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rgerum/pylustrator | matplotlib | 59 | ModuleNotFoundError: No module named 'matplotlib.axes._subplots' | ```
(biotite) ddalab@DP7820-WS:Analysis$ python -c 'import pylustrator'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/ddalab/anaconda3/envs/biotite/lib/python3.11/site-packages/pylustrator/__init__.py", line 22, in <module>
from .QtGuiDrag import initialize as start
File "/home/ddalab/anaconda3/envs/biotite/lib/python3.11/site-packages/pylustrator/QtGuiDrag.py", line 33, in <module>
from .ax_rasterisation import rasterizeAxes, restoreAxes
File "/home/ddalab/anaconda3/envs/biotite/lib/python3.11/site-packages/pylustrator/ax_rasterisation.py", line 24, in <module>
from matplotlib.axes._subplots import Axes
ModuleNotFoundError: No module named 'matplotlib.axes._subplots' | closed | 2023-02-25T21:24:30Z | 2023-02-27T19:00:58Z | https://github.com/rgerum/pylustrator/issues/59 | [] | hackerzone85 | 2 |
autokey/autokey | automation | 16 | UnicodeEncodeError: autokey-shell, autokey-gtk not starting | couldn't get the gtk version to start after some trouble with the AUR PKGBUILD (i ended up using the instructions from the wiki, i'm not sure if you maintain both) so i tried autokey-shell and produced this stack trace: http://pastebin.com/YpQ0SNJf
| closed | 2016-01-23T09:00:15Z | 2016-11-11T06:33:21Z | https://github.com/autokey/autokey/issues/16 | [] | covercash2 | 1 |
huggingface/datasets | computer-vision | 7,061 | Custom Dataset | Still Raise Error while handling errors in _generate_examples | ### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
"""
The above exception was the direct cause of the following exception:
โ โ
โ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. โ
โ py:1377 in <listcomp> โ
โ โ
โ 1374 โ โ โ โ if all(async_result.ready() for async_result in async_results) and queue โ
โ 1375 โ โ โ โ โ break โ
โ 1376 โ โ # we get the result in case there's an error to raise โ
โ โฑ 1377 โ โ [async_result.get() for async_result in async_results] โ
โ 1378 โ
โ โ
โ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ locals โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ
โ โ .0 = <list_iterator object at 0x7f2cc1f0ce20> โ โ
โ โ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> โ โ
โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โ
โ โ
โ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 โ
โ in get โ
โ โ
โ 768 โ โ if self._success: โ
โ 769 โ โ โ return self._value โ
โ 770 โ โ else: โ
โ โฑ 771 โ โ โ raise self._value โ
โ 772 โ โ
โ 773 โ def _set(self, i, obj): โ
โ 774 โ โ self._success, self._value = obj โ
โ โ
โ โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ locals โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ โ
โ โ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> โ โ
โ โ timeout = None โ โ
โ โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ โ
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9 | open | 2024-07-22T21:18:12Z | 2024-09-09T14:48:07Z | https://github.com/huggingface/datasets/issues/7061 | [] | hahmad2008 | 0 |
serengil/deepface | machine-learning | 1,086 | AttributeError: 'KerasHistory' object has no attribute 'layer' | I am running the following code but it keeps telling me that AttributeError: 'KerasHistory' object has no attribute 'layer', are there anything i can use to fix this issue.
this is the code:
TF_ENABLE_ONEDNN_OPTS=0
import cv2
import pandas as pd
import keras
from deepface import DeepFace as df
cap = cv2.VideoCapture(0)
cascade = cv2.CascadeClassifier(r"D:\FaceDetect\haarcascade_frontalface_default.xml")
while True:
face = []
ret, frame = cap.read()
if cv2.waitKey(1) and (0XFF == ord("q")):
break
for (x,y,w,h) in cascade.detectMultiScale(frame,1.15,3):
face.append(frame[x:x+w,y:y+h])
cv2.rectangle(frame,(x,y),(x+w,y+h),(255,255,255),1)
for img in face:
df.find(img,r"D:\FaceDetect\database","ArcFace")
cv2.imshow("frame",frame)
cap.release()
cv2.destroyAllWindows()
my deepface is in its latest version 0.086
this is the error message:
Traceback (most recent call last):
File "d:\FaceDetect\FaceDetect.py", line 16, in <module>
df.find(img,r"D:\FaceDetect\database","ArcFace")
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\DeepFace.py", line 301, in find
return recognition.find(
^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\modules\recognition.py", line 96, in find
model: FacialRecognition = modeling.build_model(model_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\modules\modeling.py", line 46, in build_model
model_obj[model_name] = model()
^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 54, in __init__
self.model = load_model()
^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 80, in load_model
base_model = ResNet34()
^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 130, in ResNet34
model = training.Model(img_input, x, name="ResNet34")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\trackable\base.py", line 204, in _method_wrapper
result = method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 116, in __init__
self._init_graph_network(inputs, outputs)
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\trackable\base.py", line 204, in _method_wrapper
result = method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 152, in _init_graph_network
self._validate_graph_inputs_and_outputs()
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 694, in _validate_graph_inputs_and_outputs
layer = x._keras_history.layer
^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'KerasHistory' object has no attribute 'layer'
PS C:\Users\ASUS> ^C
PS C:\Users\ASUS> & C:/Users/ASUS/AppData/Local/Microsoft/WindowsApps/python3.12.exe d:/FaceDetect/FaceDetect.py
2024-03-10 16:51:33.062190: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-10 16:51:33.441281: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-03-10 16:51:37.024612: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last):
File "d:\FaceDetect\FaceDetect.py", line 17, in <module>
df.find(img,r"D:\FaceDetect\database","ArcFace")
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\DeepFace.py", line 301, in find
return recognition.find(
^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\modules\recognition.py", line 96, in find
model: FacialRecognition = modeling.build_model(model_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\modules\modeling.py", line 46, in build_model
model_obj[model_name] = model()
^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 54, in __init__
self.model = load_model()
^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 80, in load_model
base_model = ResNet34()
^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\deepface\basemodels\ArcFace.py", line 130, in ResNet34
model = training.Model(img_input, x, name="ResNet34")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\trackable\base.py", line 204, in _method_wrapper
result = method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 116, in __init__
self._init_graph_network(inputs, outputs)
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\trackable\base.py", line 204, in _method_wrapper
result = method(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 152, in _init_graph_network
self._validate_graph_inputs_and_outputs()
File "C:\Users\ASUS\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\LocalCache\local-packages\Python312\site-packages\tensorflow\python\keras\engine\functional.py", line 694, in _validate_graph_inputs_and_outputs
layer = x._keras_history.layer
^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'KerasHistory' object has no attribute 'layer' | closed | 2024-03-10T09:54:33Z | 2024-03-10T13:48:09Z | https://github.com/serengil/deepface/issues/1086 | [
"dependencies"
] | FabioCanavarro | 10 |
healthchecks/healthchecks | django | 592 | Telegram Channel Support | Now the telegram bot can only send messages to users or groups, but I want to post the message to the channel. | closed | 2021-12-23T02:23:50Z | 2021-12-29T18:40:49Z | https://github.com/healthchecks/healthchecks/issues/592 | [] | AuroraDysis | 2 |
ckan/ckan | api | 8,126 | Editor User Permissions Issue: Dataset Created but 404 Response (API) | ## CKAN version
2.10.4
## Describe the bug
I am encountering a 404 error when attempting to create a new dataset using an editor user account. The dataset is created successfully, but the response code indicates the resource cannot be found. This issue does not occur when using a sysadmin account (API token).
### Steps to reproduce
1. In Postman, create a POST request to: {{host}}/api/3/action/package_create
2. Set the "Authorization" header to: {{token}}
3. Include the following JSON body in the request:
```json
{
"tag_string": [
"test"
],
"name": "doi-test",
"author": "Flavio Francisco (Royal Netherlands Institute for Sea Research)",
"subject": "Earth and Environmental Sciences",
"author_email": "user@nioz.nl",
"owner_org": "nioz",
"private": true,
"doi": "10.33591/nioz/7b.b.9g",
"license_id": "CC0-1.0",
"maintainer": "Flavio",
"maintainer_email": "user@nioz.nl",
"notes": "This is a test",
"title": "DOI TEST",
"url": "https://doi.org/10.33591/nioz/7b.b.9g",
"extras": [],
"creators": "[{\"firstname\":\"Flavio\",\"lastname\":\"Francisco\",\"orcid\":null,\"affiliation\":\"Royal Netherlands Institute for Sea Research\",\"iscorrespondingauthor\":true,\"contactemailaddress\":\"user@nioz.nl\",\"formatted\":\"Flavio Francisco - Royal Netherlands Institute for Sea Research - ORCID: - Corresponding Author - user@nioz.nl\"}]",
"dataset_persistent_id": "DOI:10.33591/nioz/7b.b.9g",
"distribution_date": "2024-03-19",
"deposit_date": "2024-03-19",
"depositor": "Flavio Francisco (user@nioz.nl)",
"others_id": "DOI:10.33591/nioz/7b.b.9g; DAS:84989278-89a6-4e50-8b9d-e3a76f4b265c",
"contributor": "NIOZ Royal Netherlands Institute for Sea Research in cooperation with Utrecht University",
"distributor": "Research Data Management(NIOZ Royal Netherlands Institute for Sea Research)"
}
```
### Expected behavior
The request should successfully create a new dataset and return a response code of 200 (OK) or 201 (Created).
### Additional details
N/A
| open | 2024-03-20T10:54:04Z | 2024-06-02T22:00:37Z | https://github.com/ckan/ckan/issues/8126 | [] | flaviofrancisco | 3 |
slackapi/bolt-python | fastapi | 1,229 | How is the "app_installed_team_id" in a slack request payload set? | I have a question about how app_installed_team_id is set in the slack request payload.
According to the source code mentioned here, [https://github.com/slackapi/bolt-python/blob/v1.20.1/slack_bolt/request/internals.py#L90](https://github.com/slackapi/bolt-python/blob/v1.20.1/slack_bolt/request/internals.py#L90), the team_id is extracted based on what is present in the payload. I have a situation where I have a user that is a member of two workspaces in an organization. An app is installed in both workspaces by the same user, i.e. separate installation records are created with different team_ids. When I receive an event like _app_home_open_ the payload will extract the team_id from the first authorization record, which in my case ends up being the correct team_id of the workspace that the app home tab is being loaded in. When I start an interaction, like clicking a button in the home tab, it results in an action payload and the team_id is extracted from the ["view"]["app_installed_team_id"] which has the value of the other workspace, being the workspace that the app was first installed in.
Why is it sending that value instead of the team_id of the workspace I am in? This results in the other team_id being set in the context and used by the client. I can override this, but am curious why this is the case and is it the expected behaviour?
_TL;DR: when an app is installed in multiple workspaces in an org by the same user the app_installed_team_id seems to always be the team_id of the first workspace the app was installed in. Depending on the action being handled, the team_id that is being set in the context may not always be the team_id of the workspace you are using, resulting in the webclient making requests against the wrong workspace unless you manually override it._
### Reproducible in:
#### The `slack_bolt` version
slack_bolt==1.20.1
slack_sdk==3.27.1
#### Python runtime version
Python 3.12.3
#### Steps to reproduce:
1. Create two workspaces in an org and install an app in both workspaces by the same user.
2. Open app home tab in the second workspace and review the app_installed_team_id in the request body.
### Expected result:
Expected the app_installed_team_id to be the team_id of the workspace the action was initiated in.
### Actual result:
It sets the team_id to the first workspace that the app was installed into.
####Logs:
```
{
"type": "block_actions",
"user": {
<<redacted>>
"team_id": "T07LF8EHX7V"
},
"api_app_id": "A02FZN4GRGA",
<<redacted>>
"team": {
"id": "T07LF8EHX7V",
<<redacted>>
"enterprise_id": "E07L8N9AQES",
<<redacted>>
},
<<redacted>>
"is_enterprise_install": false,
"view": {
<<redacted>>
"app_installed_team_id": "T07M45HCWF2",
}
```
I would expect the app_installed_team_id to equal T07LF8EHX7V, not T07M45HCWF2. | closed | 2024-12-18T22:35:33Z | 2025-01-10T01:52:38Z | https://github.com/slackapi/bolt-python/issues/1229 | [
"question"
] | RubberDuckyToyFactory | 3 |
Yorko/mlcourse.ai | matplotlib | 789 | AI Agent ไธญๆๆๆฏไบคๆต็พค |  | open | 2025-03-10T14:19:29Z | 2025-03-14T06:57:00Z | https://github.com/Yorko/mlcourse.ai/issues/789 | [] | aiqubits | 0 |
deezer/spleeter | tensorflow | 128 | [Discussion] anyway to change bitrate of the output files? | Whenever I use songs from the base FLAC/WAV, no matter what, the stems always come out as a really low bit rate. I've tried messing with a few config files and as far as I know there's no command to change it. Would there be a specific way/file to modify to do this?
| closed | 2019-11-23T05:42:19Z | 2019-11-25T14:15:50Z | https://github.com/deezer/spleeter/issues/128 | [
"question",
"RTMP"
] | Waffled-II | 1 |
ultralytics/ultralytics | deep-learning | 19,793 | The recall is very low. How can it be improved? | ### 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 am using the YOLOv11l model for training on a detection dataset. The current issue is that the mAP is around 0.6, but the recall is only about 0.3. Are there any parameters I can modify to improve the detection recall rate?
