEBUG - Arguments entered to CLI: ['apigatewaymanagementapi', 'get-connection', '--connection-id', '8807fd50', '--debug', '--endpoint-url', 'http://localhost:4566/_aws/execute-api/0ea7ff6b/local']
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function add_s3 at 0x110d01260>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function add_ddb at 0x110b14c20>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <bound method BasicCommand.add_command of <class 'awscli.customizations.configure.configure.ConfigureCommand'>>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function change_name at 0x110a7e700>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function change_name at 0x110a7fce0>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function alias_opsworks_cm at 0x110d03c40>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function add_history_commands at 0x110b634c0>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <bound method BasicCommand.add_command of <class 'awscli.customizations.devcommands.CLIDevCommand'>>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <function add_waiters at 0x110d03b00>
2025-03-02 01:06:31,074 - MainThread - botocore.hooks - DEBUG - Event building-command-table.main: calling handler <bound method AliasSubCommandInjector.on_building_command_table of <awscli.alias.AliasSubCommandInjector object at 0x110dd19d0>>
2025-03-02 01:06:31,075 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/data/cli.json
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <function resolve_types at 0x110c34f40>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <function no_sign_request at 0x110c35260>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <function resolve_verify_ssl at 0x110c351c0>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <function resolve_cli_read_timeout at 0x110c353a0>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <function resolve_cli_connect_timeout at 0x110c35300>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event top-level-args-parsed: calling handler <built-in method update of dict object at 0x110dcab80>
2025-03-02 01:06:31,076 - MainThread - awscli.clidriver - DEBUG - CLI version: aws-cli/2.17.25 Python/3.11.9 Darwin/24.1.0 exe/x86_64
2025-03-02 01:06:31,076 - MainThread - awscli.clidriver - DEBUG - Arguments entered to CLI: ['apigatewaymanagementapi', 'get-connection', '--connection-id', '8807fd50', '--debug', '--endpoint-url', 'http://localhost:4566/_aws/execute-api/0ea7ff6b/local']
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function add_timestamp_parser at 0x110d01b20>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function register_uri_param_handler at 0x110384860>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function add_binary_formatter at 0x110d94680>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function no_pager_handler at 0x110246a20>
2025-03-02 01:06:31,076 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function inject_assume_role_provider_cache at 0x1103ac180>
2025-03-02 01:06:31,082 - MainThread - botocore.utils - DEBUG - IMDS ENDPOINT: http://169.254.169.254/
2025-03-02 01:06:31,091 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function attach_history_handler at 0x110b4bce0>
2025-03-02 01:06:31,091 - MainThread - botocore.hooks - DEBUG - Event session-initialized: calling handler <function inject_json_file_cache at 0x110b08ea0>
2025-03-02 01:06:31,099 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/botocore/data/apigatewaymanagementapi/2018-11-29/service-2.json
2025-03-02 01:06:31,099 - MainThread - botocore.hooks - DEBUG - Event building-command-table.apigatewaymanagementapi: calling handler <function add_waiters at 0x110d03b00>
2025-03-02 01:06:31,107 - MainThread - botocore.hooks - DEBUG - Event building-command-table.apigatewaymanagementapi: calling handler <bound method AliasSubCommandInjector.on_building_command_table of <awscli.alias.AliasSubCommandInjector object at 0x110dd19d0>>
2025-03-02 01:06:31,107 - MainThread - awscli.clidriver - DEBUG - OrderedDict([('connection-id', <awscli.arguments.CLIArgument object at 0x110e34810>)])
2025-03-02 01:06:31,107 - MainThread - botocore.hooks - DEBUG - Event building-argument-table.apigatewaymanagementapi.get-connection: calling handler <function add_streaming_output_arg at 0x110d02020>
2025-03-02 01:06:31,107 - MainThread - botocore.hooks - DEBUG - Event building-argument-table.apigatewaymanagementapi.get-connection: calling handler <function add_cli_input_json at 0x1103acae0>
2025-03-02 01:06:31,107 - MainThread - botocore.hooks - DEBUG - Event building-argument-table.apigatewaymanagementapi.get-connection: calling handler <function add_cli_input_yaml at 0x1103acb80>
2025-03-02 01:06:31,107 - MainThread - botocore.hooks - DEBUG - Event building-argument-table.apigatewaymanagementapi.get-connection: calling handler <function unify_paging_params at 0x110b15260>
2025-03-02 01:06:31,117 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/botocore/data/apigatewaymanagementapi/2018-11-29/paginators-1.json
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event building-argument-table.apigatewaymanagementapi.get-connection: calling handler <function add_generate_skeleton at 0x110c07740>
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event before-building-argument-table-parser.apigatewaymanagementapi.get-connection: calling handler <bound method OverrideRequiredArgsArgument.override_required_args of <awscli.customizations.cliinput.CliInputJSONArgument object at 0x110e34c50>>
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event before-building-argument-table-parser.apigatewaymanagementapi.get-connection: calling handler <bound method OverrideRequiredArgsArgument.override_required_args of <awscli.customizations.cliinput.CliInputYAMLArgument object at 0x110e352d0>>
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event before-building-argument-table-parser.apigatewaymanagementapi.get-connection: calling handler <bound method GenerateCliSkeletonArgument.override_required_args of <awscli.customizations.generatecliskeleton.GenerateCliSkeletonArgument object at 0x110e3f110>>
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event building-command-table.apigatewaymanagementapi_get-connection: calling handler <function add_waiters at 0x110d03b00>
2025-03-02 01:06:31,117 - MainThread - botocore.hooks - DEBUG - Event building-command-table.apigatewaymanagementapi_get-connection: calling handler <bound method AliasSubCommandInjector.on_building_command_table of <awscli.alias.AliasSubCommandInjector object at 0x110dd19d0>>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event load-cli-arg.execute-api.get-connection.connection-id: calling handler <awscli.paramfile.URIArgumentHandler object at 0x11041ce90>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event process-cli-arg.apigatewaymanagementapi.get-connection: calling handler <awscli.argprocess.ParamShorthandParser object at 0x110296210>
2025-03-02 01:06:31,118 - MainThread - awscli.arguments - DEBUG - Unpacked value of '8807fd50' for parameter "connection_id": '8807fd50'
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event load-cli-arg.execute-api.get-connection.cli-input-json: calling handler <awscli.paramfile.URIArgumentHandler object at 0x11041ce90>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event load-cli-arg.execute-api.get-connection.cli-input-yaml: calling handler <awscli.paramfile.URIArgumentHandler object at 0x11041ce90>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event load-cli-arg.execute-api.get-connection.generate-cli-skeleton: calling handler <awscli.paramfile.URIArgumentHandler object at 0x11041ce90>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event calling-command.apigatewaymanagementapi.get-connection: calling handler <bound method CliInputArgument.add_to_call_parameters of <awscli.customizations.cliinput.CliInputJSONArgument object at 0x110e34c50>>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event calling-command.apigatewaymanagementapi.get-connection: calling handler <bound method CliInputArgument.add_to_call_parameters of <awscli.customizations.cliinput.CliInputYAMLArgument object at 0x110e352d0>>
2025-03-02 01:06:31,118 - MainThread - botocore.hooks - DEBUG - Event calling-command.apigatewaymanagementapi.get-connection: calling handler <bound method GenerateCliSkeletonArgument.generate_skeleton of <awscli.customizations.generatecliskeleton.GenerateCliSkeletonArgument object at 0x110e3f110>>
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - DEBUG - Looking for credentials via: env
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - DEBUG - Looking for credentials via: assume-role
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - DEBUG - Looking for credentials via: assume-role-with-web-identity
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - DEBUG - Looking for credentials via: sso
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - DEBUG - Looking for credentials via: shared-credentials-file
2025-03-02 01:06:31,118 - MainThread - botocore.credentials - INFO - Found credentials in shared credentials file: ~/.aws/credentials
2025-03-02 01:06:31,119 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/botocore/data/endpoints.json
2025-03-02 01:06:31,128 - MainThread - botocore.hooks - DEBUG - Event choose-service-name: calling handler <function handle_service_name_alias at 0x10eed7f60>
2025-03-02 01:06:31,138 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/botocore/data/apigatewaymanagementapi/2018-11-29/endpoint-rule-set-1.json
2025-03-02 01:06:31,139 - MainThread - botocore.loaders - DEBUG - Loading JSON file: /usr/local/aws-cli/awscli/botocore/data/partitions.json
2025-03-02 01:06:31,139 - MainThread - botocore.hooks - DEBUG - Event creating-client-class.apigatewaymanagementapi: calling handler <function add_generate_presigned_url at 0x10ee00c20>
2025-03-02 01:06:31,139 - MainThread - botocore.regions - DEBUG - Creating a regex based endpoint for execute-api, eu-west-1
2025-03-02 01:06:31,144 - MainThread - botocore.endpoint - DEBUG - Setting execute-api timeout as (60, 60)
2025-03-02 01:06:31,145 - MainThread - botocore.hooks - DEBUG - Event provide-client-params.apigatewaymanagementapi.GetConnection: calling handler <function base64_decode_input_blobs at 0x110d94720>
2025-03-02 01:06:31,145 - MainThread - botocore.hooks - DEBUG - Event before-parameter-build.apigatewaymanagementapi.GetConnection: calling handler <function generate_idempotent_uuid at 0x10eefa2a0>
2025-03-02 01:06:31,145 - MainThread - botocore.regions - DEBUG - Calling endpoint provider with parameters: {'Region': 'eu-west-1', 'UseDualStack': False, 'UseFIPS': False, 'Endpoint': 'http://localhost:4566/_aws/execute-api/0ea7ff6b/local'}
2025-03-02 01:06:31,145 - MainThread - botocore.regions - DEBUG - Endpoint provider result: http://localhost:4566/_aws/execute-api/0ea7ff6b/local
2025-03-02 01:06:31,146 - MainThread - botocore.hooks - DEBUG - Event before-call.apigatewaymanagementapi.GetConnection: calling handler <function inject_api_version_header_if_needed at 0x10eefbd80>
2025-03-02 01:06:31,146 - MainThread - botocore.endpoint - DEBUG - Making request for OperationModel(name=GetConnection) with params: {'url_path': '/@connections/8807fd50', 'query_string': {}, 'method': 'GET', 'headers': {'User-Agent': 'aws-cli/2.17.25 md/awscrt#0.21.2 ua/2.0 os/macos#24.1.0 md/arch#x86_64 lang/python#3.11.9 md/pyimpl#CPython cfg/retry-mode#standard md/installer#exe md/prompt#off md/command#apigatewaymanagementapi.get-connection'}, 'body': b'', 'url': 'http://localhost:4566/_aws/execute-api/0ea7ff6b/local/@connections/8807fd50', 'context': {'client_region': 'eu-west-1', 'client_config': <botocore.config.Config object at 0x11840d6d0>, 'has_streaming_input': False, 'auth_type': None}}
2025-03-02 01:06:31,146 - MainThread - botocore.hooks - DEBUG - Event request-created.apigatewaymanagementapi.GetConnection: calling handler <bound method RequestSigner.handler of <botocore.signers.RequestSigner object at 0x1183caf50>>
2025-03-02 01:06:31,147 - MainThread - botocore.hooks - DEBUG - Event choose-signer.apigatewaymanagementapi.GetConnection: calling handler <function set_operation_specific_signer at 0x10eefa160>
2025-03-02 01:06:31,147 - MainThread - botocore.auth - DEBUG - Calculating signature using v4 auth.
