repo_name stringlengths 9 75 | topic 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 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
globaleaks/globaleaks-whistleblowing-software | sqlalchemy | 3,346 | Make it possible to read statistics about returning whistleblowers | This ticket it to track the activities to make it possible to read statistics about returning whistleblowers.
It would be in fact valuable to show statistics about:
- the amount of reports where the whistleblowers logged in back vs. not
- the average amount of time that whistleblowers logged in onto the system
- the average amount of first to last interaction by whistleblowers | open | 2023-02-09T16:38:29Z | 2023-02-09T16:38:29Z | https://github.com/globaleaks/globaleaks-whistleblowing-software/issues/3346 | [
"C: Client",
"C: Backend",
"F: Statistics",
"T: Feature"
] | evilaliv3 | 0 |
widgetti/solara | flask | 243 | Internal Server Error by KeyError: 'load_extensions' | Thank you for this great library.
I tried to implement auth0 private site using Docker+Poetry and got an internal server error, and after looking at the code, it looks like there is no solution.
Apparently I should be able to set use_nbextensions=False when calling read_root in server.py, but I can't set it in the part that calls it in flask.py or starlette.py.
Actually, in starlette.py, I set use_nbextensions=False in the server.read_root section and it worked fine.
If possible, I think it would be better to be able to set the same as the port when starting such as:
`solara run sol.py --no_use_jupyter_notebook`
Log
```
Hoge | Solara server is starting at http://0.0.0.0:8080
Hoge | ERROR: Exception in ASGI application
Hoge | Traceback (most recent call last):
Hoge | File "/usr/local/lib/python3.11/site-packages/uvicorn/protocols/http/h11_impl.py", line 408, in run_asgi
Hoge | result = await app( # type: ignore[func-returns-value]
Hoge | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 84, in __call__
Hoge | return await self.app(scope, receive, send)
Hoge | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/applications.py", line 116, in __call__
Hoge | await self.middleware_stack(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 186, in __call__
Hoge | raise exc
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/errors.py", line 164, in __call__
Hoge | await self.app(scope, receive, _send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/gzip.py", line 24, in __call__
Hoge | await responder(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/gzip.py", line 44, in __call__
Hoge | await self.app(scope, receive, self.send_with_gzip)
Hoge | File "/usr/local/lib/python3.11/site-packages/solara_enterprise/auth/middleware.py", line 127, in __call__
Hoge | await self.app(scope, receive, send_wrapper)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/authentication.py", line 48, in __call__
Hoge | await self.app(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
Hoge | await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 55, in wrapped_app
Hoge | raise exc
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 44, in wrapped_app
Hoge | await app(scope, receive, sender)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 746, in __call__
Hoge | await route.handle(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 288, in handle
Hoge | await self.app(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 75, in app
Hoge | await wrap_app_handling_exceptions(app, request)(scope, receive, send)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 55, in wrapped_app
Hoge | raise exc
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/_exception_handler.py", line 44, in wrapped_app
Hoge | await app(scope, receive, sender)
Hoge | File "/usr/local/lib/python3.11/site-packages/starlette/routing.py", line 70, in app
Hoge | response = await func(request)
Hoge | ^^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/solara/server/starlette.py", line 243, in root
Hoge | content = server.read_root(request_path, root_path)
Hoge | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/solara/server/server.py", line 220, in read_root
Hoge | nbextensions = get_nbextensions()
Hoge | ^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/solara/cache.py", line 95, in __call__
Hoge | value = self.function(*args, **kwargs)
Hoge | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Hoge | File "/usr/local/lib/python3.11/site-packages/solara/server/server.py", line 300, in get_nbextensions
Hoge | load_extensions = jupytertools.get_config(paths, "notebook")["load_extensions"]
Hoge | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
Hoge | KeyError: 'load_extensions'
``` | closed | 2023-08-14T13:56:35Z | 2023-08-14T14:22:31Z | https://github.com/widgetti/solara/issues/243 | [] | Sanuki-073 | 1 |
unit8co/darts | data-science | 2,531 | How to use dump or load of joblib to save or reuse fitted model | I want to use `joblib` to dump and load the fitted model, but when I used the loaded model, it reported `AttributeError: 'NoneType' object has no attribute 'set_predict_parameters'`.
Suppose that I have trained a TSMixerModel as follows,
```model_name ='tsm'
model = TSMixerModel(
**create_params(
input_chunk_length,
output_chunk_length,
full_training=full_training,
),
hidden_size=64,
ff_size=64,
num_blocks=2,
activation="GELU",
dropout=0.2,
use_static_covariates=True,
model_name='tsm',
)
model.fit(
series=y_train,
future_covariates=x_train,
val_series=y_val,
val_future_covariates=x_val,
)
fitted_model = model.load_from_checkpoint(
model_name=model.model_name, best=True
)
```
and I want to save the fitted model using `joblib`(I know that we can find the checkpoint file from ./darts_logs directory ),
```
joblib.dump(fitted_model,'./test_tsm_model_save')
```
but when I load the model to predict, it reports error `AttributeError: 'NoneType' object has no attribute 'set_predict_parameters'
`
```
loaded_model = joblib.load('./test_tsm_model_save)
y_pred = loaded_model.predict(n=output_chunk_length,
series=y_input,
past_covariates= None,
future_covariates=x_input,
num_samples=num_samples,
predict_likelihood_parameters= False,
)
```
How should do to properly save and load the fitted model.
| closed | 2024-09-15T07:38:34Z | 2024-10-16T08:06:11Z | https://github.com/unit8co/darts/issues/2531 | [
"question"
] | ZhangAllen98 | 4 |
WZMIAOMIAO/deep-learning-for-image-processing | deep-learning | 417 | 请问为什么pytorch训练Faster-RCNN，出来的权重文件那么大？能不能修改只get它模型的权重？ | 不知道为什么，训练出来的的权重文件有几百MB大，我训练的是faster-rcnn+mobilenetv2，按道理来说应该只有十几MB，是不是包含了其他的东西？ | closed | 2021-12-06T09:00:30Z | 2021-12-06T09:23:14Z | https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/issues/417 | [] | nit-hmz | 2 |
skypilot-org/skypilot | data-science | 4,878 | [API server] Refine error message of version mismatch | One of our users reported the following error when trying to use a remote API server:
```
RuntimeError: SkyPilot API server is too old: v1 (client version is v3). Please restart the SkyPilot API server with: sky api stop; sky api start
```
This is only valid for local API servers and should be for remote API servers, including:
- `sky api stop; sky api start` just does not work for remote API server
- It might be client too old when the remote API server is upgraded by local CLI not;
- If user might have no permission to upgrade the remote API server, we may hint a downgrade
| open | 2025-03-05T03:46:23Z | 2025-03-05T07:31:35Z | https://github.com/skypilot-org/skypilot/issues/4878 | [
"Initial-User-Issue",
"interface/ux",
"api server"
] | aylei | 0 |
facebookresearch/fairseq | pytorch | 4,595 | How to use the past, future and self targets for text generation? | #### What is your question?
I noticed that new FairSeq models support past, future and self targets for text generation tasks. However, I have been trying to figure out a way of implementing a generator that would allow me to do it, but it seems that it is really hard to get that to work. The issue is mostly in the fact that it is not entirely clear, which models actually support it. The default model handler sets `model.supported_targets` as `['future']`, which mean we only have access to the old default mode, but also many models do not seem to support the feature (I have spent few days now trying to discover right combination of things to do in order to enable this).
The reason I am enthusiastic about this feature is that I have developed a writing theory based prompting system for AI generated fiction writing, where I am able to generate past-(target)-theme, future-(target)-themes for scenes in a story and character-action prompts that I could use with self-target. I would love to build a model based on this as my entry for the NaNoGenMo AI writing competition by November of this year; so I would be willing to do some contributions if I get some guidance.
I have studied this problem (of dramatic arc generation with AI) since 2016 and I strongly believe that this kind of semi-guided method where author / automation (based on theoretical framework used by real authors of fiction) provides dramatic structure and machine just bridges the gap, would be most interesting as it might actually work. Unfortunately traditional language models have lacked bidirectional causality and self-reflection due to unidirectional prompting, while this method would depend on exactly those features. I have already built an early prototype for the prompt generator.
So my questions would be:
1) Which pretrained models support past, future and self targets for language-modeling tasks?
2) Would it be sufficient if I just change the default handlers (of supported models) so that their `model.supported_targets` would be `['past', 'self', 'future']`? If not, where could I get proper guidance for doing rest of the coding (I am professional data engineer / scientist with lot of experience on NLP; I would not need that much help)?
3) If I would like to contribute a generator pipeline for this "more causal" generation task, how would we conduct such work?
Regarding the pretrained models, I would prefer some simple smallish thingie that I can run with single Quadro T1000 (4GB), though I do have occasional free access to bigger machines too. | open | 2022-07-20T21:37:49Z | 2022-07-26T11:55:06Z | https://github.com/facebookresearch/fairseq/issues/4595 | [
"question",
"needs triage"
] | AhtiAhde | 3 |
2noise/ChatTTS | python | 892 | 无法找到GPU | no GPU or NPU found, use CPU instead
如何启用GPU运行加速？ | closed | 2025-02-12T09:24:35Z | 2025-02-13T15:04:57Z | https://github.com/2noise/ChatTTS/issues/892 | [] | robin12jbj | 0 |
PaddlePaddle/ERNIE | nlp | 757 | 请问下，ERNIE-Layout是否有开源计划？如果有的话什么时候开源？ | 请问下，ERNIE-Layout是否有开源计划？如果有的话什么时候开源？ | closed | 2021-10-20T10:20:39Z | 2021-12-28T12:30:40Z | https://github.com/PaddlePaddle/ERNIE/issues/757 | [
"wontfix"
] | cjt222 | 1 |
deeppavlov/DeepPavlov | nlp | 969 | reupload BERT model without optimizer parameters in checkpoint | Currently BERT models pretrained by DeepPavlov team contain optimizers params.
It makes impossible to use these models with `pytorch-pretrained-BERT` without changing `load_tf_weights_in_bert` function.
current workaround to this issue: https://github.com/deepmipt/DeepPavlov/issues/863#issuecomment-497928718 | closed | 2019-08-13T11:58:48Z | 2019-09-06T15:18:12Z | https://github.com/deeppavlov/DeepPavlov/issues/969 | [] | yurakuratov | 0 |
modelscope/modelscope | nlp | 420 | No module named 'fa' | ```
from modelscope.tools import run_auto_label
```
```
ModuleNotFoundError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/tts_autolabel/auto_label.py](https://localhost:8080/#) in <module>
33 from tts_autolabel.speechenh.run_decode import speech_enhancement
---> 34 from tts_autolabel.fa_utils import run_fa_pipeline
35 except ImportError:
6 frames
[/usr/local/lib/python3.10/dist-packages/tts_autolabel/fa_utils.py](https://localhost:8080/#) in <module>
2 import sys
----> 3 import fa
4 import json
ModuleNotFoundError: No module named 'fa'
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/modelscope/tools/speech_tts_autolabel.py](https://localhost:8080/#) in <module>
11 try:
---> 12 from tts_autolabel import AutoLabeling
13 except ImportError:
[/usr/local/lib/python3.10/dist-packages/tts_autolabel/__init__.py](https://localhost:8080/#) in <module>
----> 1 from .auto_label import AutoLabeling
[/usr/local/lib/python3.10/dist-packages/tts_autolabel/auto_label.py](https://localhost:8080/#) in <module>
35 except ImportError:
---> 36 raise ImportError("Please install tts_autolabel.")
37
ImportError: Please install tts_autolabel.
During handling of the above exception, another exception occurred:
ImportError Traceback (most recent call last)
[<ipython-input-8-f63e0049ee8d>](https://localhost:8080/#) in <cell line: 1>()
----> 1 from modelscope.tools import run_auto_label
[/usr/local/lib/python3.10/dist-packages/modelscope/tools/__init__.py](https://localhost:8080/#) in <module>
----> 1 from .speech_tts_autolabel import run_auto_label
[/usr/local/lib/python3.10/dist-packages/modelscope/tools/speech_tts_autolabel.py](https://localhost:8080/#) in <module>
12 from tts_autolabel import AutoLabeling
13 except ImportError:
---> 14 raise ImportError('pls install tts-autolabel with \
15 "pip install tts-autolabel -f \
16 https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html"'
ImportError: pls install tts-autolabel with "pip install tts-autolabel -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html"
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
``` | closed | 2023-07-27T07:18:37Z | 2023-08-15T09:56:57Z | https://github.com/modelscope/modelscope/issues/420 | [] | nijisakai | 6 |
numba/numba | numpy | 9,882 | `literal_unroll()` silently fails to unroll `range()` | Noted first when investigating #9874 and trying to do `literal_unroll(range(2))`, I note that `literal_unroll()` seems like something that should report an error if it fails to unroll. For example, the following:
```python
from numba import njit, literal_unroll
@njit
def f(x):
for i in literal_unroll(range(x)):
print(i)
f(2)
```
runs and gives
```
0
1
```
instead of reporting that a `range()` cannot be unrolled.
The annotation seems to confirm that `i` was not literal:
```
$ NUMBA_DUMP_ANNOTATION=1 python repro2.py
-----------------------------------ANNOTATION-----------------------------------
# File: /home/gmarkall/numbadev/numba/numba/misc/literal.py
# --- LINE 22 ---
# label 0
# container = arg(0, name=container) :: range_state_int64
def impl(container):
# --- LINE 23 ---
# $6return_value.1 = cast(value=container) :: range_state_int64
# del container
# return $6return_value.1
return container
================================================================================
-----------------------------------ANNOTATION-----------------------------------
# File: /home/gmarkall/numbadev/issues/9874/repro2.py
# --- LINE 5 ---
# label 0
# x = arg(0, name=x) :: int64
@njit
# --- LINE 6 ---
def f(x):
# --- LINE 7 ---
# $4load_global.0 = global(literal_unroll: <function literal_unroll at 0x7b5eb53fce00>) :: Function(<function literal_unroll at 0x7b5eb53fce00>)
# $16load_global.2 = global(range: <class 'range'>) :: Function(<class 'range'>)
# $34call.5 = call $16load_global.2(x, func=$16load_global.2, args=[Var(x, repro2.py:5)], kws=(), vararg=None, varkwarg=None, target=None) :: (int64,) -> range_state_int64
# del x
# del $16load_global.2
# $48call.6 = call $4load_global.0($34call.5, func=$4load_global.0, args=[Var($34call.5, repro2.py:7)], kws=(), vararg=None, varkwarg=None, target=None) :: (range_state_int64,) -> range_state_int64
# del $4load_global.0
# del $34call.5
# $58get_iter.7 = getiter(value=$48call.6) :: range_iter_int64
# del $48call.6
# $phi60.0 = $58get_iter.7 :: range_iter_int64
# del $58get_iter.7
# jump 60
# label 60
# $60for_iter.1 = iternext(value=$phi60.0) :: pair<int64, bool>
# $60for_iter.2 = pair_first(value=$60for_iter.1) :: int64
# $60for_iter.3 = pair_second(value=$60for_iter.1) :: bool
# del $60for_iter.1
# $phi62.1 = $60for_iter.2 :: int64
# del $60for_iter.2
# branch $60for_iter.3, 62, 96
# label 62
# del $60for_iter.3
# i = $phi62.1 :: int64
# del $phi62.1
# label 96
# del $phi62.1
# del $phi60.0
# del $60for_iter.3
# $const96.0.0 = const(NoneType, None) :: none
# $98return_value.1 = cast(value=$const96.0.0) :: none
# del $const96.0.0
# return $98return_value.1
for i in literal_unroll(range(x)):
# --- LINE 8 ---
# $64load_global.2 = global(print: <built-in function print>) :: Function(<built-in function print>)
# del $64load_global.2
# print(i)
# del i
# $82call.5 = const(NoneType, None) :: none
# del $82call.5
# jump 60
print(i)
``` | open | 2024-12-30T10:03:28Z | 2025-01-14T15:51:42Z | https://github.com/numba/numba/issues/9882 | [
"bug - incorrect behavior"
] | gmarkall | 0 |
pytest-dev/pytest-cov | pytest | 618 | [docs] Switch to Furo | $sbj. is a nice theme that many use nowadays. I think it'd be nice to refresh the look and improve the Sphinx setup in general. | closed | 2023-11-24T14:17:52Z | 2024-03-18T12:38:53Z | https://github.com/pytest-dev/pytest-cov/issues/618 | [] | webknjaz | 0 |
ultralytics/ultralytics | python | 19,566 | Cleanest way to customize the model.val() method for custom validation. | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
Hello, I plan to customise the standard validation (see photo) to my needs (use of def ap50, def ap70 with difficulty levels). How do I have to do this to extend the validation cleanly? By default, the OBBValidator (based on DetectionValidator) is used for this.
**Current validation code:**
```python
from ultralytics import YOLO
model = YOLO('/home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/kitti_bev_yolo/run10_Adam_89.2_87.9/weights/best.pt', task='obb', verbose=False)
metrics = model.val_kitti(data='/home/heizung1/ultralytics_yolov8-obb_ob_kitti/ultralytics/cfg/datasets/kitti_bev.yaml', imgsz=640,
batch=16, save_json=False, conf=0.001, iou=0.5, max_det=300, half=False,
device='0', dnn=False, plots=False, rect=False, split='val', project=None, name=None)
```
**Current validation output:**
**Desired validation output:**
`Class` , `Images`, `Instances`, `Box(P R AP50 AP70): 100% etc.`
`AP50` and `AP70` categorized in difficulties columns: Easy, Moderate, Hard
The information for the difficulties are in my validation labels: `standard OBB format (1 0.223547 0.113517 0.223496 0.049611 0.258965 0.049583 0.259016 0.113489) difficulty infos (22.58 0.00 0)`.
For the training I modified: https://github.com/ultralytics/ultralytics/blob/23a90142dc66fbb180fe1bb513a2adc44322c978/ultralytics/data/utils.py#L97 to handle only the required label information.
I think it would be good to start in small steps, so my first consideration would be how to load the labels from a newly created .cache file that contains all the label information and then split it into two variables. One will be used to execute the standard processes (prediction, calculation IoU, metrics) and the other will be processed to develop 1 difficulty level from the 3 values.