### Additional
here is my trauining code:
from ultralytics import YOLO
import os
import warnings
warnings.filterwarnings("ignore", category=UserWarning, module="matplotlib")
print(os.getcwd())
if __name__ == '__main__':
# model = YOLO(r'yolov11l.yaml') # build a new model from YAML
# model.load(r'yolo11l.pt')
model = YOLO("yolo11l.pt") # load a pretrained model (recommended for training)
model.train(data=r'data.yaml',
imgsz=960,
epochs=800,
batch=6,
workers=8,
optimizer='AdamW',
patience=0,
device='1',
conf=0.1,
multi_scale=True,
lr0=0.001
)
the others parameters:
# Ultralytics YOLO ๐, AGPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training
task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
# Train settings -------------------------------------------------------------------------------------------------------
model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
data: '../../data.yaml' # (str, optional) path to data file, i.e. coco8.yaml
epochs: 300 # (int) number of epochs to train for
time: # (float, optional) number of hours to train for, overrides epochs if supplied
patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
batch: 4 # (int) number of images per batch (-1 for AutoBatch)
imgsz: 640 # (int | list) input images size as int for train and val modes, or list[h,w] for predict and export modes
save: True # (bool) save train checkpoints and predict results
save_period: 40 # (int) Save checkpoint every x epochs (disabled if < 1)
cache: False # (bool) True/ram, disk or False. Use cache for data loading
device: 2 # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 1 # (int) number of worker threads for data loading (per RANK if DDP)
project: # (str, optional) project name
name: # (str, optional) experiment name, results saved to 'project/name' directory
exist_ok: False # (bool) whether to overwrite existing experiment
pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
verbose: True # (bool) whether to print verbose output
seed: 0 # (int) random seed for reproducibility
deterministic: True # (bool) whether to enable deterministic mode
single_cls: False # (bool) train multi-class data as single-class
rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # (bool) use cosine learning rate scheduler
close_mosaic: 0 # (int) disable mosaic augmentation for final epochs (0 to disable)
resume: False # (bool) resume training from last checkpoint
amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
multi_scale: False # (bool) Whether to use multiscale during training
# Segmentation
overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
mask_ratio: 4 # (int) mask downsample ratio (segment train only)
# Classification
dropout: 0.0 # (float) use dropout regularization (classify train only)
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True # (bool) validate/test during training
split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
save_json: False # (bool) save results to JSON file
save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
conf: 0.2 # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.6 # (float) intersection over union (IoU) threshold for NMS
max_det: 300 # (int) maximum number of detections per image
half: False # (bool) use half precision (FP16)
dnn: False # (bool) use OpenCV DNN for ONNX inference
plots: True # (bool) save plots and images during train/val
# Predict settings -----------------------------------------------------------------------------------------------------
source: # (str, optional) source directory for images or videos
vid_stride: 1 # (int) video frame-rate stride
stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
visualize: False # (bool) visualize model features
augment: False # (bool) apply image augmentation to prediction sources
agnostic_nms: False # (bool) class-agnostic NMS
classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
retina_masks: False # (bool) use high-resolution segmentation masks
embed: # (list[int], optional) return feature vectors/embeddings from given layers
# Visualize settings ---------------------------------------------------------------------------------------------------
show: False # (bool) show predicted images and videos if environment allows
save_frames: False # (bool) save predicted individual video frames
save_txt: False # (bool) save results as .txt file
save_conf: False # (bool) save results with confidence scores
save_crop: False # (bool) save cropped images with results
show_labels: True # (bool) show prediction labels, i.e. 'person'
show_conf: True # (bool) show prediction confidence, i.e. '0.99'
show_boxes: True # (bool) show prediction boxes
line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
keras: False # (bool) use Kera=s
optimize: False # (bool) TorchScript: optimize for mobile
int8: False # (bool) CoreML/TF INT8 quantization
dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
simplify: True # (bool) ONNX: simplify model using `onnxslim`
opset: # (int, optional) ONNX: opset version
workspace: None # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
nms: False # (bool) CoreML: add NMS
# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01 # (float) final learning rate (lr0 * lrf)
momentum: 0.937 # (float) SGD momentum/Adam beta1
weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
warmup_momentum: 0.8 # (float) warmup initial momentum
warmup_bias_lr: 0.1 # (float) warmup initial bias lr
box: 7.5 # (float) box loss gain
cls: 0.5 # (float) cls loss gain (scale with pixels)
dfl: 1.5 # (float) dfl loss gain
pose: 12.0 # (float) pose loss gain
kobj: 1.0 # (float) keypoint obj loss gain
nbs: 64 # (int) nominal batch size
hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
degrees: 5.0 # (float) image rotation (+/- deg)
translate: 0.1 # (float) image translation (+/- fraction)
scale: 0.2 # (float) image scale (+/- gain)
shear: 0.0 # (float) image shear (+/- deg)
perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
flipud: 0.0 # (float) image flip up-down (probability)
fliplr: 0.5 # (float) image flip left-right (probability)
bgr: 0.0 # (float) image channel BGR (probability)
mosaic: 0.0 # (float) image mosaic (probability)
mixup: 0.0 # (float) image mixup (probability)
copy_paste: 0.0 # (float) segment copy-paste (probability)
copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
crop_fraction: 1.0 # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.
# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg: # (str, optional) for overriding defaults.yaml
# Tracker settings ------------------------------------------------------------------------------------------------------
tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
| open | 2025-03-20T06:55:07Z | 2025-03-20T20:55:54Z | https://github.com/ultralytics/ultralytics/issues/19793 | [
"question",
"detect"
] | LiangYong1216 | 4 |
vitalik/django-ninja | rest-api | 511 | [BUG] | I had trouble to use Ninja Forms, the form is not working with the PUT method, but only POST
```py
@router.post("/profile/edit", response={200: dict, 400: dict})
def edit_profile(request: Request, profile: EditProfileSchema = Form(...)):
print(profile.dict())
```
result:
`{'username': 'test', 'fullname': 'test', 'delete_image': True, 'bio': '', 'size': ''}`
this code works only if i leave the post method, if put is set this is te result:
`{'username': None, 'fullname': None, 'delete_image': None, 'bio': None, 'size': None}`
Versions:
- Python version: 3.10.3
- Django version: 4.0.6
- Django-Ninja version: 0.19.1
| closed | 2022-07-22T00:31:45Z | 2022-09-08T15:02:08Z | https://github.com/vitalik/django-ninja/issues/511 | [] | RiccardoCherchi | 2 |
zappa/Zappa | flask | 1,351 | Scheduled function truncated at 63 characters and fails to invoke | if I have a scheduled function with a name longer than 63 characters, then the name will be truncated in the CloudWatch event name/ARN:
```
{
"production": {
...
"events": [{
"function": "my_module.my_submodule.my_really_long_and_descriptive_function_name",
"expressions": ["rate(1 day)"]
}],
...
}
}
```
Event rule: `arn:aws:events:eu-west-2:000000000000:rule/-my_module.my_submodule.my_really_long_and_descriptive_function_`
This results in the following exception when the event is handled by the lambda:
```
AttributeError: module 'my_module.my_submodule' has no attribute 'my_really_long_and_descriptive_function_'
```
## Context
<!--- Provide a more detailed introduction to the issue itself, and why you consider it to be a bug -->
<!--- Also, please make sure that you are running Zappa _from a virtual environment_ and are using Python 3.8/3.9/3.10/3.11/3.12 -->
It looks like the `whole_function` value is parsed out of the event ARN here: https://github.com/zappa/Zappa/blob/39f75e76d28c1a08d4de6501e6f794fe988cbc98/zappa/handler.py#L410
Since the ARNs are limited in length, the long module path gets truncated to 63 characters (possibly because of the leading `-` making 64 total). It looks like the full module and function path remains non-truncated in the description of the event rule.
## Expected Behavior
It should invoke the non-truncated function, or should refuse to deploy with handler functions that are too long.
## Actual Behavior
It throws an exception and the scheduled task never executes.
## Possible Fix
Either:
1. Have the handler read the non-truncated `whole_function` value from the event description. This might require an extra AWS API call that existing deployments may or may not have permission to perform.
2. During deployment, a mapping of truncated names to full names could be created and embedded in the deployed app bundle, then referenced when handling events.
3. Raise an error (early) during deployment if a handler function name is too long and would result in truncation. It would be better to explicitly fail during deployment than to have guaranteed failures later on that might go unnoticed.
## Steps to Reproduce
1. Create a scheduled function whose fully qualified handler is longer than 63 characters.
2. Deploy.
3. Observe the error logs for the `AttributeError` above.
## Your Environment
<!--- Include as many relevant details about the environment you experienced the bug in -->
* Zappa version used: 0.56.1
* Operating System and Python version: Amazon Linux (lambda), Python 3.9
| closed | 2024-09-18T11:18:37Z | 2024-09-30T07:31:03Z | https://github.com/zappa/Zappa/issues/1351 | [
"duplicate"
] | eviltwin | 2 |
xonsh/xonsh | data-science | 5,753 | Inconsisted quoting behavior on Windows vs. macOS | ## Current Behavior
<!---
For general xonsh issues, please try to replicate the failure using `xonsh --no-rc --no-env`.
Short, reproducible code snippets are highly appreciated.
You can use `$XONSH_SHOW_TRACEBACK=1`, `$XONSH_TRACE_SUBPROC=2`, or `$XONSH_DEBUG=1`
to collect more information about the failure.
-->
I can run the command below on both macOS and Windows:
```xsh
conda install "python>=3.11"
```
However, the following variant only works on Windows:
```xsh
conda install @(['"python>=3.11"'])
```
On macOS, I get the traceback pasted below.
Traceback (if applicable):
<details>
```xsh
Trace run_subproc({'cmds': (['conda', 'install', '"python>=3.11"'],), 'captured': 'hiddenobject'})
Trace run_subproc({'cmds': (['/usr/local/Caskroom/miniconda/base/bin/conda', 'install', '"python>=3.11"'],), 'captured': 'hiddenobject'})
InvalidMatchSpec: Invalid spec '"python>=3.11"': Invalid version '3.11"': invalid character(s)
Exception in thread {'cls': 'ProcProxyThread', 'name': 'Thread-186', 'func': FuncAlias({'name': 'conda', 'func': '_conda_main', 'return_what': 'result'}), 'alias': 'conda', 'pid': None}
subprocess.CalledProcessError: Command '['/usr/local/Caskroom/miniconda/base/bin/conda', 'install', '"python>=3.11"']' returned non-zero exit status 1.
subprocess.CalledProcessError: Command '['conda', 'install', '"python>=3.11"']' returned non-zero exit status 1.
```
</details>
## Expected Behavior
<!--- What you expect and what is your real life use case. -->
## xonfig
<details>
```xsh
+-----------------------------+------------------------------------+
| xonsh | 0.19.0 |
| Python | 3.11.11 |
| PLY | 3.11 |
| have readline | True |
| prompt toolkit | 3.0.39 |
| shell type | prompt_toolkit |
| history backend | json |
| pygments | 2.16.1 |
| on posix | True |
| on linux | False |
| on darwin | True |
| on windows | False |
| on cygwin | False |
| on msys2 | False |
| is superuser | False |
| default encoding | utf-8 |
| xonsh encoding | utf-8 |
| encoding errors | surrogateescape |
| xontrib | [] |
| RC file 1 | /path/to/home/.config/xonsh/rc.xsh |
| UPDATE_OS_ENVIRON | False |
| XONSH_CAPTURE_ALWAYS | False |
| XONSH_SUBPROC_OUTPUT_FORMAT | stream_lines |
| THREAD_SUBPROCS | True |
| XONSH_CACHE_SCRIPTS | True |
+-----------------------------+------------------------------------+
```
</details>
## For community
โฌ๏ธ **Please click the ๐ reaction instead of leaving a `+1` or ๐ comment**
| open | 2024-12-16T17:14:03Z | 2025-01-18T19:38:35Z | https://github.com/xonsh/xonsh/issues/5753 | [
"windows",
"command-substitution",
"argparse",
"to-close-in-the-future",
"edge-case"
] | auneri | 8 |
horovod/horovod | tensorflow | 3,504 | hvd.DistributedOptimizer gradient accumulation doesn't clean up infinite gradient correctly | **Environment:**
1. Framework: (TensorFlow, Keras, PyTorch, MXNet) Keras
2. Framework version: 2.4
3. Horovod version: 2.3
4. MPI version:
5. CUDA version:
6. NCCL version:
7. Python version:
8. Spark / PySpark version:
9. Ray version:
10. OS and version:
11. GCC version:
12. CMake version:
**Checklist:**
1. Did you search issues to find if somebody asked this question before?
2. If your question is about hang, did you read [this doc](https://github.com/horovod/horovod/blob/master/docs/running.rst)?
3. If your question is about docker, did you read [this doc](https://github.com/horovod/horovod/blob/master/docs/docker.rst)?
4. Did you check if you question is answered in the [troubleshooting guide](https://github.com/horovod/horovod/blob/master/docs/troubleshooting.rst)?
**Bug report:**
We were training in TensorFlow [FP16 mixed precision](https://www.tensorflow.org/guide/mixed_precision) with keras `model.fit()` and with gradient accumulation/aggregation (`backward_pass_per_step` in `hvd.DistributedOptimizer`) and noticed that the [GradientAggregationHelperEager](https://github.com/horovod/horovod/blob/master/horovod/tensorflow/gradient_aggregation_eager.py#L8) doesn't work correctly with FP16 when the loss goes infinite. Details:
It is kind of expected that at the very first 2-15 steps of the training, the gradient out of TF [LossScaleOptimizer](https://github.com/keras-team/keras/blob/v2.8.0/keras/mixed_precision/loss_scale_optimizer.py#L258-L844) is infinite (because the default initial loss scale factor is as large as `2**15`). Dynamic LSO can handle this gracefully, it just skips applying gradient of that step and divides the scale factor by half. However horovod GradientAggregationHelper will anyway add the infinite gradient up locally, and the infinite gradient will never be correctly cleaned up in [this way](https://github.com/horovod/horovod/blob/133ef0725253db83cfb82a4ed4003df76d189829/horovod/tensorflow/gradient_aggregation_eager.py#L119-L123):
```
def _clear_vars(self):
self.counter.assign(0)
for idx in self.locally_aggregated_grads.keys():
self.locally_aggregated_grads[idx].assign_add(
-1 * self.locally_aggregated_grads[idx])
```
as the result of adding inf value by its negative val will be NaN.
| closed | 2022-04-06T05:12:54Z | 2022-04-15T17:39:00Z | https://github.com/horovod/horovod/issues/3504 | [
"bug"
] | yundai424 | 0 |
wkentaro/labelme | computer-vision | 527 | Instance segmentation not working | SegmentationObjectPNG and SegmentationClassPNG have same type of images and not showing different colors for different instances.
<img src=https://user-images.githubusercontent.com/55757328/71172201-9db37500-2285-11ea-9758-e8decca2be09.png width=30% > <img src=https://user-images.githubusercontent.com/55757328/71172208-a441ec80-2285-11ea-92f0-5c145f4059dc.png width=30%>
Even though while labelling I labelled as classname-1, classname-2
The labels.txt file has only classname once as you have shown in the instance segmentation example.
What could I be doing wrong? I really want different instances in different colors | closed | 2019-12-19T12:03:40Z | 2020-03-15T00:00:21Z | https://github.com/wkentaro/labelme/issues/527 | [] | aditya-krish | 2 |
horovod/horovod | tensorflow | 3,535 | Distributed validation with Keras and Tensorflow | **Is your feature request related to a problem? Please describe.**
Referring to the Keras+Tensorflow example at https://github.com/horovod/horovod/blob/master/examples/tensorflow2/tensorflow2_keras_mnist.py, I'm considering distinct training and validation datasets by expanding l42 as follows:
```
train_dataset = train_dataset.repeat().shuffle(10000).batch(128)
val_dataset = val_dataset.batch(128)
```
And the adapted ``model.fit`` call as follows:
```
mnist_model.fit(train_dataset, steps_per_epoch=500 // hvd.size(), callbacks=callbacks,
validation_data=val_dataset, validation_steps=val_steps, epochs=24, verbose=verbose)
```
When running this code on a cluster, the model training phase uses all computation nodes as expected, but the validation phase operates only on the rank 0 node. When the validation set is pretty large, validation may take even more time than actual training.
**Describe the solution you'd like**
Ideally, validation would use all computation nodes under the hood.
**Describe alternatives you've considered**
I could not find any working solution which did not require abandonning Keras. As the optimizer object is the cornerstone for data distribution with Keras+Tensorflow, I have little idea on cues to investigate. [This page mentions a distributed validation step](http://www.idris.fr/eng/jean-zay/gpu/jean-zay-gpu-hvd-tf-multi-eng.html#distributed_validation), but I could not figure out a way to incorporate it in a working custom validation step made following [the official Tensorflow guidelines](https://www.tensorflow.org/guide/keras/customizing_what_happens_in_fit#providing_your_own_evaluation_step).
| open | 2022-05-04T12:18:14Z | 2022-05-04T12:18:14Z | https://github.com/horovod/horovod/issues/3535 | [
"enhancement"
] | pbruneau | 0 |
CTFd/CTFd | flask | 2,667 | Undeclared variable link in media library | **Environment**:
- CTFd Version/Commit: bbf1ffc7aa7ce6c505f8cb6c9afd2da0ddd1c609
- Operating System: Linux (docker)
- Web Browser and Version: Firefox 132 & Chrome 123
**What happened?**
In the page editor, I am unable to insert a media link.
In the console, it throws an error which indicates that the `link` variable is not defined.
**What did you expect to happen?**
To work ...