2025-03-02 01:06:31,147 - MainThread - botocore.auth - DEBUG - CanonicalRequest:
GET
/_aws/execute-api/0ea7ff6b/local/%40connections/8807fd50
host:localhost:4566
x-amz-date:20250302T010631Z
host;x-amz-date
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
2025-03-02 01:06:31,147 - MainThread - botocore.auth - DEBUG - StringToSign:
AWS4-HMAC-SHA256
20250302T010631Z
20250302/eu-west-1/execute-api/aws4_request
c7e0f8bbcf7ff24541071dc1cd177b4a807b9d6164524f500b6b5a5107d9bd37
2025-03-02 01:06:31,148 - MainThread - botocore.auth - DEBUG - Signature:
a9c5238786639dbc9a5113fef1f7e0315d98b522f8eea57d7948baf66b4b0fde
2025-03-02 01:06:31,148 - MainThread - botocore.endpoint - DEBUG - Sending http request: <AWSPreparedRequest stream_output=False, method=GET, url=http://localhost:4566/_aws/execute-api/0ea7ff6b/local/@connections/8807fd50, headers={'User-Agent': b'aws-cli/2.17.25 md/awscrt#0.21.2 ua/2.0 os/macos#24.1.0 md/arch#x86_64 lang/python#3.11.9 md/pyimpl#CPython cfg/retry-mode#standard md/installer#exe md/prompt#off md/command#apigatewaymanagementapi.get-connection', 'X-Amz-Date': b'20250302T010631Z', 'Authorization': b'AWS4-HMAC-SHA256 Credential=test/20250302/eu-west-1/execute-api/aws4_request, SignedHeaders=host;x-amz-date, Signature=a9c5238786639dbc9a5113fef1f7e0315d98b522f8eea57d7948baf66b4b0fde'}>
2025-03-02 01:06:31,149 - MainThread - urllib3.connectionpool - DEBUG - Starting new HTTP connection (1): localhost:4566
2025-03-02 01:06:31,160 - MainThread - urllib3.connectionpool - DEBUG - http://localhost:4566 "GET /_aws/execute-api/0ea7ff6b/local/@connections/8807fd50 HTTP/1.1" 500 2453
2025-03-02 01:06:31,161 - MainThread - botocore.parsers - DEBUG - Response headers: {'Server': 'TwistedWeb/24.3.0', 'Date': 'Sun, 02 Mar 2025 01:06:31 GMT', 'Content-Type': 'application/json', 'X-Amzn-Errortype': 'InternalError', 'Content-Length': '2453', 'x-amzn-requestid': '5e066851-ef66-44d5-8f70-5f36109d2173', 'x-amz-request-id': '5e066851-ef66-44d5-8f70-5f36109d2173'}
2025-03-02 01:06:31,161 - MainThread - botocore.parsers - DEBUG - Response body:
b'{"__type": "InternalError", "message": "exception while calling iot with unknown operation: Traceback (most recent call last):\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/chain.py\\", line 166, in handle\\n handler(self, self.context, response)\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/handlers.py\\", line 27, in __call__\\n router_response = self.router.dispatch(context.request)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/router.py\\", line 326, in dispatch\\n return self.dispatcher(request, handler, args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 64, in __call__\\n result = self.invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/pydantic.py\\", line 82, in invoke_endpoint\\n return super().invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 73, in invoke_endpoint\\n return endpoint(request, **request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets/apigatewaymanagementapi/core.py.enc\\", line 22, in call_apigwmgmtapi\\n try:H=G(A,connection_id,**F)\\n ^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets_legacy/management_api_routes.py.enc\\", line 19, in get_connection\\n D={\'SourceIp\':A[_B].remote_address[0],\'UserAgent\':A[_B].request_headers[\'User-Agent\']};return GetConnectionResponse(ConnectedAt=A[\'connected_at\'],Identity=D,LastActiveAt=A[\'last_active_at\'])\\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/websockets/datastructures.py\\", line 107, in __getitem__\\n value = self._dict[key.lower()]\\n ~~~~~~~~~~^^^^^^^^^^^^^\\nKeyError: \'user-agent\'\\n"}'
2025-03-02 01:06:31,161 - MainThread - botocore.hooks - DEBUG - Event needs-retry.apigatewaymanagementapi.GetConnection: calling handler <bound method RetryHandler.needs_retry of <botocore.retries.standard.RetryHandler object at 0x11840f410>>
2025-03-02 01:06:31,161 - MainThread - botocore.retries.standard - DEBUG - Retry needed, retrying request after delay of: 0.082056313224804
2025-03-02 01:06:31,161 - MainThread - botocore.endpoint - DEBUG - Response received to retry, sleeping for 0.082056313224804 seconds
2025-03-02 01:06:31,248 - MainThread - botocore.hooks - DEBUG - Event request-created.apigatewaymanagementapi.GetConnection: calling handler <bound method RequestSigner.handler of <botocore.signers.RequestSigner object at 0x1183caf50>>
2025-03-02 01:06:31,248 - MainThread - botocore.hooks - DEBUG - Event choose-signer.apigatewaymanagementapi.GetConnection: calling handler <function set_operation_specific_signer at 0x10eefa160>
2025-03-02 01:06:31,248 - MainThread - botocore.auth - DEBUG - Calculating signature using v4 auth.
2025-03-02 01:06:31,249 - MainThread - botocore.auth - DEBUG - CanonicalRequest:
GET
/_aws/execute-api/0ea7ff6b/local/%40connections/8807fd50
host:localhost:4566
x-amz-date:20250302T010631Z
host;x-amz-date
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
2025-03-02 01:06:31,249 - MainThread - botocore.auth - DEBUG - StringToSign:
AWS4-HMAC-SHA256
20250302T010631Z
20250302/eu-west-1/execute-api/aws4_request
c7e0f8bbcf7ff24541071dc1cd177b4a807b9d6164524f500b6b5a5107d9bd37
2025-03-02 01:06:31,249 - MainThread - botocore.auth - DEBUG - Signature:
a9c5238786639dbc9a5113fef1f7e0315d98b522f8eea57d7948baf66b4b0fde
2025-03-02 01:06:31,249 - MainThread - botocore.endpoint - DEBUG - Sending http request: <AWSPreparedRequest stream_output=False, method=GET, url=http://localhost:4566/_aws/execute-api/0ea7ff6b/local/@connections/8807fd50, headers={'User-Agent': b'aws-cli/2.17.25 md/awscrt#0.21.2 ua/2.0 os/macos#24.1.0 md/arch#x86_64 lang/python#3.11.9 md/pyimpl#CPython cfg/retry-mode#standard md/installer#exe md/prompt#off md/command#apigatewaymanagementapi.get-connection', 'X-Amz-Date': b'20250302T010631Z', 'Authorization': b'AWS4-HMAC-SHA256 Credential=test/20250302/eu-west-1/execute-api/aws4_request, SignedHeaders=host;x-amz-date, Signature=a9c5238786639dbc9a5113fef1f7e0315d98b522f8eea57d7948baf66b4b0fde'}>
2025-03-02 01:06:31,251 - MainThread - urllib3.connectionpool - DEBUG - http://localhost:4566 "GET /_aws/execute-api/0ea7ff6b/local/@connections/8807fd50 HTTP/1.1" 500 2453
2025-03-02 01:06:31,252 - MainThread - botocore.parsers - DEBUG - Response headers: {'Server': 'TwistedWeb/24.3.0', 'Date': 'Sun, 02 Mar 2025 01:06:31 GMT', 'Content-Type': 'application/json', 'X-Amzn-Errortype': 'InternalError', 'Content-Length': '2453', 'x-amzn-requestid': 'f6da2c9c-2f79-4d95-9aa9-15481c0315bb', 'x-amz-request-id': 'f6da2c9c-2f79-4d95-9aa9-15481c0315bb'}
2025-03-02 01:06:31,252 - MainThread - botocore.parsers - DEBUG - Response body:
b'{"__type": "InternalError", "message": "exception while calling iot with unknown operation: Traceback (most recent call last):\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/chain.py\\", line 166, in handle\\n handler(self, self.context, response)\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/handlers.py\\", line 27, in __call__\\n router_response = self.router.dispatch(context.request)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/router.py\\", line 326, in dispatch\\n return self.dispatcher(request, handler, args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 64, in __call__\\n result = self.invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/pydantic.py\\", line 82, in invoke_endpoint\\n return super().invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 73, in invoke_endpoint\\n return endpoint(request, **request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets/apigatewaymanagementapi/core.py.enc\\", line 22, in call_apigwmgmtapi\\n try:H=G(A,connection_id,**F)\\n ^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets_legacy/management_api_routes.py.enc\\", line 19, in get_connection\\n D={\'SourceIp\':A[_B].remote_address[0],\'UserAgent\':A[_B].request_headers[\'User-Agent\']};return GetConnectionResponse(ConnectedAt=A[\'connected_at\'],Identity=D,LastActiveAt=A[\'last_active_at\'])\\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/websockets/datastructures.py\\", line 107, in __getitem__\\n value = self._dict[key.lower()]\\n ~~~~~~~~~~^^^^^^^^^^^^^\\nKeyError: \'user-agent\'\\n"}'
2025-03-02 01:06:31,252 - MainThread - botocore.hooks - DEBUG - Event needs-retry.apigatewaymanagementapi.GetConnection: calling handler <bound method RetryHandler.needs_retry of <botocore.retries.standard.RetryHandler object at 0x11840f410>>
2025-03-02 01:06:31,252 - MainThread - botocore.retries.standard - DEBUG - Retry needed, retrying request after delay of: 1.7712764872614646
2025-03-02 01:06:31,252 - MainThread - botocore.endpoint - DEBUG - Response received to retry, sleeping for 1.7712764872614646 seconds
2025-03-02 01:06:33,028 - MainThread - botocore.hooks - DEBUG - Event request-created.apigatewaymanagementapi.GetConnection: calling handler <bound method RequestSigner.handler of <botocore.signers.RequestSigner object at 0x1183caf50>>
2025-03-02 01:06:33,029 - MainThread - botocore.hooks - DEBUG - Event choose-signer.apigatewaymanagementapi.GetConnection: calling handler <function set_operation_specific_signer at 0x10eefa160>
2025-03-02 01:06:33,030 - MainThread - botocore.auth - DEBUG - Calculating signature using v4 auth.