Thank you for any useful input and guidance during this customization!
### Additional
_No response_ | open | 2025-03-07T09:44:22Z | 2025-03-15T03:28:25Z | https://github.com/ultralytics/ultralytics/issues/19566 | [
"question",
"OBB"
] | Petros626 | 13 |
ageitgey/face_recognition | machine-learning | 1,383 | Is it eligible to use in commercial? | Does this library or library dependencies requires permission to use in commercial?
| closed | 2021-10-24T23:31:29Z | 2021-11-09T23:29:25Z | https://github.com/ageitgey/face_recognition/issues/1383 | [] | 0x77dev | 3 |
wkentaro/labelme | deep-learning | 410 | Save after Undo results in corrupted JSON file | If I start annotation, create 2 shapes, then press ctrl+Z and save, it results in a JSON file with:
"flags": null
Such a file causes error when you opening it as flags is expected to be dict [here](https://github.com/wkentaro/labelme/blob/master/labelme/app.py#L961)
In general it is caused by the fact, that in many some flags is expected to be dict, but in other plases it is assigned by None. So it is nessesary a) to open files sabed before (with "flags": null) and b) remove this ambigulity | closed | 2019-05-16T09:18:15Z | 2019-05-16T12:09:54Z | https://github.com/wkentaro/labelme/issues/410 | [] | IlyaOvodov | 0 |
RobertCraigie/prisma-client-py | asyncio | 1,046 | About UnicodeEncodeError | Error Message
Error:
Traceback (most recent call last):
File "D:\Pyenv\pyenv-win-master\pyenv-win\versions\3.10.8\lib\site-packages\prisma\generator\generator.py", line 106, in run
self._on_request(request)
File "D:\Pyenv\pyenv-win-master\pyenv-win\versions\3.10.8\lib\site-packages\prisma\generator\generator.py", line 159, in _on_request
self.generate(data)
File "D:\Pyenv\pyenv-win-master\pyenv-win\versions\3.10.8\lib\site-packages\prisma\generator\generator.py", line 241, in generate
packaged_schema.write_text(data.datamodel)
File "D:\Pyenv\pyenv-win-master\pyenv-win\versions\3.10.8\lib\pathlib.py", line 1155, in write_text
return f.write(data)
UnicodeEncodeError: 'gbk' codec can't encode character '\udc80' in position 253: illegal multibyte sequence
My prisma is 0.15.0
My schema.prisma file
```
datasource db {
// provider = "sqlite"
provider = "postgresql"
url = env("DATABASE_URL")
}
generator client {
provider = "prisma-client-py"
recursive_type_depth = 5
interface = "asyncio"
}
// 教职工基础数据事实表 DW_JG_JZGJCSJ
model DW_JG_JZGJCSJ {
ID String @id @default(cuid()) // 主键
GH String? // 工号
DWH String? // 单位号
XM String? // 姓名
}
```
what should I do? | open | 2024-11-19T14:47:16Z | 2024-11-19T15:19:28Z | https://github.com/RobertCraigie/prisma-client-py/issues/1046 | [] | wxxtaiyang | 1 |
RobertCraigie/prisma-client-py | pydantic | 396 | Table not found error with SQLite | ## Bug description
When trying to create a database entry I get the following result:
"The table `main.MenuItem` does not exist in the current database."
## How to reproduce
I'm using FastApi and Uvicorn
The script is run from routes.api.v1.endpoints.menu
### Script:
```python
@router.post("/")
async def handler():
try:
await prisma.connect()
item = await prisma.menuitem.create(data={
'item_name': 'Test',
'price': 1.2
})
return item
except Exception as ex:
print(ex)
return str(ex)
finally:
await prisma.disconnect()
```
### My Directory Structure
```
.
├── __init__.py
├── app.py
├── db
│ ├── client.py
│ ├── dev.db
│ └── prisma
│ ├── migrations
│ └── schema.prisma
├── requirements.txt
└── routes
├── __init__.py
└── api
├── __init__.py
└── v1
├── __init__.py
├── endpoints
│ ├── __init__.py
│ └── menu.py
└── router.py
```
## Prisma information
```prisma
generator client {
provider = "prisma-client-py"
}
datasource db {
provider = "sqlite"
url = "file:../dev.db"
}
model MenuItem {
id Int @id @default(autoincrement())
item_name String
price Float
}
```
## Environment & setup
- OS: Ubuntu20.04
- Database: SQLite
- Python version: 3.10
- Prisma version:
```
prisma : 3.13.0
prisma client python : 0.6.5
platform : debian-openssl-1.1.x
engines : efdf9b1183dddfd4258cd181a72125755215ab7b
install path : /home/karry/projects/clients/idhb/.venv/lib/python3.10/site-packages/prisma
installed extras : []
```
| closed | 2022-05-13T16:20:26Z | 2022-05-13T16:53:38Z | https://github.com/RobertCraigie/prisma-client-py/issues/396 | [
"kind/question"
] | kcrtr | 2 |
alpacahq/alpaca-trade-api-python | rest-api | 631 | [Feature Request] GTC + EXT | ### Is there an existing issue for this?
- [X] I have searched the existing issues
### Is your feature request related to a problem? Please describe.
Yes, there is a problem.
We are not able to open/close positions with GTC orders on extended hours.
There are other brokers that allows you to do this.
### Describe the solution you'd like.
There are several brokers, including TD Ameritrade, that already support orders `GTC + EXT`
Alpaca is now able to trade in full extended hours (4:00 AM - 8:00 PM EST) Great!
But is not enough, as a customer, we also need the possibility of using `GTC + EXT`
I'm sure there are a lot of traders that will thank you for this.
If this is something that goes beyond your jurisdiction or need to be escalated, please forward it to the appropriate team.
### Describe an alternate solution.
An alternate solution could be the ability to replace the order during extended hours without having the error:
`pending_cancel`
### Anything else? (Additional Context)
Tested with alpaca-trade-api 2.2.0 | closed | 2022-06-20T21:59:03Z | 2022-10-23T14:22:02Z | https://github.com/alpacahq/alpaca-trade-api-python/issues/631 | [] | sshcli | 1 |
iterative/dvc | data-science | 9,740 | Support packaged (pkg://) configs for Hydra | Hi,
Hydra supports several ways to define config directories. The two relevant to this issue are:
1. Relative to some directory. This translates to "relative to `.dvc/config`" in DVC and is the supported mode.
2. As a Python module `pkg://some.python.module`.
Would it be possible to support the second route too?
It seems likely that replacing `initialize_config_dir` [here](https://github.com/iterative/dvc/blob/ea5f7d40c131aa949f307e31e14e5d99024c20da/dvc/utils/hydra.py#L40) with `initialize_config_module` ([docs](https://hydra.cc/docs/1.3/advanced/compose_api/)) would be enough to enable packaged configs and a simple check for a `pkg://` prefix should be enough to distinguish between the two cases.
The reason for this feature request is that I'd like to set up automatic unit tests which involves copying `.dvc` into a temporary directory. That would break the relative path but a packaged config would still resolve correctly.
Thanks! :)
Edit: I was able to get around this with a bit of surgery but it'd still be a nice feature for more readable and transferable configs. :) | closed | 2023-07-16T20:36:32Z | 2023-08-06T08:39:14Z | https://github.com/iterative/dvc/issues/9740 | [
"feature request",
"p2-medium",
"A: hydra"
] | exs-dmiketa | 6 |
xuebinqin/U-2-Net | computer-vision | 18 | U-2-Net for binary segmentation | Hey @NathanUA,
just to share my experience here, using the small U-2-Net architecture:
- I'm comparing the results to a baseline model (DeepLabV3, ResNet101 ~450MB) and achieved 82,... mIOU after 500 Epochs on my rather small benchmarking Dataset (license plate binary segmentation).
- The small U-2-Net model achieved 78,... mIOU (~5MB) after roughly 600 Epochs
I did the following modifications to the model in order to fine-tune my results:
- ~I introduced a distillation loss between each nested Unet and used temperature annealing on the sigmoid. Based on the assumption that the more nested Unets have more computational power, we can define a second loss specifying the bce between adjacent layers. The model converges way faster with this approach, however the 3 new hyperparameters ("temperature", alpha and a scaling factor for the loss) seem to be quite "sensitive".~ was not working.
- I changed to SGD with a poly learning rate starting at 0.01
- I'm currently exploring the options to further prune the network or use an early exit while inferencing to reduce inferencing time.
I just wanted to share my experience here, maybe it can be helpful for this repository.
Best,
Dennis
| closed | 2020-05-14T13:41:24Z | 2020-07-19T15:16:57Z | https://github.com/xuebinqin/U-2-Net/issues/18 | [] | dbpprt | 3 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 755 | [FEATURE]: More info on failed/skipped applications | ### Feature summary
Add 'reason' in failed and skipped json
### Feature description
We have two JSON files that list the failed and skipped job applications where the bot either failed to apply or chose to skip for some reason. The "reason" in these files provides valuable information that can help improve our bot settings or resume tuning, enhancing our chances of securing better job matches.
### Motivation
Improve our bot settings or resume tuning
### Alternatives considered
add reason to skipped.json and failed.json
### Additional context
_No response_ | closed | 2024-11-05T06:10:30Z | 2024-11-15T06:55:17Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/755 | [
"enhancement"
] | javad3zafar | 4 |
gradio-app/gradio | data-science | 9,872 | Custom components don't work on Lite | ### Describe the bug
Apps including custom components don't start being bound to the loading state.
Successor to #7476
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
from gradio_pdf import PDF
demo = gr.Interface(lambda x: x,
PDF(),
gr.File())
if __name__ == "__main__":
demo.launch()
```
### Screenshot
_No response_
### Logs
```shell
17:20:20.216 playground:1 [Deprecation] Custom state pseudo classes have been changed from ":--color-var" to ":state(color-var)". See more here: https://github.com/w3c/csswg-drafts/issues/4805
17:20:26.191 style.css:1
Failed to load resource: net::ERR_NAME_NOT_RESOLVED
17:20:26.191 index.js:1
Failed to load resource: net::ERR_NAME_NOT_RESOLVED
17:20:26.192 Blocks.svelte:81 Uncaught (in promise) undefined
```
### System Info
```shell
Lite on 85731e7eba3ee79499e353445990ccff662367ae
```
### Severity
I can work around it | open | 2024-10-30T16:23:15Z | 2024-10-30T16:23:16Z | https://github.com/gradio-app/gradio/issues/9872 | [
"bug",
"gradio-lite"
] | whitphx | 0 |
TvoroG/pytest-lazy-fixture | pytest | 30 | `pytest.lazy_fixture` doesn't exist in 0.4.2 | When I downgraded it to 0.4.1, the problem is fixed.
Examples
- Broken: https://travis-ci.org/youknowone/itunes-iap/jobs/438342600
- Fixed: https://travis-ci.org/youknowone/itunes-iap/jobs/438538498
- Patch: https://github.com/youknowone/itunes-iap/commit/ef4a927db45c4f9c9e62ce9653d8a5efff906548 | open | 2018-10-08T09:53:19Z | 2019-02-13T05:50:07Z | https://github.com/TvoroG/pytest-lazy-fixture/issues/30 | [] | youknowone | 5 |
ageitgey/face_recognition | machine-learning | 1,107 | how to "face_recognition" a video | i don't know how to recognize videos,please teach me how to do | open | 2020-04-08T09:26:24Z | 2020-04-10T09:44:00Z | https://github.com/ageitgey/face_recognition/issues/1107 | [] | tim94173 | 7 |
SALib/SALib | numpy | 248 | Unexpected distribution of Morris samples with more than 4 grid levels | Hi there !
I am using SALib to run Morris sensitivity analyses, and I am facing a problem with Morris sample generation.
When using 4 grid levels, each parameter is distributed over its 4 grid levels, i.e. 0, 1/3, 2/3 and 1 when normalized. When using more grid levels, the delta value is modified accordingly but only four grid values appear in the samples.
For instance, in the following example the number of levels is set to 6:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
```python
from SALib.sample.morris import sample
#Manual definition of the problem
problem = {
'num_vars': 8,
'names': ['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8'],
'groups': None,
'bounds': [[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0],
[0.0, 1.0]]
}
#Generate Morris samples
param_values = sample(problem, N=1000, num_levels=6,
optimal_trajectories=None)
```
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
The delta value is modified accordingly, i.e. that the step for elementary effects calculation is then 0.6, but the variables only take the values 0, 0.4, 0.6 and 1, while 0.2 and 0.8 never appear. The same behavior occurs for larger number of levels as well.
Thanks a lot for your help !
Martin | closed | 2019-06-05T08:18:02Z | 2019-11-07T23:03:10Z | https://github.com/SALib/SALib/issues/248 | [
"bug"
] | mjeanner | 6 |
scikit-tda/kepler-mapper | data-visualization | 2 | 'list' object has no attribute 'fit_transform' when running "make_circles" example | ---
AttributeError Traceback (most recent call last)
<ipython-input-16-934e94845567> in <module>()
9
10 # Fit to and transform the data
---> 11 projected_data = mapper.fit_transform(data, projection=[0,1]) # X-Y axis
12
13 # Create dictionary called 'complex' with nodes, edges and meta-information
C:\Users\admin\Anaconda3\lib\km.py in fit_transform(self, X, projection, scaler)
62 pass
63 print("\n..Projecting data using: \n\t%s\n"%str(projection))
---> 64 X = reducer.fit_transform(X)
65
66 # Detect if projection is a string (for standard functions)
AttributeError: 'list' object has no attribute 'fit_transform'
| closed | 2016-08-20T14:12:03Z | 2017-11-16T04:07:39Z | https://github.com/scikit-tda/kepler-mapper/issues/2 | [] | drmingle | 7 |
StackStorm/st2 | automation | 5,185 | WFL editor crashes if I click on some tasks | ## SUMMARY
The new WFL editor crashes if I click on some specific tasks. The affected actions are all part of self developed packs. It affects multiple actions belonging to multiple packs.
The same action is used two times in the workflow, one task can be edited the other one crashes the editor. -> I don't think it is related to my pack and action. But till now I have not identified what exactly makes the difference for the tasks working and the ones not working.
### STACKSTORM VERSION
st2 3.4.0, on Python 3.6.8
##### OS, environment, install method
redhat:enterprise_linux:7.9
## Steps to reproduce the problem
I can not share my current workflow and self developed packs, but I will check later if I can create an example workflow. Therefore I need to know what exactly the issue is, maybe someone can already give me a hint by looking at the error I get from Firefox or can tell me what I should check in advance. Otherwise I need more time to identify it on my own.
## Expected Results
Move and edit tasks like others.
## Actual Results
The editor crashes. (white page) used Browser: Firefox 86.0
Debug Output:
<img width="1664" alt="Bildschirmfoto 2021-03-10 um 11 15 50" src="https://user-images.githubusercontent.com/73697233/110613967-23c12180-8192-11eb-883a-8762a952de19.png">
| open | 2021-03-10T10:19:06Z | 2021-04-10T13:25:53Z | https://github.com/StackStorm/st2/issues/5185 | [
"bug",
"component:st2web"
] | wingiti | 3 |
CatchTheTornado/text-extract-api | api | 54 | [feat] Add `docling` support | https://github.com/DS4SD/docling?tab=readme-ov-file
- [x] Change `ocr_strategies` to `strategies` (same for strategy)
- [x] Install docling
- [x] PoC research integration - convert one format example
- [ ] Docling Strategy
- [ ] Universal Document (which will have DoclingDocument)
- [ ] ... tbd | open | 2025-01-08T10:31:55Z | 2025-01-19T11:09:48Z | https://github.com/CatchTheTornado/text-extract-api/issues/54 | [
"feature"
] | pkarw | 0 |
pytest-dev/pytest-django | pytest | 999 | How to use mysql back-end from a file for tests | Could somebody point me to how I can set mysql back-end up?
At the moment I have sqlite which simply reads from a file, I want something similar but with mysql.
```
DATABASES = {
"default": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": "tests/test_db.sqlite",
},
"reader": {
"ENGINE": "django.db.backends.sqlite3",
"NAME": "tests/test_db.sqlite",
},
}
``` | open | 2022-03-16T15:19:08Z | 2022-03-16T16:08:27Z | https://github.com/pytest-dev/pytest-django/issues/999 | [] | manycoding | 2 |
amidaware/tacticalrmm | django | 2,071 | Easier grouping for bulk scripts | To run bulk script on all similar machines, regardless of which client or site they located in, one needs to choose them manually in bulk script each time or run script on each machine individually.
I think it would be cool to be able to run script in automation policy as it already possible for windows. Or to run script depending on some sort of tag or depending on the value of custom field. | open | 2024-11-19T09:27:53Z | 2024-11-19T13:23:12Z | https://github.com/amidaware/tacticalrmm/issues/2071 | [] | VadymKhvoinytskyi | 1 |
Anjok07/ultimatevocalremovergui | pytorch | 571 | ValueError: "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 6" | Is there a limit on size for wav's?
Getting this error but only for a ```.wav``` input files e.g.: the same input audio in ```.mp3``` works fine.
Here is the error log:
```
Last Error Received:
Process: MDX-Net
If this error persists, please contact the developers with the error details.
Raw Error Details:
ValueError: "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 2 and the array at index 1 has size 6"
Traceback Error: "
File "UVR.py", line 4716, in process_start
File "separate.py", line 287, in seperate
File "separate.py", line 360, in demix_base
File "separate.py", line 343, in initialize_mix
File "<__array_function__ internals>", line 180, in concatenate
"
Error Time Stamp [2023-05-23 21:19:11]
Full Application Settings:
vr_model: Choose Model
aggression_setting: 10
window_size: 512
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: Choose Model
segment: Default
overlap: 0.25
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: UVR-MDX-NET Inst HQ 2
chunks: Auto
margin: 44100
compensate: Auto
is_denoise: False
is_invert_spec: False
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Manual Ensemble
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 2.0
is_gpu_conversion: True
is_primary_stem_only: False
is_secondary_stem_only: True
is_testing_audio: False
is_add_model_name: False
is_accept_any_input: False
is_task_complete: False
is_normalization: True
is_create_model_folder: False
mp3_bit_set: 320k
save_format: WAV
wav_type_set: PCM_16
help_hints_var: True
model_sample_mode: False
model_sample_mode_duration: 30
demucs_stems: All Stems
``` | open | 2023-05-23T20:30:18Z | 2023-06-16T22:54:45Z | https://github.com/Anjok07/ultimatevocalremovergui/issues/571 | [] | pablo-01 | 1 |
ansible/awx | django | 15,552 | Private Instance Group | ### Please confirm the following
- [X] I agree to follow this project's [code of conduct](https://docs.ansible.com/ansible/latest/community/code_of_conduct.html).