**How to reproduce your issue**
- Start a fresh instance of CTFd
- Edit any page, then open the media library
- Upload an image, then try to insert it
- Nothing happen, an error is written into the console
**Any associated stack traces or error logs**
 | closed | 2024-11-25T19:05:44Z | 2024-11-25T20:52:39Z | https://github.com/CTFd/CTFd/issues/2667 | [] | adam-lebon | 1 |
holoviz/panel | plotly | 7,689 | AttributeError when attempting to add PyComponent to column in FastListTemplate main/modal |
### ALL software version info
<details>
<summary>Software Version Info</summary>
```plaintext
Python == 3.11.11 (but it also happens on lesser versions, such as 3.10 -- not sure for higher versions))
panel==1.6.0 (but it also happens on lesser versions)
bokeh==3.6.3
param==2.2.0
```
</details>
### Description of expected behavior and the observed behavior
In my full project, I am attempting to add a custom `PyComponent` to a column which I have in the modal of a `FastListTemplate`. When I append my component and then open the modal, I get the error shown below, and the modal doesn't open (as expected since an error occurred). If I open the modal first and then append, I get the error, and my component shows correctly with some style errors (not sure if that is reproduced in the example below).
I was able to semi-work around this issue by directly inheriting from `pn.reactive.Reactive`, which implements `_process_param_change`. Then, I started getting other errors which may be seen by adding in `Reactive` to the `FeatureInput` inheritance below. (I already imported it at the top of the example.)
The example I provide below is the minimal example I could get to reproduce the issue.
The expected behavior is to be able to append a `PyComponent` to these columns and have it work with no errors.
### Complete, minimal, self-contained example code that reproduces the issue
This is the `FeatureInput` example from the [`PyComponent` example page](https://panel.holoviz.org/how_to/custom_components/python/create_custom_widget.html), except I changed it to display in a `FastListTemplate` main or modal.
Remember to test adding `Reactive` to the inheritance of `FeatureInput` as well.
<details>
```python
import panel as pn
import param
from panel.widgets.base import WidgetBase
from panel.custom import PyComponent
from panel.reactive import Reactive
class FeatureInput(WidgetBase, PyComponent):
"""
The `FeatureInput` enables a user to select from a list of features and set their values.
"""
value = param.Dict(
doc="The names of the features selected and their set values", allow_None=False
)
features = param.Dict(
doc="The names of the available features and their default values"
)
selected_features = param.ListSelector(
doc="The list of selected features"
)
_selected_widgets = param.ClassSelector(
class_=pn.Column, doc="The widgets used to edit the selected features"
)
def __init__(self, **params):
params["value"] = params.get("value", {})
params["features"] = params.get("features", {})
params["selected_features"] = params.get("selected_features", [])
params["_selected_widgets"] = self.param._selected_widgets.class_()
super().__init__(**params)
self._selected_features_widget = pn.widgets.MultiChoice.from_param(
self.param.selected_features, sizing_mode="stretch_width"
)
def __panel__(self):
return pn.Column(self._selected_features_widget, self._selected_widgets)
@param.depends("features", watch=True, on_init=True)
def _reset_selected_features(self):
selected_features = []
for feature in self.selected_features.copy():
if feature in self.features.copy():
selected_features.append(feature)
self.param.selected_features.objects = list(self.features)
self.selected_features = selected_features
@param.depends("selected_features", watch=True, on_init=True)
def _handle_selected_features_change(self):
org_value = self.value
self._update_selected_widgets(org_value)
self._update_value()
def _update_value(self, *args): # pylint: disable=unused-argument
new_value = {}
for widget in self._selected_widgets:
new_value[widget.name] = widget.value
self.value = new_value
def _update_selected_widgets(self, org_value):
new_widgets = {}
for feature in self.selected_features:
value = org_value.get(feature, self.features[feature])
widget = self._new_widget(feature, value)
new_widgets[feature] = widget
self._selected_widgets[:] = list(new_widgets.values())
def _new_widget(self, feature, value):
widget = pn.widgets.FloatInput(
name=feature, value=value, sizing_mode="stretch_width"
)
pn.bind(self._update_value, widget, watch=True)
return widget
features = {
"Blade Length (m)": 73.5,
"Cut-in Wind Speed (m/s)": 3.5,
"Cut-out Wind Speed (m/s)": 25,
"Grid Connection Capacity (MW)": 5,
"Hub Height (m)": 100,
"Rated Wind Speed (m/s)": 12,
"Rotor Diameter (m)": 150,
"Turbine Efficiency (%)": 45,
"Water Depth (m)": 30,
"Wind Speed (m/s)": 10,
}
_selected_features = ["Wind Speed (m/s)", "Rotor Diameter (m)"]
_widget = FeatureInput(
features=features,
selected_features=_selected_features,
width=500,
)
##### My code starts here #####
main_column = pn.Column()
modal_column = pn.Column()
flt = pn.template.FastListTemplate(
main=[main_column],
modal=[modal_column]
)
def add_to_main(_):
main_column.append(pn.FlexBox(
pn.Column(
"## Widget",
_widget,
),
pn.Column(
"## Value",
pn.pane.JSON(_widget.param.value, width=500, height=200),
),
))
def add_to_modal(_):
modal_column.append(
pn.FlexBox(
pn.Column(
"## Widget",
_widget,
),
pn.Column(
"## Value",
pn.pane.JSON(_widget.param.value, width=500, height=200),
),
)
)
def add_then_open(_):
modal_column.append(
pn.FlexBox(
pn.Column(
"## Widget",
_widget,
),
pn.Column(
"## Value",
pn.pane.JSON(_widget.param.value, width=500, height=200),
),
)
)
flt.open_modal()
def open_then_add(_):
flt.open_modal()
modal_column.append(
pn.FlexBox(
pn.Column(
"## Widget",
_widget,
),
pn.Column(
"## Value",
pn.pane.JSON(_widget.param.value, width=500, height=200),
),
)
)
main_column.append(pn.widgets.Button(
name='add to main', on_click=add_to_main))
main_column.append(pn.widgets.Button(
name='add to modal', on_click=add_to_modal))
main_column.append(pn.widgets.Button(
name="add to modal, then open modal", on_click=add_then_open))
main_column.append(pn.widgets.Button(
name="open modal, then add to modal", on_click=open_then_add))
flt.servable()
```
</details>
### Stack traceback and/or browser JavaScript console output
Errors without inheriting from Reactive:
<details>
```plaintext
AttributeError: 'FeatureInput' object has no attribute '_process_param_change'
2025-02-06 19:21:37,267 WebSocket connection closed: code=1001, reason=None
2025-02-06 19:21:37,826 WebSocket connection opened
2025-02-06 19:21:37,826 ServerConnection created
2025-02-06 19:21:39,019 error handling message
message: Message 'PATCH-DOC' content: {'events': [{'kind': 'MessageSent', 'msg_type': 'bokeh_event', 'msg_data': {'type': 'event', 'name': 'button_click', 'values': {'type': 'map', 'entries': [['model', {'id': 'p1289'}]]}}}]}
error: AttributeError("'FeatureInput' object has no attribute '_process_param_change'")
Traceback (most recent call last):
File "/code/venv/lib/python3.11/site-packages/bokeh/server/protocol_handler.py", line 94, in handle
work = await handler(message, connection)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/server/session.py", line 94, in _needs_document_lock_wrapper
result = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/server/session.py", line 286, in _handle_patch
message.apply_to_document(self.document, self)
File "/code/venv/lib/python3.11/site-packages/bokeh/protocol/messages/patch_doc.py", line 104, in apply_to_document
invoke_with_curdoc(doc, lambda: doc.apply_json_patch(self.payload, setter=setter))
File "/code/venv/lib/python3.11/site-packages/bokeh/document/callbacks.py", line 453, in invoke_with_curdoc
return f()
^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/protocol/messages/patch_doc.py", line 104, in <lambda>
invoke_with_curdoc(doc, lambda: doc.apply_json_patch(self.payload, setter=setter))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/document/document.py", line 391, in apply_json_patch
DocumentPatchedEvent.handle_event(self, event, setter)
File "/code/venv/lib/python3.11/site-packages/bokeh/document/events.py", line 244, in handle_event
event_cls._handle_event(doc, event)
File "/code/venv/lib/python3.11/site-packages/bokeh/document/events.py", line 279, in _handle_event
cb(event.msg_data)
File "/code/venv/lib/python3.11/site-packages/bokeh/document/callbacks.py", line 400, in trigger_event
model._trigger_event(event)
File "/code/venv/lib/python3.11/site-packages/bokeh/util/callback_manager.py", line 111, in _trigger_event
self.document.callbacks.notify_event(cast(Model, self), event, invoke)
File "/code/venv/lib/python3.11/site-packages/bokeh/document/callbacks.py", line 262, in notify_event
invoke_with_curdoc(doc, callback_invoker)
File "/code/venv/lib/python3.11/site-packages/bokeh/document/callbacks.py", line 453, in invoke_with_curdoc
return f()
^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/util/callback_manager.py", line 107, in invoke
cast(EventCallbackWithEvent, callback)(event)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 580, in _server_event
self._comm_event(doc, event)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 567, in _comm_event
state._handle_exception(e)
File "/code/venv/lib/python3.11/site-packages/panel/io/state.py", line 484, in _handle_exception
raise exception
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 565, in _comm_event
self._process_bokeh_event(doc, event)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 502, in _process_bokeh_event
self._process_event(event)
File "/code/venv/lib/python3.11/site-packages/panel/widgets/button.py", line 241, in _process_event
self.clicks += 1
^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 515, in _f
instance_param.__set__(obj, val)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameters.py", line 541, in __set__
super().__set__(obj,val)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 1564, in __set__
obj.param._call_watcher(watcher, event)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2604, in _call_watcher
self_._execute_watcher(watcher, (event,))
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/workspaces/dexter-ui/test.py", line 125, in add_to_modal
modal_column.append(
File "/code/venv/lib/python3.11/site-packages/panel/layout/base.py", line 474, in append
self.objects = new_objects
^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 515, in _f
instance_param.__set__(obj, val)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/panel/viewable.py", line 1184, in __set__
super().__set__(obj, self._transform_value(val))
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 1564, in __set__
obj.param._call_watcher(watcher, event)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2604, in _call_watcher
self_._execute_watcher(watcher, (event,))
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 451, in _param_change
applied &= self._apply_update(named_events, properties, model, ref)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 339, in _apply_update
self._update_model(events, msg, root, model, doc, comm)
File "/code/venv/lib/python3.11/site-packages/panel/layout/base.py", line 135, in _update_model
state._views[ref][0]._preprocess(root, self, old_children)
File "/code/venv/lib/python3.11/site-packages/panel/viewable.py", line 619, in _preprocess
hook(self, root, changed, old_models)
File "/code/venv/lib/python3.11/site-packages/panel/theme/base.py", line 150, in _apply_hooks
self._reapply(changed, root, old_models, isolated=False, cache=cache, document=root.document)
File "/code/venv/lib/python3.11/site-packages/panel/theme/base.py", line 138, in _reapply
self._apply_modifiers(o, ref, self.theme, isolated, cache, document)
File "/code/venv/lib/python3.11/site-packages/panel/theme/base.py", line 253, in _apply_modifiers
cls._apply_params(viewable, mref, modifiers, document)
File "/code/venv/lib/python3.11/site-packages/panel/theme/base.py", line 273, in _apply_params
props = viewable._process_param_change(params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'FeatureInput' object has no attribute '_process_param_change'
```
</details>
Errors when inheriting from Reactive and trying to add something in the UI:
<details>
```plaintext
2025-02-06 19:52:36,522 WebSocket connection closed: code=1001, reason=None
2025-02-06 19:52:37,391 WebSocket connection opened
2025-02-06 19:52:37,392 ServerConnection created
2025-02-06 19:52:42,574 ERROR: panel.reactive - Callback failed for object named 'Selected features' changing property {'value': ['Rotor Diameter (m)', 'Wind Speed (m/s)', 'Blade Length (m)']}
Traceback (most recent call last):
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 470, in _process_events
self.param.update(**self_params)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2406, in update
restore = dict(self_._update(arg, **kwargs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2439, in _update
self_._batch_call_watchers()
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2624, in _batch_call_watchers
self_._execute_watcher(watcher, events)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/panel/param.py", line 526, in link_widget
self.object.param.update(**{p_name: change.new})
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2406, in update
restore = dict(self_._update(arg, **kwargs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2439, in _update
self_._batch_call_watchers()
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2624, in _batch_call_watchers
self_._execute_watcher(watcher, events)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 767, in _sync_caller
return function()
^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/depends.py", line 85, in _depends
return func(*args, **kw)
^^^^^^^^^^^^^^^^^
File "/workspaces/dexter-ui/test.py", line 61, in _handle_selected_features_change
self._update_value()
File "/workspaces/dexter-ui/test.py", line 69, in _update_value
self.value = new_value
^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 515, in _f
instance_param.__set__(obj, val)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 1564, in __set__
obj.param._call_watcher(watcher, event)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2604, in _call_watcher
self_._execute_watcher(watcher, (event,))
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 451, in _param_change
applied &= self._apply_update(named_events, properties, model, ref)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 339, in _apply_update
self._update_model(events, msg, root, model, doc, comm)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 711, in _update_model
super()._update_model(events, msg, root, model, doc, comm)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 371, in _update_model
model_val = getattr(model, attr)
^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/core/has_props.py", line 369, in __getattr__
self._raise_attribute_error_with_matches(name, properties)
File "/code/venv/lib/python3.11/site-packages/bokeh/core/has_props.py", line 377, in _raise_attribute_error_with_matches
raise AttributeError(f"unexpected attribute {name!r} to {self.__class__.__name__}, {text} attributes are {nice_join(matches)}")
AttributeError: unexpected attribute 'value' to Column, possible attributes are align, aspect_ratio, auto_scroll_limit, children, context_menu, css_classes, css_variables, disabled, elements, flow_mode, height, height_policy, js_event_callbacks, js_property_callbacks, margin, max_height, max_width, min_height, min_width, name, resizable, scroll_button_threshold, scroll_index, scroll_position, sizing_mode, spacing, styles, stylesheets, subscribed_events, syncable, tags, view_latest, visible, width or width_policy
2025-02-06 19:52:42,578 Exception in callback functools.partial(<bound method IOLoop._discard_future_result of <tornado.platform.asyncio.AsyncIOMainLoop object at 0x774f97e62390>>, <Task finished name='Task-1212' coro=<ServerSession.with_document_locked() done, defined at /code/venv/lib/python3.11/site-packages/bokeh/server/session.py:77> exception=AttributeError("unexpected attribute 'value' to Column, possible attributes are align, aspect_ratio, auto_scroll_limit, children, context_menu, css_classes, css_variables, disabled, elements, flow_mode, height, height_policy, js_event_callbacks, js_property_callbacks, margin, max_height, max_width, min_height, min_width, name, resizable, scroll_button_threshold, scroll_index, scroll_position, sizing_mode, spacing, styles, stylesheets, subscribed_events, syncable, tags, view_latest, visible, width or width_policy")>)
Traceback (most recent call last):
File "/code/venv/lib/python3.11/site-packages/tornado/ioloop.py", line 750, in _run_callback
ret = callback()
^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/tornado/ioloop.py", line 774, in _discard_future_result
future.result()
File "/code/venv/lib/python3.11/site-packages/bokeh/server/session.py", line 98, in _needs_document_lock_wrapper
result = await result
^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 517, in _change_coroutine
state._handle_exception(e)
File "/code/venv/lib/python3.11/site-packages/panel/io/state.py", line 484, in _handle_exception
raise exception
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 515, in _change_coroutine
self._change_event(doc)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 533, in _change_event
self._process_events(events)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 470, in _process_events
self.param.update(**self_params)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2406, in update
restore = dict(self_._update(arg, **kwargs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2439, in _update
self_._batch_call_watchers()
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2624, in _batch_call_watchers
self_._execute_watcher(watcher, events)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/panel/param.py", line 526, in link_widget
self.object.param.update(**{p_name: change.new})
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2406, in update
restore = dict(self_._update(arg, **kwargs))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2439, in _update
self_._batch_call_watchers()
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2624, in _batch_call_watchers
self_._execute_watcher(watcher, events)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 767, in _sync_caller
return function()
^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/depends.py", line 85, in _depends
return func(*args, **kw)
^^^^^^^^^^^^^^^^^
File "/workspaces/dexter-ui/test.py", line 61, in _handle_selected_features_change
self._update_value()
File "/workspaces/dexter-ui/test.py", line 69, in _update_value
self.value = new_value
^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 515, in _f
instance_param.__set__(obj, val)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 517, in _f
return f(self, obj, val)
^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 1564, in __set__
obj.param._call_watcher(watcher, event)
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2604, in _call_watcher
self_._execute_watcher(watcher, (event,))
File "/code/venv/lib/python3.11/site-packages/param/parameterized.py", line 2586, in _execute_watcher
watcher.fn(*args, **kwargs)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 451, in _param_change
applied &= self._apply_update(named_events, properties, model, ref)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 339, in _apply_update
self._update_model(events, msg, root, model, doc, comm)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 711, in _update_model
super()._update_model(events, msg, root, model, doc, comm)
File "/code/venv/lib/python3.11/site-packages/panel/reactive.py", line 371, in _update_model
model_val = getattr(model, attr)
^^^^^^^^^^^^^^^^^^^^
File "/code/venv/lib/python3.11/site-packages/bokeh/core/has_props.py", line 369, in __getattr__
self._raise_attribute_error_with_matches(name, properties)
File "/code/venv/lib/python3.11/site-packages/bokeh/core/has_props.py", line 377, in _raise_attribute_error_with_matches
raise AttributeError(f"unexpected attribute {name!r} to {self.__class__.__name__}, {text} attributes are {nice_join(matches)}")
AttributeError: unexpected attribute 'value' to Column, possible attributes are align, aspect_ratio, auto_scroll_limit, children, context_menu, css_classes, css_variables, disabled, elements, flow_mode, height, height_policy, js_event_callbacks, js_property_callbacks, margin, max_height, max_width, min_height, min_width, name, resizable, scroll_button_threshold, scroll_index, scroll_position, sizing_mode, spacing, styles, stylesheets, subscribed_events, syncable, tags, view_latest, visible, width or width_policy
2025-02-06 19:52:42,620 Dropping a patch because it contains a previously known reference (id='p1637'). Most of the time this is harmless and usually a result of updating a model on one side of a communications channel while it was being removed on the other end.