2025-03-02 01:06:33,030 - MainThread - botocore.auth - DEBUG - CanonicalRequest:
GET
/_aws/execute-api/0ea7ff6b/local/%40connections/8807fd50
host:localhost:4566
x-amz-date:20250302T010633Z
host;x-amz-date
e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
2025-03-02 01:06:33,030 - MainThread - botocore.auth - DEBUG - StringToSign:
AWS4-HMAC-SHA256
20250302T010633Z
20250302/eu-west-1/execute-api/aws4_request
2e0eeae4c9f3f7e36dd448d74ef2d63713fd286a3eee49dd142efa88cb1351c0
2025-03-02 01:06:33,030 - MainThread - botocore.auth - DEBUG - Signature:
8a584434b234270fa0478f3b67a6db66be513eb07ea3e9abe40d5d5d537eef72
2025-03-02 01:06:33,030 - MainThread - botocore.endpoint - DEBUG - Sending http request: <AWSPreparedRequest stream_output=False, method=GET, url=http://localhost:4566/_aws/execute-api/0ea7ff6b/local/@connections/8807fd50, headers={'User-Agent': b'aws-cli/2.17.25 md/awscrt#0.21.2 ua/2.0 os/macos#24.1.0 md/arch#x86_64 lang/python#3.11.9 md/pyimpl#CPython cfg/retry-mode#standard md/installer#exe md/prompt#off md/command#apigatewaymanagementapi.get-connection', 'X-Amz-Date': b'20250302T010633Z', 'Authorization': b'AWS4-HMAC-SHA256 Credential=test/20250302/eu-west-1/execute-api/aws4_request, SignedHeaders=host;x-amz-date, Signature=8a584434b234270fa0478f3b67a6db66be513eb07ea3e9abe40d5d5d537eef72'}>
2025-03-02 01:06:33,034 - MainThread - urllib3.connectionpool - DEBUG - http://localhost:4566 "GET /_aws/execute-api/0ea7ff6b/local/@connections/8807fd50 HTTP/1.1" 500 2453
2025-03-02 01:06:33,035 - MainThread - botocore.parsers - DEBUG - Response headers: {'Server': 'TwistedWeb/24.3.0', 'Date': 'Sun, 02 Mar 2025 01:06:33 GMT', 'Content-Type': 'application/json', 'X-Amzn-Errortype': 'InternalError', 'Content-Length': '2453', 'x-amzn-requestid': '85bfadc6-e106-4ce0-a5da-160ed72ac253', 'x-amz-request-id': '85bfadc6-e106-4ce0-a5da-160ed72ac253'}
2025-03-02 01:06:33,035 - MainThread - botocore.parsers - DEBUG - Response body:
b'{"__type": "InternalError", "message": "exception while calling iot with unknown operation: Traceback (most recent call last):\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/chain.py\\", line 166, in handle\\n handler(self, self.context, response)\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/handlers.py\\", line 27, in __call__\\n router_response = self.router.dispatch(context.request)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/router.py\\", line 326, in dispatch\\n return self.dispatcher(request, handler, args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 64, in __call__\\n result = self.invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/pydantic.py\\", line 82, in invoke_endpoint\\n return super().invoke_endpoint(request, endpoint, request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py\\", line 73, in invoke_endpoint\\n return endpoint(request, **request_args)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets/apigatewaymanagementapi/core.py.enc\\", line 22, in call_apigwmgmtapi\\n try:H=G(A,connection_id,**F)\\n ^^^^^^^^^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets_legacy/management_api_routes.py.enc\\", line 19, in get_connection\\n D={\'SourceIp\':A[_B].remote_address[0],\'UserAgent\':A[_B].request_headers[\'User-Agent\']};return GetConnectionResponse(ConnectedAt=A[\'connected_at\'],Identity=D,LastActiveAt=A[\'last_active_at\'])\\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\\n File \\"/opt/code/localstack/.venv/lib/python3.11/site-packages/websockets/datastructures.py\\", line 107, in __getitem__\\n value = self._dict[key.lower()]\\n ~~~~~~~~~~^^^^^^^^^^^^^\\nKeyError: \'user-agent\'\\n"}'
2025-03-02 01:06:33,036 - MainThread - botocore.hooks - DEBUG - Event needs-retry.apigatewaymanagementapi.GetConnection: calling handler <bound method RetryHandler.needs_retry of <botocore.retries.standard.RetryHandler object at 0x11840f410>>
2025-03-02 01:06:33,036 - MainThread - botocore.retries.standard - DEBUG - Max attempts of 3 reached.
2025-03-02 01:06:33,036 - MainThread - botocore.retries.standard - DEBUG - Not retrying request.
2025-03-02 01:06:33,036 - MainThread - botocore.hooks - DEBUG - Event after-call.apigatewaymanagementapi.GetConnection: calling handler <bound method RetryQuotaChecker.release_retry_quota of <botocore.retries.standard.RetryQuotaChecker object at 0x11840edd0>>
2025-03-02 01:06:33,037 - MainThread - awscli.clidriver - DEBUG - Exception caught in main()
Traceback (most recent call last):
File "awscli/clidriver.py", line 499, in main
File "awscli/clidriver.py", line 634, in __call__
File "awscli/clidriver.py", line 837, in __call__
File "awscli/clidriver.py", line 963, in invoke
File "awscli/clidriver.py", line 975, in _make_client_call
File "awscli/botocore/client.py", line 360, in _api_call
File "awscli/botocore/client.py", line 739, in _make_api_call
botocore.exceptions.ClientError: An error occurred (InternalError) when calling the GetConnection operation (reached max retries: 2): exception while calling iot with unknown operation: Traceback (most recent call last):
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/chain.py", line 166, in handle
handler(self, self.context, response)
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/gateway/handlers.py", line 27, in __call__
router_response = self.router.dispatch(context.request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/router.py", line 326, in dispatch
return self.dispatcher(request, handler, args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py", line 64, in __call__
result = self.invoke_endpoint(request, endpoint, request_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/pydantic.py", line 82, in invoke_endpoint
return super().invoke_endpoint(request, endpoint, request_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/rolo/routing/handler.py", line 73, in invoke_endpoint
return endpoint(request, **request_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets/apigatewaymanagementapi/core.py.enc", line 22, in call_apigwmgmtapi
try:H=G(A,connection_id,**F)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/localstack/pro/core/services/apigatewayv2/next_gen/execute_api/websockets_legacy/management_api_routes.py.enc", line 19, in get_connection
D={'SourceIp':A[_B].remote_address[0],'UserAgent':A[_B].request_headers['User-Agent']};return GetConnectionResponse(ConnectedAt=A['connected_at'],Identity=D,LastActiveAt=A['last_active_at'])
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^
File "/opt/code/localstack/.venv/lib/python3.11/site-packages/websockets/datastructures.py", line 107, in __getitem__
value = self._dict[key.lower()]
~~~~~~~~~~^^^^^^^^^^^^^
KeyError: 'user-agent'
```
### Expected Behavior
Would expect the cli command to return the connection details
### How are you starting LocalStack?
With a docker-compose file
### Steps To Reproduce
#### How are you starting localstack (e.g., `bin/localstack` command, arguments, or `docker-compose.yml`)
docker compose with apigateway,apigatewayv2 and apigatewaymanagementapi enabled
#### Client commands (e.g., AWS SDK code snippet, or sequence of "awslocal" commands)
```
aws apigatewaymanagementapi get-connection \
--connection-id 8807fd50 \
--debug --endpoint-url http://localhost:4566/_aws/execute-api/0ea7ff6b/local
```
### Environment
```markdown
- OS: MacOS Sequoia 15.1.1 (but running in Docker)
- LocalStack:
LocalStack version: latest pro 4.2.1.dev8
LocalStack Docker image sha: sha256:ea9ce6df7278e06988dbafc768fa410c37230fb24aeee2f44c42f2bf22f1b201
LocalStack build date: 2025-02-28
LocalStack build git hash: 9ae2f1f7d
```
### Anything else?
`aws apigatewaymanagementapi post-to-connection` works ok to same connection and api
|
closed
|
2025-03-02T01:30:14Z
|
2025-03-07T17:30:39Z
|
https://github.com/localstack/localstack/issues/12321
|
[
"type: bug",
"status: resolved/fixed",
"aws:apigatewayv2"
] |
slegs
| 5
|
PaddlePaddle/PaddleHub
|
nlp
| 2,084
|
使用出现RuntimeError
|
- 版本、环境信息
1）PaddleHub和PaddlePaddle版本：paddlehub==1.8.3，paddlepaddle==2.3.2
2）系统环境：Windows 7
- 复现信息：如为报错，请给出复现环境、复现步骤
```
import paddlehub as hub
def genImage():
module = hub.Module(name="ernie_vilg")
results = module.generate_image(text_prompts=["王者之心"])
def textAna():
lac = hub.Module(name="lac")
test_text = ["今天是个好天气。"]
results = lac.cut(
text=test_text, use_gpu=False, batch_size=1, return_tag=True)
print(results)
#{'word': ['今天', '是', '个', '好天气', '。'], 'tag': ['TIME', 'v', 'q', 'n', 'w']}
genImage()
#textAna()
```
无论调用genImage()还是textAna()函数，都会出现RuntimeError
```
[2022-10-24 09:40:04,237] [ INFO] - Installing ernie_vilg module
Traceback (most recent call last):
File "e:/MyProjects/py-demo/aiApp/ai02.py", line 26, in <module>
genImage()
File "e:/MyProjects/py-demo/aiApp/ai02.py", line 12, in genImage
module = hub.Module(name="ernie_vilg")
File "E:\Python37\lib\site-packages\paddlehub\module\module.py", line 102, in __new__
name=name, version=version, **kwargs)
File "E:\Python37\lib\site-packages\paddlehub\module\module.py", line 171, in init_with_name
module_name=name, module_version=version, extra=extra)
File "E:\Python37\lib\site-packages\paddlehub\module\manager.py", line 127, in install_module
self.all_modules(update=True)
File "E:\Python37\lib\site-packages\paddlehub\module\manager.py", line 105, in all_modules
valid, info = self.check_module_valid(sub_dir_path)
File "E:\Python37\lib\site-packages\paddlehub\module\manager.py", line 72, in check_module_valid
"{}.module".format(basename))
File "E:\Python37\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "C:\Users\liaoxuewei\.paddlehub\modules\ernie_vilg\module.py", line 29, in <module>
author_email="paddle-dev@baidu.com")
File "E:\Python37\lib\site-packages\paddlehub\module\module.py", line 80, in _wrapper
raise RuntimeError
RuntimeError
```
|
closed
|
2022-10-24T01:43:57Z
|
2022-10-24T02:23:36Z
|
https://github.com/PaddlePaddle/PaddleHub/issues/2084
|
[] |
liaoxuewei
| 4
|
keras-team/keras
|
tensorflow
| 20,382
|
Low frequency issue using the tensorflow-GPU backend
|
The following code
```python
import keras
print(keras.__version__)
import tensorflow as tf
print(tf.__version__)
from keras.src import layers
from keras.src import models
import numpy as np
from keras.src.metrics import iou_metrics as metrics
m_obj = metrics.MeanIoU(num_classes=2, ignore_class=0)
model = models.Sequential([layers.Dense(2, activation="softmax"), ])
model.compile(optimizer="rmsprop", loss="mse", metrics=[m_obj], jit_compile=False)
for i in range(20000):
print(i)
model.fit(np.array([[1.0, 1.0]]), np.array([[1.0, 0.0]]))
```
Eventually ends up with:
```
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 62ms/step - loss: 0.2777 - mean_io_u: 0.0000e+00
237
1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 56ms/step - loss: 0.2761 - mean_io_u: 0.0000e+00
238
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-2-9e7607e81947>](https://localhost:8080/#) in <cell line: 15>()
15 for i in range(20000):
16 print(i)
---> 17 model.fit(np.array([[1.0, 1.0]]), np.array([[1.0, 0.0]]))
1 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/ops/operation_utils.py](https://localhost:8080/#) in compute_take_along_axis_output_shape(input_shape, indices_shape, axis)
359
360 if len(input_shape) != len(indices_shape):
--> 361 raise ValueError(
362 "`x` and `indices` must have the same number of dimensions, "
363 f"but receive shape {input_shape} and {indices_shape}."
ValueError: `x` and `indices` must have the same number of dimensions, but receive shape [2, 2] and [2].
```
|
closed
|
2024-10-18T19:26:21Z
|
2024-10-19T21:06:33Z
|
https://github.com/keras-team/keras/issues/20382
|
[] |
nicolaspi
| 1
|
joke2k/django-environ
|
django
| 369
|
UnicodeDecodeError: 'ascii' codec can't decode
|
I have an error when I run server with wsgi
I am using python3.6 and Django3.2.
I tried both django-environ==0.8.1 and django-environ==0.7.0
```
App 10334 output: from .base import *
App 10334 output: File "myproject/config/settings/base.py", line 11, in <module>
App 10334 output: environ.Env.read_env(BASE_DIR / '.env')
App 10334 output: File ".../env/lib/python3.6/site-packages/environ/environ.py", line 801, in read_env
App 10334 output: content = f.read()
App 10334 output: File "/usr/lib/python3.6/encodings/ascii.py", line 26, in decode
App 10334 output: return codecs.ascii_decode(input, self.errors)[0]
App 10334 output: UnicodeDecodeError: 'ascii' codec can't decode byte 0xc2 in position 379: ordinal not in range(128)
```
|
open
|
2022-03-02T20:57:02Z
|
2022-06-16T15:11:20Z
|
https://github.com/joke2k/django-environ/issues/369
|
[
"bug"
] |
kkutayozgun
| 4
|
thp/urlwatch
|
automation
| 406
|
Some help for a noob please
|
If this is the wrong place please let me know and I'll post somewhere else.