- [X] I have checked the [current issues](https://github.com/ansible/awx/issues) for duplicates.
- [X] I understand that AWX is open source software provided for free and that I might not receive a timely response.
### Feature type
New Feature
### Feature Summary
Hello,
As an admin of ansible automation platform I would like to be able to create a private instance group that only a certain organisation can see but not the rest.
I want to do a PoC on container groups, but I do not want my customers (users in the rest of organisations), to see this PoC instance group.
Is there a way to achieve this somehow already? If not, would it be a nice to have?
### Select the relevant components
- [X] UI
- [X] API
- [X] Docs
- [X] Collection
- [X] CLI
- [ ] Other
### Steps to reproduce
As an admin of ansible automation platform I would like to be able to create a private instance group that only a certain organisation can see but not the rest.
### Current results
As an admin of ansible automation platform I would like to be able to create a private instance group that only a certain organisation can see but not the rest.
### Sugested feature result
Being able to create a private instance group only visible for a certain organisation
### Additional information
_No response_ | open | 2024-09-26T15:48:15Z | 2024-09-26T15:48:32Z | https://github.com/ansible/awx/issues/15552 | [
"type:enhancement",
"component:api",
"component:ui",
"component:docs",
"component:awx_collection",
"needs_triage",
"community"
] | jangel97 | 0 |
AntonOsika/gpt-engineer | python | 1,112 | Missing support for the vision capabilities in the new model gpt-4-turbo | ## Expected Behavior
When using the model gpt-4-turbo, I expect the vision capabilities to work so that I can send in image inputs using the --image_directory flag.
## Current Behavior
When using the --image_directory flag and the model gpt-4-turbo, the images in the directory are ignored and not sent to the model.
##
A simple fix would be to update the dependency to langchain to reference v0.1.16 or later that have better support for gpt-4-turbo.
https://github.com/langchain-ai/langchain/releases/tag/v0.1.16
And:
In core/ai.py one could change this on row 110:
`self.vision = "vision" in model_name`
to
`self.vision = "vision" in model_name or model_name in ["gpt-4-turbo", "gpt-4-turbo-2024-04-09"]`
| closed | 2024-04-13T21:44:07Z | 2024-04-18T09:43:08Z | https://github.com/AntonOsika/gpt-engineer/issues/1112 | [
"bug",
"triage"
] | fre-bod | 5 |
Johnserf-Seed/TikTokDownload | api | 315 | TikTokMulti报错 | Expecting value: line 1 column 1 (char 0)
| closed | 2023-02-10T14:28:18Z | 2023-02-10T17:48:19Z | https://github.com/Johnserf-Seed/TikTokDownload/issues/315 | [] | navyNIE | 1 |
SciTools/cartopy | matplotlib | 1,618 | Add Oblique Mercator | Will there be support for the oblique Mercator projection in Cartopy? I'd like to use it to plot some line-style geographic features that are sometimes best aligned with a rotation. Thanks!
_Originally posted by @shipengcheng1230 in https://github.com/SciTools/cartopy/issues/501#issuecomment-657857834_ | closed | 2020-07-29T19:49:20Z | 2023-03-11T08:58:27Z | https://github.com/SciTools/cartopy/issues/1618 | [
"Type: Enhancement"
] | dopplershift | 2 |
Gozargah/Marzban | api | 921 | How to find out on what day and time the traffic? | - How to find out on what day and time the traffic will be reset, there are parameters in the database such as the month. How can I make changes to the database at the time when it will be reset?
- Как узнать в какой день и время произойдет сброс трафика, есть такие параметры в базе данных как например месяц. Как я могу в БД внести изменения и поставить то время, когда он будет сброшен? | closed | 2024-04-04T14:19:26Z | 2024-04-28T17:50:43Z | https://github.com/Gozargah/Marzban/issues/921 | [
"Bug"
] | whitehatqq | 8 |
allenai/allennlp | pytorch | 5,582 | Loading a HuggingFace model into AllenNLP gives different predictions | ## Checklist
<!-- To check an item on the list replace [ ] with [x]. -->
- [x] I have verified that the issue exists against the `main` branch of AllenNLP.
- [x] I have read the relevant section in the [contribution guide](https://github.com/allenai/allennlp/blob/main/CONTRIBUTING.md#bug-fixes-and-new-features) on reporting bugs.
- [x] I have checked the [issues list](https://github.com/allenai/allennlp/issues) for similar or identical bug reports.
- [x] I have checked the [pull requests list](https://github.com/allenai/allennlp/pulls) for existing proposed fixes.
- [x] I have checked the [CHANGELOG](https://github.com/allenai/allennlp/blob/main/CHANGELOG.md) and the [commit log](https://github.com/allenai/allennlp/commits/main) to find out if the bug was already fixed in the main branch.
- [x] I have included in the "Description" section below a traceback from any exceptions related to this bug.
- [x] I have included in the "Related issues or possible duplicates" section beloew all related issues and possible duplicate issues (If there are none, check this box anyway).
- [x] I have included in the "Environment" section below the name of the operating system and Python version that I was using when I discovered this bug.
- [x] I have included in the "Environment" section below the output of `pip freeze`.
- [x] I have included in the "Steps to reproduce" section below a minimally reproducible example.
## Description
When loading a model created with HuggingFace (transformer library) into AllenNLP, the models works but predictions are different from what I get if running the model with transformers. Both model are running on evaluation mode.
### Details
I have a custom classification model trained using transformers library based on a BERT model. The model classifies text into 7 different categories. It is persisted in a directory using:
```python
trainer.save_model(model_name)
tokenizer.save_pretrained(model_name)
```
I'm trying to load such persisted model using the `allennlp` library for further analysis. However, when running the model inside the `allennlp` framework, the model tends to predict very different from the predictions I get when I run it using `transformers`, which lead me think some part of the loading was not done correctly. There are no errors during the inference, it is just that the predictions don't match.
There is little documentation about how to load an existing model (persisted in a path for instance), so I'm wondering if someone faced the same situation before. There is just one example of how to do QA classification with ROBERTA, but couldn't extrapolate to what I'm looking for. This is how I'm loading the trained model:
```python
transformer_vocab = Vocabulary.from_pretrained_transformer(model_name)
transformer_tokenizer = PretrainedTransformerTokenizer(model_name)
transformer_encoder = BertPooler(model_name)
params = Params(
{
"token_embedders": {
"tokens": {
"type": "pretrained_transformer",
"model_name": model_name,
}
}
}
)
token_embedder = BasicTextFieldEmbedder.from_params(vocab=vocab, params=params)
token_indexer = PretrainedTransformerIndexer(model_name)
transformer_model = BasicClassifier(vocab=transformer_vocab,
text_field_embedder=token_embedder,
seq2vec_encoder=transformer_encoder,
dropout=0.1,
num_labels=7)
transformer_model.eval()
```
I also had to implement my own DatasetReader as follows:
```python
class ClassificationTransformerReader(DatasetReader):
def __init__(
self,
tokenizer: Tokenizer,
token_indexer: TokenIndexer,
max_tokens: int,
**kwargs
):
super().__init__(**kwargs)
self.tokenizer = tokenizer
self.token_indexers: Dict[str, TokenIndexer] = { "tokens": token_indexer }
self.max_tokens = max_tokens
self.vocab = vocab
def text_to_instance(self, text: str, label: str = None) -> Instance:
tokens = self.tokenizer.tokenize(text)
if self.max_tokens:
tokens = tokens[: self.max_tokens]
inputs = TextField(tokens, self.token_indexers)
fields: Dict[str, Field] = { "tokens": inputs }
if label:
fields["label"] = LabelField(label)
return Instance(fields)
```
It is instantiated as follows:
```python
dataset_reader = ClassificationTransformerReader(tokenizer=transformer_tokenizer,
token_indexer=token_indexer,
max_tokens=400)
```
To run the model and test out if it works I'm doing the following:
```python
instance = dataset_reader.text_to_instance("some sample text here")
dataset = Batch([instance])
dataset.index_instances(transformer_vocab)
model_input = util.move_to_device(dataset.as_tensor_dict(),
transformer_model._get_prediction_device())
outputs = transformer_model.make_output_human_readable(transformer_model(**model_input))
```
<details>
<summary><b>Python traceback:</b></summary>
<p>
<!-- Paste the traceback from any exception (if there was one) in between the next two lines below -->
```
```
</p>
</details>
## Related issues or possible duplicates
- None
## Environment
OS: `Ubuntu 20.04 LTS`
Python version: `3.8`
<details>
<summary><b>Output of <code>pip freeze</code>:</b></summary>
<p>
<!-- Paste the output of `pip freeze` in between the next two lines below -->
```
absl-py==1.0.0
alabaster==0.7.12
albumentations==0.1.12
allennlp==2.9.0
altair==4.2.0
appdirs==1.4.4
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arviz==0.11.4
astor==0.8.1
astropy==4.3.1
astunparse==1.6.3
atari-py==0.2.9
atomicwrites==1.4.0
attrs==21.4.0
audioread==2.1.9
autograd==1.3
Babel==2.9.1
backcall==0.2.0
backports.csv==1.0.7
base58==2.1.1
beautifulsoup4==4.6.3
bleach==4.1.0
blis==0.4.1
bokeh==2.3.3
boto3==1.21.9
botocore==1.24.9
Bottleneck==1.3.2
branca==0.4.2
bs4==0.0.1
CacheControl==0.12.10
cached-path==1.0.2
cached-property==1.5.2
cachetools==4.2.4
catalogue==1.0.0
certifi==2021.10.8
cffi==1.15.0
cftime==1.5.2
chardet==3.0.4
charset-normalizer==2.0.12
checklist==0.0.11
cheroot==8.6.0
CherryPy==18.6.1
click==7.1.2
cloudpickle==1.3.0
cmake==3.12.0
cmdstanpy==0.9.5
colorcet==3.0.0
colorlover==0.3.0
community==1.0.0b1
contextlib2==0.5.5
convertdate==2.4.0
coverage==3.7.1
coveralls==0.5
crcmod==1.7
cryptography==36.0.1
cufflinks==0.17.3
cupy-cuda111==9.4.0
cvxopt==1.2.7
cvxpy==1.0.31
cycler==0.11.0
cymem==2.0.6
Cython==0.29.28
daft==0.0.4
dask==2.12.0
datascience==0.10.6
debugpy==1.0.0
decorator==4.4.2
defusedxml==0.7.1
descartes==1.1.0
dill==0.3.4
distributed==1.25.3
dlib @ file:///dlib-19.18.0-cp37-cp37m-linux_x86_64.whl
dm-tree==0.1.6
docker-pycreds==0.4.0
docopt==0.6.2
docutils==0.17.1
dopamine-rl==1.0.5
earthengine-api==0.1.299
easydict==1.9
ecos==2.0.10
editdistance==0.5.3
en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz
entrypoints==0.4
ephem==4.1.3
et-xmlfile==1.1.0
fa2==0.3.5
fairscale==0.4.5
fastai==1.0.61
fastdtw==0.3.4
fastprogress==1.0.2
fastrlock==0.8
fbprophet==0.7.1
feather-format==0.4.1
feedparser==6.0.8
filelock==3.4.2
firebase-admin==4.4.0
fix-yahoo-finance==0.0.22
Flask==1.1.4
flatbuffers==2.0
folium==0.8.3
future==0.16.0
gast==0.5.3
GDAL==2.2.2
gdown==4.2.1
gensim==3.6.0
geographiclib==1.52
geopy==1.17.0
gin-config==0.5.0
gitdb==4.0.9
GitPython==3.1.27
glob2==0.7
google==2.0.3
google-api-core==1.26.3
google-api-python-client==1.12.10
google-auth==1.35.0
google-auth-httplib2==0.0.4
google-auth-oauthlib==0.4.6
google-cloud-bigquery==1.21.0
google-cloud-bigquery-storage==1.1.0
google-cloud-core==1.7.2
google-cloud-datastore==1.8.0
google-cloud-firestore==1.7.0
google-cloud-language==1.2.0
google-cloud-storage==1.40.0
google-cloud-translate==1.5.0
google-colab @ file:///colabtools/dist/google-colab-1.0.0.tar.gz
google-crc32c==1.3.0
google-pasta==0.2.0
google-resumable-media==1.3.3
googleapis-common-protos==1.54.0
googledrivedownloader==0.4
graphviz==0.10.1
greenlet==1.1.2
grpcio==1.44.0
gspread==3.4.2
gspread-dataframe==3.0.8
gym==0.17.3
h5py==3.1.0
HeapDict==1.0.1
hijri-converter==2.2.3
holidays==0.10.5.2
holoviews==1.14.8
html5lib==1.0.1
httpimport==0.5.18
httplib2==0.17.4
httplib2shim==0.0.3
huggingface-hub==0.2.1
humanize==0.5.1
hyperopt==0.1.2
ideep4py==2.0.0.post3
idna==2.10
imageio==2.4.1
imagesize==1.3.0
imbalanced-learn==0.8.1
imblearn==0.0
imgaug==0.2.9
importlib-metadata==4.11.1
importlib-resources==5.4.0
imutils==0.5.4
inflect==2.1.0
iniconfig==1.1.1
intel-openmp==2022.0.2
intervaltree==2.1.0
ipykernel==4.10.1
ipython==5.5.0
ipython-genutils==0.2.0
ipython-sql==0.3.9
ipywidgets==7.6.5
iso-639==0.4.5
itsdangerous==1.1.0
jaraco.classes==3.2.1
jaraco.collections==3.5.1
jaraco.context==4.1.1
jaraco.functools==3.5.0
jaraco.text==3.7.0
jax==0.3.1
jaxlib @ https://storage.googleapis.com/jax-releases/cuda11/jaxlib-0.3.0+cuda11.cudnn805-cp37-none-manylinux2010_x86_64.whl
jedi==0.18.1
jieba==0.42.1
Jinja2==2.11.3
jmespath==0.10.0
joblib==1.1.0
jpeg4py==0.1.4
jsonnet==0.18.0
jsonschema==4.3.3
jupyter==1.0.0
jupyter-client==5.3.5
jupyter-console==5.2.0
jupyter-core==4.9.2
jupyterlab-pygments==0.1.2
jupyterlab-widgets==1.0.2
kaggle==1.5.12
kapre==0.3.7
keras==2.8.0
Keras-Preprocessing==1.1.2
keras-vis==0.4.1
kiwisolver==1.3.2
korean-lunar-calendar==0.2.1
libclang==13.0.0
librosa==0.8.1
lightgbm==2.2.3
llvmlite==0.34.0
lmdb==0.99
LunarCalendar==0.0.9
lxml==4.2.6
Markdown==3.3.6
MarkupSafe==2.0.1
matplotlib==3.2.2
matplotlib-inline==0.1.3
matplotlib-venn==0.11.6
missingno==0.5.0
mistune==0.8.4
mizani==0.6.0
mkl==2019.0
mlxtend==0.14.0
more-itertools==8.12.0
moviepy==0.2.3.5
mpmath==1.2.1
msgpack==1.0.3
multiprocess==0.70.12.2
multitasking==0.0.10
munch==2.5.0
murmurhash==1.0.6
music21==5.5.0
natsort==5.5.0
nbclient==0.5.11
nbconvert==5.6.1
nbformat==5.1.3
nest-asyncio==1.5.4
netCDF4==1.5.8
networkx==2.6.3
nibabel==3.0.2
nltk==3.2.5
notebook==5.3.1
numba==0.51.2
numexpr==2.8.1
numpy==1.21.5
nvidia-ml-py3==7.352.0
oauth2client==4.1.3
oauthlib==3.2.0
okgrade==0.4.3
opencv-contrib-python==4.1.2.30
opencv-python==4.1.2.30
openpyxl==3.0.9
opt-einsum==3.3.0
osqp==0.6.2.post0
packaging==21.3
palettable==3.3.0
pandas==1.3.5
pandas-datareader==0.9.0
pandas-gbq==0.13.3
pandas-profiling==1.4.1
pandocfilters==1.5.0
panel==0.12.1
param==1.12.0
parso==0.8.3
pathlib==1.0.1
pathtools==0.1.2
patsy==0.5.2
patternfork-nosql==3.6
pdfminer.six==20211012
pep517==0.12.0
pexpect==4.8.0
pickleshare==0.7.5
Pillow==7.1.2
pip-tools==6.2.0
plac==1.1.3
plotly==5.5.0
plotnine==0.6.0
pluggy==0.7.1
pooch==1.6.0
portend==3.1.0
portpicker==1.3.9
prefetch-generator==1.0.1
preshed==3.0.6
prettytable==3.1.1
progressbar2==3.38.0
prometheus-client==0.13.1
promise==2.3
prompt-toolkit==1.0.18
protobuf==3.17.3
psutil==5.4.8
psycopg2==2.7.6.1
ptyprocess==0.7.0
py==1.11.0
pyarrow==6.0.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycocotools==2.0.4
pycparser==2.21
pyct==0.4.8
pydata-google-auth==1.3.0
pydot==1.3.0
pydot-ng==2.0.0
pydotplus==2.0.2
PyDrive==1.3.1
pyemd==0.5.1
pyerfa==2.0.0.1
pyglet==1.5.0
Pygments==2.6.1
pygobject==3.26.1
pymc3==3.11.4
PyMeeus==0.5.11
pymongo==4.0.1
pymystem3==0.2.0
PyOpenGL==3.1.5
pyparsing==3.0.7
pyrsistent==0.18.1
pysndfile==1.3.8
PySocks==1.7.1
pystan==2.19.1.1
pytest==3.6.4
python-apt==0.0.0
python-chess==0.23.11
python-dateutil==2.8.2
python-docx==0.8.11
python-louvain==0.16
python-slugify==6.0.1
python-utils==3.1.0
pytz==2018.9
pyviz-comms==2.1.0
PyWavelets==1.2.0
PyYAML==6.0
pyzmq==22.3.0
qdldl==0.1.5.post0
qtconsole==5.2.2
QtPy==2.0.1
regex==2019.12.20
requests==2.23.0
requests-oauthlib==1.3.1
resampy==0.2.2
rpy2==3.4.5
rsa==4.8
s3transfer==0.5.2
sacremoses==0.0.47
scikit-image==0.18.3
scikit-learn==1.0.2
scipy==1.4.1
screen-resolution-extra==0.0.0
scs==3.1.0
seaborn==0.11.2
semver==2.13.0
Send2Trash==1.8.0
sentencepiece==0.1.96
sentry-sdk==1.5.6
setuptools-git==1.2
sgmllib3k==1.0.0
Shapely==1.8.1.post1
shortuuid==1.0.8
simplegeneric==0.8.1
six==1.15.0
sklearn==0.0
sklearn-pandas==1.8.0
smart-open==5.2.1
smmap==5.0.0
snowballstemmer==2.2.0
sortedcontainers==2.4.0
SoundFile==0.10.3.post1
spacy==2.2.4
Sphinx==1.8.6
sphinxcontrib-serializinghtml==1.1.5
sphinxcontrib-websupport==1.2.4
SQLAlchemy==1.4.31
sqlparse==0.4.2
srsly==1.0.5
statsmodels==0.10.2
sympy==1.7.1
tables==3.7.0
tabulate==0.8.9
tblib==1.7.0
tempora==5.0.1
tenacity==8.0.1
tensorboard==2.8.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.5
tensorflow @ file:///tensorflow-2.8.0-cp37-cp37m-linux_x86_64.whl
tensorflow-datasets==4.0.1
tensorflow-estimator==2.8.0
tensorflow-gcs-config==2.8.0
tensorflow-hub==0.12.0
tensorflow-io-gcs-filesystem==0.24.0
tensorflow-metadata==1.6.0
tensorflow-probability==0.16.0
termcolor==1.1.0
terminado==0.13.1
testpath==0.5.0
text-unidecode==1.3
textblob==0.15.3
Theano-PyMC==1.1.2
thinc==7.4.0
threadpoolctl==3.1.0
tifffile==2021.11.2
tokenizers==0.10.3
tomli==2.0.1
toolz==0.11.2
torch==1.9.0
torchaudio @ https://download.pytorch.org/whl/cu111/torchaudio-0.10.0%2Bcu111-cp37-cp37m-linux_x86_64.whl
torchsummary==1.5.1
torchtext==0.11.0
torchvision==0.10.0
tornado==5.1.1
tqdm==4.62.3
traitlets==5.1.1
transformers==4.12.3
tweepy==3.10.0
typeguard==2.7.1
typing-extensions==3.10.0.2
tzlocal==1.5.1
uritemplate==3.0.1
urllib3==1.25.11
vega-datasets==0.9.0
wandb==0.12.10
wasabi==0.9.0
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==1.0.1
widgetsnbextension==3.5.2
wordcloud==1.5.0
wrapt==1.13.3
xarray==0.18.2
xgboost==0.90
xkit==0.0.0
xlrd==1.1.0
xlwt==1.3.0
yaspin==2.1.0
zc.lockfile==2.0
zict==2.0.0
zipp==3.7.0
```
</p>
</details>
## Steps to reproduce
Follow the description of the isssue.