2025-02-06 19:52:42,621 Dropping a patch because it contains a previously known reference (id='p1638'). Most of the time this is harmless and usually a result of updating a model on one side of a communications channel while it was being removed on the other end.
```
</details>
| open | 2025-02-06T20:04:24Z | 2025-03-11T14:33:24Z | https://github.com/holoviz/panel/issues/7689 | [] | 14lclark | 0 |
microsoft/qlib | machine-learning | 1,619 | dump_bin.py dump_allๆถ๏ผไธช่กไธบไฝ่ฆ้็จๆฅๅ็็ดขๅผ๏ผๅฏผ่ดๅ็ๆฅๆ ๆฐๆฎ็่ฎฐๅฝ | ๅจdump_bin.py dump_all็้ป่พไธญ๏ผไผๅฐไธช่ก็็ดขๅผ้็จๆฅๅ็็ดขๅผ๏ผๅฆไธใ่ฟๅฏผ่ดbinๆฐๆฎไธญไธช่กๅ็ๆฅๅบ็ฐๆ ไปทๆ ผ๏ผๆ ๆไบค้็่ฎฐๅฝ๏ผไพๅฆsz000001ไธญๅฝๅนณๅฎๅจ2014-7-15ๆฅๅ็๏ผไฝbinๆฐๆฎไธญไป็ถๅบ็ฐไธๆก็ฉบ่ฎฐๅฝ๏ผๅชๆๆฅๆ๏ผๅ
ถไปๅญๆฎต้ฝๆฏ็ฉบใ
ๆ็้ฎ้ขๆฏ๏ผ่ฟๆ ทๅค็ๅฏนไธๅฏน๏ผ่ฟ็งๆฒกๆไปทๆ ผ็่ฎฐๅฝไผๅไธ่ฎญ็ปๅ๏ผๆไธชไบบ็ๆ่งๆฏๅ็ๆฅbinไธญไธๅบ่ฏฅๅญๅจ็ธๅ
ณ่ฎฐๅฝใ
def data_merge_calendar(self, df: pd.DataFrame, calendars_list: List[pd.Timestamp]) -> pd.DataFrame:
# calendars
calendars_df = pd.DataFrame(data=calendars_list, columns=[self.date_field_name])
calendars_df[self.date_field_name] = calendars_df[self.date_field_name].astype(np.datetime64)
cal_df = calendars_df[
(calendars_df[self.date_field_name] >= df[self.date_field_name].min())
& (calendars_df[self.date_field_name] <= df[self.date_field_name].max())
]
# align index
cal_df.set_index(self.date_field_name, inplace=True)
df.set_index(self.date_field_name, inplace=True)
r_df = df.reindex(cal_df.index)
return r_df | closed | 2023-08-04T10:03:05Z | 2023-11-12T00:06:41Z | https://github.com/microsoft/qlib/issues/1619 | [
"question",
"stale"
] | quant2008 | 2 |
graphistry/pygraphistry | jupyter | 210 | [DOCS] requirements | An explicit hw/sw requirements doc may help. It can cover only the Python client side, and defer viz client + GPU server discussion to https://github.com/graphistry/graphistry-cli/blob/master/hardware-software.md .
Would help w/ issues like https://github.com/graphistry/pygraphistry/issues/203
README.md is quite big, so probably a separate page w/ a README.md link to it | open | 2021-02-08T17:18:50Z | 2021-02-08T17:21:27Z | https://github.com/graphistry/pygraphistry/issues/210 | [
"docs"
] | lmeyerov | 0 |
browser-use/browser-use | python | 169 | Streamlit error while using Browesr-use | How do develop streamlit app using Browser-use? I am using simple code as below:
import os
import sys
import asyncio
from langchain_openai import ChatOpenAI
from browser_use import Agent
import streamlit as st
os.environ['SSL_CERT_FILE'] = 'C:\\Users\\RSPRASAD\\AppData\\Local\\.certifi\\cacert.pem'
os.environ['REQUESTS_CA_BUNDLE'] = 'C:\\Users\\RSPRASAD\\AppData\\Local\\.certifi\\cacert.pem'
os.environ["OPENAI_API_KEY"] = 'my_api_key'
llm = ChatOpenAI(base_url = 'https://models.inference.ai.azure.com', model='gpt-4o')
agent = Agent(
task='Go to google and then go to hindu.com and give me summary of 1 editorial. ',
llm=llm,
)
async def main():
await agent.run(max_steps=50)
# agent.create_history_gif()
asyncio.run(main())
But i encounter the below error:
future: <Task finished name='Task-16155' coro=<Connection.run() done, defined at C:\Users\RSPRASAD\AppData\Local\anaconda3\envs\new_env\Lib\site-packages\playwright\_impl\_connection.py:272> exception=NotImplementedError()>
Seems there is an issue of asynchronous behavior that Streamlit doesnt like and playwright cant function properly. | closed | 2025-01-06T19:02:08Z | 2025-03-12T10:01:51Z | https://github.com/browser-use/browser-use/issues/169 | [] | ravi6389 | 1 |
darrenburns/posting | rest-api | 188 | customize font family | 1. is there any way to use own font family in posting by changing the .scss file? is this restricted by textual for now?
2. ~~also not sure about the render code of $surface-darken-1 and $surface-lighten-1~~
Can you shed a light for these questions?
After check the textual api, i got answer of question<2>, but really need help for custom font support. | closed | 2025-02-15T09:07:07Z | 2025-02-16T09:15:17Z | https://github.com/darrenburns/posting/issues/188 | [] | zeyutt | 1 |
2noise/ChatTTS | python | 265 | ็ๆ่ฏญ้ณ่ดจ้ไธ้๏ผไฝ้ๅบฆๅคชๆ
ข ๆฒกๆณ็จ | OS: MX x86_64
Host: Z390 AORUS ELITE
Kernel: 6.8.12-1-liquorix-amd64
CPU: Intel i7-9700K (8) @ 3.601GHz
GPU: NVIDIA GeForce RTX 3090
Memory: 10768MiB / 64230MiB
Cuda 11.8
็ๆ12็ง็่ฏญ้ณ ้่ฆ120็งไปฅไธ๏ผ ๆฏไธๆฏๆๅช้่ฎพ็ฝฎ็ไธๅฏน๏ผ
| closed | 2024-06-05T12:04:13Z | 2024-09-09T04:01:21Z | https://github.com/2noise/ChatTTS/issues/265 | [
"stale"
] | Yeeler | 12 |
httpie/cli | rest-api | 674 | Follow doesn't work (on showing intermediary responses) | Per [ Showing intermediary redirect responses](https://httpie.org/doc#showing-intermediary-redirect-responses) documentation I should be able to follow redirects. However with the latest httpie on Ubuntu (Ubuntu bash on Windows 10), this doesn't work. On running:
`http --follow --all httpbin.org/redirect/3`
I get:
```
HTTPie 0.9.2
HTTPie data: /home/xxxxx/.httpie
Requests 2.9.1
Pygments 2.1
Python 2.7.12 (default, Dec 4 2017, 14:50:18)
[GCC 5.4.0 20160609] linux2
usage: http [--json] [--form] [--pretty {all,colors,format,none}]
[--style STYLE] [--print WHAT] [--verbose] [--headers] [--body]
[--stream] [--output FILE] [--download] [--continue]
[--session SESSION_NAME_OR_PATH | --session-read-only SESSION_NAME_OR_PATH]
[--auth USER[:PASS]] [--auth-type {basic,digest}]
[--proxy PROTOCOL:PROXY_URL] [--follow] [--verify VERIFY]
[--cert CERT] [--cert-key CERT_KEY] [--timeout SECONDS]
[--check-status] [--ignore-stdin] [--help] [--version]
[--traceback] [--debug]
[METHOD] URL [REQUEST_ITEM [REQUEST_ITEM ...]]
http: error: unrecognized arguments: --all
```
| closed | 2018-04-27T05:44:35Z | 2018-07-11T12:21:49Z | https://github.com/httpie/cli/issues/674 | [] | viper25 | 1 |
BeastByteAI/scikit-llm | scikit-learn | 30 | Sentiment | Having Sentiment.py in https://github.com/iryna-kondr/scikit-llm/tree/main/skllm/datasets. would it be needed and essential ? @iryna-kondr @Nadav-Barak | closed | 2023-06-05T23:05:39Z | 2023-06-05T23:18:42Z | https://github.com/BeastByteAI/scikit-llm/issues/30 | [] | Adesoji1 | 0 |
sammchardy/python-binance | api | 618 | Cryptos lack of Ranking number | Dear All,
I have looked to get the Ranking number of any given symbol such as "BTC" 1 "ETH" 2.
However, either API does not have such a function or it is not documented.
Thanks. | open | 2020-11-22T11:52:47Z | 2020-11-23T10:06:43Z | https://github.com/sammchardy/python-binance/issues/618 | [] | mmaxus35 | 3 |
frol/flask-restplus-server-example | rest-api | 42 | Trouble distinguishing flask-marshmallow problems from embedded hack | Hi -
Thanks for a GREAT example! I am trying use it as the starting point for a small service I am writing. I do not seem to be able to get the custom validators and or pre and post request processing to work. However, I am not well versed in Marshmallow, so do not know if they are my problems or problems with the integration hack.
Would you consider adding a field to teams with a custom validator (I think this is the marshmallow way) The one I am trying to add is a custom date field, simplest case - existing or string 'now'.
The other things I am looking at integrating (once I have a bit more understanding about 'schemas' are:
guid instead of or in addition to id fields
jwt authentication (then I can separate out the oauth part)
| closed | 2016-12-21T14:11:35Z | 2017-01-11T08:27:07Z | https://github.com/frol/flask-restplus-server-example/issues/42 | [] | joshStillerman | 4 |
stanfordnlp/stanza | nlp | 1,294 | Tokenizer doesn't respect combined_electra-large's max_length | **Describe the bug**
When parsing a long text using the latest "combined_electra-large" model, I get the error:
```
Token indices sequence length is longer than the specified maximum sequence length for this
model (630 > 512). Running this sequence through the model will result in indexing errors
Exception in thread parse_chunks:
Traceback (most recent call last):
File "/home1/malouf/.pyenv/versions/3.11.3/lib/python3.11/threading.py", line 1038, in
_bootstrap_inner
self.run()
File "/home1/malouf/batch/treebank/threadpipe.py", line 113, in run
for tag, result in zip(tags, self.function(items)):
File "/home1/malouf/batch/treebank/parse.py", line 125, in parse_chunks
for doc_id, doc in zip(
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/pipeline/core.py",
line 456, in stream
batch = self.bulk_process(batch, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/pipeline/core.py",
line 433, in bulk_process
return self.process(docs, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/pipeline/core.py",
line 422, in process
doc = process(doc)
^^^^^^^^^^^^
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/pipeline/processor.
py", line 258, in bulk_process
self.process(combined_doc) # annotations are attached to sentence objects
^^^^^^^^^^^^^^^^^^^^^^^^^^
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/pipeline/pos_proces
sor.py", line 84, in process
batch.doc.set([doc.UPOS, doc.XPOS, doc.FEATS], [y for x in preds for y in x])
File
"/home1/malouf/.pyenv/versions/treebank/lib/python3.11/site-packages/stanza/models/common/doc.p
y", line 254, in set
assert (to_token and self.num_tokens == len(contents)) or self.num_words == len(contents),
\
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: Contents must have the same length as the original file.
```
**Environment (please complete the following information):**
- OS: MacOS 14.0
- Python version: Python 3.11.3
- Stanza version: 1.6.1 (and transformers 4.34.0)
| open | 2023-10-09T03:26:15Z | 2024-05-24T15:23:12Z | https://github.com/stanfordnlp/stanza/issues/1294 | [
"bug"
] | rmalouf | 9 |
allenai/allennlp | nlp | 5,262 | allennlp.common.checks.ConfigurationError: | allennlp.common.checks.ConfigurationError: from_params was passed a `params` object that was not a `Params`. This probably indicates malformed parameters in a configuration file, where something that should have been a dictionary was actually a list, or something else. This happened when constructing an object of type <class 'allennlp.nn.initializers.InitializerApplicator'>. | closed | 2021-06-15T09:17:00Z | 2021-06-29T16:14:26Z | https://github.com/allenai/allennlp/issues/5262 | [
"question",
"stale"
] | vicky-abr | 2 |
microsoft/nni | tensorflow | 5,064 | nni cannot use tensorboard | When I use NNI to conduct hyperparameter searching, I cannot use tensorboard.
As I selected the experiment and click tensorboard button, it will quickly pop up a python window (fig.1), and then the tensorboard page shows error (fig.2).
for the path in trail source code, I use
log_dir = os.path.join(os.environ["NNI_OUTPUT_DIR"], 'tensorboard')
Indeed, I can see tensorboard log file in each exp directory, but I cannot open the file via nni platform.
However, when I open terminal, and use the tensorboard from there, open the file in the trail output dir, the tensorboard can work well.