I'm currently migrating my systems from Windows to Ubuntu. So far so good. With 1 exception.
I use a windows tool called Website Watcher that monitors web site changes via a gui and some nice UI friendly tools.
I've moved most of the URLs that I want to check over to urlwatch. However I would like a little help if possible.
1) In WSW I could specify an item of text on a particular page and WSW would ignore any changes BEFORE that text was found on the page. This is useful for ignoring dynamic menus etc. I Could not find anything similar so far in urlwatch
2) Similar to the above WSW lets me specify an item of text and then any changes AFTER that text would be Excluded. Again useful for dynamic footers/dates/time/years at the end of pages etc
3) Sometimes the underlying CSS/HMTL changes on a page. Is there a way so that these changes do not get flagged as changed. I'm typically only interested visible readable text on the page. eg lots of Google based sites use generated class names/ids. These normally change but the text etc on the readable page stays the same. So far urlwatch is flagging these css/html changes.
Many thanks in advance or any help/links/examples
|
closed
|
2019-05-29T14:54:44Z
|
2020-07-10T12:50:21Z
|
https://github.com/thp/urlwatch/issues/406
|
[] |
darkufo
| 1
|
ivy-llc/ivy
|
numpy
| 28,523
|
Fix Frontend Failing Test: tensorflow - tensor.paddle.Tensor.mean
|
To-do List: https://github.com/unifyai/ivy/issues/27499
|
open
|
2024-03-09T20:50:30Z
|
2024-03-09T20:50:30Z
|
https://github.com/ivy-llc/ivy/issues/28523
|
[
"Sub Task"
] |
ZJay07
| 0
|
mirumee/ariadne
|
api
| 986
|
Create GraphQL Chat app with subscriptions example
|
People ask us once in a while for example of subscriptions app. I while ago I've implemented simple GraphQL chat using encode/broadcaster for messaging. I should cleanup my code and put it on Github for people to see.
<img width="1168" alt="Zrzut ekranu 2022-12-5 o 18 21 28" src="https://user-images.githubusercontent.com/750553/205704022-7b815e5b-35b8-4e33-9c85-16dc24fa1076.png">
|
closed
|
2022-12-05T17:34:01Z
|
2022-12-13T16:15:56Z
|
https://github.com/mirumee/ariadne/issues/986
|
[
"meta"
] |
rafalp
| 2
|
google/seq2seq
|
tensorflow
| 311
|
Optimizing time complexity of LCS calculation in ROUGE calculation using Suffix Trees
|
In seq2seq/seq2seq/metrics/rouge.py,
The longest common sequence code implementation for calculating ROUGE L score for comparing two sequences is implemented using Dynamic Programming technique which yields a time complexity of O(n*m) where n is the length of sequence 1 and m is the length of sequence 2, the sequences which are to be compared with.
Using Suffix Trees, the time complexity could be dropped to O(m+n), essentially bring the time complexity down by nearly an order of magnitude to linear bounds.
|
open
|
2018-01-19T08:25:51Z
|
2018-01-19T08:25:51Z
|
https://github.com/google/seq2seq/issues/311
|
[] |
Anirudhsekar96
| 0
|
geex-arts/django-jet
|
django
| 251
|
After deploying website Through Heroku, Mixed content error is summoned by chrome
|
After deploying website Through Heroku, Mixed content error is summoned by chrome, and also a missing js file 404 occurs, I dont understand why because everything else in my static files goes through except this particular folder "jet/js/build"
Basically, all the forms in jet are not working after deployment but in local server it works fine.
|
open
|
2017-09-13T21:09:52Z
|
2017-09-15T11:16:26Z
|
https://github.com/geex-arts/django-jet/issues/251
|
[] |
fauziame
| 1
|
Sanster/IOPaint
|
pytorch
| 170
|
Adding "--precision full" and "--no-half" args to sd
|
If I'm trying inpainting with sd1.5 or sd2, the mask is replaced by black. I used sd a few months back and it had the same problem, but adding "--precision full --no-half" solved my issue. The problem is that I've searched and I couldn't find where to pass the args.
|
closed
|
2022-12-30T07:09:21Z
|
2023-01-04T18:50:46Z
|
https://github.com/Sanster/IOPaint/issues/170
|
[] |
yattsu
| 1
|
pydata/pandas-datareader
|
pandas
| 26
|
Add pandas-data reader to pypi
|
closed
|
2015-03-26T03:32:32Z
|
2015-03-26T03:55:38Z
|
https://github.com/pydata/pandas-datareader/issues/26
|
[] |
davidastephens
| 1
|
|
danimtb/dasshio
|
dash
| 111
|
Where to put the config file ?
|
Hi
I have installed it easily in Hassio but I'm quite confused where to put the /data/options.json as I have no data directory in all hassio directories visible through samba share :(
Thanks for clarification ;)
Vincèn
|
closed
|
2022-10-18T14:09:10Z
|
2023-06-12T07:39:48Z
|
https://github.com/danimtb/dasshio/issues/111
|
[] |
vincegre
| 2
|
FlareSolverr/FlareSolverr
|
api
| 834
|
[yggtorrent] Error connecting to FlareSolverr server: System.Net.Http.HttpRequestException: Connection refused (
|
### Have you checked our README?
- [X] I have checked the README
### Have you followed our Troubleshooting?
- [X] I have followed your Troubleshooting
### Is there already an issue for your problem?
- [X] I have checked older issues, open and closed
### Have you checked the discussions?
- [X] I have read the Discussions
### Environment
```markdown
- FlareSolverr version: 2.2.1.0
- Last working FlareSolverr version:2.2.1.0
- Operating system:dsm 7
- Are you using Docker: [yes/no] yes
- FlareSolverr User-Agent (see log traces or / endpoint):
- Are you using a VPN: [yes/no] yes
- Are you using a Proxy: [yes/no] no
- Are you using Captcha Solver: [yes/no] yes
- If using captcha solver, which one: harvester
- URL to test this issue:
```
### Description
Connection refused
Delete indexer
Unable to add and connect indexer
### Logged Error Messages
```text
FlareSolverrSharp.Exceptions.FlareSolverrException: Error connecting to FlareSolverr server: System.Net.Http.HttpRequestException: Connection refused (192.168.1.44:8191)
---> System.Net.Sockets.SocketException (111): Connection refused
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.ThrowException(SocketError error, CancellationToken cancellationToken)
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.System.Threading.Tasks.Sources.IValueTaskSource.GetResult(Int16 token)
at System.Net.Sockets.Socket.<ConnectAsync>g__WaitForConnectWithCancellation|277_0(AwaitableSocketAsyncEventArgs saea, ValueTask connectTask, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
--- End of inner exception stack trace ---
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.CreateHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.AddHttp11ConnectionAsync(HttpRequestMessage request)
at System.Threading.Tasks.TaskCompletionSourceWithCancellation`1.WaitWithCancellationAsync(CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.GetHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.SendWithVersionDetectionAndRetryAsync(HttpRequestMessage request, Boolean async, Boolean doRequestAuth, CancellationToken cancellationToken)
at System.Net.Http.DiagnosticsHandler.SendAsyncCore(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.RedirectHandler.SendAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpClient.<SendAsync>g__Core|83_0(HttpRequestMessage request, HttpCompletionOption completionOption, CancellationTokenSource cts, Boolean disposeCts, CancellationTokenSource pendingRequestsCts, CancellationToken originalCancellationToken)
at FlareSolverrSharp.Solvers.FlareSolverr.<>c__DisplayClass10_0.<<SendFlareSolverrRequest>b__0>d.MoveNext()
Exception in GetConfigurationForSetup (yggtorrent): FlareSolverrSharp.Exceptions.FlareSolverrException: Error connecting to FlareSolverr server: System.Net.Http.HttpRequestException: Connection refused (192.168.1.44:8191)
---> System.Net.Sockets.SocketException (111): Connection refused
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.ThrowException(SocketError error, CancellationToken cancellationToken)
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.System.Threading.Tasks.Sources.IValueTaskSource.GetResult(Int16 token)
at System.Net.Sockets.Socket.<ConnectAsync>g__WaitForConnectWithCancellation|277_0(AwaitableSocketAsyncEventArgs saea, ValueTask connectTask, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
--- End of inner exception stack trace ---
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.CreateHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.AddHttp11ConnectionAsync(HttpRequestMessage request)
at System.Threading.Tasks.TaskCompletionSourceWithCancellation`1.WaitWithCancellationAsync(CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.GetHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.SendWithVersionDetectionAndRetryAsync(HttpRequestMessage request, Boolean async, Boolean doRequestAuth, CancellationToken cancellationToken)
at System.Net.Http.DiagnosticsHandler.SendAsyncCore(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.RedirectHandler.SendAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpClient.<SendAsync>g__Core|83_0(HttpRequestMessage request, HttpCompletionOption completionOption, CancellationTokenSource cts, Boolean disposeCts, CancellationTokenSource pendingRequestsCts, CancellationToken originalCancellationToken)
at FlareSolverrSharp.Solvers.FlareSolverr.<>c__DisplayClass10_0.<<SendFlareSolverrRequest>b__0>d.MoveNext()
at FlareSolverrSharp.Solvers.FlareSolverr.<>c__DisplayClass10_0.<<SendFlareSolverrRequest>b__0>d.MoveNext()
--- End of stack trace from previous location ---
at FlareSolverrSharp.Utilities.SemaphoreLocker.LockAsync[T](Func`1 worker)
at FlareSolverrSharp.Solvers.FlareSolverr.SendFlareSolverrRequest(HttpContent flareSolverrRequest)
at FlareSolverrSharp.Solvers.FlareSolverr.Solve(HttpRequestMessage request, String sessionId)
at FlareSolverrSharp.ClearanceHandler.SendAsync(HttpRequestMessage request, CancellationToken cancellationToken)
at System.Net.Http.HttpClient.<SendAsync>g__Core|83_0(HttpRequestMessage request, HttpCompletionOption completionOption, CancellationTokenSource cts, Boolean disposeCts, CancellationTokenSource pendingRequestsCts, CancellationToken originalCancellationToken)
at Jackett.Common.Utils.Clients.HttpWebClient2.Run(WebRequest webRequest) in ./Jackett.Common/Utils/Clients/HttpWebClient2.cs:line 178
at Jackett.Common.Utils.Clients.WebClient.GetResultAsync(WebRequest request) in ./Jackett.Common/Utils/Clients/WebClient.cs:line 186
at Jackett.Common.Indexers.BaseWebIndexer.RequestWithCookiesAsync(String url, String cookieOverride, RequestType method, String referer, IEnumerable`1 data, Dictionary`2 headers, String rawbody, Nullable`1 emulateBrowser) in ./Jackett.Common/Indexers/BaseIndexer.cs:line 531
at Jackett.Common.Indexers.CardigannIndexer.GetConfigurationForSetup(Boolean automaticlogin) in ./Jackett.Common/Indexers/CardigannIndexer.cs:line 966
at Jackett.Common.Indexers.CardigannIndexer.GetConfigurationForSetup() in ./Jackett.Common/Indexers/CardigannIndexer.cs:line 943
FlareSolverrSharp.Exceptions.FlareSolverrException: Error connecting to FlareSolverr server: System.Net.Http.HttpRequestException: Connection refused (192.168.1.44:8191)
---> System.Net.Sockets.SocketException (111): Connection refused
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.ThrowException(SocketError error, CancellationToken cancellationToken)
at System.Net.Sockets.Socket.AwaitableSocketAsyncEventArgs.System.Threading.Tasks.Sources.IValueTaskSource.GetResult(Int16 token)
at System.Net.Sockets.Socket.<ConnectAsync>g__WaitForConnectWithCancellation|277_0(AwaitableSocketAsyncEventArgs saea, ValueTask connectTask, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
--- End of inner exception stack trace ---
at System.Net.Http.HttpConnectionPool.ConnectToTcpHostAsync(String host, Int32 port, HttpRequestMessage initialRequest, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.ConnectAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.CreateHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.AddHttp11ConnectionAsync(HttpRequestMessage request)
at System.Threading.Tasks.TaskCompletionSourceWithCancellation`1.WaitWithCancellationAsync(CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.GetHttp11ConnectionAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpConnectionPool.SendWithVersionDetectionAndRetryAsync(HttpRequestMessage request, Boolean async, Boolean doRequestAuth, CancellationToken cancellationToken)
at System.Net.Http.DiagnosticsHandler.SendAsyncCore(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.RedirectHandler.SendAsync(HttpRequestMessage request, Boolean async, CancellationToken cancellationToken)
at System.Net.Http.HttpClient.<SendAsync>g__Core|83_0(HttpRequestMessage request, HttpCompletionOption completionOption, CancellationTokenSource cts, Boolean disposeCts, CancellationTokenSource pendingRequestsCts, CancellationToken originalCancellationToken)
at FlareSolverrSharp.Solvers.FlareSolverr.<>c__DisplayClass10_0.<<SendFlareSolverrRequest>b__0>d.MoveNext()
```
### Screenshots
_No response_
|
closed
|
2023-07-30T18:41:27Z
|
2023-07-31T10:14:53Z
|
https://github.com/FlareSolverr/FlareSolverr/issues/834
|
[
"duplicate"
] |
DaGreenX
| 6
|
ScrapeGraphAI/Scrapegraph-ai
|
machine-learning
| 733
|
SearchGraph Invalid json output
|
**Describe the bug**
when I use searchGraph, I meet the following error.