<details>
<summary><b>Example source:</b></summary>
<p>
<!-- Add a fully runnable example in between the next two lines below that will reproduce the bug -->
```
## Provided above.
```
</p>
</details>
| closed | 2022-02-28T22:59:13Z | 2022-06-11T18:29:05Z | https://github.com/allenai/allennlp/issues/5582 | [
"bug"
] | santiagxf | 12 |
Netflix/metaflow | data-science | 1,640 | Documentation is missing information for local Kubernetes deployment | Documentation is missing information for local K8S deployment
https://docs.metaflow.org/getting-started/infrastructure
Point 5 links to AWS-K8S
We're currently evaluating Metaflow, but would like to understand on premise requirements (or possibilities).
In our case we have on premise a GPU cluster, and use some Cloud resources too.
Understanding if/how we can mix that would be great. | open | 2023-11-21T15:33:54Z | 2025-01-07T13:12:29Z | https://github.com/Netflix/metaflow/issues/1640 | [] | Felix-N | 6 |
jupyter-book/jupyter-book | jupyter | 1,836 | Issue on page /content/executable/output-insert.html | Typo "historgram" in the code block following "You can also glue visualizations, such as Matplotlib figures" | closed | 2022-09-18T12:35:43Z | 2022-09-21T04:28:59Z | https://github.com/jupyter-book/jupyter-book/issues/1836 | [] | clairedavid | 1 |
deeppavlov/DeepPavlov | nlp | 1,085 | Is there any way to build docker image for new trained NER model? | How to build a docker image for the newly trained NER model by DeepPavlov? | closed | 2019-11-27T08:21:03Z | 2020-05-06T10:18:56Z | https://github.com/deeppavlov/DeepPavlov/issues/1085 | [] | DLNSimha | 1 |
plotly/dash-core-components | dash | 919 | dcc.RangeSlider tooltip broken between 0 and 10 | The tooltip in the `dcc.RangeSlider` breaks between 0 and 10, but appears to work properly in the `dcc.Slider`.
This issue appeared in the past for the `dcc.Slider` as a result of a bug with the library that `dcc.Slider` is wrapped around: https://github.com/plotly/dash-core-components/issues/763
If this is the case, and it appears to be fixed, than perhaps the `dcc.RangeSlider` library needs to be updated as well?
The top slider in the `.gif` below is a `dcc.RangeSlider` and the bottom is a `dcc.Slider`.
Sample code:
```
from dash import Dash
import dash_core_components as dcc
import dash_html_components as html
# Build App
app = Dash(__name__)
app.layout = html.Div([
html.Div(
[
dcc.RangeSlider(
min=-50,
max=50,
step=0.5,
value=[5, 15],
# allowCross=False,
tooltip={"always_visible": True}
),
]
),
html.Div(
[
dcc.Slider(
min=0,
max=20,
step=0.5,
value=5,
tooltip={"always_visible": True}
)
], style={"marginTop": "25%"})
]
)
app.run_server(port=8060, debug=True)
```
Dependencies:
```
dash==1.19.0
dash-core-components==1.15.0
dash-html-components==1.1.2
dash-renderer==1.9.0
dash-table==4.11.2
```
CC: @kully | closed | 2021-01-20T05:23:19Z | 2021-05-14T00:41:58Z | https://github.com/plotly/dash-core-components/issues/919 | [] | mtwichan | 2 |
httpie/cli | python | 732 | httpie does not allow to redirect binary data | I'm trying to do this:
```
> http https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh | bash
Please run using "bash" or "sh", but not "." or "source"\nbash: eilutė 14: return: galima grįžti (return) tik iš funkcijos ar scenarijaus
wc: /home/sirex/devel/talks/2018/cartocon/bash: Toks failas ar aplankas neegzistuoja
ERROR: size of bash should be 62574861 bytes
```
Documentation says:
```
The default behaviour is 'hb' (i.e., the response headers and body
is printed), if standard output is not redirected. If the output is piped
to another program or to a file, then only the response body is printed
by default.
```
I thin check for binary data for redirected output should be disabled or at least there should be a flag to disable the check. For example I might want to redirect binary data to a file or to a program that expects binary data.
Full output:
```
> http https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
HTTP/1.1 200 OK
Accept-Ranges: bytes
Connection: keep-alive
Content-Length: 62574861
Content-Type: application/x-sh
...
+-----------------------------------------+
| NOTE: binary data not shown in terminal |
+-----------------------------------------+
``` | closed | 2018-11-24T11:17:52Z | 2018-11-24T14:36:16Z | https://github.com/httpie/cli/issues/732 | [] | sirex | 1 |
proplot-dev/proplot | data-visualization | 365 | Wrong position of xlabel in Cartesian subplot mixed with Geographic subplots | ### Description
The location of xlabel is usually in the middle of subplots. But, it doesn't work for Cartesian and Geographic mixed plots.
### Steps to reproduce
```python
import proplot as pplt
fig, axs = pplt.subplots(nrows=2, ncols=2, proj=['npstere', 'npstere', None, 'npstere'])
axs[2].format(xlabel='xlabel for Cartesian subplot')
```
**Expected behavior**:
The xlabel in the middle of subplot3 xaxis.
**Actual behavior**:
### Proplot version
```
3.5.1
0.9.5.post202
``` | open | 2022-06-05T13:50:13Z | 2023-03-29T10:02:21Z | https://github.com/proplot-dev/proplot/issues/365 | [
"enhancement"
] | zxdawn | 2 |
mars-project/mars | scikit-learn | 2,956 | Submit query condition to remote node instead of fetch to local then query | <!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Is your feature request related to a problem? Please describe.**
Curently if ray fetcher gets objects with condition, it will fetch objects to local, then filter local objects with conditions. This will incur much objects transfer cost and high memory footsprint in local node.
**Describe the solution you'd like**
We should submit query to objects' node then fetch queried result instead.
| closed | 2022-04-24T12:06:51Z | 2022-04-25T03:45:07Z | https://github.com/mars-project/mars/issues/2956 | [] | chaokunyang | 0 |
mwaskom/seaborn | matplotlib | 3,002 | Global matplotlib style with objects interface | Hey! thanks for the amazing 0.12 release 🚀 !
I want to use the new object interface while using a global `matplotlib` style following https://seaborn.pydata.org/generated/seaborn.objects.Plot.theme.html#seaborn.objects.Plot.theme
I have tried:
```python
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn.objects as so
anscombe = sns.load_dataset("anscombe")
plt.style.use("bmh")
sns.set_theme({**style.library["bmh"]})
sns.set_style({**style.library["bmh"]})
sns.set_theme(rc=style.library["bmh"])
sns.set_style(rc=style.library["bmh"])
p = (
so.Plot(anscombe, "x", "y")
.facet("dataset", wrap=2)
.add(so.Line(), so.PolyFit(order=1))
.add(so.Dot())
)
p
```
but I am still getting:
Any tips on how to make this work? Thanks!
**Remark:** With the "old" seaborn's functional API it works as expected. | closed | 2022-09-06T13:18:09Z | 2022-09-08T10:38:00Z | https://github.com/mwaskom/seaborn/issues/3002 | [] | juanitorduz | 2 |
piccolo-orm/piccolo | fastapi | 1,013 | Add more operators to `QueryString` | In recent releases we have been adding more SQL functions to Piccolo.
These functions return `QueryString` - the building block of queries in Piccolo.
By overriding magic methods in `QueryString` we can make a more intuitive API. For example:
```python
await Ticket.select(Round(Ticket.price) * 2)
```
In the example above, `Round` returns a `QueryString`, and by overriding the `__mul__` magic method on `QueryString`, we can make it so `Round(Ticket.price) * 2` returns a new `QueryString` which compiles down to `round(price) * 2` in SQL.
| closed | 2024-06-10T20:27:35Z | 2024-06-10T21:07:58Z | https://github.com/piccolo-orm/piccolo/issues/1013 | [
"enhancement"
] | dantownsend | 0 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 802 | Discriminator loss (D_real & D_fake loss) is always between 0.001 to 1.5 | Hi,
I training a pix2pix model with my own dataset with 8000 images. The loss for G_L1 converges very fast like in 20 epochs or so. But the other losses like D_real and D_fake always remains between 0.0001 to 1.5 in almost all 64 epochs i have trained so far. Is my model is learning or not? I have attached the logs as txt file.
[logloss.txt](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/files/3738812/logloss.txt)
| closed | 2019-10-17T10:51:36Z | 2022-05-15T16:11:42Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/802 | [] | kalai2033 | 4 |
huggingface/diffusers | pytorch | 10,045 | Problem with importing pipelines | ### Describe the bug
When trying to import pipelines in colab i have the following error:
RuntimeError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/transformers/utils/import_utils.py](https://localhost:8080/#) in _get_module(self, module_name)
1777 try:
-> 1778 return importlib.import_module("." + module_name, self.__name__)
1779 except Exception as e:
34 frames
RuntimeError: operator torchvision::nms does not exist
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
RuntimeError: Failed to import transformers.models.clip.image_processing_clip because of the following error (look up to see its traceback):
operator torchvision::nms does not exist
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
[/usr/local/lib/python3.10/dist-packages/diffusers/utils/import_utils.py](https://localhost:8080/#) in _get_module(self, module_name)
853 return importlib.import_module("." + module_name, self.__name__)
854 except Exception as e:
--> 855 raise RuntimeError(
856 f"Failed to import {self.__name__}.{module_name} because of the following error (look up to see its"
857 f" traceback):\n{e}"
RuntimeError: Failed to import diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl because of the following error (look up to see its traceback):
Failed to import transformers.models.clip.image_processing_clip because of the following error (look up to see its traceback):
operator torchvision::nms does not exist
### Reproduction
!pip install -Upgrade torch torchvision
!pip install -Upgrade diffusers["torch"] transformers
!pip install -U accelerate
!pip install -U peft
!pip install -U compel
from diffusers import StableDiffusionXLPipeline
### Logs
_No response_
### System Info
Cannot send.
When trying to run diffusers-cli env have the following error:
RuntimeError: Failed to import transformers.models.bloom.modeling_bloom because of the following error (look up to see its traceback):
operator torchvision::nms does not exist
### Who can help?
_No response_ | closed | 2024-11-28T12:50:50Z | 2024-11-28T18:03:57Z | https://github.com/huggingface/diffusers/issues/10045 | [
"bug"
] | justahumanb | 0 |
streamlit/streamlit | streamlit | 10,389 | First Day of the week is still Sunday | ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar issues.
- [x] I added a very descriptive title to this issue.
- [x] I have provided sufficient information below to help reproduce this issue.
### Summary
As @jrieke said in the [issue](https://github.com/streamlit/streamlit/issues/5215#issuecomment-2654284129), I'm opening a new issue.
I use `Streamlit v1.42.0` but for me it's still showing Sunday (USA style) as the first day of the week.
I set my region as `Ireland` but it didn't fix the issues. (python3.10 and Ubuntu 24.04 x86_64).
### Reproducible Code Example
```Python
import streamlit as st
from streamlit import runtime
from subprocess import Popen
def main():
st.date_input("Select the cutoff date:", format="DD/MM/YYYY")
if __name__ == "__main__":
if runtime.exists():
main()
else:
process = Popen(["streamlit", "run", "datehandler.py"])
```
### Steps To Reproduce
_No response_
### Expected Behavior
_No response_
### Current Behavior
_No response_
### Is this a regression?
- [ ] Yes, this used to work in a previous version.
### Debug info
- Streamlit version: 1.42.0
- Python version: 3.10.12 (`cpython-3.10.12-linux-x86_64-gnu` from `uv`)
- Operating System: GNU/Linux - Ubuntu 24.04 x86_64
- Browser: Opera
### Additional Information
_No response_ | closed | 2025-02-13T09:05:01Z | 2025-02-14T08:29:35Z | https://github.com/streamlit/streamlit/issues/10389 | [
"upstream"
] | sglbl | 2 |
chaos-genius/chaos_genius | data-visualization | 868 | Port number changes automatically on UI datasource/channel config Form | ## Describe the bug
when on Datasource add/Alert channel(Email) add form if port number field is focused and someone scrolls or presses up/down key the port number changes
## Explain the environment
- **Chaos Genius version**: 0.5.0
- **OS Version / Instance**: Debian 11
- **Deployment type**: Docker
## Current behavior
Port number changes and user might not realise how it changed and mistaking that they put wrong port
## Expected behavior
should not change without user's explicit editing
## Screenshots
## Additional context
- occurs on both Datasource add form and Email channel add form
- The port number input field is of input type "number" and this is because number fields behave in a way that whenever someone presses up/down key or scrolls while focus is on field it will decrease or increase the value
## Logs
N/A
| open | 2022-04-01T04:07:49Z | 2022-04-01T04:08:50Z | https://github.com/chaos-genius/chaos_genius/issues/868 | [
"🐛 bug",
"🖥️ frontend"
] | rjdp | 0 |
graphql-python/gql | graphql | 225 | Phoenix transport: breaking early from async generator loop requires canceling receive_data_task | I don't know if there is a Slack channel or other forum for the gql project, so I'm posing this here as an issue. I would like to see an example of a subscription that uses one of the async transports (such as PhoenixChannelWebsocketsTransport) for the use case below. It's something that I had working with gql 2.x, after adding my own (probably naive) implementation of Phoenix channels. The target server is Absinthe GraphQL.
The inputs for the process are:
* a mutation document and variables
* a subscription document and variables (subscription updates are triggered by the mutation)
* a coroutine (or asyncio task?) that can act on each subscription update received, as indicated below
* an optional timeout, after which the subscription will be ended
Then the process is:
1. Send a subscription request with the coroutine as a parameter.
2. If the subscription is successful, send a mutation request that will trigger subscription updates.
3. As each update arrives on the subscription (inside an async generator for loop), the coroutine acts on it (for example printing, or parsing the ExecutionResult and processing results). The coroutine may also, as it parses an update, decide to end the subscription if it decides that no more updates are expected, and return any data it wants.
4. If the timeout is exceeded, the process will unsubscribe, and return any results collected by the coroutine, as well as an indication that the process ended via a timeout.
It looks like the standard way a subscription is ended is when it receives a "complete" message on the websocket, but in my case I want the client to decide when to stop subscribing. | closed | 2021-08-10T02:20:33Z | 2021-08-22T16:40:43Z | https://github.com/graphql-python/gql/issues/225 | [
"type: bug"
] | pzingg | 13 |
Lightning-AI/pytorch-lightning | data-science | 19,810 | Issue in Manual optimisation, during self.manual_backward call | ### Bug description
I have set automatic_optimization to False, and am using self.manual_backward to calculate and populate the gradients. The code breaks during the self.manual_backward call, raising the error "RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn". I have posted the code below for replicating the issue.