**Environment**:
- NNI version: v2.8
- Training service (local|remote|pai|aml|etc): local
- Client OS: windows
- Server OS (for remote mode only):
- Python version: 3.9.12
- PyTorch/TensorFlow version: pytorch
- Is conda/virtualenv/venv used?: conda
- Is running in Docker?: no
**Configuration**:
- Experiment config : hyperparameter tuning
| closed | 2022-08-12T09:48:59Z | 2023-05-12T02:34:38Z | https://github.com/microsoft/nni/issues/5064 | [
"bug",
"user raised",
"support",
"tensorboard"
] | cehw | 2 |
albumentations-team/albumentations | machine-learning | 1,568 | PadIfNeeded doesn't serialize position parameter | ## ๐ Bug
The `PadIfNeeded` transform doesn't serialize the position parameter.
## To Reproduce
Steps to reproduce the behavior:
```python
import albumentations as A
transform = A.PadIfNeeded(min_height=512, min_width=512, p=1, border_mode=0, value=[124, 116, 104], position="top_left")
transform.to_dict()
```
Output:
```
{'__version__': '1.4.1', 'transform': {'__class_fullname__': 'PadIfNeeded', 'always_apply': False, 'p': 1, 'min_height': 512, 'min_width': 512, 'pad_height_divisor': None, 'pad_width_divisor': None, 'border_mode': 0, 'value': [124, 116, 104], 'mask_value': None}}
```
## Expected behavior
The position parameter should be included in the `dict`.
## Environment
- Albumentations version: 1.4.1
- Python version: 3.8
- OS: Linux
- How you installed albumentations (`conda`, `pip`, source): `pip`
| closed | 2024-03-06T22:11:21Z | 2024-03-09T02:28:49Z | https://github.com/albumentations-team/albumentations/issues/1568 | [
"bug",
"good first issue"
] | margilt | 2 |
supabase/supabase-py | fastapi | 464 | Handling the Password Reset for a user by Supabase itself. | **Is your feature request related to a problem? Please describe.**
The function `supabase.auth.reset_password_email(email)` only sends a reset mail to the particular email address mentioned, but not actually handles the password reset of that account, unlike firebase. This function `reset_password_email()` should not only just send a password reset mail but also handle password reset with a unique form link specific to that user, so that the particular user can change the password. I hope that clarifies the problem.
**Describe the solution you'd like**
The function `reset_password_email(email)` should generate a unique supabase link which would contain a form to reset the password and this link should be sent to the particular mail as a password reset mail. This way supabase users wouldn't have to worry about password reset themselves. They just have to call this function and rest, we would handle. ***I would be more than happy to take up on this issue and contribute to the supabase community!***
**Describe alternatives you've considered**
***Firebase Authentication*** is a clear distinctive alternative for this. They have this function `send_password_reset_email()` that does the same thing described above.
**Additional context**
Do checkout this Loom:- [Firebase Password Reset Flow](https://www.loom.com/share/211759ff187e4a8f9dd5d14dd434c7e5)
| closed | 2023-06-14T11:37:08Z | 2023-06-14T14:47:43Z | https://github.com/supabase/supabase-py/issues/464 | [] | MBSA-INFINITY | 2 |
hpcaitech/ColossalAI | deep-learning | 5,391 | [BUG]: Wrong import in ColossalAuto's meta_registry/binary_elementwise_ops.py | ### ๐ Describe the bug
# Problem description
The file `colossalai/auto_parallel/meta_profiler/meta_registry/binary_elementwise_ops.py` contains the following line:
```python
from ..constants import BCAST_FUNC_OP
```
However, the file `colossalai/auto_parallel/meta_profiler/constants.py` which this import refers to does not contain any `BCAST_FUNC_OP`. This leads to an `ImportError` when running ColossalAuto in release 0.3.3 and newer.
This constant can be found in the file `colossalai/auto_parallel/tensor_shard/constants.py`. The last commit to `colossalai/auto_parallel/meta_profiler/constants.py` (commit ID `079bf3cb`) removes the import of tensor_shard's `constants.py` from meta_profiler's `constants.py` (seemingly due to an automated refactoring).
# Solution
Since no other file in the `meta_registry` module uses constants from the `tensor_shard/constants.py` and to avoid automated removal of "unused" imports in the future, the import statement in question in above-mentioned `binary_elementwise_ops.py` could be changed to:
```python
from colossalai.auto_parallel.tensor_shard.constants import BCAST_FUNC_OP
```
### Environment
- Python 3.8
- Torch 1.12.0
- no CUDA | closed | 2024-02-20T06:29:51Z | 2024-02-20T11:24:45Z | https://github.com/hpcaitech/ColossalAI/issues/5391 | [
"bug"
] | stephankoe | 0 |
mars-project/mars | numpy | 2,560 | Reduction over different columns of a single DataFrame can be merged | When calculating series aggregations like `execute(df.a.sum(),df.b.mean())`, aggregations over different columns can be merged as `df.agg({'a': 'sum', 'b': 'mean'})`. An optimizer can be added to reduce num of subtasks. | open | 2021-10-28T08:55:47Z | 2021-10-28T08:56:50Z | https://github.com/mars-project/mars/issues/2560 | [
"type: enhancement",
"mod: dataframe"
] | wjsi | 0 |
pennersr/django-allauth | django | 3,375 | SAML and OIDC organization sso | I'm happy to see the recent commits of SAML into main, and the implementation appears great to me. I intend to put the main branch into my staging environment to test, give feedback, and contribute if that proves useful. I do have a couple questions, if you have the opportunity to answer:
1. How can I best assist you in completing this feature? Are there specific places that you'd like me to particularly pay attention to?
2. What's your feeling about extending the approach you're taking for saml to OpenID Connect? I'm wanting to provide OIDC as an alternative to SAML for SSO for the organizations we have as clients. | closed | 2023-08-09T22:02:57Z | 2023-08-10T07:52:23Z | https://github.com/pennersr/django-allauth/issues/3375 | [] | ryanhiebert | 1 |
scikit-learn/scikit-learn | machine-learning | 30,425 | Make sklearn.neighbors algorithms treat all samples as neighbors when `n_neighbors is None`/`radius is None` | ### Describe the workflow you want to enable
The proposed feature is that algorithms in `sklearn.neighbors`, when created with parameter `n_neighbors is None` or `radius is None`, treat all samples used for fitting (or all samples to which distances are `'precomputed'`) as neighbors of every sample for which prediction is requested.
This makes sense when algorithm parameter `weights` is not `'uniform'` but `distance` or callable, distributing voting power among fitted samples unevenly. It expands which customized algorithms (that use distance-dependent voting) are available with scikit-learn API.
### Describe your proposed solution
The solution:
1. allow the algorithm parameters `n_neighbors`/`radius` to have the value `None`;
2. allow the public method `KNeighborsMixin.kneighbors` to return ragged arrays instead of 2D arrays (for the case of working on graphs instead of dense matrices);
3. make routines that process indices/distances of neighbors of samples work with ragged arrays;
4. add the special case for the parameter being `None` in routines that find indices of neighbors of a sample.
Examples of relevant code for k-neighbors algorithms:
1. `sklearn.neighbors._base._kneighbors_from_graph`
Add special case to return a ragged array of indices of all non-zero elements in every row (an array per row, taken directly from `graph.indptr`).
1. `sklearn.neighbors._base.KNeighborsMixin._kneighbors_reduce_func`
Add special case to produce `neigh_ind` from `numpy.arange(...)` instead of `numpy.argpartition(...)[...]`.
3. `sklearn.neighbors._base.KNeighborsMixin.kneighbors`
In the end, where the false extra neighbor is removed for every sample, add case for a ragged array.
4. `sklearn.neighbors._base.KNeighborsMixin.kneighbors_graph`
Add special case to forward results of `.kneighbors(...)` to output.
5. `sklearn.metrics._pairwise_distances_reduction`
I don't comprehend Cython yet and have no ide what is going on there. Anyway, it's probable that the best case would be to deem the Cython implementation unsuitable for the case discussed (as it is already deemed for other conditions).
### Describe alternatives you've considered, if relevant
The alternative is to build the application in such way that a k-neighbors estimator is instantiated only when the size of dataset is known, setting `n_neighbors` to this size. This possibly causes unwarranted overhead cost for operations that do seek n neighbors, and, as I understand, this is not the conventional way to employ estimators.
### Additional context
When I started investigating the necessary changes, I didn't realize there will be so much code to rewrite because of graphs. I have not found a way to do what I originally needed to do (with dense matrices of precomputed distances) without modifying library's code deeply.
I have also not found this feature proposed earlier. I see why this idea is not so very good, and this issue can tell others. | closed | 2024-12-07T13:29:05Z | 2024-12-19T14:02:54Z | https://github.com/scikit-learn/scikit-learn/issues/30425 | [
"New Feature"
] | asrelo | 3 |
miguelgrinberg/python-socketio | asyncio | 351 | wss://url:port is not an accepted origin. | Recently I've started to see the following error when connecting using the `socket.io-client-swift`. Connecting using `socket.io-client-java` works fine.
```
wss://myip:port is not an accepted origin.
```
Using:
```
Name: Flask-SocketIO
Version: 4.2.1
---
Name: python-engineio
Version: 3.9.3
``` | closed | 2019-09-10T21:23:46Z | 2019-11-17T19:11:33Z | https://github.com/miguelgrinberg/python-socketio/issues/351 | [
"question"
] | ffleandro | 1 |
OpenVisualCloud/CDN-Transcode-Sample | dash | 33 | ffplay and VLC can't play x265 clips with HTTP which is generated in xcode server | **Describe the bug**
Using 1 xcode server + 1 cdn server to do H264/MPEG2 transcoding to x265, the transcoding is ok and the output file index.m3u8 or index.mpd is also generated with normal size, but while using ffplay or VLC to play the x265 clips with HLS or DASH, there will be no output in screen and error log is printed by ffplay.
**Command line and Log information from FFPLAY:**
command:
ffplay http://host_ip:port_id/hls/4kCamera/index.m3u8
Log:
ffplay version 4.1 Copyright (c) 2003-2018 the FFmpeg developers
built with gcc 7 (Ubuntu 7.3.0-27ubuntu1~18.04)
configuration:
libavutil 56. 22.100 / 56. 22.100
libavcodec 58. 35.100 / 58. 35.100
libavformat 58. 20.100 / 58. 20.100
libavdevice 58. 5.100 / 58. 5.100
libavfilter 7. 40.101 / 7. 40.101
libswscale 5. 3.100 / 5. 3.100
libswresample 3. 3.100 / 3. 3.100
[hls,applehttp @ 0x7f803c000b80] Opening 'http://host_ip:port_id/hls/4kCamera/0.ts' for reading
[NULL @ 0x7f803c025dc0] PPS id out of range: 0
[hevc @ 0x7f803c027a00] PPS id out of range: 0
[hevc @ 0x7f803c027a00] Error parsing NAL unit #1.
[NULL @ 0x7f803c025dc0] PPS id out of range: 0
[hevc @ 0x7f803c027a00] PPS id out of range: 0
[hevc @ 0x7f803c027a00] Error parsing NAL unit #1.
[NULL @ 0x7f803c025dc0] PPS id out of range: 0
[hevc @ 0x7f803c027a00] PPS id out of range: 0
[hevc @ 0x7f803c027a00] Error parsing NAL unit #1.
[NULL @ 0x7f803c025dc0] PPS id out of range: 0
[hevc @ 0x7f803c027a00] PPS id out of range: 0
[hevc @ 0x7f803c027a00] Error parsing NAL unit #1.
[NULL @ 0x7f803c025dc0] PPS id out of range: 0
[hevc @ 0x7f803c027a00] PPS id out of range: 0
**NOTE:** If streaming output x265 stream with RTMP during transcoding, then use ffplay to playback with RTMP, the output is ok but has about 15s delay; If play with VLC also no output in screen.
**To Reproduce**
Steps to reproduce the behavior:
1. Run transcode command in xcode server: ffmpeg -re -stream_loop -1 -i /var/www/Mpeg2_1080.m2v -c:v libx265 -s 4096x2160 -f flv rtmp://ngnix_id:port_id/hls/4kCamera
2. Run ffplay (with HEVC enabled patch) command in another Linux machine:
ffplay http://host_ip:port_id/hls/4kCamera/index.m3u8 or play with VLC on your windows PC.
**Expected behavior**
Video should be played smoothly with FFPLAY or VLC, no error and no delay.
| closed | 2019-04-28T02:28:49Z | 2019-05-30T08:37:36Z | https://github.com/OpenVisualCloud/CDN-Transcode-Sample/issues/33 | [] | liweki | 1 |
BeanieODM/beanie | pydantic | 1,110 | [BUG] ODM model can't get datetime timezone | **Describe the bug**
When saving and loading datetime type information using ODM, timezone information disappears.
**To Reproduce**
- model
```python
class TestDocs(Document, extra="allow", populate_by_name=True):
id: Indexed(str) = Field(..., alias="_id") # type: ignore
created: Optional[datetime] = None
modified: Optional[datetime] = None
```
**Expected behavior**
When saving, information should be saved to mongodb and loaded according to the timezone information of the datetime in the model.
**Additional context**
When imported, it is loaded without timezone information. | closed | 2025-01-21T05:25:29Z | 2025-02-26T21:38:03Z | https://github.com/BeanieODM/beanie/issues/1110 | [] | DotJM | 3 |
microsoft/nni | tensorflow | 4,938 | when I run quantization_speedup.py in /examples/tutorials, get erros like this: | IndexError Traceback (most recent call last)
/home/chenwz/code/pycharm/test2.ipynb Cell 6' in <cell line: 4>()
[2](vscode-notebook-cell://ssh-remote%2Bchenwz/home/chenwz/code/pycharm/test2.ipynb#ch0000005vscode-remote?line=1) input_shape = (32, 1, 28, 28)
[3](vscode-notebook-cell://ssh-remote%2Bchenwz/home/chenwz/code/pycharm/test2.ipynb#ch0000005vscode-remote?line=2) engine = ModelSpeedupTensorRT(model, input_shape, config=calibration_config, batchsize=32)
----> [4](vscode-notebook-cell://ssh-remote%2Bchenwz/home/chenwz/code/pycharm/test2.ipynb#ch0000005vscode-remote?line=3) engine.compress()
[5](vscode-notebook-cell://ssh-remote%2Bchenwz/home/chenwz/code/pycharm/test2.ipynb#ch0000005vscode-remote?line=4) test_trt(engine)
File ~/.conda/envs/nni-wenze/lib/python3.8/site-packages/nni/compression/pytorch/quantization_speedup/integrated_tensorrt.py:291, in ModelSpeedupTensorRT.compress(self)
288 assert self.input_shape is not None
290 # Convert pytorch model to onnx model and save onnx model in onnx_path
--> 291 _, self.onnx_config = fonnx.torch_to_onnx(self.model, self.config, input_shape=self.input_shape,
292 model_path=self.onnx_path, input_names=self.input_names, output_names=self.output_names)
294 if self.calib_data_loader is not None:
295 assert self.calibrate_type is not None
File ~/.conda/envs/nni-wenze/lib/python3.8/site-packages/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py:144, in torch_to_onnx(model, config, input_shape, model_path, input_names, output_names)
142 # Load onnx model
143 model_onnx = onnx.load(model_path)
--> 144 model_onnx, onnx_config = unwrapper(model_onnx, index2name, config)
145 onnx.save(model_onnx, model_path)
147 onnx.checker.check_model(model_onnx)
File ~/.conda/envs/nni-wenze/lib/python3.8/site-packages/nni/compression/pytorch/quantization_speedup/frontend_to_onnx.py:82, in unwrapper(model_onnx, index2name, config)
80 mul_nd = model_onnx.graph.node[idx-1]
...