```bash
Traceback (most recent call last):
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/output_parsers/json.py", line 83, in parse_result
return parse_json_markdown(text)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/utils/json.py", line 144, in parse_json_markdown
return _parse_json(json_str, parser=parser)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/utils/json.py", line 160, in _parse_json
return parser(json_str)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/utils/json.py", line 118, in parse_partial_json
return json.loads(s, strict=strict)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/json/__init__.py", line 359, in loads
return cls(**kw).decode(s)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/json/decoder.py", line 355, in raw_decode
raise JSONDecodeError("Expecting value", s, err.value) from None
json.decoder.JSONDecodeError: Expecting value: line 16 column 5 (char 504)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/Users/miyamo/pdfscrawl.py", line 90, in <module>
result = search_graph.run()
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/search_graph.py", line 120, in run
self.final_state, self.execution_info = self.graph.execute(inputs)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 259, in execute
return self._execute_standard(initial_state)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 180, in _execute_standard
raise e
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 164, in _execute_standard
result = current_node.execute(state)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/nodes/graph_iterator_node.py", line 73, in execute
state = asyncio.run(self._async_execute(state, batchsize))
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
return future.result()
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/nodes/graph_iterator_node.py", line 136, in _async_execute
answers = await tqdm.gather(
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/tqdm/asyncio.py", line 79, in gather
res = [await f for f in cls.as_completed(ifs, loop=loop, timeout=timeout,
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/tqdm/asyncio.py", line 79, in <listcomp>
res = [await f for f in cls.as_completed(ifs, loop=loop, timeout=timeout,
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/asyncio/tasks.py", line 571, in _wait_for_one
return f.result() # May raise f.exception().
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/tqdm/asyncio.py", line 76, in wrap_awaitable
return i, await f
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/nodes/graph_iterator_node.py", line 126, in _async_run
return await asyncio.to_thread(graph.run)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/asyncio/threads.py", line 25, in to_thread
return await loop.run_in_executor(None, func_call)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/smart_scraper_graph.py", line 115, in run
self.final_state, self.execution_info = self.graph.execute(inputs)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 259, in execute
return self._execute_standard(initial_state)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 180, in _execute_standard
raise e
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/graphs/base_graph.py", line 164, in _execute_standard
result = current_node.execute(state)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/scrapegraphai/nodes/generate_answer_node.py", line 111, in execute
batch_results = async_runner.invoke({"question": user_prompt})
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3723, in invoke
output = {key: future.result() for key, future in zip(steps, futures)}
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3723, in <dictcomp>
output = {key: future.result() for key, future in zip(steps, futures)}
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3707, in _invoke_step
return context.run(
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3022, in invoke
input = context.run(step.invoke, input, config)
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/output_parsers/base.py", line 192, in invoke
return self._call_with_config(
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1926, in _call_with_config
context.run(
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/runnables/config.py", line 394, in call_func_with_variable_args
return func(input, **kwargs) # type: ignore[call-arg]
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/output_parsers/base.py", line 193, in <lambda>
lambda inner_input: self.parse_result(
File "/Users/miyamo/miniconda3/envs/mofa/lib/python3.10/site-packages/langchain_core/output_parsers/json.py", line 86, in parse_result
raise OutputParserException(msg, llm_output=text) from e
langchain_core.exceptions.OutputParserException: Invalid json output: ```json
{
"answer": null,
"question": {
"title": "Finding the first positive number in the array",
"url": "/questions/74095793/finding-the-first-positive-number-in-the-array"
},
"relatedQuestions": [
{
"title": "Question score (upvotes - downvotes)",
"url": "/questions/74095793/finding-the-first-positive-number-in-the-array"
},
{
"title": "Question score (upvotes - downvotes)",
"url": "/questions/21225399/first-non-negative-element-in-an-array"
},
// ... Other related questions...
]
}
```
**To Reproduce**
source code
```python
"""
Example of Search Graph
"""
import os
from dotenv import load_dotenv
from scrapegraphai.graphs import SearchGraph
from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
# ************************************************
# Define the configuration for the graph
# ************************************************
load_dotenv()
groq_key = os.getenv("GROQ_APIKEY")
graph_config = {
"llm": {
"model": "groq/gemma-7b-it",
"api_key": "",
"temperature": 0
},
"headless": False
}
# ************************************************
# Create the SearchGraph instance and run it
# ************************************************
search_graph = SearchGraph(
prompt="give me the first positive number",
config=graph_config
)
result = search_graph.run()
print(result)
```
**Desktop :**
- OS: MacOS
|
closed
|
2024-10-09T03:22:32Z
|
2024-10-10T08:33:55Z
|
https://github.com/ScrapeGraphAI/Scrapegraph-ai/issues/733
|
[] |
Gege-Wang
| 3
|
apify/crawlee-python
|
automation
| 427
|
Add support for `preNavigationHooks` in Playwright
|
As the title, I want to know does the crawlee-python support the `preNavigationHooks` function?
|
closed
|
2024-08-13T11:42:22Z
|
2024-10-31T08:39:42Z
|
https://github.com/apify/crawlee-python/issues/427
|
[
"enhancement",
"t-tooling",
"hacktoberfest"
] |
viewv
| 8
|
3b1b/manim
|
python
| 1,463
|
Chained Animations not working
|
I was using the CoordinateSystemExample class from the examples page. The last animations (rescaling and movement) were not running in parallel. Only the first animation was rendered.
I'm running manim on arch linux in python 3.9.
|
closed
|
2021-04-07T21:43:58Z
|
2021-04-08T21:21:41Z
|
https://github.com/3b1b/manim/issues/1463
|
[
"bug"
] |
mortimervonchappuis
| 1
|
modin-project/modin
|
pandas
| 6,710
|
Don't materialize index in `_groupby_shuffle` internal function
|
closed
|
2023-11-05T16:32:09Z
|
2023-11-07T09:47:05Z
|
https://github.com/modin-project/modin/issues/6710
|
[
"Performance 🚀"
] |
anmyachev
| 0
|
|
deeppavlov/DeepPavlov
|
nlp
| 789
|
Answers broken
|
|
closed
|
2019-04-04T15:00:44Z
|
2019-09-24T19:22:44Z
|
https://github.com/deeppavlov/DeepPavlov/issues/789
|
[] |
alexriabtsev
| 2
|
Morizeyao/GPT2-Chinese
|
nlp
| 195
|
同样的代码，在win笔记本可以train，在linux台式机下显示killed
|
代码完全一样，复制到google colab运行一会儿之后，不显示loss，然后出现一个^C就结束了。
我就把代码复制到linux台式机上去跑，结果能看到2行step和loss，然后就直接显示Killed，可能是什么原因？
|
closed
|
2021-02-28T22:26:39Z
|
2021-06-08T05:21:36Z
|
https://github.com/Morizeyao/GPT2-Chinese/issues/195
|
[] |
libralibra
| 1
|
automagica/automagica
|
automation
| 120
|
read_cell_formula() is not working
|
When I try below read_cell_formula() example in Automagica Documentation Release 2(Apr 02, 2020).
>excel = Excel()
> excel.write_cell_formula(1, 1, '=1+1')
> excel.read_cell_formula(1, 1)
'=1+1'
error message is below :
File "C:\automagica\utilities.py", line 17, in wrapper
return func(*args, **kwargs)
TypeError: read_cell_formula() missing 1 required positional argument: 'formula'
|
closed
|
2020-04-13T03:44:24Z
|
2020-09-07T21:37:29Z
|
https://github.com/automagica/automagica/issues/120
|
[] |
taesikkim
| 1
|
mljar/mljar-supervised
|
scikit-learn
| 124
|
Issues during installation and with module shap
|
I tried to install but already get an error during the installation process:
I didn't see, that Visual Studio is a requirement for "Mljar-Supervised". After having downloaded "Visual Studio" I tried to use your example "binary_classifier.py" but it says, that the "module shap is missing" even if it is there in the correct folder:
And when I have a look into "shap.py" I see another "import shap" there (I don't know if this causes the error):
|
closed
|
2020-07-17T14:07:20Z
|
2020-10-26T17:42:28Z
|
https://github.com/mljar/mljar-supervised/issues/124
|
[
"installation"
] |
AndreasTraut
| 2
|
SciTools/cartopy
|
matplotlib
| 2,251
|
Mask A Contour With Coastlines
|
Hello,
I am in the process from switching from `matplotlib.basemap` to cartopy and the standout feature that is missing is the `maskoceans` / `maskland` feature set.
I see that there are `ax.addFeature(LAND/OCEAN...)` however this completely ruins the nice basemap I have included.
The data is already there, so how do we make a mask out of it. I understand that projection transforms are somewhat complicated but it should be doable ultimately.
Example of the map I am trying to make.