The issue does not arise when I set args['use_minibatch_clip_loss'] = False, or when I set args['batch_size'] = args['minibatch_size'] = 16. I suspect the issue only arises when I try to do backwards after running the model under torch.no_grad()
### What version are you seeing the problem on?
v2.2
### How to reproduce the bug
```python
import os
import math
import torch
from torch import nn
from torch.utils.data import TensorDataset, DataLoader
import lightning.pytorch as pl
from lightning.pytorch import loggers as pl_loggers
class TestModule(nn.Module):
def __init__(self, in_dim=512, out_dim=16):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.query = nn.Linear(self.in_dim, self.out_dim, bias=True)
def forward(self, input):
return self.query(input)
class TestLitModel(pl.LightningModule):
def __init__(self, args):
super().__init__()
self.test_module_obj = TestModule(args['in_dim'], args['out_dim'])
self.use_minibatch_clip_loss = args['use_minibatch_clip_loss']
if self.use_minibatch_clip_loss:
self.batch_size = args['batch_size']
self.minibatch_size = args['minibatch_size']
self.accumulate_grad_batches_mb = args['accumulate_grad_batches_mb']
self.automatic_optimization = False
def get_mini_batches(self, input):
num_mb = math.ceil(self.batch_size / self.minibatch_size)
return torch.chunk(input, num_mb)
def shared_step(self, input):
output = self.test_module_obj(input)
loss = output.mean()
return loss
def train_step_minibatch(self, input, batch_idx):
if self.batch_size > self.minibatch_size:
mini_batches = self.get_mini_batches(input)
mb_model_output_list = list()
with torch.no_grad():
for mb in mini_batches:
mb_model_output_list.append(self.shared_step(mb).detach())
all_loss = sum(mb_model_output_list)
self.test_module_obj.train()
self.test_module_obj.requires_grad_(True)
torch.set_grad_enabled(True)
assert torch.is_grad_enabled()
assert all(p.requires_grad for p in self.test_module_obj.parameters())
for _, mb in enumerate(mini_batches):
mb_model_output = self.shared_step(mb)
self.manual_backward(mb_model_output)
else:
all_loss = self.shared_step(input)
self.manual_backward(all_loss)
# get optimizers and scheduler
if (batch_idx + 1) % self.accumulate_grad_batches_mb == 0:
optimizer = self.optimizers()
if isinstance(optimizer, list):
optimizer = optimizer[0]
optimizer.step()
optimizer.zero_grad()
return all_loss
def training_step(self, batch, batch_idx):
input = batch[0]
if self.use_minibatch_clip_loss:
loss = self.train_step_minibatch(input, batch_idx)
else:
loss = self.shared_step(input)
return loss
def validation_step(self, batch, batch_idx):
input = batch[0]
loss = self.shared_step(input)
return loss
def configure_optimizers(self):
self.optimizer = torch.optim.AdamW(list(self.test_module_obj.parameters()), lr= 0.0002, weight_decay= 0.01)
return {"optimizer": self.optimizer}
if __name__ == '__main__':
args = {
'in_dim': 512,
'out_dim': 16,
'train_batch_size': 16,
'val_batch_size': 64,
'use_minibatch_clip_loss': True,
'batch_size': 16,
'minibatch_size': 4,
'accumulate_grad_batches_mb': 1,
}
x_dummy = torch.randn(512, args['in_dim']) # 512 samples, args['in_dim'] features each
test_data_loader = DataLoader(TensorDataset(x_dummy), batch_size=args['train_batch_size'], shuffle=False) # Dummy dataset
test_lit_model = TestLitModel(args)
# -- LOGGING
checkpoint_dir = 'test_logs/'
tb_logger = pl_loggers.TensorBoardLogger(save_dir=os.path.join(checkpoint_dir, "logs"))
trainer = pl.Trainer(
logger=tb_logger,
accelerator='gpu',
devices=[1],
strategy='auto',
precision='16-mixed',
max_epochs=1,
accumulate_grad_batches=1,
num_sanity_val_steps=0,
inference_mode=False,
)
trainer.fit(test_lit_model, test_data_loader)
```
### Error messages and logs
```
Epoch 0: 0%| | 0/32 [00:00<?, ?it/s]Traceback (most recent call last):
File "/home/users/pranav.rao/foundational_models/test1.py", line 119, in <module>
trainer.fit(test_lit_model, test_data_loader)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py", line 44, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 580, in _fit_impl
self._run(model, ckpt_path=ckpt_path)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 987, in _run
results = self._run_stage()
^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 1033, in _run_stage
self.fit_loop.run()
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py", line 205, in run
self.advance()
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py", line 363, in advance
self.epoch_loop.run(self._data_fetcher)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 140, in run
self.advance(data_fetcher)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/training_epoch_loop.py", line 252, in advance
batch_output = self.manual_optimization.run(kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/manual.py", line 94, in run
self.advance(kwargs)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/manual.py", line 114, in advance
training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py", line 309, in _call_strategy_hook
output = fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/strategies/strategy.py", line 391, in training_step
return self.lightning_module.training_step(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/foundational_models/test1.py", line 74, in training_step
loss = self.train_step_minibatch(input, batch_idx)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/users/pranav.rao/foundational_models/test1.py", line 57, in train_step_minibatch
self.manual_backward(mb_model_output)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/core/module.py", line 1071, in manual_backward
self.trainer.strategy.backward(loss, None, *args, **kwargs)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/strategies/strategy.py", line 213, in backward
self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/plugins/precision/precision.py", line 72, in backward
model.backward(tensor, *args, **kwargs)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/lightning/pytorch/core/module.py", line 1090, in backward
loss.backward(*args, **kwargs)
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/torch/_tensor.py", line 525, in backward
torch.autograd.backward(
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/torch/autograd/__init__.py", line 267, in backward
_engine_run_backward(
File "/home/users/pranav.rao/miniconda3/envs/pytorch_lightning_bug_report/lib/python3.11/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
```
### Environment
<details>
<summary>Current environment</summary>
* CUDA:
- GPU:
- NVIDIA A100 80GB PCIe
- NVIDIA A100 80GB PCIe
- NVIDIA A100 80GB PCIe
- NVIDIA A100 80GB PCIe
- available: True
- version: 12.1
* Lightning:
- lightning: 2.2.3
- lightning-utilities: 0.11.2
- pytorch-lightning: 2.2.3
- torch: 2.3.0
- torchmetrics: 1.3.2
* Packages:
- absl-py: 2.1.0
- aiohttp: 3.9.5
- aiosignal: 1.3.1
- attrs: 23.2.0
- filelock: 3.13.4
- frozenlist: 1.4.1
- fsspec: 2024.3.1
- grpcio: 1.62.2
- idna: 3.7
- jinja2: 3.1.3
- lightning: 2.2.3
- lightning-utilities: 0.11.2
- markdown: 3.6
- markupsafe: 2.1.5
- mpmath: 1.3.0
- multidict: 6.0.5
- networkx: 3.3
- numpy: 1.26.4
- nvidia-cublas-cu12: 12.1.3.1
- nvidia-cuda-cupti-cu12: 12.1.105
- nvidia-cuda-nvrtc-cu12: 12.1.105
- nvidia-cuda-runtime-cu12: 12.1.105
- nvidia-cudnn-cu12: 8.9.2.26
- nvidia-cufft-cu12: 11.0.2.54
- nvidia-curand-cu12: 10.3.2.106
- nvidia-cusolver-cu12: 11.4.5.107
- nvidia-cusparse-cu12: 12.1.0.106
- nvidia-nccl-cu12: 2.20.5
- nvidia-nvjitlink-cu12: 12.4.127
- nvidia-nvtx-cu12: 12.1.105
- packaging: 24.0
- pip: 23.3.1
- protobuf: 5.26.1
- pytorch-lightning: 2.2.3
- pyyaml: 6.0.1
- setuptools: 68.2.2
- six: 1.16.0
- sympy: 1.12
- tensorboard: 2.16.2
- tensorboard-data-server: 0.7.2
- torch: 2.3.0
- torchmetrics: 1.3.2
- tqdm: 4.66.2
- triton: 2.3.0
- typing-extensions: 4.11.0
- werkzeug: 3.0.2
- wheel: 0.41.2
- yarl: 1.9.4
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.11.9
- release: 5.15.0-69-generic
- version: #76~20.04.1-Ubuntu SMP Mon Mar 20 15:54:19 UTC 2023
</details>
### More info
I am training a Vision Language model with CLIP loss. The batch size I want to use is large, which requires to calculate the embeddings in mini batches and then calculate the gradient in mini batches as done in the repo https://github.com/Zasder3/train-CLIP/tree/main (See lines: https://github.com/Zasder3/train-CLIP/blob/79d4c7960072047a9e0d39335ab60dcb150640c3/models/wrapper.py#L64-L109 )
The issue arose when I implemented the similar algorithm as above for my use case and tried to train it. I have tried to isolate the problem as much I could, and produce a simple script reproducing the same error I get.
cc @carmocca @justusschock @awaelchli | closed | 2024-04-25T10:23:19Z | 2024-10-28T14:36:26Z | https://github.com/Lightning-AI/pytorch-lightning/issues/19810 | [
"question",
"precision: amp",
"ver: 2.0.x"
] | pranavrao-qure | 3 |
minimaxir/textgenrnn | tensorflow | 62 | Multi-GPU Support not working great | I added a Multi-GPU function that extended Keras's `multi_gpu_model()` and verified that it indeed utilizes multiple GPUs (via `nvidia-smi`). However, for smaller models, training speed is about the same, if not *worse*.
I suspect that the `CuDNNLSTM`s are so efficient that it mitigates the gains of using multiple GPUs are lost from overhead.
Would using Horovod fix this? I have no issues implementing that instead. | open | 2018-08-08T05:40:17Z | 2019-12-12T05:25:21Z | https://github.com/minimaxir/textgenrnn/issues/62 | [] | minimaxir | 3 |
DistrictDataLabs/yellowbrick | matplotlib | 453 | Implement Detection Error Tradeoff Curves (DET) Visualizer | A community suggestion from reddit, implement a DET curve visualizer for model comparison.
From [wikipedia](https://en.wikipedia.org/wiki/Detection_error_tradeoff):
`A detection error tradeoff (DET) graph is a graphical plot of error rates for binary classification systems, plotting the false rejection rate vs. false acceptance rate.[1] The x- and y-axes are scaled non-linearly by their standard normal deviates (or just by logarithmic transformation), yielding tradeoff curves that are more linear than ROC curves, and use most of the image area to highlight the differences of importance in the critical operating region.`
Aspiration Wikipedia image comparing multiple models:
Sample Code for plotting one model on a mpt object. Reference link below:
```
from matplotlib import pyplot as plt
def DETCurve(fps,fns):
"""
Given false positive and false negative rates, produce a DET Curve.
The false positive rate is assumed to be increasing while the false
negative rate is assumed to be decreasing.
"""
axis_min = min(fps[0],fns[-1])
fig,ax = plt.subplots()
plot(fps,fns)
yscale('log')
xscale('log')
ticks_to_use = [0.001,0.002,0.005,0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10,20,50]
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_xticks(ticks_to_use)
ax.set_yticks(ticks_to_use)
axis([0.001,50,0.001,50])
```
**Suggested Visualizer Interface**
```
models = [LogisticRegression(), AnotherModel(), DifferentModel()]
viz = DETCurve(models)
viz.fit(X, y)
viz.poof()
```
Sample Code Reference Link
https://jeremykarnowski.wordpress.com/2015/08/07/detection-error-tradeoff-det-curves/
Source Reddit comment
https://www.reddit.com/r/MachineLearning/comments/8mbif5/news_new_release_of_python_ml_visualization/dzpford/
| closed | 2018-05-28T14:14:00Z | 2020-06-12T05:07:19Z | https://github.com/DistrictDataLabs/yellowbrick/issues/453 | [
"type: feature",
"priority: medium",
"level: intermediate"
] | ndanielsen | 2 |
1313e/CMasher | matplotlib | 53 | Matplotlib deprecation warnings | On matplotlib 3.7 I am getting the following errors when importing and using cmasher
MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later.
Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
cmap = mplcm.get_cmap(cmap)
And the following:
MatplotlibDeprecationWarning: The register_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later.
Use ``matplotlib.colormaps.register(name)`` instead.
mplcm.register_cmap(cmap=cmap_mpl_r)
These methods should be updated as described.
| closed | 2023-08-31T13:09:19Z | 2023-12-18T07:46:29Z | https://github.com/1313e/CMasher/issues/53 | [
"feature request"
] | christopherlovell | 3 |
chezou/tabula-py | pandas | 258 | Characters out of the desired area are appearing in my tables | # Summary of your issue
Some characters are leaking from useless cells to the output dataframe
# Check list before submit
<!--- Write and check the following questionaries. -->
- [ ] Did you read [FAQ](https://tabula-py.readthedocs.io/en/latest/faq.html)? yes
- [ ] (Optional, but really helpful) Your PDF URL: https://www.cge.cl/wp-content/uploads/2020/08/Tarifas-de-Suministro-CGE-Agosto-2020.pdf
- [ ] Paste the output of `import tabula; tabula.environment_info()` on Python REPL: ?
>>> tabula.environment_info()
Python version:
3.6.9 (default, Jul 17 2020, 12:50:27)
[GCC 8.4.0]
Java version:
openjdk version "1.8.0_265"
OpenJDK Runtime Environment (build 1.8.0_265-8u265-b01-0ubuntu2~18.04-b01)
OpenJDK 64-Bit Server VM (build 25.265-b01, mixed mode)
tabula-py version: 2.1.1
platform: Linux-5.4.0-42-generic-x86_64-with-Ubuntu-18.04-bionic
uname:
uname_result(system='Linux', node='papo-MACH-WX9', release='5.4.0-42-generic', version='#46~18.04.1-Ubuntu SMP Fri Jul 10 07:21:24 UTC 2020', machine='x86_64', processor='x86_64')
linux_distribution: ('Ubuntu', '18.04', 'bionic')
mac_ver: ('', ('', '', ''), '')
# What did you do when you faced the problem?
Here is a specific problem, but it happens in a lot of parts in the results of any page
## Code:
top = 177.0
left = 66.0
buttom = 187.0
right = 698.0
table = tabula.read_pdf('https://www.cge.cl/wp-content/uploads/2020/08/Tarifas-de-Suministro-CGE-Agosto-2020.pdf',area= [top,left,buttom,right],pages=2,lattice=True, pandas_options={'header': None})
print(table[0].loc[0][25])
## Expected behavior:
20,274
## Actual behavior:
fijo2 0m,2e7n4s
Some of the characters in the index rows randomly appear in the dataframe | closed | 2020-08-18T20:43:54Z | 2020-08-24T13:53:51Z | https://github.com/chezou/tabula-py/issues/258 | [
"tabula-java limitation"
] | Pgovalle | 1 |
scikit-hep/awkward | numpy | 2,656 | Unflattening typetracer on axis > 0 fails | ### Version of Awkward Array
2.3.3
### Description and code to reproduce
```python3
import json
import awkward as ak
fromjson = {
"class": "RecordArray",
"fields": ["muon", "anindex"],
"contents": [
{
"class": "ListOffsetArray",
"offsets": "i64",
"content": {
"class": "RecordArray",
"fields": ["pt", "eta", "phi", "crossref", "thing1"],
"contents": [
{
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "muon_pt!",
},
{
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "muon_eta!",
},
{
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "muon_phi!",
},
{
"class": "ListOffsetArray",
"offsets": "i64",
"content": {
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "muon_crossref_content!",
},
"parameters": {},
"form_key": "muon_crossref_index!",
},
{
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "muon_thing1!",
},
],
"parameters": {},
"form_key": "muon_record!",
},
"parameters": {},
"form_key": "muon_list_outer!",
},
{
"class": "NumpyArray",
"primitive": "int64",
"inner_shape": [],
"parameters": {},
"form_key": "anindex!",
},
],
"parameters": {},
"form_key": "outer!",
}
form = ak.forms.from_json(json.dumps(fromjson))
ttlayout, report = ak._nplikes.typetracer.typetracer_with_report(form, forget_length=True)
ttarray = ak.Array(ttlayout, behavior=ak.behavior)
for i in range(2):
print(f"unflattening axis={i}")
ak.unflatten(ttarray.muon.pt, ttarray.anindex, axis=i)
```
fails with:
```
(coffea-dev) lgray@Lindseys-MacBook-Pro coffea % python form_madness15.py
unflattening axis=0
unflattening axis=1
Traceback (most recent call last):
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_dispatch.py", line 60, in dispatch
next(gen_or_result)
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/operations/ak_unflatten.py", line 83, in unflatten
return _impl(array, counts, axis, highlevel, behavior)
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/operations/ak_unflatten.py", line 222, in _impl
out = ak._do.recursively_apply(layout, apply)
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_do.py", line 35, in recursively_apply
return layout._recursively_apply(
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/contents/listoffsetarray.py", line 2157, in _recursively_apply
result = action(
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/operations/ak_unflatten.py", line 211, in apply
if not backend.index_nplike.array_equal(
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_nplikes/typetracer.py", line 473, in __bool__
raise RuntimeError("cannot realise an unknown value")
RuntimeError: cannot realise an unknown value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "form_madness15.py", line 84, in <module>
ak.unflatten(ttarray.muon.pt, ttarray.anindex, axis=i)
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_dispatch.py", line 68, in dispatch
return gen_or_result
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_errors.py", line 67, in __exit__
self.handle_exception(exception_type, exception_value)
File "/Users/lgray/miniforge3/envs/coffea-dev/lib/python3.8/site-packages/awkward/_errors.py", line 82, in handle_exception
raise self.decorate_exception(cls, exception)
RuntimeError: cannot realise an unknown value
This error occurred while calling
ak.unflatten(
<Array-typetracer [...] type='## * var * int64'>
<Array-typetracer [...] type='## * int64'>
axis = 1
)
```
@agoose77 this appears to be required for https://github.com/CoffeaTeam/coffea/pull/822 (or at least the studies required to get cleaner distillations of the objects we really want) | closed | 2023-08-17T18:51:51Z | 2023-08-17T19:55:44Z | https://github.com/scikit-hep/awkward/issues/2656 | [
"bug (unverified)"
] | lgray | 0 |
tartiflette/tartiflette | graphql | 92 | "official" Introspection Query doesn't work | It seems that the [official introspection Query](https://github.com/graphql/graphql-js/blob/master/src/utilities/introspectionQuery.js#L18) doesn't work. Thus, we can't use the full potential of tools which use it to provide auto-completion, like insomnia, graphiql etc...