---> 82 index = int(onnx.numpy_helper.to_array(const_nd.attribute[0].t))
83 if index != -1:
84 name = index2name[index]
IndexError: list index (0) out of range
**Environment**:
- NNI version:2.7
- Training service (local|remote|pai|aml|etc):
- Client OS:
- Server OS (for remote mode only):
- Python version:3.8
- onnx version:1.11.0
- PyTorch/TensorFlow version:1.11.0
- Is conda/virtualenv/venv used?:
- Is running in Docker?:
**Configuration**:
- Experiment config (remember to remove secrets!):
- Search space:
**Log message**:
- nnimanager.log:
- dispatcher.log:
- nnictl stdout and stderr:
<!--
Where can you find the log files:
LOG: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/HowToDebug.md#experiment-root-director
STDOUT/STDERR: https://nni.readthedocs.io/en/stable/reference/nnictl.html#nnictl-log-stdout
-->
**How to reproduce it?**: | open | 2022-06-15T03:37:15Z | 2022-07-15T06:58:24Z | https://github.com/microsoft/nni/issues/4938 | [
"bug",
"quantize"
] | Shining-Tears | 4 |
waditu/tushare | pandas | 1,006 | ๅปบ่ฎฎๅขๅ ๆช้ขๆฐๆฎ่ทๅๅ่ฝ | ็ฎๅๅช็ๅฐdaily_basic()ๆ้จๅๅ่ฝ๏ผไฝๆฏไธฅ้ไธๅ
จใๅปบ่ฎฎๅขๅ ไธ้จ็ๆช้ขๆฐๆฎ่ทๅๅ่ฝ๏ผ่พๅ
ฅๅๆฐๅฏไปฅๅ
ๆฌ๏ผ่ก็ฅจไปฃ็ list๏ผไบคๆๆฅๆ๏ผๆช้ขๆฐๆฎๅฆk็บฟๆฐๆฎใ่ดขๅกๆๆ ็ญ๏ผใ็ฑปไผผwind็wss()ใ
ๅปบ่ฎฎid๏ผpbzysb@163.com | open | 2019-04-12T06:22:56Z | 2019-04-13T11:53:12Z | https://github.com/waditu/tushare/issues/1006 | [] | acehwong | 1 |
microsoft/unilm | nlp | 711 | UniLM v2 checkpoint | **Describe**
I would like to try out the UniLM v2 model but am unable to find it. Is the checkpoint available (here or from HuggingFace)? Thank you! | closed | 2022-05-09T18:40:51Z | 2022-05-10T13:40:29Z | https://github.com/microsoft/unilm/issues/711 | [] | natuan | 2 |
pyeve/eve | flask | 697 | patch_internal with USRA complains about missing items in a list with data_relation set | This is on 0.5.3.
I'm trying to use patch_internal to maintain a list of "invites" for a given "event". Suppose the "event" belongs to User 1, and it contains a list (named "inviteIds") referencing "invites" 1, 2, and 3 belonging to Users 1, 2 and 3 - respectively with USRA.
Since User 1 has no direct access to the invites owned by 2 and 3, I wanted a way to allow User 1 to delete their invites: by simply removing the Id's from the "inviteId" list -- which triggers a hook that does the deleting. Items in "inviteIds" are ObjectIds that have a data-relation to the collection "eventInvites"
patch_internal complains about "value '55e64ced4018db0bd0d13fe2' must exist in resource 'eventInvites', field '_id'." (along with every other invite that isn't owned by the event owner).
I have a feeling that authentication/USRA isn't fully disabled for patch_internal when performing data validation against its schema..
| closed | 2015-09-02T01:41:57Z | 2018-05-18T18:19:27Z | https://github.com/pyeve/eve/issues/697 | [
"stale"
] | kenmaca | 1 |
deezer/spleeter | tensorflow | 404 | [Discussion] Will higher cpu cores and ram speed up spleeter? | <!-- Please respect the title [Discussion] tag. -->
I currently have a quad core with 8gb ram and it gets stuck with spleeter. Will using for example an 8 core cpu with 16gb ram make it faster or it wont make a difference? | closed | 2020-05-29T04:20:18Z | 2020-06-06T14:26:12Z | https://github.com/deezer/spleeter/issues/404 | [
"question"
] | Scylla2020 | 7 |
vitalik/django-ninja | django | 549 | Whether django-ninja has a caching mechanism, and whether django's cache configuration is valid for ninja. | Whether django-ninja has a caching mechanism, and whether django's cache configuration is valid for ninja. | open | 2022-09-01T09:34:43Z | 2024-07-03T06:21:18Z | https://github.com/vitalik/django-ninja/issues/549 | [] | ddzyx | 5 |
google-research/bert | tensorflow | 815 | Will BERT learn from ERNIE 2.0? | ERNIE 2.0 is a new state of the art language model that has a major innovation:
Continual learning.
I hope researchers take the great ideas from others instead of working in isolation.
So will BERT 2.0 take the good ideas from ERNIE 2.0 and implement continual leaning ?
https://www.infoq.com/news/2019/08/Baidu-OpenSources-ERNIE/
The paper:
https://www.semanticscholar.org/paper/ERNIE-2-.-0-%3A-A-CONTINUAL-PRE-TRAINING-FRAMEWORK-Sun/90251aa6225fcd5687542eab5819db18afb6a20f | open | 2019-08-21T01:11:40Z | 2019-08-21T01:11:40Z | https://github.com/google-research/bert/issues/815 | [] | LifeIsStrange | 0 |
paperless-ngx/paperless-ngx | django | 7,809 | [BUG] Cannot access bottom items on left pane | ### Description
I cannot access the bottom items on left pane (dashboard, documents, saved views, manage...). Moving the mouse circle only moves the center part with documents.
### Steps to reproduce
1. Open Paperless on any view.
2. Move mouse over the left pane.
3. Try to move the mouse wheel to access not visible part of the left pane.
4. Only the center part appears.
### Webserver logs
```bash
-
```
### Browser logs
_No response_
### Paperless-ngx version
Actually, I cannot even access this part to see the version
### Host OS
Docker on Windows 11
### Installation method
Docker - official image
### System status
_No response_
### Browser
tried Brave and Edge
### Configuration changes
_No response_
### Please confirm the following
- [X] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [X] This issue is not about the OCR or archive creation of a specific file(s). Otherwise, please see above regarding OCR tools.
- [X] I have already searched for relevant existing issues and discussions before opening this report.
- [X] I have updated the title field above with a concise description. | closed | 2024-09-30T15:30:45Z | 2024-10-31T03:10:36Z | https://github.com/paperless-ngx/paperless-ngx/issues/7809 | [
"duplicate"
] | akumiszcza | 2 |
postmanlabs/httpbin | api | 411 | /status/100 will crash | Going to http://httpbin.org/status/100 with show a Heroku error page. | closed | 2017-12-11T16:38:27Z | 2018-04-26T17:51:16Z | https://github.com/postmanlabs/httpbin/issues/411 | [] | ROMTypo | 1 |
fastapi-users/fastapi-users | asyncio | 102 | Dependabot can't resolve your Python dependency files | Dependabot can't resolve your Python dependency files.
As a result, Dependabot couldn't update your dependencies.
The error Dependabot encountered was:
```
ERROR: ERROR: Could not find a version that matches pymdown-extensions<6.3,>=6.2,>=6.3
Tried: 1.0.0, 1.0.0, 1.0.1, 1.0.1, 1.1, 1.1, 1.2, 1.2, 1.3, 1.3, 1.4, 1.4, 1.5, 1.5, 1.6, 1.6, 1.6.1, 1.6.1, 1.7, 1.7, 1.8, 1.8, 2.0, 2.0, 3.0, 3.0, 3.1, 3.1, 3.2, 3.2, 3.2.1, 3.2.1, 3.3, 3.3, 3.4, 3.4, 3.5, 3.5, 4.0, 4.0, 4.1, 4.1, 4.2, 4.2, 4.3, 4.3, 4.4, 4.4, 4.5, 4.5, 4.5.1, 4.5.1, 4.6, 4.6, 4.7, 4.7, 4.8, 4.8, 4.9, 4.9, 4.9.1, 4.9.1, 4.9.2, 4.9.2, 4.10, 4.10, 4.10.1, 4.10.1, 4.10.2, 4.10.2, 4.11, 4.11, 4.12, 4.12, 5.0, 5.0, 6.0, 6.0, 6.1, 6.1, 6.2, 6.2, 6.2.1, 6.2.1, 6.3, 6.3
There are incompatible versions in the resolved dependencies.
[pipenv.exceptions.ResolutionFailure]: req_dir=requirements_dir
[pipenv.exceptions.ResolutionFailure]: File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 726, in resolve_deps
[pipenv.exceptions.ResolutionFailure]: req_dir=req_dir,
[pipenv.exceptions.ResolutionFailure]: File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 480, in actually_resolve_deps
[pipenv.exceptions.ResolutionFailure]: resolved_tree = resolver.resolve()
[pipenv.exceptions.ResolutionFailure]: File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 395, in resolve
[pipenv.exceptions.ResolutionFailure]: raise ResolutionFailure(message=str(e))
[pipenv.exceptions.ResolutionFailure]: pipenv.exceptions.ResolutionFailure: ERROR: ERROR: Could not find a version that matches pymdown-extensions<6.3,>=6.2,>=6.3
[pipenv.exceptions.ResolutionFailure]: Tried: 1.0.0, 1.0.0, 1.0.1, 1.0.1, 1.1, 1.1, 1.2, 1.2, 1.3, 1.3, 1.4, 1.4, 1.5, 1.5, 1.6, 1.6, 1.6.1, 1.6.1, 1.7, 1.7, 1.8, 1.8, 2.0, 2.0, 3.0, 3.0, 3.1, 3.1, 3.2, 3.2, 3.2.1, 3.2.1, 3.3, 3.3, 3.4, 3.4, 3.5, 3.5, 4.0, 4.0, 4.1, 4.1, 4.2, 4.2, 4.3, 4.3, 4.4, 4.4, 4.5, 4.5, 4.5.1, 4.5.1, 4.6, 4.6, 4.7, 4.7, 4.8, 4.8, 4.9, 4.9, 4.9.1, 4.9.1, 4.9.2, 4.9.2, 4.10, 4.10, 4.10.1, 4.10.1, 4.10.2, 4.10.2, 4.11, 4.11, 4.12, 4.12, 5.0, 5.0, 6.0, 6.0, 6.1, 6.1, 6.2, 6.2, 6.2.1, 6.2.1, 6.3, 6.3
[pipenv.exceptions.ResolutionFailure]: Warning: Your dependencies could not be resolved. You likely have a mismatch in your sub-dependencies.
First try clearing your dependency cache with $ pipenv lock --clear, then try the original command again.
Alternatively, you can use $ pipenv install --skip-lock to bypass this mechanism, then run $ pipenv graph to inspect the situation.
Hint: try $ pipenv lock --pre if it is a pre-release dependency.
ERROR: ERROR: Could not find a version that matches pymdown-extensions<6.3,>=6.2,>=6.3
Tried: 1.0.0, 1.0.0, 1.0.1, 1.0.1, 1.1, 1.1, 1.2, 1.2, 1.3, 1.3, 1.4, 1.4, 1.5, 1.5, 1.6, 1.6, 1.6.1, 1.6.1, 1.7, 1.7, 1.8, 1.8, 2.0, 2.0, 3.0, 3.0, 3.1, 3.1, 3.2, 3.2, 3.2.1, 3.2.1, 3.3, 3.3, 3.4, 3.4, 3.5, 3.5, 4.0, 4.0, 4.1, 4.1, 4.2, 4.2, 4.3, 4.3, 4.4, 4.4, 4.5, 4.5, 4.5.1, 4.5.1, 4.6, 4.6, 4.7, 4.7, 4.8, 4.8, 4.9, 4.9, 4.9.1, 4.9.1, 4.9.2, 4.9.2, 4.10, 4.10, 4.10.1, 4.10.1, 4.10.2, 4.10.2, 4.11, 4.11, 4.12, 4.12, 5.0, 5.0, 6.0, 6.0, 6.1, 6.1, 6.2, 6.2, 6.2.1, 6.2.1, 6.3, 6.3
There are incompatible versions in the resolved dependencies.
['Traceback (most recent call last):\n', ' File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 501, in create_spinner\n yield sp\n', ' File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 649, in venv_resolve_deps\n c = resolve(cmd, sp)\n', ' File "/usr/local/.pyenv/versions/3.7.6/lib/python3.7/site-packages/pipenv/utils.py", line 539, in resolve\n sys.exit(c.return_code)\n', 'SystemExit: 1\n']
```
If you think the above is an error on Dependabot's side please don't hesitate to get in touch - we'll do whatever we can to fix it.
[View the update logs](https://app.dependabot.com/accounts/frankie567/update-logs/22892841). | closed | 2020-02-10T04:50:47Z | 2020-02-11T04:50:40Z | https://github.com/fastapi-users/fastapi-users/issues/102 | [] | dependabot-preview[bot] | 0 |
deezer/spleeter | deep-learning | 198 | Error when freezing model | <!-- PLEASE READ THIS CAREFULLY :
- Any issue which does not respect following template or lack of information will be considered as invalid and automatically closed
- First check FAQ from wiki to see if your problem is not already known
-->
## Description
gives error when I'm trying to freez model
<!-- Give us a clear and concise description of the bug you are reporting. -->
## Step to reproduce
<!-- Indicates clearly steps to reproduce the behavior: -->
1. checked using `tensorflow 1.14` and `tensorflow 1.15`
2. using output_node_names as `output_node_names = "save_1/restore_all"`
3. Got `Input 0 of node import/save_1/AssignVariableOp was passed float from import/batch_normalization/beta:0 incompatible with expected resource.` error
## Output
```bash
Share what your terminal says when you run the script (as well as what you would expect).