Several requests for similar features, the only way to do this would be to plot twice and use PIL to mask the image from the LAND data
https://stackoverflow.com/questions/72078262/how-to-mask-data-that-appears-in-the-ocean-using-cartopy-and-matplotlib
|
open
|
2023-09-27T18:49:40Z
|
2023-10-02T21:40:15Z
|
https://github.com/SciTools/cartopy/issues/2251
|
[] |
SoundsSerious
| 3
|
AutoGPTQ/AutoGPTQ
|
nlp
| 606
|
[BUG] TypeError: LlamaRotaryEmbedding.forward() got an unexpected keyword argument 'seq_len'
|
**Describe the bug**
Encounter this when running genreration_speed.py. Input model is Llama-2-7b-chat-gptq. Complete error log:
Traceback (most recent call last):
File "AutoGPTQ/examples/benchmark/generation_speed.py", line 326, in <module>
main()
File "AutoGPTQ/examples/benchmark/generation_speed.py", line 316, in main
benchmark_generation_speed(model, tokenizer, examples, generation_config)
File "AutoGPTQ/examples/benchmark/generation_speed.py", line 197, in benchmark_generation_speed
outputs_ids = model.generate(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/auto_gptq/modeling/_base.py", line 532, in generate
return self.model.generate(**kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/transformers/generation/utils.py", line 1527, in generate
result = self._greedy_search(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/transformers/generation/utils.py", line 2411, in _greedy_search
outputs = self(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 1196, in forward
outputs = self.model(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 1016, in forward
layer_outputs = decoder_layer(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/transformers/models/llama/modeling_llama.py", line 739, in forward
hidden_states, self_attn_weights, present_key_value = self.self_attn(
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/auto_gptq/nn_modules/fused_llama_attn.py", line 76, in forward
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1511, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1520, in _call_impl
return forward_call(*args, **kwargs)
File "venv/auto-gptq-cuda/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
TypeError: LlamaRotaryEmbedding.forward() got an unexpected keyword argument 'seq_len'
**Hardware details**
Information about CPU and GPU, such as RAM, number, etc.
Nvidia V100
**Software version**
Version of relevant software such as operation system, cuda toolkit, python, auto-gptq, pytorch, transformers, accelerate, etc.
CUDA: 12.3 , Pytorch: 2.2.1, transformers: 4.39.1, accelerate: 0.28.0
|
open
|
2024-03-25T08:59:52Z
|
2024-03-29T20:40:34Z
|
https://github.com/AutoGPTQ/AutoGPTQ/issues/606
|
[
"bug"
] |
timefliesfang
| 3
|
ray-project/ray
|
python
| 51,277
|
[core][gpu-objects] Overlap compute / communication of CUDA streams
|
### Description
as title
### Use case
_No response_
|
open
|
2025-03-11T22:44:30Z
|
2025-03-11T22:44:50Z
|
https://github.com/ray-project/ray/issues/51277
|
[
"enhancement",
"P2",
"core",
"gpu-objects"
] |
kevin85421
| 0
|
CatchTheTornado/text-extract-api
|
api
| 71
|
[feat] research minerU + pdf-extract-api
|
Let's check if it's somehow useful for us to add an extraction integration with the https://github.com/opendatalab/MinerU/tree/master/demo
By the way I really like their pipelines architecture which makes multistage extraction and parsing more scalable
|
open
|
2025-01-11T09:30:11Z
|
2025-01-19T16:55:22Z
|
https://github.com/CatchTheTornado/text-extract-api/issues/71
|
[] |
pkarw
| 0
|
SALib/SALib
|
numpy
| 506
|
How to handle integer parameter？
|
What I want to do is building energy consumption analysis, some variables are integer types (such as weather file 1, weather file 2), how can I get integer parameters when sampling？
|
closed
|
2022-04-28T08:16:48Z
|
2022-04-29T10:05:52Z
|
https://github.com/SALib/SALib/issues/506
|
[] |
FOANFAN
| 3
|
CorentinJ/Real-Time-Voice-Cloning
|
tensorflow
| 490
|
How do I train a model with my own data? Where can I find the instruction?
|
How do I train a model with my own data? Where can I find the instructions on how to do it?
Need help
|
closed
|
2020-08-13T12:58:53Z
|
2020-08-25T23:29:24Z
|
https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/490
|
[] |
justinjohn0306
| 4
|
mars-project/mars
|
pandas
| 3,039
|
Support get_chunk_meta in RayExecutionContext
|
Currently `RayExecutionContext.get_chunk_meta` is not supported, which will make any operands relied on this API failed on tiling, such as when call `DataFrame.groupby`:
```
df = md.DataFrame(mt.random.rand(300, 4, chunk_size=100), columns=list("abcd"))
df["a"], df["b"] = (df["a"] * 5).astype(int), (df["b"] * 2).astype(int)
df.groupby(["a", "b"]).apply(lambda pdf: pdf.sum()).execute()
```
Will got following error:
```
================================================================================== FAILURES ==================================================================================
________________________________________________________________________________ test_shuffle ________________________________________________________________________________
ray_start_regular_shared2 = RayContext(dashboard_url='127.0.0.1:8265', python_version='3.8.2', ray_version='1.12.0', ray_commit='f18fc31c756299095...127.0.0.1:55710', 'address': '127.0.0.1:55710', 'node_id': '38787319e06bc89f95d7600524069ed4dfba256068c917c261fe697f'})
create_cluster = (<mars.deploy.oscar.local.LocalClient object at 0x7fb22aaf38b0>, {})
@require_ray
@pytest.mark.asyncio
async def test_shuffle(ray_start_regular_shared2, create_cluster):
df = md.DataFrame(mt.random.rand(300, 4, chunk_size=100), columns=list("abcd"))
# `describe` contains multiple shuffle.
df.describe().execute()
arr = np.random.RandomState(0).rand(31, 27)
t1 = mt.tensor(arr, chunk_size=10).reshape(27, 31)
t1.op.extra_params["_reshape_with_shuffle"] = True
np.testing.assert_almost_equal(arr.reshape(27, 31), t1.to_numpy())
np.testing.assert_equal(mt.bincount(mt.arange(5, 10)).to_numpy(), np.bincount(np.arange(5, 10)))
# `RayExecutionContext.get_chunk_meta` not supported, skip dataframe.groupby
df["a"], df["b"] = (df["a"] * 5).astype(int), (df["b"] * 2).astype(int)
> df.groupby(["a", "b"]).apply(lambda pdf: pdf.sum()).execute()
mars/deploy/oscar/tests/test_ray_dag.py:147:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
mars/core/entity/tileables.py:462: in execute
result = self.data.execute(session=session, **kw)
mars/core/entity/executable.py:144: in execute
return execute(self, session=session, **kw)
mars/deploy/oscar/session.py:1855: in execute
return session.execute(
mars/deploy/oscar/session.py:1649: in execute
execution_info: ExecutionInfo = fut.result(
../../../../../opt/anaconda3/envs/mars-py3.8-dev/lib/python3.8/concurrent/futures/_base.py:439: in result
return self.__get_result()
../../../../../opt/anaconda3/envs/mars-py3.8-dev/lib/python3.8/concurrent/futures/_base.py:388: in __get_result
raise self._exception
mars/deploy/oscar/session.py:1835: in _execute
await execution_info
mars/deploy/oscar/session.py:105: in wait
return await self._aio_task
mars/deploy/oscar/session.py:953: in _run_in_background
raise task_result.error.with_traceback(task_result.traceback)
mars/services/task/supervisor/processor.py:364: in run
async for stage_args in self._iter_stage_chunk_graph():
mars/services/task/supervisor/processor.py:158: in _iter_stage_chunk_graph
chunk_graph = await self._get_next_chunk_graph(chunk_graph_iter)
mars/services/task/supervisor/processor.py:149: in _get_next_chunk_graph
chunk_graph = await fut
mars/lib/aio/_threads.py:36: in to_thread
return await loop.run_in_executor(None, func_call)
../../../../../opt/anaconda3/envs/mars-py3.8-dev/lib/python3.8/concurrent/futures/thread.py:57: in run
result = self.fn(*self.args, **self.kwargs)
mars/services/task/supervisor/processor.py:144: in next_chunk_graph
return next(chunk_graph_iter)
mars/services/task/supervisor/preprocessor.py:194: in tile
for chunk_graph in chunk_graph_builder.build():
mars/core/graph/builder/chunk.py:440: in build
yield from self._build()
mars/core/graph/builder/chunk.py:434: in _build
graph = next(tile_iterator)
mars/services/task/supervisor/preprocessor.py:74: in _iter_without_check
to_update_tileables = self._iter()
mars/core/graph/builder/chunk.py:317: in _iter
self._tile(
mars/core/graph/builder/chunk.py:211: in _tile
need_process = next(tile_handler)
mars/core/graph/builder/chunk.py:183: in _tile_handler
tiled_tileables = yield from handler.tile(tiled_tileables)
mars/core/entity/tileables.py:79: in tile
tiled_result = yield from tile_handler(op)
mars/dataframe/groupby/apply.py:151: in tile
return [auto_merge_chunks(get_context(), ret)]
mars/dataframe/utils.py:1333: in auto_merge_chunks
metas = ctx.get_chunks_meta(
mars/services/context.py:188: in get_chunks_meta
return self._call(self._get_chunks_meta(data_keys, fields=fields, error=error))
mars/services/context.py:84: in _call
return fut.result()
../../../../../opt/anaconda3/envs/mars-py3.8-dev/lib/python3.8/concurrent/futures/_base.py:439: in result
return self.__get_result()
../../../../../opt/anaconda3/envs/mars-py3.8-dev/lib/python3.8/concurrent/futures/_base.py:388: in __get_result
raise self._exception
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
self = <mars.services.task.execution.ray.context.RayExecutionContext object at 0x7fb22b3485e0>
data_keys = ['9f92dcd8196d32f25e43e33ba1f56e02_0', '223590f1093c414359f466c42a698006_0', 'dc80798f45b8ed8bb358a7b39b6d8170_0'], fields = ['memory_size'], error = 'ignore'
async def _get_chunks_meta(
self, data_keys: List[str], fields: List[str] = None, error: str = "raise"
) -> List[Dict]:
# get chunks meta
get_metas = []
for data_key in data_keys:
meta = self._meta_api.get_chunk_meta.delay(
data_key, fields=["bands"], error=error
)
get_metas.append(meta)
metas = await self._meta_api.get_chunk_meta.batch(*get_metas)
api_to_keys_calls = defaultdict(lambda: (list(), list()))
for data_key, meta in zip(data_keys, metas):
> addr = meta["bands"][0][0]
E TypeError: 'NoneType' object is not subscriptable
mars/services/context.py:145: TypeError
```
We need to support get_chunk_meta for ray task backend.
|
open
|
2022-05-17T03:57:35Z
|
2022-05-17T05:58:26Z
|
https://github.com/mars-project/mars/issues/3039
|
[] |
chaokunyang
| 0
|
alteryx/featuretools
|
scikit-learn
| 2,017
|
release Featuretools v1.9.0
|
closed
|
2022-04-13T14:51:25Z
|
2022-04-27T17:09:14Z
|
https://github.com/alteryx/featuretools/issues/2017
|
[] |
gsheni
| 0
|
|
sebp/scikit-survival
|
scikit-learn
| 489
|
Documentation: clarification of the estimate argument for each metric
|
**Problem**
I spent a day trying to understand the `cumulative_dynamic_auc` function because I was consistently getting values in the 0.15-0.3 range. After experimenting, I realized that for the `estimate` argument, I was using the output of `predict_survival_function` (in a `RandomSurvivalForest` model) instead of `predict`, and switching this fixed the issue and gave me reasonable AUCs.