* **Tartiflette version:** 0.3.2
* **Python version:** 3.7.1
* **Executed in docker:** No
* **Is a regression from a previous versions?** No
## GraphQL Query
```gql
query IntrospectionQuery {
__schema {
queryType {
name
}
mutationType {
name
}
subscriptionType {
name
}
types {
...FullType
}
directives {
name
locations
args {
...InputValue
}
}
}
}
fragment FullType on __Type {
kind
name
fields(includeDeprecated: true) {
name
args {
...InputValue
}
type {
...TypeRef
}
isDeprecated
deprecationReason
}
inputFields {
...InputValue
}
interfaces {
...TypeRef
}
enumValues(includeDeprecated: true) {
name
isDeprecated
deprecationReason
}
possibleTypes {
...TypeRef
}
}
fragment InputValue on __InputValue {
name
type {
...TypeRef
}
defaultValue
}
fragment TypeRef on __Type {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
ofType {
kind
name
}
}
}
}
}
}
}
}
```
## Stacktrace
```json
{
"data": null,
"errors": [
{
"message": "Undefined fragment < FullType >.",
"path": null,
"locations": [
{
"line": 7,
"column": 11
}
]
},
{
"message": "field `__Type.directives` was not found in GraphQL schema.",
"path": [
"__schema",
"types",
"directives"
],
"locations": [
{
"line": 9,
"column": 9
}
]
},
{
"message": "Undefined fragment < InputValue >.",
"path": null,
"locations": [
{
"line": 13,
"column": 13
}
]
},
{
"message": "Fragment < TypeRef > is never used.",
"path": null,
"locations": [
{
"line": 53,
"column": 5
}
]
}
]
}
``` | closed | 2019-01-17T10:29:08Z | 2019-01-22T13:28:08Z | https://github.com/tartiflette/tartiflette/issues/92 | [
"bug"
] | tsunammis | 2 |
xorbitsai/xorbits | numpy | 583 | CHORE: too many warnings when running TPCH | Note that the issue tracker is NOT the place for general support. For
discussions about development, questions about usage, or any general questions,
contact us on https://discuss.xorbits.io/.
```python
/Users/codingl2k1/Work/xorbits/python/xorbits/_mars/dataframe/tseries/to_datetime.py:195: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
```
```python
/Users/codingl2k1/Work/xorbits/python/xorbits/_mars/dataframe/groupby/aggregation.py:1288: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
pd.set_option("mode.use_inf_as_na", op.use_inf_as_na)
```
```python
/Users/codingl2k1/Work/xorbits/python/xorbits/_mars/dataframe/groupby/aggregation.py:1298: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
pd.reset_option("mode.use_inf_as_na")
``` | closed | 2023-07-06T08:25:41Z | 2023-08-28T08:55:38Z | https://github.com/xorbitsai/xorbits/issues/583 | [
"good first issue"
] | codingl2k1 | 1 |
jupyterhub/repo2docker | jupyter | 1,324 | repo2docker.ref contains HEAD rather than the actual commit ID at the time of creation | <!-- Thank you for contributing. These HTML commments will not render in the issue, but you can delete them once you've read them if you prefer! -->
### Bug description
<!-- Use this section to clearly and concisely describe the bug. -->
When creating an image, if HEAD is used, then the `repo2docker.ref` label will contain that value which is not really useful. One must start a container and use git to know which commit was used to build the container.
The same goes if `--ref` is not used, then the ref will be `None`
#### Expected behaviour
<!-- Tell us what you thought would happen. -->
When HEAD is used, the commit hash should be put it in `repo2docker.ref`.
Same goes if `--ref` is not used.
#### Actual behaviour
<!-- Tell us what you actually happens. -->
`repo2docker.ref` contains the literal `HEAD` when `--ref HEAD` is used.
`repo2docker.ref` contains the literal `None` when `--ref` is not used.
### How to reproduce
<!-- Use this section to describe the steps that a user would take to experience this bug. -->
1. clone a repo2docker repository
2. run `repo2docker --no-run --no-push --image-name label-test .`
3. docker image inspect label-test -f '{{ index .Config.Labels "repo2docker.ref" }}'
4. See `None`
### Your personal set up
<!-- Tell us a little about the system you're using. You can see the guidelines for setting up and reporting this information at https://repo2docker.readthedocs.io/en/latest/contributing/contributing.html#setting-up-for-local-development. -->
- OS: [linux, OSX]
- Docker version: Docker Desktop 4.25.1 (128006)
- repo2docker version: 2023.06.0+47.g1bb3cba (development installation)
| open | 2023-12-13T17:36:18Z | 2023-12-22T16:34:05Z | https://github.com/jupyterhub/repo2docker/issues/1324 | [] | sgaist | 1 |
pydantic/pydantic-ai | pydantic | 646 | Documentation on agent control flow | I've been trying to understand the control flow for agent runs, which I think others are also trying to learn about. Here are some of the related issues for context:
https://github.com/pydantic/pydantic-ai/issues/642
https://github.com/pydantic/pydantic-ai/pull/142
https://github.com/pydantic/pydantic-ai/issues/127
It would be very helpful for someone with deep knowledge of the library to create a documentation section for control flow within agents. These are some examples of what I want to understand:
1. inside `agent.run()` what is executed sequential v.s. concurrently (post to LLM provider, tool calls, result validation, etc.)?
2. are there points where coroutines within `agent.run()` can be cancelled?
3. at what points will control be given back to the caller of `agent.run()`?
4. which of these points are considered "success" cases and which ones "error" cases?
I'm trying to write a user application, where the agent might be sending data back to the user while it's running (see example below), so it's really helpful to be crystal clear on what's happening inside the agent.
```py
import dataclasses
from fastapi import WebSocket
from pydantic_ai import Agent, RunContext
@dataclasses.dataclass
class AgentDeps:
websocket: WebSocket
agent = Agent(
"openai:gpt-4o",
)
@agent.tool
async def do_something(ctx: RunContext[AgentDeps], message: str) -> None:
"""Do the thing.
Args:
ctx: The call context.
message: The info from my llm.
"""
await ctx.deps.websocket.send(f"The llm sent me this: {message}")
print("Agent did the thing.")
async def run_context(websocket: WebSocket, user_message: str):
# Doing something before running the agent
print("Doing something before...")
# Blocks while agent is running
await agent.run(user_prompt=user_message, deps=AgentDeps(websocket=websocket))
# Doing something after running the agent
print("Doing something after...")
```
I've also seen some examples thrown around where agent-runs are initiated in tools. The documentation would also help us reason about how that pattern would be executed under the hood. | open | 2025-01-08T22:47:01Z | 2025-01-11T21:10:56Z | https://github.com/pydantic/pydantic-ai/issues/646 | [
"documentation"
] | dyllamt | 4 |
Johnserf-Seed/TikTokDownload | api | 102 | 脚本并不行，要不给你试试看？ | 可以使用bat脚本实现，例如
@echo off
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
TikTokDownlaod -u xxx -m no
echo 下载完成
单视频保存路径选项将会更新
_Originally posted by @Johnserf-Seed in https://github.com/Johnserf-Seed/TikTokDownload/issues/99#issuecomment-1056104005_
```
@echo off
.\TikTokDownload.exe -u https://v.douyin.com/LWnxUn8/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/LoYqnQd/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/Lo6ug5Y/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/Lo6w2qX/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NdpjNG6/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/Ndp8Smj/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/Ndpbxbh/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/Ndpxp68/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NRfWbY4/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NRPYd2p/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NRPSdJs/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NRP5F8x/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NRPfFPm/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NFya8Gj/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NFfMKr2/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYBXHHH/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYBCETq/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYAsYTB/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYAp4X9/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYUUJKN/ -m no
.\TikTokDownload.exe -u https://v.douyin.com/NYUtpXh/ -m no
echo 下载完成
``` | closed | 2022-03-08T09:08:34Z | 2022-03-22T14:10:02Z | https://github.com/Johnserf-Seed/TikTokDownload/issues/102 | [] | huangsijun17 | 3 |
dask/dask | scikit-learn | 11,420 | merge_asof Throws error when merging to single partitions | **Describe the issue**:
Encountered this issue while trying to debug other issues using a subset of the data I typically use, i.e. the data fit into a single partition. One might argue this isn't the appropriate use case for the Dask library but the error message is also confusing.
**Minimal Complete Verifiable Example**:
```python
import dask.dataframe as ddf
import pandas as pd
left = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
right = pd.DataFrame({"a": [1, 2, 3], "c": [4, 5, 6]})
ddf_left = ddf.from_pandas(left, npartitions=1)
ddf_left = ddf_left.set_index("a", sort=True)
ddf_right = ddf.from_pandas(right, npartitions=1)
ddf_right = ddf_right.set_index("a", sort=True)
ddf.merge_asof(ddf_left, ddf_right, left_index=True, right_index=True, direction="nearest").compute()
```
**Anything else we need to know?**:
**Environment**:
- Dask version: 2024.8.0
- Python version: 3.9
- Operating System: PopOS
- Install method (conda, pip, source): conda
| closed | 2024-10-08T15:12:20Z | 2024-10-10T16:50:25Z | https://github.com/dask/dask/issues/11420 | [
"dask-expr"
] | kylehiroyasu | 0 |
miguelgrinberg/microblog | flask | 337 | How to solve the syntax conflict between JinJia2 and CSS in chapter 21.3 ? | I met such a question that JinJia2 used in CSS inline style will cause a conflict that the default block string will be misunderstood as a css property value in visual studio code . The detail is :
```
<span id='message_count' class="badge"
style="visibility: {% if new_messages %}visible
{% else %}hidden {% endif %};">
```
---
I happend to find a related plausible solution that is to set <code>app.jinja_env.block_start_string = '(%'</code>, this solution asks me to alter every existed block start string , that would be boring. So is there anyway to solve such question besides this? | closed | 2023-03-02T01:40:59Z | 2023-12-03T10:54:23Z | https://github.com/miguelgrinberg/microblog/issues/337 | [
"question"
] | JTP-123 | 6 |
mouredev/Hello-Python | fastapi | 447 | 网络赌博被黑怎么办？不给出款该怎么办?平台提示账号出现异常该怎么办？注单维护系统审核该怎么办？ | 被黑了不能出款找我们啊超出黑团队多年经验，如果你被黑了请联系我们帮忙把损失降到最低/微x:<>《微信：xiaolu460570》前期出不收任何费用，我们团队都是先出款/后收费。不成功不收费、
当网赢了不能提款的问题怎么办呢？
首先我们应该保持冷静，如果提现被拒绝不重复点了，切不能跟平台客服称代理人有任何争执，一旦激怒对方，极有可能账号造成冻结之类的情况，这样问题就很难处理了，此时对方的理由或者我们要表示相信，并迅速处理，在稳定住对方后希望。
第一时间联系专业出黑团队，通过藏分锁卡等手段分批出款，这样问题我们就顺利解决了，如果您目前遇到网赢钱无法提款的，请及时联系专业出黑团队为您处理↓↓↓↓↓
 | closed | 2025-03-03T12:39:57Z | 2025-03-04T08:41:38Z | https://github.com/mouredev/Hello-Python/issues/447 | [] | 376838 | 0 |
521xueweihan/HelloGitHub | python | 1,980 | https://github.com/mstegen/open-chargeport | ## 项目推荐
- 项目地址：仅收录 GitHub 的开源项目，请填写 GitHub 的项目地址
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| closed | 2021-11-24T02:02:00Z | 2021-11-24T02:02:04Z | https://github.com/521xueweihan/HelloGitHub/issues/1980 | [
"恶意issue"
] | fenglong2018 | 1 |
ivy-llc/ivy | pytorch | 28,419 | Fix Frontend Failing Test: torch - math.paddle.floor | To-do List: https://github.com/unifyai/ivy/issues/27498 | closed | 2024-02-25T14:26:02Z | 2024-03-06T09:54:21Z | https://github.com/ivy-llc/ivy/issues/28419 | [
"Sub Task"
] | Sai-Suraj-27 | 0 |
graphdeco-inria/gaussian-splatting | computer-vision | 620 | Coordinate Transformation | Thank you very much for your open-source code.
I have a question to consult with you. After obtaining a mesh or point cloud using 3D Gaussian splatting, and knowing the GPS information for each frame in the video, how can I transform the relative coordinates in the three-dimensional model into absolute coordinates? Thank you very much! | open | 2024-01-17T12:18:03Z | 2024-01-17T12:18:03Z | https://github.com/graphdeco-inria/gaussian-splatting/issues/620 | [] | oym050922021 | 0 |
gevent/gevent | asyncio | 1,163 | Make libuv the default Windows loop for 1.3? | * gevent version: 1.3
* Python version: All
* Operating System: Windows
I'd like to make the libuv backend the default on Windows for 1.3 for all supported Python implementations (currently it's the default for PyPy). This would benefit Windows users by granting access to more sockets by avoiding libev's use of the C runtime functions that have a fixed size cap, and potentially being more efficient (although since Windows is currently not recommended for production, those may be only nominal benefits). It would benefit the gevent project by (very slightly) increasing the number of testers of the libuv backend.
Windows is currently 14th in download stats for the last 90 days with an order of magnitude fewer downloads than first place.
Our Linux wheels cannot be tested with libuv at all because manylinux1 doesn't support libuv. We'll have to wait for manylinux2010, which PEP is expected to be approved about April 9th, and then see how long it takes to get support for that across the ecosystem.
rank | Version | File | system | download_count
------|--------|-----|--------|-----------------
| 1 | 1.2.2 | gevent-1.2.2-cp27-cp27mu-manylinux1_x86_64.whl | Linux | 632080 |
| 2 | 1.2.2 | gevent-1.2.2-cp35-cp35m-manylinux1_x86_64.whl | Linux | 131471 |
| 3 | 1.2.2 | gevent-1.2.2-cp36-cp36m-manylinux1_x86_64.whl | Linux | 129704 |
| 4 | 1.2.2 | gevent-1.2.2.tar.gz | Linux | 95025 |
| 5 | 1.2.1 | gevent-1.2.1-cp27-cp27mu-manylinux1_x86_64.whl | Linux | 42103 |
|6 | 1.2.1 | gevent-1.2.1.tar.gz | Linux | 29716 |
| 7 | 1.2.2 | gevent-1.2.2-cp34-cp34m-manylinux1_x86_64.whl | Linux | 13935 |
| 8 | 1.2.2 | gevent-1.2.2.tar.gz | null | 11906 |
| 9 | 1.2.1 | gevent-1.2.1-cp36-cp36m-manylinux1_x86_64.whl | Linux | 11860 |
| 10 | 1.2.2 | gevent-1.2.2-cp27-cp27m-macosx_10_6_intel.whl | Darwin | 11405 |
| 11 | 1.3a1 | gevent-1.3a1.tar.gz | null | 9215 |
| 12 | 1.2.2 | gevent-1.2.2-cp36-cp36m-macosx_10_6_intel.whl | Darwin | 6554 |
| 13 | 1.2.1 | gevent-1.2.1-cp35-cp35m-manylinux1_x86_64.whl | Linux | 6290 |
| 14 | 1.2.2 | gevent-1.2.2-cp27-cp27m-win32.whl | Windows | 6281 |
| 15 | 1.2.2 | gevent-1.2.2-cp36-cp36m-win_amd64.whl | Windows | 5800 |
| 16 | 1.2.2 | gevent-1.2.2-cp27-cp27m-manylinux1_x86_64.whl | Linux | 5291 |
| 17 | 1.2.2 | gevent-1.2.2-cp27-cp27mu-manylinux1_x86_64.whl | null | 4980 |
| 18 | 1.2.0 | gevent-1.2.0-cp34-cp34m-manylinux1_x86_64.whl | Linux | 3882 |
| 19 | 1.2.2 | gevent-1.2.2-cp36-cp36m-win32.whl | Windows | 3702 |
| 20 | 1.2.2 | gevent-1.2.2-cp27-cp27m-win_amd64.whl | Windows | 3471 |
Any reason not to do that?
| closed | 2018-03-30T20:16:38Z | 2018-04-14T17:52:25Z | https://github.com/gevent/gevent/issues/1163 | [
"Type: Enhancement",
"PyVer: python3",
"Platform: Windows",
"Loop: libuv"
] | jamadden | 4 |
strawberry-graphql/strawberry-django | graphql | 22 | TypeError: No type defined for 'ImageField' | Hey,
in my User model I've got this field:
```python
def user_directory_path(instance, filename):
filename, ext = os.path.splitext(filename)
return os.path.join(
f'profile_pictures/{instance.uuid}/',
f'{uuid.uuid4()}{ext}'
)
...
profile_picture = models.ImageField(
verbose_name=_('profile picture'),
upload_to=user_directory_path,
blank=True,
)
```
and I guess I'm doing something wrong because I can't define the type of the ImageField.
```python
@types.register
@strawberry_django.type(User, types=types)
class User:
profile_picture: any
profilePicture: any
```
Whatever I try to define it keeps raising the error. What am I missing?
Also: Does this package support all kind of types like "PhoneNumberField" or "versatileimagefield"? Do I have to define these "special" fields alsways as any?
Thanks! | closed | 2021-04-14T08:28:37Z | 2025-03-20T15:56:59Z | https://github.com/strawberry-graphql/strawberry-django/issues/22 | [] | holtergram | 13 |
ultralytics/ultralytics | machine-learning | 19,139 | tracking using a custom dataset where objects have IDs | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
I have a dataset where each of the binding boxes has a unique ID. How can I create a tracking that uses these IDs?
I tried removing the ID and tracking as a normal dataset, but then I run into a problem in the tracking section where the IDs of the objects keep changing. I also used a Kalman filter, but it had no effect.