```
## Environment
<!-- Fill the following table -->
| | |
| ----------------- | ------------------------------- |
| OS | MacOS
| Installation type | pip
## Additional context
<!-- Add any other context about the problem here, references, cites, etc.. -->
| closed | 2019-12-26T09:09:03Z | 2020-01-27T16:46:01Z | https://github.com/deezer/spleeter/issues/198 | [
"bug",
"invalid",
"model",
"training"
] | waqasakram117 | 1 |
opengeos/streamlit-geospatial | streamlit | 90 | Can't open app | closed | 2022-10-26T04:16:57Z | 2022-10-29T17:04:42Z | https://github.com/opengeos/streamlit-geospatial/issues/90 | [] | haizhupan | 0 |
|
sherlock-project/sherlock | python | 2,434 | False positive for: (several websites) | ### Additional info
Consistent:
- Cults3D
- GNOME VCS
- LibraryThing
- Mydramalist
- NationStates Nation
- NationStates Region
- ProductHunt
Inconsistent:
- AllMyLinks (4/10)
- Twitter/X (4/10)
- HackerEarth (6/10)
- TorrentGalaxy (2/10)
- Reddit (2/10)
Usernames tried:
- [own username]
- extremelymostlyfake
- mostlylikelyfake
- mostlymaybefake
- extremelymostlyfakeu
- extremelymostlyfakeus
- audienceatm12
- devicemare03
- w3av3n3sc4f3
- w3av3n3sc4f
Regions used to test:
- Hungary
- Turkey
- USA
- Sweden
### Code of Conduct
- [x] I agree to follow this project's Code of Conduct | open | 2025-03-15T00:05:54Z | 2025-03-15T00:05:54Z | https://github.com/sherlock-project/sherlock/issues/2434 | [
"false positive"
] | forestbitter | 0 |
OFA-Sys/Chinese-CLIP | computer-vision | 31 | ๆฏๅฆๅฏไปฅไฝไธบstable diffusion็text encoder? | ๅฐ่ฏๅฐChinese CLIPไฝไธบstable diffusion็text encoder๏ผไฝๆฏไธ็ด็ๆ็บฏ้ปๅพๅ๏ผๅฎๅ
จๆฃๆฅๅทฒ็ปๅ
ณ้ญ๏ผ๏ผๆๆณ้ฎไธๆฏๅฆๅฏไปฅไฝไธบsd็text encoderๅข๏ผๅฎๆนๆฏๅฆๅ่ฟๆต่ฏใ | open | 2022-12-13T10:42:05Z | 2024-08-15T07:44:32Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/31 | [] | zhaop-l | 9 |
jumpserver/jumpserver | django | 14,341 | [Question] ๆฌๅฐ็จๅบไบๆบ็ ๅฏๅจ็jumpserver core็ปไปถ๏ผ้ป่ฎค็จๆทๆ ๆณ็ป้ | ### Product Version
v4.20
### Product Edition
- [X] Community Edition
- [ ] Enterprise Edition
- [ ] Enterprise Trial Edition
### Installation Method
- [ ] Online Installation (One-click command installation)
- [ ] Offline Package Installation
- [ ] All-in-One
- [ ] 1Panel
- [ ] Kubernetes
- [ ] Source Code
### Environment Information
mac
python 3.11
mysql ๏ผredis ไฝฟ็จdocker
### ๐ค Question Description
ๆๅจๆฌๅฐไปฃ็ ไปๅบ้๏ผๆง่ก` python ./apps/manage migrate` ่ฟ่กไบ่กจ็ปๆ่ฟ็งป๏ผ็ถๅไฝฟ็จ `python ./apps/manage.py runserver`ๅฝไปคๅฏๅจ้กน็ฎ๏ผ็ปๅฝ็้ขๆญฃๅธธๅฑ็คบ๏ผไฝๆฏๆไฝฟ็จadmin่ฟไธช้ป่ฎค็จๆทๅป็ป้๏ผๅฏ็ admin๏ผๅด็ปๅฝไธ่ฟๅป๏ผ่ฏท้ฎ๏ผ่ฟ้่ฆๆทปๅ ไปไน้
็ฝฎ๏ผๆ่
้่ฆๅ
ถไฝ็็ปไปถ่ฟ่ก้
ๅๅ๏ผ
่ฟๆฏๆ็้
็ฝฎๆไปถ๏ผ
> // SECURITY WARNING: keep the secret key used in production secret!
> // ๅ ๅฏๅฏ้ฅ ็ไบง็ฏๅขไธญ่ฏทไฟฎๆนไธบ้ๆบๅญ็ฌฆไธฒ๏ผ่ฏทๅฟๅคๆณ, ๅฏไฝฟ็จๅฝไปค็ๆ
> // $ cat /dev/urandom | tr -dc A-Za-z0-9 | head -c 49;echo
> SECRET_KEY: abcdefg
>
> // SECURITY WARNING: keep the bootstrap token used in production secret!
> // ้ขๅ
ฑไบซToken cocoๅguacamole็จๆฅๆณจๅๆๅก่ดฆๅท๏ผไธๅจไฝฟ็จๅๆฅ็ๆณจๅๆฅๅๆบๅถ
> BOOTSTRAP_TOKEN: abcdefg
>
> // Development env open this, when error occur display the full process track, Production disable it
> // DEBUG ๆจกๅผ ๅผๅฏDEBUGๅ้ๅฐ้่ฏฏๆถๅฏไปฅ็ๅฐๆดๅคๆฅๅฟ
> DEBUG: true
>
> // DEBUG, INFO, WARNING, ERROR, CRITICAL can set. See https://docs.djangoproject.com/en/1.10/topics/logging/
> // ๆฅๅฟ็บงๅซ
> LOG_LEVEL: DEBUG
> // LOG_DIR:
>
> // Session expiration setting, Default 1 hour, Also set expired on on browser close
> // ๆต่งๅจSession่ฟๆๆถ้ด๏ผ้ป่ฎค 1 ๅฐๆถ, ไนๅฏไปฅ่ฎพ็ฝฎๆต่งๅจๅ
ณ้ญๅ่ฟๆ
> SESSION_COOKIE_AGE: 3600
> SESSION_EXPIRE_AT_BROWSER_CLOSE: false
>
> // Database setting, Support sqlite3, mysql, postgres ....
> // ๆฐๆฎๅบ่ฎพ็ฝฎ
> // See https://docs.djangoproject.com/en/1.10/ref/settings/#databases
>
> // SQLite setting:
> // ไฝฟ็จๅๆไปถsqliteๆฐๆฎๅบ
> // DB_ENGINE: sqlite3
> // DB_NAME:
> // MySQL or postgres setting like:
> // ไฝฟ็จ PostgreSQL ไฝไธบๆฐๆฎๅบ
> DB_ENGINE: mysql
> DB_HOST: 127.0.0.1
> DB_PORT: 3306
> DB_USER: root
> DB_PASSWORD: '123456'
> DB_NAME: jumpserver
>
> // When Django start it will bind this host and port
> // ./manage.py runserver 127.0.0.1:8080
> // ่ฟ่กๆถ็ปๅฎ็ซฏๅฃ
> HTTP_BIND_HOST: 0.0.0.0
> HTTP_LISTEN_PORT: 8080
> WS_LISTEN_PORT: 8070
>
> // Use Redis as broker for celery and web socket
> // Redis้
็ฝฎ
> REDIS_HOST: 127.0.0.1
> REDIS_PORT: 6379
> REDIS_PASSWORD: '123456'
> REDIS_DB_CELERY: 3
> REDIS_DB_CACHE: 4
>
> // LDAP/AD settings
> // LDAP ๆ็ดขๅ้กตๆฐ้
> // AUTH_LDAP_SEARCH_PAGED_SIZE: 1000
> //
> // ๅฎๆถๅๆญฅ็จๆท
> // ๅฏ็จ / ็ฆ็จ
> // AUTH_LDAP_SYNC_IS_PERIODIC: True
> // ๅๆญฅ้ด้ (ๅไฝ: ๆถ) (ไผๅ
๏ผ
> // AUTH_LDAP_SYNC_INTERVAL: 12
> // Crontab ่กจ่พพๅผ
> // AUTH_LDAP_SYNC_CRONTAB: * 6 * * *
> //
> // LDAP ็จๆท็ปๅฝๆถไป
ๅ
่ฎธๅจ็จๆทๅ่กจไธญ็็จๆทๆง่ก LDAP Server ่ฎค่ฏ
> AUTH_LDAP_USER_LOGIN_ONLY_IN_USERS: False
> //
> // LDAP ่ฎค่ฏๆถๅฆๆๆฅๅฟไธญๅบ็ฐไปฅไธไฟกๆฏๅฐๅๆฐ่ฎพ็ฝฎไธบ 0 (่ฏฆๆ
ๅ่ง๏ผhttps://www.python-ldap.org/en/latest/faq.html)
> // In order to perform this operation a successful bind must be completed on the connection
> // AUTH_LDAP_OPTIONS_OPT_REFERRALS: -1
>
> // OTP settings
> // OTP/MFA ้
็ฝฎ
> // OTP_VALID_WINDOW: 0
> // OTP_ISSUER_NAME: JumpServer
>
> // ๅฏ็จๅฎๆถไปปๅก
> // PERIOD_TASK_ENABLED: True
>
> // ๆฏๅฆๅผๅฏ Luna ๆฐดๅฐ
> // SECURITY_WATERMARK_ENABLED: False
>
> // ๆต่งๅจๅ
ณ้ญ้กต้ขๅ๏ผไผ่ฏ่ฟๆ
> // SESSION_EXPIRE_AT_BROWSER_CLOSE: False
>
> // ๆฏๆฌก api ่ฏทๆฑ๏ผsession ็ปญๆ
> // SESSION_SAVE_EVERY_REQUEST: True
>
> // ไป
ๅ
่ฎธ็จๆทไปๆฅๆบๅค็ปๅฝ
> // ONLY_ALLOW_AUTH_FROM_SOURCE: False
>
> // ไป
ๅ
่ฎธๅทฒๅญๅจ็็จๆท็ปๅฝ๏ผไธๅ
่ฎธ็ฌฌไธๆน่ฎค่ฏๅ๏ผ่ชๅจๅๅปบ็จๆท
> // ONLY_ALLOW_EXIST_USER_AUTH: False
### Expected Behavior
_No response_
### Additional Information
_No response_ | closed | 2024-10-22T08:32:52Z | 2024-11-28T08:30:06Z | https://github.com/jumpserver/jumpserver/issues/14341 | [
"๐ค Question"
] | yxxchange | 1 |
TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | matplotlib | 11 | The Jupyter Notebooks Links are not found | the following links from
Theano to Keras Models
Not found
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.2%20Introduction%20-%20Theano.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.3%20Introduction%20-%20Keras.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/1.4%20(Extra)%20A%20Simple%20Implementation%20of%20ANN%20for%20MNIST.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.1%20Supervised%20Learning%20-%20ConvNets.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.1%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20I.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.2.2%20Supervised%20Learning%20-%20ConvNet%20HandsOn%20Part%20II.ipynb
- http://nbviewer.jupyter.org/github/leriomaggio/deep-learning-keras-tensorflow/blob/master/2.3%20Supervised%20Learning%20-%20Famous%20Models%20with%20Keras.ipynb | open | 2018-11-17T08:17:16Z | 2018-11-17T08:26:59Z | https://github.com/TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials/issues/11 | [] | navidalvee | 0 |
dynaconf/dynaconf | fastapi | 640 | Migrate to Github Actions | Move from Azure Pipelines to Github Actions
- Keep the same job matrix
- Move the tag/release process to GHA | closed | 2021-08-12T23:35:54Z | 2021-09-08T18:16:27Z | https://github.com/dynaconf/dynaconf/issues/640 | [
"Not a Bug",
"RFC",
"HIGH"
] | rochacbruno | 0 |
mljar/mljar-supervised | scikit-learn | 739 | error in docker installation | ```
> [15/25] RUN pip install mljar-supervised:
#0 0.914 Collecting mljar-supervised
#0 0.926 Downloading mljar-supervised-1.1.9.tar.gz (127 kB)
#0 0.974 Preparing metadata (setup.py): started
#0 1.285 Preparing metadata (setup.py): finished with status 'done'
#0 1.294 Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.8/dist-packages (from mljar-supervised) (1.24.4)
#0 1.296 Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from mljar-supervised) (2.0.3)
#0 1.542 Collecting scipy<=1.11.4,>=1.6.1 (from mljar-supervised)
#0 1.546 Downloading scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (58 kB)
#0 1.785 Collecting scikit-learn>=1.0 (from mljar-supervised)
#0 1.793 Downloading scikit_learn-1.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)
#0 1.855 Collecting xgboost>=2.0.0 (from mljar-supervised)
#0 1.860 Downloading xgboost-2.1.0-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)
#0 1.905 Collecting lightgbm>=3.0.0 (from mljar-supervised)
#0 1.910 Downloading lightgbm-4.5.0-py3-none-manylinux_2_28_x86_64.whl.metadata (17 kB)
#0 2.051 Collecting catboost>=0.24.4 (from mljar-supervised)
#0 2.056 Downloading catboost-1.2.5-cp38-cp38-manylinux2014_x86_64.whl.metadata (1.2 kB)
#0 2.097 Collecting joblib>=1.0.1 (from mljar-supervised)
#0 2.102 Downloading joblib-1.4.2-py3-none-any.whl.metadata (5.4 kB)
#0 2.128 Collecting tabulate>=0.8.7 (from mljar-supervised)
#0 2.133 Downloading tabulate-0.9.0-py3-none-any.whl.metadata (34 kB)
#0 2.336 Collecting matplotlib>=3.2.2 (from mljar-supervised)
#0 2.340 Downloading matplotlib-3.7.5-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl.metadata (5.7 kB)
#0 2.384 Collecting dtreeviz>=2.2.2 (from mljar-supervised)
#0 2.389 Downloading dtreeviz-2.2.2-py3-none-any.whl.metadata (2.4 kB)
#0 2.460 Collecting shap>=0.42.1 (from mljar-supervised)
#0 2.466 Downloading shap-0.44.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (24 kB)
#0 2.505 Collecting seaborn>=0.11.1 (from mljar-supervised)
#0 2.510 Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)
#0 2.582 Collecting wordcloud>=1.8.1 (from mljar-supervised)
#0 2.587 Downloading wordcloud-1.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)
#0 2.613 Collecting category_encoders>=2.2.2 (from mljar-supervised)
#0 2.624 Downloading category_encoders-2.6.3-py2.py3-none-any.whl.metadata (8.0 kB)
#0 2.709 Collecting optuna>=2.7.0 (from mljar-supervised)
#0 2.714 Downloading optuna-3.6.1-py3-none-any.whl.metadata (17 kB)
#0 2.746 INFO: pip is looking at multiple versions of mljar-supervised to determine which version is compatible with other requirements. This could take a while.
#0 2.747 Collecting mljar-supervised
#0 2.752 Downloading mljar-supervised-1.1.8.tar.gz (127 kB)
#0 2.798 Preparing metadata (setup.py): started
#0 3.097 Preparing metadata (setup.py): finished with status 'done'
#0 3.106 Collecting mljar-scikit-plot>=0.3.8 (from mljar-supervised)
#0 3.117 Downloading mljar-scikit-plot-0.3.10.tar.gz (25 kB)
#0 3.139 Preparing metadata (setup.py): started
#0 3.332 Preparing metadata (setup.py): finished with status 'error'
#0 3.339 error: subprocess-exited-with-error
#0 3.339
#0 3.339 ร python setup.py egg_info did not run successfully.
#0 3.339 โ exit code: 1
#0 3.339 โฐโ> [6 lines of output]
#0 3.339 Traceback (most recent call last):
#0 3.339 File "<string>", line 2, in <module>
#0 3.339 File "<pip-setuptools-caller>", line 34, in <module>
#0 3.339 File "/tmp/pip-install-gq9olotk/mljar-scikit-plot_ddca47a3e5b3486eb05e8139f91ea0d8/setup.py", line 3, in <module>
#0 3.339 from setuptools.command.test import test as TestCommand
#0 3.339 ModuleNotFoundError: No module named 'setuptools.command.test'
#0 3.339 [end of output]
#0 3.339
#0 3.339 note: This error originates from a subprocess, and is likely not a problem with pip.
#0 3.347 error: metadata-generation-failed
#0 3.347
#0 3.347 ร Encountered error while generating package metadata.
#0 3.347 โฐโ> See above for output.
#0 3.347
#0 3.347 note: This is an issue with the package mentioned above, not pip.
#0 3.347 hint: See above for details.
------
failed to solve: executor failed running [/bin/sh -c pip install mljar-supervised]: exit code: 1
``` | closed | 2024-07-29T07:24:09Z | 2024-09-25T07:46:28Z | https://github.com/mljar/mljar-supervised/issues/739 | [] | pplonski | 1 |
ultralytics/yolov5 | machine-learning | 13,036 | ๐ Feature Request: Simplified Method for Changing Label Names in YOLOv5 Model | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests.
### Description
Background
Many users have reported issues with misspelled label names in their trained YOLOv5 models. Often, they are unaware of how to update these labels easily and end up retraining the entire model, which is impractical and time-consuming. Issues such as [#12156](https://github.com/ultralytics/yolov5/issues/12156) and [#3577](https://github.com/ultralytics/yolov5/issues/3577) highlight the need for a straightforward solution to update label names in an existing YOLOv5 model.