**Proposed solution**
The documentation for `cumulative_dynamic_auc` is not very descriptive on what kind of outputted predictions (e.g., probabilities, estimated times-to-event) the `estimate` argument should be, and I think this could be clearer for all metrics.
|
open
|
2024-10-29T14:21:21Z
|
2024-11-02T07:38:41Z
|
https://github.com/sebp/scikit-survival/issues/489
|
[
"documentation"
] |
RandallJEllis
| 1
|
browser-use/browser-use
|
python
| 814
|
Ollama - Does not support tools (status code: 400) Issue
|
### Bug Description
I am new here but i am facing this issue. When I am trying to use browse-use with Ollama but i am getting "does not support tools error". Intrestingly my code is working if i use Deepeek but not when using qwen, phi, llama
### Reproduction Steps
1. Use Ollama with either of these models - qwen:14b, phi4, llama3.1, deepseek-coder:6.7b
### Code Sample
```python
llm=ChatOllama(model="qwen:14b", num_ctx=32000)
async def main():
agent = Agent( task=task, llm=llm , controller=controller, system_prompt_class=MySystemPrompt)
result = await agent.run()
print(result)
# Access (some) useful information
result.urls() # List of visited URLs
result.screenshots() # List of screenshot paths
result.action_names() # Names of executed actions
result.extracted_content() # Content extracted during execution
result.errors() # Any errors that occurred
result.model_actions() # All actions with their parameters
asyncio.run(main())
```
### Version
0.1.37
### LLM Model
Other (specify in description)
### Operating System
macOS 14.7.2
### Relevant Log Output
```shell
INFO [agent] 📍 Step 1
ERROR [agent] ❌ Result failed 1/3 times:
qwen:14b does not support tools (status code: 400)
INFO [agent] 📍 Step 1
ERROR [agent] ❌ Result failed 2/3 times:
qwen:14b does not support tools (status code: 400)
INFO [agent] 📍 Step 1
ERROR [agent] ❌ Result failed 3/3 times:
qwen:14b does not support tools (status code: 400)
ERROR [agent] ❌ Stopping due to 3 consecutive failures
INFO [agent] Created GIF at agent_history.gif
```
|
open
|
2025-02-22T06:09:02Z
|
2025-03-05T04:39:59Z
|
https://github.com/browser-use/browser-use/issues/814
|
[
"bug"
] |
ArpitSureka
| 6
|
microsoft/unilm
|
nlp
| 931
|
ViT-S for BeiT v2
|
Hi!
Thank you for this great repo.
In the Table 4 of your paper you show ablation studies with a ViT-S (Small & 1x384x6).
Is it possible to have access to those pretrained weights or to have the command to reproduce this results?
Thanks!
Elias
|
open
|
2022-11-25T14:46:09Z
|
2022-11-28T13:03:13Z
|
https://github.com/microsoft/unilm/issues/931
|
[] |
elias-ramzi
| 2
|
pallets-eco/flask-sqlalchemy
|
flask
| 585
|
Dynamically creating Tables
|
I'd like to dynamically creating tables for a user when the user signs up , but could not find any resources for it.
I tried using SQLAlchemys declarative_base() function , but could not get it to work.
Would really appreciate the help.
Also Thanks for the awesome work!
|
closed
|
2018-01-13T16:12:26Z
|
2020-12-05T20:46:34Z
|
https://github.com/pallets-eco/flask-sqlalchemy/issues/585
|
[] |
highoncarbs
| 1
|
huggingface/datasets
|
machine-learning
| 6,788
|
A Question About the Map Function
|
### Describe the bug
Hello,
I have a question regarding the map function in the Hugging Face datasets.
The situation is as follows: when I load a jsonl file using load_dataset(..., streaming=False), and then utilize the map function to process it, I specify that the returned example should be of type Torch.tensor. However, I noticed that after applying the map function, the datatype automatically changes to List, which leads to errors in my program.
I attempted to use load_dataset(..., streaming=True), and this issue no longer occurs. I'm not entirely clear on why this happens. Could you please provide some insights into this?
### Steps to reproduce the bug
1.dataset = load_dataset(xxx, streaming = False)
2. dataset.map(function), function will return torch.Tensor.
3. you will find the format of data in dataset is List.
### Expected behavior
I expected to receieve the format of data is torch.Tensor.
### Environment info
2.18.0
|
closed
|
2024-04-06T11:45:23Z
|
2024-04-11T05:29:35Z
|
https://github.com/huggingface/datasets/issues/6788
|
[] |
Klein-Lan
| 2
|
dask/dask
|
scikit-learn
| 11,802
|
assertionerror when trying to compute reversed cumulative sum
|
<!-- Please include a self-contained copy-pastable example that generates the issue if possible.
Please be concise with code posted. See guidelines below on how to provide a good bug report:
- Craft Minimal Bug Reports http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples https://stackoverflow.com/help/mcve
Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->
**Describe the issue**:
**Minimal Complete Verifiable Example**:
```python
import pandas as pd
import dask.dataframe as dd
df = dd.from_pandas(pd.DataFrame({'a': [1,2,3], 'b': [4,5,6]}), npartitions=2)
df['a'][::-1].cumsum()
```
throws
```
In [7]: df['a'][::-1].cumsum().compute()
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
Cell In[7], line 1
----> 1 df['a'][::-1].cumsum().compute()
File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/_collection.py:4134, in Series.__getitem__(self, key)
4132 if isinstance(key, Series) or self.npartitions == 1:
4133 return super().__getitem__(key)
-> 4134 return self.loc[key]
File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/_indexing.py:84, in LocIndexer.__getitem__(self, key)
81 if isinstance(cindexer, np.generic):
82 cindexer = cindexer.item()
---> 84 return self._loc(iindexer, cindexer)
File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/_indexing.py:103, in LocIndexer._loc(self, iindexer, cindexer)
100 iindexer = self._maybe_partial_time_string(iindexer, unit=unit)
102 if isinstance(iindexer, slice):
--> 103 return self._loc_slice(iindexer, cindexer)
104 elif is_series_like(iindexer) and not is_bool_dtype(iindexer.dtype):
105 return new_collection(LocList(self.obj, iindexer.values, cindexer))
File ~/polars-api-compat-dev/.venv/lib/python3.12/site-packages/dask/dataframe/dask_expr/_indexing.py:157, in LocIndexer._loc_slice(self, iindexer, cindexer)
155 def _loc_slice(self, iindexer, cindexer):
156 assert isinstance(iindexer, slice)
--> 157 assert iindexer.step in (None, 1)
158 return new_collection(LocSlice(self.obj, iindexer, cindexer))
AssertionError:
```
**Anything else we need to know?**:
**Environment**:
- Dask version: 2025.2.0
- Python version: 3.12
- Operating System: linux
- Install method (conda, pip, source): pip
|
closed
|
2025-03-03T15:08:53Z
|
2025-03-04T12:01:02Z
|
https://github.com/dask/dask/issues/11802
|
[
"needs triage"
] |
MarcoGorelli
| 1
|
vllm-project/vllm
|
pytorch
| 14,446
|
[Usage]: After starting the QwQ-32B model normally, it was found that the model could not output the thought tag normally
|
### Your current environment
INFO 03-08 00:00:39 __init__.py:190] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: CentOS Linux release 7.9.2009 (Core) (x86_64)
GCC version: (GCC) 11.2.0
Clang version: Could not collect
CMake version: version 3.31.5
Libc version: glibc-2.17
Python version: 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.92.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A10
GPU 1: NVIDIA A10
GPU 2: NVIDIA A10
GPU 3: NVIDIA A10
GPU 4: NVIDIA A10
GPU 5: NVIDIA A10
GPU 6: NVIDIA A10
GPU 7: NVIDIA A10
Nvidia driver version: 550.127.08
cuDNN version: Probably one of the following:
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_adv.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_cnn.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_graph.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_ops.so.9.2.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
Stepping: 6
CPU MHz: 3499.859
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5800.00
Virtualization: VT-x
L1d cache: 48K
L1i cache: 32K
L2 cache: 1280K
L3 cache: 49152K
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 invpcid_single ssbd mba rsb_ctxsw ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq md_clear pconfig spec_ctrl intel_stibp flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.49.0
[pip3] triton==3.1.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-ml-py 12.570.86 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] pyzmq 26.2.1 pypi_0 pypi
[conda] torch 2.5.1 pypi_0 pypi
[conda] torchaudio 2.5.1 pypi_0 pypi
[conda] torchvision 0.20.1 pypi_0 pypi
[conda] transformers 4.49.0 pypi_0 pypi
[conda] triton 3.1.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU1 PIX X NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU2 NODE NODE X PIX SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU3 NODE NODE PIX X SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU4 SYS SYS SYS SYS X PIX NODE NODE 32-63,96-127 1 N/A
GPU5 SYS SYS SYS SYS PIX X NODE NODE 32-63,96-127 1 N/A
GPU6 SYS SYS SYS SYS NODE NODE X PIX 32-63,96-127 1 N/A
GPU7 SYS SYS SYS SYS NODE NODE PIX X 32-63,96-127 1 N/A
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
CUDA_PATH=/usr/local/cuda-12.4
LD_LIBRARY_PATH=/root/anaconda3/envs/vllm/lib/python3.12/site-packages/cv2/../../lib64:/usr/local/cuda/lib64:/usr/local/lib64:/usr/local/cuda-12.4/lib64:
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
### 🐛 Describe the bug
The call example is as follows, the model normally replies to the content, you can see that there is thinking content, but there is no thinking label
I don't know if this is a feature of the model or a vllm problem, but I remember wrapping it with the <think> tag
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.
|
open
|
2025-03-07T16:05:25Z
|
2025-03-10T08:23:34Z
|
https://github.com/vllm-project/vllm/issues/14446
|
[
"usage"
] |
shatang123
| 8
|
tensorpack/tensorpack
|
tensorflow
| 569
|
AttributeError: 'PrefetchDataZMQ' object has no attribute 'socket'
|
when i'm using dataflow.dataloader, i got this exception.
By scanning the source code in prefetch.py, i find that **reset_state** method is **not called** in the construction function of PrefetchDataZMQ, but the 'socket' property is set in the _reset_once function which is not called.
I think reset_state can be called when initializing PrefetchDataZMQ, that will be more convenient.
|
closed
|
2017-12-25T03:21:47Z
|
2018-05-30T20:59:30Z
|
https://github.com/tensorpack/tensorpack/issues/569
|
[
"usage"
] |
youkaichao
| 2
|
neuml/txtai
|
nlp
| 258
|
Unit tests failing with transformers==4.18.0
|
CLIP models through sentence_transformers are failing to load. Issue https://github.com/UKPLab/sentence-transformers/issues/1515 has been filed with sentence_transformers to determine where the issue is coming from.
|
closed
|
2022-04-09T13:05:58Z
|
2022-04-13T14:58:03Z
|
https://github.com/neuml/txtai/issues/258
|
[
"bug"
] |
davidmezzetti
| 0
|
ageitgey/face_recognition
|
python
| 1,082
|
AttributeError: 'Image' object has no attribute 'read' in Google Colab
|
* face_recognition version:
* Python version: 3.0
* Operating System: Google Colab
### Description
I am trying to implement the library in Google Colab.
Here is my code so far
#pip install dependencies
!pip install face_recognition
!pip install os
!pip install cv2
import face_recognition
import os
import cv2
#Load the Drive helper and mount
from google.colab import drive
#This will prompt for authorization.
drive.mount('/content/drive')
#After executing the cell above, Drive
#files will be present in "/content/drive/My Drive".
!ls "/content/drive/My Drive"
!ls "/content/drive/My Drive/faces/unknown"
#'sigrid.jpeg' is in unknown
from IPython.display import Image
from IPython.display import display
Embed = Image('sigrid.jpeg')
Embed
#Here the image doesn't fully show in the notebook
import face_recognition
image = face_recognition.load_image_file(Embed)
face_locations = face_recognition.face_locations(image)
#Then I get this error
AttributeError: 'Image' object has no attribute 'read'
Can anyone please tell me how to solve this? Thanks.
|
closed
|
2020-03-10T02:08:47Z
|
2020-03-25T22:36:49Z
|
https://github.com/ageitgey/face_recognition/issues/1082
|
[] |
aanis
| 2
|
freqtrade/freqtrade
|
python
| 10,788
|
How do I get my strategy to place sell trades with trading_mode spot?
|
<!--
Have you searched for similar issues before posting it? Yes
Did you have a VERY good look at the [documentation](https://www.freqtrade.io/en/latest/) and are sure that the question is not explained there Yes
Please do not use the question template to report bugs or to request new features.