### Additional
This is an example of a data set.
The first column is the unique ID of each box, the second column is the object type, and the subsequent columns are the box specifications.
0 0 0.27576742256637166 0.4125491642084562 0.0265625 0.0375
1 0 0.23828125 0.5833039906103287 0.0265625 0.0375
2 0 0.24607907863849765 0.8479557902973396 0.0265625 0.0375
3 0 0.19453125 0.8458333333333333 0.0265625 0.0375
4 0 0.08359375 0.80625 0.0265625 0.0375
5 0 0.030446742957746475 0.7271028951486698 0.0265625 0.0375
6 0 0.13515625 0.18958333333333333 0.0265625 0.0375
7 0 0.19001736111111112 0.10254629629629629 0.0265625 0.0375
8 0 0.31472582547169814 0.09382861635220126 0.0265625 0.0375
9 0 0.2851506294964029 0.2166741606714628 0.0265625 0.0375
10 0 0.31482907863849763 0.30419600938967134 0.0265625 0.0375
11 0 0.46828125 0.10291666666666667 0.0265625 0.0375
12 0 0.5866453931924883 0.8730046948356808 0.0265625 0.0375
13 0 0.6476291546242774 0.562530105973025 0.0265625 0.0375
14 0 0.48828125 0.86875 0.0265625 0.0375
15 0 0.42578125 0.7666666666666667 0.0265625 0.0375
16 0 0.8492099842215989 0.8375262973352035 0.0265625 0.0375
17 0 0.8398349842215989 0.9625262973352035 0.0265625 0.0375
18 0 0.8554621756661991 0.7479254324450678 0.0265625 0.0375 | open | 2025-02-08T17:57:35Z | 2025-02-09T15:40:49Z | https://github.com/ultralytics/ultralytics/issues/19139 | [
"question",
"track"
] | asghariarad | 6 |
microsoft/unilm | nlp | 977 | VALL-E ETA? | I've been eager to use VALL-E since I read about its creation, but the directory created for it is empty.
Is there an ETA on when it will be made available? | open | 2023-01-11T22:46:17Z | 2023-05-16T10:46:23Z | https://github.com/microsoft/unilm/issues/977 | [] | randolpho | 9 |
microsoft/Bringing-Old-Photos-Back-to-Life | pytorch | 7 | [QUESTION] Colab images with scretches | I have added the `--with_scratch` option to the section
```
!python run.py --input_folder /content/photo_restoration/test_images/upload --output_folder /content/photo_restoration/upload_output --GPU 0 --with_scratch
```
in order to process images with scratches.
This is the result that I obtain:
```
Running Stage 1: Overall restoration
initializing the dataloader
model weights loaded
directory of testing image: /content/photo_restoration/test_images/upload
processing EMILIA 55.png
You are using NL + Res
Now you are processing EMILIA 55.png
Skip EMILIA 55.png
Finish Stage 1 ...
Running Stage 2: Face Detection
1
Finish Stage 2 ...
Running Stage 3: Face Enhancement
The main GPU is
0
dataset [FaceTestDataset] of size 1 was created
The size of the latent vector size is [8,8]
Network [SPADEGenerator] was created. Total number of parameters: 92.1 million. To see the architecture, do print(network).
hi :)
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:3121: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1614: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Finish Stage 3 ...
Running Stage 4: Blending
Finish Stage 4 ...
All the processing is done. Please check the results.
```
and the image (I have intentionally obscured the face) for privacy:
<img width="611" alt="Schermata_2020-09-19_alle_20_51_58" src="https://user-images.githubusercontent.com/163333/93686993-68f57800-faba-11ea-81ab-05590e477bfb.png">
Is the pipeline command correct? Thank you!
| closed | 2020-09-19T18:56:19Z | 2020-11-07T08:46:57Z | https://github.com/microsoft/Bringing-Old-Photos-Back-to-Life/issues/7 | [] | loretoparisi | 2 |
unionai-oss/pandera | pandas | 1,661 | Support Data synthesis strategies for polars | **Is your feature request related to a problem? Please describe.**
As stated in the docs polars lacks comprehensive data synthesis strategies
<img width="838" alt="image" src="https://github.com/user-attachments/assets/355a5151-190b-4ee8-a8e5-b11b867be97c">
**Describe the solution you'd like**
I would like to see the implementation of various data synthesis strategies within Polars similar to how it is done with pandas
**Describe alternatives you've considered**
use the [patito](https://github.com/JakobGM/patito) library | open | 2024-05-29T16:57:36Z | 2024-09-13T15:53:59Z | https://github.com/unionai-oss/pandera/issues/1661 | [
"enhancement"
] | elyase | 2 |
flasgger/flasgger | api | 260 | uiversion 3 get method array says: Could not render this component, see the console. | Hi
When I specify an array using uiversion 3 on a get method. I get "Could not render this component, see the console.". Am I doing something wrong?
Below is a screenshot of what I see
| open | 2018-11-13T18:50:39Z | 2021-03-15T11:25:48Z | https://github.com/flasgger/flasgger/issues/260 | [] | wobeng | 5 |
LAION-AI/Open-Assistant | python | 3,744 | Can't open dashboard | ERROR: type should be string, got "\r\nhttps://github.com/LAION-AI/Open-Assistant/assets/29770761/254db19d-a284-41ca-8612-99103df12fac\r\n\r\n" | closed | 2024-01-06T16:14:36Z | 2024-01-06T17:25:55Z | https://github.com/LAION-AI/Open-Assistant/issues/3744 | [] | DRYN07 | 1 |
graphistry/pygraphistry | pandas | 290 | [FEA] UMAP umap_learn integration metaissue | **Is your feature request related to a problem? Please describe.**
Using umap_learn with graphistry should be more visible and easier!
See: https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/umap_learn/umap_learn.ipynb
**Describe the solution you'd like**
- [ ] umap_learn: `embedding.plot()` <- maintainers already signaled interest
- [ ] method
- [ ] docs on their side
- [x] pygraphistry: `g = g.nodes(...).umap(engine='umap_learn', params=...); print(g._embeddings); g.plot()`
- Unclear what input params + outputs for typical cases... maybe just raw parent objs until we understand what to standardize?
- [ ] attributes: options not just for embedding, but original properties + potentially vectorized features
- [x] option to autovectorize, nodes & maybe label propagated edges <-- feels like a separate issue
- [ ] maybe some recommended settings or enrichments, like:
- compact layout
- color edges by similarity
## Implementation notes
May be easiest if mostly implemented as a shim to our side, so we can update with less friction, and they just version bump whenever ready
Try to keep varargs in most places to be more robust against changes on either side
| open | 2021-12-18T18:02:08Z | 2022-09-07T14:18:16Z | https://github.com/graphistry/pygraphistry/issues/290 | [
"enhancement",
"help wanted",
"good-first-issue",
"integrations",
"umap"
] | lmeyerov | 2 |
miguelgrinberg/microblog | flask | 166 | Data not getting stored into the Database. | I've been working on a similar project like your microblog, where I created a "personal details" (an alternative registration form) table instead of a "posts table", My web App is working perfectly, but when I try to access the stored data through the shell, I'm not getting any output or error, it just returns NULL. I've tried recreating my databases multiple times , but i'm getting the same error. Could you please help me out.
models.py :
```python
from newapp import login
from newapp import db,login
from flask_login import UserMixin
from werkzeug.security import generate_password_hash, check_password_hash
class User(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String(64), index=True, unique=True)
email = db.Column(db.String(120), index=True, unique=True)
password_hash = db.Column(db.String(128))
def __repr__(self):
return '<User {}>'.format(self.username)
def set_password(self, password):
self.password_hash = generate_password_hash(password)
def check_password(self, password):
return check_password_hash(self.password_hash, password)
@login.user_loader
def load_user(id):
return User.query.get(int(id))
class Pdetails(UserMixin, db.Model):
id = db.Column(db.Integer, primary_key=True)
firstname = db.Column(db.String(64), index=True)
lastname = db.Column(db.String(64), index=True)
email = db.Column(db.String(120), index=True, unique=True)
phone = db.Column(db.Integer, index=True, unique=True)
gender = db.Column(db.Boolean, nullable=False, default=True)
age = db.Column(db.Integer)
occupation = db.Column(db.String)
salary = db.Column(db.Integer, index=True)
def __repr__(self):
return '<Pdetails {}>'.format(self.firstname)
```
routes.py:
```python
from newapp import newapp, db
from flask import render_template, flash, redirect, url_for, request
from newapp.forms import LoginForm, RegistrationForm, ResetPasswordRequestForm, PdetailsForm
from flask_login import login_user, logout_user, current_user, login_required
from werkzeug.urls import url_parse
from newapp.models import User, Pdetails
.....
@newapp.route('/pdetails',methods=['GET','POST'])
def pdetails():
form =PdetailsForm()
if form.validate_on_submit():
pdetails = Pdetails(email=form.email.data,firstname=form.firstname.data,lastname=form.lastname.data, phone=form.phone.data, salary=form.salary.data)
db.session.add(pdetails)
db.session.commit()
flash('Congratulations, you are now a registered user!')
return redirect(url_for('home'))
return render_template('details.html', title='Personal Details',form=form)
``` | closed | 2019-06-24T11:02:55Z | 2019-11-17T19:02:53Z | https://github.com/miguelgrinberg/microblog/issues/166 | [
"question"
] | NooriaAli | 6 |
d2l-ai/d2l-en | deep-learning | 2,605 | d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16, 2 num_layers=2) | ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[21], [line 4](vscode-notebook-cell:?execution_count=21&line=4)
[1](vscode-notebook-cell:?execution_count=21&line=1) encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,
[2](vscode-notebook-cell:?execution_count=21&line=2) num_layers=2)
[3](vscode-notebook-cell:?execution_count=21&line=3) encoder.eval()
----> [4](vscode-notebook-cell:?execution_count=21&line=4) decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,
[5](vscode-notebook-cell:?execution_count=21&line=5) num_layers=2)
[6](vscode-notebook-cell:?execution_count=21&line=6) decoder.eval()
[7](vscode-notebook-cell:?execution_count=21&line=7) X = torch.zeros((4, 7), dtype=torch.long) # (batch_size,num_steps)
Cell In[20], [line 5](vscode-notebook-cell:?execution_count=20&line=5)
[2](vscode-notebook-cell:?execution_count=20&line=2) def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
[3](vscode-notebook-cell:?execution_count=20&line=3) dropout=0, **kwargs):
[4](vscode-notebook-cell:?execution_count=20&line=4) super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)
----> [5](vscode-notebook-cell:?execution_count=20&line=5) self.attention = d2l.AdditiveAttention(
[6](vscode-notebook-cell:?execution_count=20&line=6) num_hiddens, num_hiddens, num_hiddens, dropout)
[7](vscode-notebook-cell:?execution_count=20&line=7) self.embedding = nn.Embedding(vocab_size, embed_size)
[8](vscode-notebook-cell:?execution_count=20&line=8) self.rnn = nn.GRU(
[9](vscode-notebook-cell:?execution_count=20&line=9) embed_size + num_hiddens, num_hiddens, num_layers,
[10](vscode-notebook-cell:?execution_count=20&line=10) dropout=dropout)
TypeError: __init__() takes 3 positional arguments but 5 were given | open | 2024-06-17T07:45:10Z | 2024-06-17T08:29:55Z | https://github.com/d2l-ai/d2l-en/issues/2605 | [] | achaosss | 2 |
piskvorky/gensim | nlp | 2,949 | test_lda_callback.py test stalls | I've seen multiple PRs fail (red) with the same problem in log, e.g. in #2947:
https://travis-ci.org/github/RaRe-Technologies/gensim/jobs/727593115
https://travis-ci.org/github/RaRe-Technologies/gensim/jobs/727515466
<img width="1440" alt="Screen Shot 2020-09-16 at 13 49 59" src="https://user-images.githubusercontent.com/610412/93333393-8a890200-f823-11ea-9605-944b7641b70f.png">
Restarting the build job helps = the re-run succeeds.
This only seems to affect Travis and py3.6; I never saw this problem in our Azure (windows) tests or other Pythons. | open | 2020-09-16T11:51:29Z | 2020-09-16T13:02:50Z | https://github.com/piskvorky/gensim/issues/2949 | [
"bug",
"testing",
"housekeeping"
] | piskvorky | 0 |
fbdesignpro/sweetviz | data-visualization | 70 | Different Scores for Uncertainty Coefficient | When showing the categorical variable's report, we have two information zones:
1. [feature_name] PROVIDES INFORMATION ON...
2. THESE FEATURES GIVE INFORMATION ON [feature_name]
This is an example from the [Kaggle Housing Price dataset](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data):
We can see that **MSZoning** provides information on **Alley** and **Neighborhood** and many more features.
We can also see that **Alley** and **Neighborhood** and many more features provide information on **MSZoning**.
The uncertainty coefficient of **MSZoning** on **Alley** is `0.27` and the uncertainty coefficient of **Alley** on **MSZoning** is `0.11`.
The uncertainty coefficient of **MSZoning** on **Neighborhood** is `0.16` and the uncertainty coefficient of **Neighborhood** on **MSZoning** is `0.67`.
Why arent these numbers the same from every direction? if **MSZoning** affects **Neighborhood**, it should be the same as the effect of **Neighborhood** on **MSZoning** (if we are talking about correlation).
How are these numbers calculated?
Thank you,
| closed | 2020-12-22T11:59:27Z | 2021-02-15T22:19:15Z | https://github.com/fbdesignpro/sweetviz/issues/70 | [
"question"
] | aviasd | 1 |
plotly/jupyter-dash | dash | 19 | Cannot stop server in JupyterLab using CTRL+C | I'm running the following code:
_Cell [2]_
```
app = JupyterDash(__name__)
app.layout = html.Div('Minimal')
app.run_server(mode="external", debug=False)
```
_Cel[2] Output_
```
* Running on http://127.0.0.1:8050/ (Press CTRL+C to quit)
127.0.0.1 - - [10/Jun/2020 14:17:49] "GET /_alive_a6195403-4d44-472b-8ea6-99427edf92c3 HTTP/1.1" 200 -
Dash app running on http://127.0.0.1:8050/
127.0.0.1 - - [10/Jun/2020 14:18:07] "GET / HTTP/1.1" 200 -
127.0.0.1 - - [10/Jun/2020 14:18:08] "GET /_dash-dependencies HTTP/1.1" 200 -
127.0.0.1 - - [10/Jun/2020 14:18:08] "GET /_dash-layout HTTP/1.1" 200 -
```
I cannot stop the server using _**(Press CTRL+C to quit)**_ as indicated in the output log. This message is probably only applicable to execution in the terminal? Is there an alternate way of stopping the server in JupyterLab?
Environment setup:
- conda install
- Windows 10
- JupyterLab 2.1
- localhost
| open | 2020-06-10T13:31:17Z | 2022-07-31T03:21:08Z | https://github.com/plotly/jupyter-dash/issues/19 | [] | msbuckas | 5 |
PeterL1n/RobustVideoMatting | computer-vision | 155 | 推理 | 我把四个阶段训练完之后，应该拿哪个阶段的权重去推理呢，是不是stage4啊 | closed | 2022-04-03T02:46:53Z | 2022-04-09T14:02:10Z | https://github.com/PeterL1n/RobustVideoMatting/issues/155 | [] | TonFard | 1 |
holoviz/panel | plotly | 7,080 | Linking tab's "active" to a Parameter does not work as expected | [panel 1.4.4, python 3.11]
I am not quite sure this is supposed to work, but figured I'd report this. If it is not a bug I'd like to know the right way to do this.
I have a Parameterized class which holds the global states of my app. The app has two main tabs, and I would like to know which tab is active though my global state class. So I did something like this:
```import panel as pn
import param
pn.extension()
class States(param.Parameterized):
mode = param.Integer()
ss = States()
main_tabs = pn.Tabs(
("tab 0", pn.panel("tab 0 content")),
("tab 1", pn.panel("tab 1 content")),
active=ss.param.mode,
).servable()
def this_works(active_tab):
print(f"this_works: SWITCHED TO TAB {active_tab}")
pn.bind(this_works, main_tabs.param.active, watch=True)
def this_doesnt_work(active_tab):
print(f"this_doesnt_work: SWITCHED TO TAB {active_tab}")
pn.bind(this_doesnt_work, ss.param.mode, watch=True)
```
From reading [this ](https://panel.holoviz.org/explanation/dependencies/param.html)doc I would expect both this_works() and this_doesnt_work() to trigger when I switch tabs, but only this_works() does. Is this expected? | closed | 2024-08-05T21:52:23Z | 2024-08-06T08:58:12Z | https://github.com/holoviz/panel/issues/7080 | [] | samimia-swks | 1 |
deezer/spleeter | deep-learning | 415 | [Bug] IndexError: index 1 is out of bounds | Dear Sirs,
Some songs can not be spleted after I installed the new Spleeter. Pls see below:
```
Traceback (most recent call last):
File "/root/anaconda3/bin/spleeter", line 10, in <module>
sys.exit(entrypoint())
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/__main__.py", line 54, in entrypoint
main(sys.argv)
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/__main__.py", line 46, in main
entrypoint(arguments, params)
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/commands/separate.py", line 45, in entrypoint
synchronous=False
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/separator.py", line 191, in separate_to_file
sources = self.separate(waveform, audio_descriptor)
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/separator.py", line 157, in separate
return self.separate_librosa(waveform, audio_descriptor)
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/separator.py", line 145, in separate_librosa
stft = self.stft(waveform)
File "/root/anaconda3/lib/python3.7/site-packages/spleeter/separator.py", line 126, in stft
dl, dr = (data[:, :, 0].T, data[:, :, 1].T) if inverse else (data[:, 0], data[:, 1])
IndexError: index 1 is out of bounds for axis 1 with size 1
```
## Environment
| OS | Ubuntu |
| Installation type | Conda |
| Hardware spec | CPU |
| closed | 2020-06-09T13:46:47Z | 2021-02-12T14:37:40Z | https://github.com/deezer/spleeter/issues/415 | [
"bug",
"invalid",
"inactive"
] | eminfan | 5 |
opengeos/leafmap | streamlit | 50 | Add GUI for loading COG | This feature allows users to load Cloud Optimized GeoTIFF without coding. | closed | 2021-06-21T00:43:32Z | 2021-06-21T01:21:40Z | https://github.com/opengeos/leafmap/issues/50 | [
"Feature Request"
] | giswqs | 1 |
BeanieODM/beanie | pydantic | 297 | QUESTION: Is events + method an atomic action, or do we need to do verbose transaction management? | Hi,
We are trying to implement an "accession number" field called ```acc``` in our Beanie Documents. Since ```acc``` is a counter that follows a specific format, we decided to make a new field instead of using MongoDB's ObjectID.
Implementation-wise, we are still deciding which approach to follow, since we are not entirely sure with Beanie's behaviour. Here is an example to illustrate:
```python
next_accession_number = 1
def get_accession_number():
return next_accession_number
def increment_accession_number():
next_accession_number += 1
class Record(Document):
title: str
acc: int
```
The ```Record``` object is the Beanie Document that we are saving to the database, while ```next_acc_number``` is the next accession number that will be assigned to a record, and is incremented whenever a new Document is saved to the database. We are avoiding to use Pydantic's ```default_factory``` since we only want to assign the accession number by the time the record will be saved to the database.
Currently here are our ideas:
## Assign and increment as a before insert event:
```Python
class Record(Document):
title: str
acc: int
@before_event(Insert)
def assign_accession_number(self):
self.acc = get_accession_number()
increment_accession_number()
```
The problem that we see here is when the Insert operation fails for whatever reason, the accession number has already incremented when it shouldn't.
## Assign accession number as a before insert event, increment accession number as an after insert event:
```Python
class Record(Document):
title: str
acc: int
@before_event(Insert)
def assign_accession_number(self):
self.acc = get_accession_number()
@after_event(Insert)
def_update_accession_number(self):
increment_accession_number()
```
Here, my concern is more on concurrency. Is it possible that two different records have the same accession number assigned (e.g., before_event on second record is executed before the after_event of the first record)? I have checked the [source code](https://github.com/roman-right/beanie/blob/05bc515592c5334160118f3560bea313c9129a4b/beanie/odm/actions.py#L185) on how the events are executed. Since the wrapper function is async, can we make an atomic operation (before, action, after) if we simply use ```await```(e.g., ```await record.save()```) or does our use case call for the use of transactions (i.e., no events, enclose assign-save-increment in a transaction using the AsyncIOMotorClient)? | closed | 2022-06-30T10:18:32Z | 2023-01-20T02:37:34Z | https://github.com/BeanieODM/beanie/issues/297 | [
"Stale"
] | edgarfelizmenio | 3 |
PablocFonseca/streamlit-aggrid | streamlit | 271 | aggrid with fastapi request | Hey !!
I also build an aggrid that works on streamlit but uses fastapi!
I don't have a pip install setup yet for it since its on my private repo but I would like to show you it and also possibly get your help with some of the features I want to add/fix.
please let me know if you would consider -- you can reach me here stefanstapinski@gmail.com
~Stefan | open | 2024-05-13T22:37:49Z | 2024-07-07T18:18:49Z | https://github.com/PablocFonseca/streamlit-aggrid/issues/271 | [] | nafets33 | 4 |
Evil0ctal/Douyin_TikTok_Download_API | web-scraping | 230 | [BUG] i don't understand coding | I don't understand coding, so I use the douyin.wtf web app, when I click "video download no watermark" but that result comes out, how can I download the video? actually we can if we click "video url no watermark" but the video title uses a random title, what if I want the video title to match the title on douyin, because I download a lot of videos 1 by 1 it is very time consuming
| closed | 2023-07-30T09:53:58Z | 2023-08-04T09:31:08Z | https://github.com/Evil0ctal/Douyin_TikTok_Download_API/issues/230 | [
"help wanted"
] | radenhendriawan | 1 |
vanna-ai/vanna | data-visualization | 118 | 'DataFrame' object is not callable | I have tried to connect vanna to my database using SQL Server. But when I used vn.ask() from my model, it cannot show table from DataFrame. What Should I do?
SELECT * FROM datasetSapi WHERE Ingredients LIKE '%pare%'
Couldn't run sql: 'DataFrame' object is not callable | closed | 2023-09-21T09:05:04Z | 2023-09-22T04:20:59Z | https://github.com/vanna-ai/vanna/issues/118 | [] | bryanjo28 | 1 |
Morizeyao/GPT2-Chinese | nlp | 277 | GPU9GB能跑起来吗请问 | closed | 2023-03-14T08:06:58Z | 2023-03-22T10:54:03Z | https://github.com/Morizeyao/GPT2-Chinese/issues/277 | [] | SunshineWQI | 0 | |
GibbsConsulting/django-plotly-dash | plotly | 386 | Unable to find app in conjunction with Wagtail 404 URL | I am trying to add a Dash app to a Wagtail/CoderedCMS site, but I cannot get the URL for the Dash app to be found. I followed the instructions in the documentation as well as other threads here, but I'm stuck with this. It's probably a simple issue, but I'm just not finding the solution.
Initialize site with [CoderedCMS](https://github.com/coderedcorp/coderedcms) (`pip install coderedcms` first):
```
coderedcms start mysite --sitename mysite --domain www.example.com
cd mysite/
python manage.py migrate
python manage.py createsuperuser
python manage.py runserver
```
Site is running. Now add the simple app from the Django Plotly Dash example. From `mysite/`
```
mkdir apps
cd apps
mkdir django_plotly_dash
cd django_plotly_dash
touch app.py
```
[Copy example app](https://django-plotly-dash.readthedocs.io/en/latest/simple_use.html) into `app.py`.
Create `urls.py`:
```
# urls.py
from django.urls import path
from . import views
app_name = "SimpleExample"
urlpatterns = [
path("example", views.example, name="example")
]
```
Create `views.py`:
```
# views.py
from django.shortcuts import render
def example(request, **kwargs):
context = {}
template_name = "simple.html"
return render(request, template_name=template_name, context=context)
```
Navigate to `mysite/website/templates/` and create `simple.html`:
```
{% load plotly_dash %}
{% plotly_app name="SimpleExample"}
```
Now modify the settings and urls files.
Go to `mysite/mysite/urls.py` and add `path('django_plotly_dash/', include('apps.django_plotly_dash.urls')),` to the `urlpatterns` list.
Go to `mysite/mysite/settings/base.py` add `django_plotly_dash.apps.DjangoPlotlyDashConfig` to `INSTALLED_APPS`.
Add `X_FRAME_OPTIONS = 'SAMEORIGIN'`.
From `mysite/` run:
```
python manage.py makemigrations
python manage.py migrate
```
Go to http://127.0.0.1:8000/django_plotly_dash/example/ (restart server if needed) and get 404 error.
New files and structure:
mysite
--apps
----django_plotly_dash
------urls.py
------app.py
------views.py
Most frustrating is that I ran this once, and it worked, at least I saw the template but no app. I removed the `django_plotly_dash` from the path to see if I could get it from just http://127.0.0.1:8000/example/ and I couldn't get it to revert, let alone display the app. I feel like I tried every variation of imports, urls, etc. and couldn't get around this, so any help would be greatly appreciated!
Running locally with:
Python 3.9.7
MacOS Monterey 12.2.1
django==3.2.12
dash==2.2.0
django-plotly-dash==1.6.6
coderedcms==0.22.3 | closed | 2022-03-08T01:20:51Z | 2022-03-10T14:37:22Z | https://github.com/GibbsConsulting/django-plotly-dash/issues/386 | [] | hubbs5 | 12 |
autokey/autokey | automation | 612 | Importing / running a long list of autocorrect | ## Classification: New Feature
## Notes
Thanks for making such an amazing piece of software - I use it daily and find that AutoKey really improves my efficiency.
I used to use AutoHotKey on windows back in the day. One of the things that I did was to import a very long list of autocorrect rules into the software. Since it was all defined in a flat-file (exported from a popular word processor) it was pretty trivial to load it into the software and get the benefit of having autocorrect basically work in every piece of software.
Would you consider adding in an import feature that processes CSV, text files or something to import the large list of autocorrects? You could de-dupe the words and load them into the current structure you use to store shortcuts.
Another alternate would be to have a separate autocorrect feature that just consumes a flat text file for all autocorrects, instead of defining it in both the JSON and the text file.
Thanks so much in advance for considering this, Have a great day.
| open | 2021-09-20T14:46:03Z | 2021-09-30T20:36:18Z | https://github.com/autokey/autokey/issues/612 | [
"enhancement"
] | haydonryan | 6 |
benbusby/whoogle-search | flask | 164 | [BUG] pictures not displayed anymore | **Describe the bug**
With the latest docker version, I started to not see images (pictures) anymore since yesterday. I normally run whoogle via a reverse proxy (nginx) but the issue persists if I access it directly via the port 5000 . It is really just images - youtube preview in the main search page and in the videos tab are displayed.
**To Reproduce**
Steps to reproduce the behavior:
1. search for anything
2. click on images tab (or look at the images preview box on the main page)
**Deployment Method**
- [ ] Heroku (one-click deploy)
- [x] Docker
**Version of Whoogle Search**
- [latest,beta,experimental] Latest build from [Docker Hub] (i.e. GitHub, Docker Hub, pip, etc)
**Desktop (please complete the following information):**
- OS: Windows 10, iOS
- Browser: Chrome, MS Edge, Firefox (latest)
- Version: in all cases the latest version available
**Smartphone (please complete the following information):**
- Device: iPhone XR
- OS: 14.3
- Browser: Safari, Firefox
- Version: latest
**Additional context**
Server has access to the web with outgoing ports 80 & 443 open - all others closed.
Location is Germany.
| closed | 2021-01-12T22:41:55Z | 2021-01-13T15:01:11Z | https://github.com/benbusby/whoogle-search/issues/164 | [
"bug"
] | somebody-somewhere-over-the-rainbow | 10 |
MaartenGr/BERTopic | nlp | 1,997 | Feature (Watsonx): representations using Llama-3-70b and Mixtral-8x7b | Topic representation using Llama-3-70b and/or Mixtral-8x7b via IBM Watsonx. | closed | 2024-05-19T14:28:50Z | 2024-05-21T17:41:32Z | https://github.com/MaartenGr/BERTopic/issues/1997 | [] | andreswagner | 1 |
yt-dlp/yt-dlp | python | 12,477 | [cultureunplugged] some URL types return as Unsupported | ### Checklist
- [x] I'm requesting a site-specific feature
- [x] I've verified that I have **updated yt-dlp to nightly or master** ([update instructions](https://github.com/yt-dlp/yt-dlp#update-channels))
- [x] I've checked that all provided URLs are playable in a browser with the same IP and same login details
- [x] I've searched the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=is%3Aissue%20-label%3Aspam%20%20) for similar requests **including closed ones**. DO NOT post duplicates
- [x] I've read about [sharing account credentials](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#are-you-willing-to-share-account-details-if-needed) and I'm willing to share it if required
### Region
_No response_
### Example URLs
https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-
### Provide a description that is worded well enough to be understood
Older link types and url schemes that are in the extractor are still valid. However, this one with /play/ structure returns as unsupported.
in case this in not just regex edit needed, content is accessed and can be downloaded via this link: https://s3.amazonaws.com/cdn.cultureunplugged.com/lg/KOI_SUNTA_HAI_2833.mp4
Older ones like this, work just fine. https://www.cultureunplugged.com/documentary/watch-online/play/53662/The-Next--Best-West
### Provide verbose output that clearly demonstrates the problem
- [x] Run **your** yt-dlp command with **-vU** flag added (`yt-dlp -vU <your command line>`)
- [x] If using API, add `'verbose': True` to `YoutubeDL` params instead
- [x] Copy the WHOLE output (starting with `[debug] Command-line config`) and insert it below
### Complete Verbose Output
```shell
./yt-dlp -Uv -F "https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-"
[debug] Command-line config: ['-Uv', '-F', 'https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-']
[debug] Encodings: locale UTF-8, fs utf-8, pref UTF-8, out utf-8, error utf-8, screen utf-8
[debug] yt-dlp version stable@2025.02.19 from yt-dlp/yt-dlp [4985a4041] (zip)
[debug] Python 3.12.2 (CPython arm64 64bit) - macOS-15.3-arm64-arm-64bit (OpenSSL 3.0.13 30 Jan 2024)
[debug] exe versions: phantomjs 2.1.1
[debug] Optional libraries: Cryptodome-3.20.0, brotli-1.1.0, certifi-2024.07.04, mutagen-1.47.0, requests-2.32.3, sqlite3-3.45.1, urllib3-2.2.2, websockets-12.0
[debug] Proxy map: {}
[debug] Request Handlers: urllib, requests
[debug] Extractor Plugins: GetPOT (YoutubeIE), SamplePluginIE
[debug] Post-Processor Plugins: SamplePluginPP
[debug] Loaded 1974 extractors
[debug] Fetching release info: https://api.github.com/repos/yt-dlp/yt-dlp/releases/latest
Latest version: stable@2025.02.19 from yt-dlp/yt-dlp
yt-dlp is up to date (stable@2025.02.19 from yt-dlp/yt-dlp)
[generic] Extracting URL: https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-
[generic] Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-: Downloading webpage
WARNING: [generic] Falling back on generic information extractor
[generic] Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-: Extracting information
[debug] Looking for embeds
ERROR: Unsupported URL: https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-
Traceback (most recent call last):
File "./yt-dlp/yt_dlp/YoutubeDL.py", line 1656, in wrapper
return func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "./yt-dlp/yt_dlp/YoutubeDL.py", line 1791, in __extract_info
ie_result = ie.extract(url)
^^^^^^^^^^^^^^^
File "./yt-dlp/yt_dlp/extractor/common.py", line 747, in extract
ie_result = self._real_extract(url)
^^^^^^^^^^^^^^^^^^^^^^^
File "./yt-dlp/yt_dlp/extractor/generic.py", line 2566, in _real_extract
raise UnsupportedError(url)
yt_dlp.utils.UnsupportedError: Unsupported URL: https://www.cultureunplugged.com/play/2833/Koi-Sunta-Hai--Journeys-with-Kumar---Kabir--Someone-is-Listening-
``` | closed | 2025-02-25T19:01:01Z | 2025-02-26T18:39:51Z | https://github.com/yt-dlp/yt-dlp/issues/12477 | [
"site-bug"
] | someziggyman | 0 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 1,637 | Add ROI specific loss function to generator | I would like to add an additional L1 loss function directly on the region of interest (bounding boxes) during training (Pix2pix) to try to improve clarity for object detection. Is it expected to yield better results? What are the steps that I have to follow to accomplish this? | open | 2024-03-23T12:13:01Z | 2024-03-23T12:13:29Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1637 | [] | Diogo-Valente2111 | 0 |
tflearn/tflearn | data-science | 670 | Basic example "use_hdf5.py" gets FileNotFoundError | When running "tflearn/examples/basics/use_hdf5.py", I get the error below:
```
Traceback (most recent call last):
File ".\use_hdf5_1.py", line 22, in <module>
(X, Y), (X_test, Y_test) = cifar10.load_data()
File "C:\Anaconda3\lib\site-packages\tflearn\datasets\cifar10.py", line 34, in load_data
data, labels = load_batch(fpath)
File "C:\Anaconda3\lib\site-packages\tflearn\datasets\cifar10.py", line 60, in load_batch
with open(fpath, 'rb') as f:
FileNotFoundError: [Errno 2] No such file or directory: 'cifar-10-batches-py\\cifar-10-batches-py\\cifar-10-batches-py\\data_batch_1'
```
I think it's related to a typo in "tflearn/tflearn/datasets/cifar10.py" and will try to create a Pull Request next. | open | 2017-03-18T13:07:10Z | 2017-03-18T13:47:31Z | https://github.com/tflearn/tflearn/issues/670 | [] | EricPerbos | 1 |
facebookresearch/fairseq | pytorch | 4,962 | Converting of unspecified list arguments fails | There is a CoLA-based RoBERTa classifier [[1][1]]. The model was trained with `pairseq` 0.10.2 (I guess). And it is impossible to load the pretrained model with the latest `pairseq` releases (e.g. 0.12.2 or 0.12.3).
```python
from fairseq.models.roberta import RobertaModel
model = RobertaModel.from_pretrained(checkpoint_dir,
checkpoint_file='checkpoint_best.pt',
data_name_or_path=data_path)
# Traceback (most recent call last):
# ...
# File "/usr/lib/python3.10/site-packages/fairseq/dataclass/utils.py", line 264, in _override_attr
# val = list(map(t_args[0], val))
# TypeError: 'NoneType' object is not iterable
```
It seems that `None` values are not checked for list types (see [here][2]).
[1]: https://drive.google.com/drive/folders/1p6_3lCbw3J0MhlidvKkRbG73qwmtWuRp
### Work around
Load, fix learning rate value and save with plain `torch` package.
```python
state_dict = T.load('path/to/checkpoint.py')
state_dict['args'].lr = []
T.save(state_dict, 'path/to/fixed-checkpoint.py')
```
### Environment
- `fairseq` Version. Model was saved with old release (~0.10.2) and can't be loaded with new release (e.g. 0.12.3).
- Other components have no effect. | open | 2023-01-29T19:25:48Z | 2023-01-29T19:25:48Z | https://github.com/facebookresearch/fairseq/issues/4962 | [
"bug",
"needs triage"
] | daskol | 0 |
google/trax | numpy | 1,284 | [Question][Feature Request] Support for fine tuning | ### Description
After venturing a while with trax for novel and some predefined models I started looking into fine tuning.
As far as I've seen, I can use a pretrained model as a part of a bigger model (since models are nothing else but stacked layers) and could somehow adapt them.
But I also understand that there are other ways of fine tuning, such as freezing some layers and changing others (e.g.: the input and output layers).
So far I didn't find a way to do it. Am I missing something or is this a feature yet to be included?
### Environment information
```
OS: Google Colabs (linux?)
$ pip freeze | grep trax
#trax==1.3.6
``` | open | 2020-12-08T12:25:45Z | 2022-01-29T23:44:23Z | https://github.com/google/trax/issues/1284 | [] | Sirsirious | 8 |
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