-->
## Describe your environment
* Operating system: Docker (Mac OS 15.0.1)
* Python Version: Python 3.12.5 (`python -V`)
* CCXT version: ccxt==4.3.88 (`pip freeze | grep ccxt`)
* Freqtrade Version: freqtrade 2024.8 (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
## Your question
I am building a strategy based off the sample strategy. I see within the `populate_entry_trend` method
"enter_long" & "enter_short"
I am using Binance and Binance allows me to "buy" and "sell" on the spot market
My strategy places "buy" trades just fine, but it does not place "sell" trades.
I do not want to use futures and short trades, I simply want to place sell trades. My trading mode in the config is `"trading_mode": "spot",`
In the strategy Bandtastic I see `populate_buy_trend` and `populate_sell_trend` methods. Is this the way to do it?
|
closed
|
2024-10-14T02:35:29Z
|
2024-10-15T00:48:20Z
|
https://github.com/freqtrade/freqtrade/issues/10788
|
[
"Question"
] |
WinstonN
| 4
|
deepspeedai/DeepSpeed
|
deep-learning
| 6,618
|
[BUG] Long sequence parallelism (Ulysses) got error list index out of range
|
**Describe the bug**
test ulysses got error list index out of range
**To Reproduce**
test ulysses with [test_ulysses.py](https://github.com/microsoft/DeepSpeedExamples/blob/uly-hf/post_training/sequence_parallelism/test_ulysses.py)
torchrun --nproc_per_node=8 test_ulysses.py
**Expected behavior**
work fine
**ds_report output**
```
collect2: error: ld returned 1 exit status
gds .................... [NO] ....... [NO]
transformer_inference .. [NO] ....... [OKAY]
inference_core_ops ..... [NO] ....... [OKAY]
cutlass_ops ............ [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
ragged_device_ops ...... [NO] ....... [OKAY]
ragged_ops ............. [NO] ....... [OKAY]
random_ltd ............. [NO] ....... [OKAY]
[WARNING] sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING] using untested triton version (2.3.0), only 1.0.0 is known to be compatible
sparse_attn ............ [NO] ....... [NO]
spatial_inference ...... [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/opt/conda/lib/python3.10/site-packages/torch']
torch version .................... 2.3.0+cu121
deepspeed install path ........... ['/opt/conda/lib/python3.10/site-packages/deepspeed']
deepspeed info ................... 0.15.2, unknown, unknown
torch cuda version ............... 12.1
torch hip version ................ None
nvcc version ..................... 12.1
deepspeed wheel compiled w. ...... torch 2.3, cuda 12.1
shared memory (/dev/shm) size .... 100.00 GB
```
**Screenshots**
got error: list index out of range
```
[rank6]: Traceback (most recent call last):
[rank6]: File "/data1/nfs15/nfs/zhanglei335/mlsys/train/long-context-train/llm-train/uly_sp_test.py", line 166, in <module>
[rank6]: get_loss(model, data_loader, DS_CONFIG)
[rank6]: File "/data1/nfs15/nfs/zhanglei335/mlsys/train/long-context-train/llm-train/uly_sp_test.py", line 112, in get_loss
[rank6]: model, _, _, _ = deepspeed.initialize(model=model,
[rank6]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/__init__.py", line 193, in initialize
[rank6]: engine = DeepSpeedEngine(args=args,
[rank6]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 269, in __init__
[rank6]: self._configure_distributed_model(model)
[rank6]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1188, in _configure_distributed_model
[rank6]: self.data_parallel_group = groups._get_data_parallel_group()
[rank6]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/utils/groups.py", line 405, in _get_data_parallel_group
[rank6]: return mesh_device.get_group(mesh_dim="data_parallel")
[rank6]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/device_mesh.py", line 423, in get_group
[rank6]: _find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2])
[rank6]: IndexError: list index out of range
[rank7]: Traceback (most recent call last):
[rank7]: File "/data1/nfs15/nfs/zhanglei335/mlsys/train/long-context-train/llm-train/uly_sp_test.py", line 166, in <module>
[rank7]: get_loss(model, data_loader, DS_CONFIG)
[rank7]: File "/data1/nfs15/nfs/zhanglei335/mlsys/train/long-context-train/llm-train/uly_sp_test.py", line 112, in get_loss
[rank7]: model, _, _, _ = deepspeed.initialize(model=model,
[rank7]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/__init__.py", line 193, in initialize
[rank7]: engine = DeepSpeedEngine(args=args,
[rank7]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 269, in __init__
[rank7]: self._configure_distributed_model(model)
[rank7]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 1188, in _configure_distributed_model
[rank7]: self.data_parallel_group = groups._get_data_parallel_group()
[rank7]: File "/opt/conda/lib/python3.10/site-packages/deepspeed/utils/groups.py", line 405, in _get_data_parallel_group
[rank7]: return mesh_device.get_group(mesh_dim="data_parallel")
[rank7]: File "/opt/conda/lib/python3.10/site-packages/torch/distributed/device_mesh.py", line 423, in get_group
[rank7]: _find_pg_by_ranks_and_tag(*self._dim_group_infos[mesh_dim][:2])
[rank7]: IndexError: list index out of range
```
**Launcher context**
Are you launching your experiment with the `deepspeed` launcher, MPI, or something else?
|
closed
|
2024-10-10T03:41:30Z
|
2024-10-14T02:21:57Z
|
https://github.com/deepspeedai/DeepSpeed/issues/6618
|
[
"bug",
"training"
] |
Lzhang-hub
| 2
|
NVlabs/neuralangelo
|
computer-vision
| 218
|
Lego Sequence Video link not working
|
The Lego Sequence Video link not working, it is asking to request access but its been many days the access is not given yet
|
open
|
2025-02-12T16:56:24Z
|
2025-02-12T16:56:24Z
|
https://github.com/NVlabs/neuralangelo/issues/218
|
[] |
Mandar800
| 0
|
gradio-app/gradio
|
data-visualization
| 10,713
|
The progress bar still exists when the backend raise an error
|
### Describe the bug
In the previous version (5.12.0), when an error was raised, the progress bar would immediately disappear and display a closable alert.
gradio 5.20.0
gradio 5.12.0
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
def submit(input_value):
raise gr.Error("test")
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
input = gr.Textbox()
input.submit(submit, inputs=input, outputs=[input, chatbot])
demo.launch()
```
### Screenshot
_No response_
### Logs
```shell
```
### System Info
```shell
gradio version: 5.20.0
gradio_client version: 1.7.2
```
### Severity
Blocking usage of gradio
|
closed
|
2025-03-03T11:40:38Z
|
2025-03-04T22:17:14Z
|
https://github.com/gradio-app/gradio/issues/10713
|
[
"bug",
"Regression"
] |
Col0ring
| 1
|
remorses/mongoke
|
graphql
| 4
|
[Improvement] Better plurals
|
This is just an improvement I thought about while demoing on a live stream today. But we had a type called `Activity` and Mongoke made it `Activitys` which is a wrong plural, should be "Activities"
|
closed
|
2020-04-10T02:16:17Z
|
2020-04-11T00:58:49Z
|
https://github.com/remorses/mongoke/issues/4
|
[] |
khaosdoctor
| 3
|
mljar/mljar-supervised
|
scikit-learn
| 398
|
Hard coded `n_jobs` during computing importance
|
Dear @pplonski ,
I observe significant overload during training despite setting or not setting the n_jobs flag.
I am not absolutely sure why this happens, but is there a chance that n_jobs is passed both to CV and the algorithms, i.e. doubling the effective number of available cores?
|
closed
|
2021-05-19T06:40:27Z
|
2021-06-08T10:27:38Z
|
https://github.com/mljar/mljar-supervised/issues/398
|
[
"bug"
] |
BeZie
| 5
|
docarray/docarray
|
pydantic
| 1,409
|
docs: Small fixes to README
|
Light copy-editing of README.md.
|
closed
|
2023-04-18T07:32:49Z
|
2023-04-18T12:51:17Z
|
https://github.com/docarray/docarray/issues/1409
|
[] |
scott-martens
| 0
|
mirumee/ariadne-codegen
|
graphql
| 67
|
Plugin system
|
We could have a plugin system for code generator where plugin would be a class instance given with a method called with at least one argument: Python AST from codegen or previous plugin
For example:
```python
class MyPlugin:
def __init__(self, settings, schema_ast, query_ast):
...
def generate_enum(self, enum_ast):
# Do something with enum_ast or return it as it is
...
```
|
closed
|
2023-02-01T19:53:28Z
|
2023-03-16T07:55:37Z
|
https://github.com/mirumee/ariadne-codegen/issues/67
|
[
"roadmap"
] |
rafalp
| 0
|
fastapi/sqlmodel
|
sqlalchemy
| 44
|
My mobile phones are hacked and duplicated, I need help please
|
closed
|
2021-08-27T17:31:29Z
|
2022-08-30T16:38:56Z
|
https://github.com/fastapi/sqlmodel/issues/44
|
[] |
Joshuagriffin9
| 1
|
|
psf/black
|
python
| 3,711
|
Decouple using .gitignore from `exclude`
|
**Is your feature request related to a problem? Please describe.**
This is the current behaviour for handling `.gitignore`:
> https://black.readthedocs.io/en/stable/usage_and_configuration/file_collection_and_discovery.html#gitignore
>
> If --exclude is not set, Black will automatically ignore files and directories in .gitignore file(s), if present.
>
> If you want Black to continue using .gitignore while also configuring the exclusion rules, please use --extend-exclude.
However, my case does not fill in those two scenarios.
- Our project is a build system, and one of its subpackages is named `build`.
- The directory named `build` is listed in `DEFAULT_EXCLUDES`:
https://github.com/psf/black/blob/a4032dce645b83e1faccc7274864869ddfe279fc/src/black/const.py#L2
- Black does not have an option to remove items from the default list, so my only alternative to workaround this is redefining the `exclude` option/setting. By doing so, black stops honouring my `.gitignore` file 😭
**Alternatives I've tried**
- `exclude` has precedence over `include`, so doing `--include '/build/|\.pyi?$'` does not work.
- The only way I could make it work was additionally informing the full path for the Python files inside the `build` directories: `black . foo/cli/build/*.py tests/foo/cli/build/*.py`. But this is not an ideal solution since I cannot specify this as black configuration in `pyproject.toml`. As a result, developers running `black .` will think they're formatting everything (no warning about the skipped files), and those paths will have to be hardcoded somewhere else (Makefile, tox.ini, ...).
**Describe the solution you'd like**
I want `black .` to format all the Python files in the repository, skipping all the paths listed in `.gitignore`.
I have the same problem with **ruff** and **isort**, since they also exclude `build` by default. But both have specific options to control the reading of the `.gitignore` file:
- [respect-gitignore](https://beta.ruff.rs/docs/settings/#respect-gitignore) in ruff
- [skip-gitignore](https://pycqa.github.io/isort/docs/configuration/options.html#skip-gitignore) in isort
Then I can use settings to workaround this issue:
```toml
[tool.ruff]
# Files in `foo/cli/build/` are ignored by ruff because the `exclude`
# list contains the `build` word by default. We redefine it with just
# `.git`, since the other paths are already listed in `.gitignore`.
respect-gitignore = true
exclude = [".git"]
```
|
open
|
2023-05-30T13:03:55Z
|
2023-05-30T13:19:14Z
|
https://github.com/psf/black/issues/3711
|
[
"T: enhancement"
] |
aureliojargas
| 0
|
ultralytics/yolov5
|
pytorch
| 12,882
|
mAP@0.5 of YOLOv5n on COCO val
|
### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
Can you provide mAP@0.5 of YOLOv5n on COCO val? (For detection)
Thanks
### Additional
_No response_
|
closed
|
2024-04-04T12:49:34Z
|
2024-10-20T19:42:59Z
|
https://github.com/ultralytics/yolov5/issues/12882
|
[
"question",
"Stale"
] |
YairSmadar
| 3
|
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