Proposed Solution
I propose adding a guide and utility script to the YOLOv5 documentation that explains how to change label names without retraining the model. This solution leverages PyTorch to load, update, and save the model with new label names.
Implementation Guide
The following Python script demonstrates how to change the label names of a YOLOv5 model:
```python
import torch
# Load your trained YOLOv5 model
model = torch.load("path/to/best.pt")
# Define New Label Names
model['model'].names = ["name1", "name2", ...]
# Save the updated model
torch.save(model, "path/to/update_best.pt")
```
Adding this guide to the YOLOv5 documentation will greatly benefit users who face issues with label name errors. It addresses a common problem and provides a practical, efficient solution.
Thank you for considering this feature request.
### Use case
_No response_
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | closed | 2024-05-21T10:04:31Z | 2024-10-20T19:46:32Z | https://github.com/ultralytics/yolov5/issues/13036 | [
"enhancement",
"Stale"
] | osalhi-kali | 3 |
amidaware/tacticalrmm | django | 1,522 | internal server error 500 - conversion between UTF8 and SQL_ASCII is not supported | **Server Info (please complete the following information):**
- OS: [Ubuntu 20.04, Debian 10, Debian 11]
- Browser: [chrome, edge]
- RMM Version (0.15.11): latest
**Installation Method:** - [ ] Standard
**Agent Info (please complete the following information):**
- Agent version (2.4.8):
- Agent OS: [Win 11 22H2]
**Describe the bug**
After clean standard install , generated winOS agent and added my first 2 computers. Double clicked on any of the clients and a window for editing is shown , but when I try so save ( no matter if anything is changed or not) it shows "internal server error 500" and nothing is saved. In django_debug.log found this error :
django.db.utils.NotSupportedError: conversion between UTF8 and SQL_ASCII is not supported
LINE 1: ...12:09:34.131767+00:00'::timestamptz, "services" = '[{"pid": ...
Tried with clean install on debian 10 and 11, Ubuntu 20.04 - problem is the same on all systems
**To Reproduce**
Steps to reproduce the behavior:
1. Go to '...'
2. Click on '....'
3. Scroll down to '....'
4. See error
| open | 2023-05-27T12:18:42Z | 2023-05-28T15:14:45Z | https://github.com/amidaware/tacticalrmm/issues/1522 | [] | gogo0618 | 5 |
mwaskom/seaborn | pandas | 3,338 | Is there a way to set tick label rotations using the objects interface? | In the new objects interface, is there a way to set, say, the xticklabel rotation? I read the [seaborn.objects.Plot.theme](https://seaborn.pydata.org/generated/seaborn.objects.Plot.theme.html#) and am not sure if it belongs here and is not implemented yet. Thank you. | closed | 2023-04-24T06:14:38Z | 2023-04-25T21:39:44Z | https://github.com/mwaskom/seaborn/issues/3338 | [] | frfeng | 3 |
jupyter-incubator/sparkmagic | jupyter | 744 | [BUG] Executing pyspark notebook cells with magics in Intellij PyCharm would fail | **Describe the bug**
While this may be due to PyCharm's auto-insertion of leading blank line for every cell, we should note that the regular python kernel wouldn't suffer from the same. So the pyspark kernel may be too restrictive on some execution environment?
See https://youtrack.jetbrains.com/issue/PY-52486
**To Reproduce**
See https://youtrack.jetbrains.com/issue/PY-52486
**Expected behavior**
Cells to be executed successfully
**Screenshots**
See https://youtrack.jetbrains.com/issue/PY-52486
**Versions:**
- SparkMagic: 0.19.1
- Livy ?
- Spark 3.1.2
| open | 2022-01-12T00:16:42Z | 2022-01-12T00:16:42Z | https://github.com/jupyter-incubator/sparkmagic/issues/744 | [] | winston-zillow | 0 |
RobertCraigie/prisma-client-py | asyncio | 370 | Potentially unnecessary binary files on generate | ## Bug description
In a basic Docker container:
```
FROM python:3.8
WORKDIR /app
COPY requirements.txt requirements.txt
RUN pip3 install -r requirements.txt
COPY schema.prisma schema.prisma
RUN prisma generate
COPY . .
CMD ["python3", "main.py"]
```
I was inspecting the Docker container file size using `dive`. Looking in the `/tmp` folder it looks like there may be some unused binaries. It appears that the binaries used are in `/tmp/prisma/binaries/engines/<version>` according to [constants.py](https://github.com/RobertCraigie/prisma-client-py/blob/main/src/prisma/binaries/constants.py#L41).
There is another folder `/tmp/prisma-binaries` with 114MB of binaries with the same name, but is missing the `prisma-cli-linux` but includes the others, e.g. `prisma-query-engine-debian-openssl-1.1.x`. If I remove this folder, the prisma query engine still appears to work.
It may be that the prisma generate and binary download process is downloading duplicate files.
## How to reproduce
```
docker build --platform linux/amd64 -t test-prisma
dive test-prisma
```
## Environment & setup
- OS: MacOS Docker building for linux/amd64
- Database:
- Python version: 3.8
- Prisma version: prisma==0.6.4 | closed | 2022-04-22T07:10:40Z | 2022-12-03T17:03:44Z | https://github.com/RobertCraigie/prisma-client-py/issues/370 | [
"kind/improvement",
"level/advanced",
"priority/medium",
"topic: binaries"
] | danfang | 1 |
JaidedAI/EasyOCR | deep-learning | 835 | Variable length images | Can we train EasyOCR on variable length images? | open | 2022-08-27T14:38:22Z | 2022-08-28T09:20:06Z | https://github.com/JaidedAI/EasyOCR/issues/835 | [] | sameearif88 | 2 |
pytest-dev/pytest-django | pytest | 1,073 | django.db.utils.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory | I've docker-compose configuration for django and postgres, it works fine. However, when I'm trying to run pytest inside a django container it fails with an error:
```shell
pytest apps/service/tests/test_api.py::TestCreate::test_new
====================================================================== test session starts =======================================================================
platform linux -- Python 3.8.18, pytest-7.4.1, pluggy-1.3.0
django: settings: project.settings.local (from env)
rootdir: /app/code
configfile: pytest.ini
plugins: mock-3.11.1, django-4.5.2, Faker-19.6.1, celery-4.4.2
collected 1 item
apps/service/tests/test_api.py E [100%]
============================================================================= ERRORS =============================================================================
__________________________________________________ ERROR at setup of TestCreate.test_new __________________________________________________
self = <django.db.backends.postgresql.base.DatabaseWrapper object at 0xffff71cedf70>
@async_unsafe
def ensure_connection(self):
"""Guarantee that a connection to the database is established."""
if self.connection is None:
with self.wrap_database_errors:
> self.connect()
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:200: in connect
self.connection = self.get_new_connection(conn_params)
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:187: in get_new_connection
connection = Database.connect(**conn_params)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
dsn = 'dbname=postgres', connection_factory = None, cursor_factory = None, kwargs = {'database': 'postgres'}, kwasync = {}
def connect(dsn=None, connection_factory=None, cursor_factory=None, **kwargs):
"""
Create a new database connection.
The connection parameters can be specified as a string:
conn = psycopg2.connect("dbname=test user=postgres password=secret")
or using a set of keyword arguments:
conn = psycopg2.connect(database="test", user="postgres", password="secret")
Or as a mix of both. The basic connection parameters are:
- *dbname*: the database name
- *database*: the database name (only as keyword argument)
- *user*: user name used to authenticate
- *password*: password used to authenticate
- *host*: database host address (defaults to UNIX socket if not provided)
- *port*: connection port number (defaults to 5432 if not provided)
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created. *async_* is
a valid alias (for Python versions where ``async`` is a keyword).
Any other keyword parameter will be passed to the underlying client
library: the list of supported parameters depends on the library version.
"""
kwasync = {}
if 'async' in kwargs:
kwasync['async'] = kwargs.pop('async')
if 'async_' in kwargs:
kwasync['async_'] = kwargs.pop('async_')
if dsn is None and not kwargs:
raise TypeError('missing dsn and no parameters')
dsn = _ext.make_dsn(dsn, **kwargs)
> conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
E psycopg2.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory
E Is the server running locally and accepting connections on that socket?
/usr/local/lib/python3.8/site-packages/psycopg2/__init__.py:127: OperationalError
The above exception was the direct cause of the following exception:
self = <django.db.backends.postgresql.base.DatabaseWrapper object at 0xffff7f398d30>
@contextmanager
def _nodb_cursor(self):
try:
> with super()._nodb_cursor() as cursor:
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:301:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.8/contextlib.py:113: in __enter__
return next(self.gen)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:620: in _nodb_cursor
with conn.cursor() as cursor:
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:259: in cursor
return self._cursor()
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:235: in _cursor
self.ensure_connection()
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219: in ensure_connection
self.connect()
/usr/local/lib/python3.8/site-packages/django/db/utils.py:90: in __exit__
raise dj_exc_value.with_traceback(traceback) from exc_value
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219: in ensure_connection
self.connect()
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:200: in connect
self.connection = self.get_new_connection(conn_params)
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:187: in get_new_connection
connection = Database.connect(**conn_params)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
dsn = 'dbname=postgres', connection_factory = None, cursor_factory = None, kwargs = {'database': 'postgres'}, kwasync = {}
def connect(dsn=None, connection_factory=None, cursor_factory=None, **kwargs):
"""
Create a new database connection.
The connection parameters can be specified as a string:
conn = psycopg2.connect("dbname=test user=postgres password=secret")
or using a set of keyword arguments:
conn = psycopg2.connect(database="test", user="postgres", password="secret")
Or as a mix of both. The basic connection parameters are:
- *dbname*: the database name
- *database*: the database name (only as keyword argument)
- *user*: user name used to authenticate
- *password*: password used to authenticate
- *host*: database host address (defaults to UNIX socket if not provided)
- *port*: connection port number (defaults to 5432 if not provided)
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created. *async_* is
a valid alias (for Python versions where ``async`` is a keyword).
Any other keyword parameter will be passed to the underlying client
library: the list of supported parameters depends on the library version.
"""
kwasync = {}
if 'async' in kwargs:
kwasync['async'] = kwargs.pop('async')
if 'async_' in kwargs:
kwasync['async_'] = kwargs.pop('async_')
if dsn is None and not kwargs:
raise TypeError('missing dsn and no parameters')
dsn = _ext.make_dsn(dsn, **kwargs)
> conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
E django.db.utils.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory
E Is the server running locally and accepting connections on that socket?
/usr/local/lib/python3.8/site-packages/psycopg2/__init__.py:127: OperationalError
During handling of the above exception, another exception occurred:
self = <django.db.backends.postgresql.base.DatabaseWrapper object at 0xffff71593ca0>
@async_unsafe
def ensure_connection(self):
"""Guarantee that a connection to the database is established."""
if self.connection is None:
with self.wrap_database_errors:
> self.connect()
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:200: in connect
self.connection = self.get_new_connection(conn_params)
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:187: in get_new_connection
connection = Database.connect(**conn_params)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
dsn = 'dbname=test_project', connection_factory = None, cursor_factory = None, kwargs = {'database': 'test_project'}, kwasync = {}
def connect(dsn=None, connection_factory=None, cursor_factory=None, **kwargs):
"""
Create a new database connection.
The connection parameters can be specified as a string:
conn = psycopg2.connect("dbname=test user=postgres password=secret")
or using a set of keyword arguments:
conn = psycopg2.connect(database="test", user="postgres", password="secret")
Or as a mix of both. The basic connection parameters are:
- *dbname*: the database name
- *database*: the database name (only as keyword argument)
- *user*: user name used to authenticate
- *password*: password used to authenticate
- *host*: database host address (defaults to UNIX socket if not provided)
- *port*: connection port number (defaults to 5432 if not provided)
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created. *async_* is
a valid alias (for Python versions where ``async`` is a keyword).
Any other keyword parameter will be passed to the underlying client
library: the list of supported parameters depends on the library version.
"""
kwasync = {}
if 'async' in kwargs:
kwasync['async'] = kwargs.pop('async')
if 'async_' in kwargs:
kwasync['async_'] = kwargs.pop('async_')
if dsn is None and not kwargs:
raise TypeError('missing dsn and no parameters')
dsn = _ext.make_dsn(dsn, **kwargs)
> conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
E psycopg2.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory
E Is the server running locally and accepting connections on that socket?
/usr/local/lib/python3.8/site-packages/psycopg2/__init__.py:127: OperationalError
The above exception was the direct cause of the following exception:
request = <SubRequest '_django_db_marker' for <Function test_new>>
@pytest.fixture(autouse=True)
def _django_db_marker(request) -> None:
"""Implement the django_db marker, internal to pytest-django."""
marker = request.node.get_closest_marker("django_db")
if marker:
> request.getfixturevalue("_django_db_helper")
/usr/local/lib/python3.8/site-packages/pytest_django/plugin.py:465:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
/usr/local/lib/python3.8/site-packages/pytest_django/fixtures.py:122: in django_db_setup
db_cfg = setup_databases(
/usr/local/lib/python3.8/site-packages/django/test/utils.py:179: in setup_databases
connection.creation.create_test_db(
/usr/local/lib/python3.8/site-packages/django/db/backends/base/creation.py:57: in create_test_db
self._create_test_db(verbosity, autoclobber, keepdb)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/creation.py:191: in _create_test_db
with self._nodb_cursor() as cursor:
/usr/local/lib/python3.8/contextlib.py:113: in __enter__
return next(self.gen)
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:319: in _nodb_cursor
with conn.cursor() as cursor:
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:259: in cursor
return self._cursor()
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:235: in _cursor
self.ensure_connection()
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219: in ensure_connection
self.connect()
/usr/local/lib/python3.8/site-packages/django/db/utils.py:90: in __exit__
raise dj_exc_value.with_traceback(traceback) from exc_value
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:219: in ensure_connection
self.connect()
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/base/base.py:200: in connect
self.connection = self.get_new_connection(conn_params)
/usr/local/lib/python3.8/site-packages/django/utils/asyncio.py:33: in inner
return func(*args, **kwargs)
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:187: in get_new_connection
connection = Database.connect(**conn_params)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
dsn = 'dbname=test_project', connection_factory = None, cursor_factory = None, kwargs = {'database': 'test_project'}, kwasync = {}
def connect(dsn=None, connection_factory=None, cursor_factory=None, **kwargs):
"""
Create a new database connection.
The connection parameters can be specified as a string:
conn = psycopg2.connect("dbname=test user=postgres password=secret")
or using a set of keyword arguments:
conn = psycopg2.connect(database="test", user="postgres", password="secret")
Or as a mix of both. The basic connection parameters are:
- *dbname*: the database name
- *database*: the database name (only as keyword argument)
- *user*: user name used to authenticate
- *password*: password used to authenticate
- *host*: database host address (defaults to UNIX socket if not provided)
- *port*: connection port number (defaults to 5432 if not provided)
Using the *connection_factory* parameter a different class or connections
factory can be specified. It should be a callable object taking a dsn
argument.
Using the *cursor_factory* parameter, a new default cursor factory will be
used by cursor().
Using *async*=True an asynchronous connection will be created. *async_* is
a valid alias (for Python versions where ``async`` is a keyword).
Any other keyword parameter will be passed to the underlying client
library: the list of supported parameters depends on the library version.
"""
kwasync = {}
if 'async' in kwargs:
kwasync['async'] = kwargs.pop('async')
if 'async_' in kwargs:
kwasync['async_'] = kwargs.pop('async_')
if dsn is None and not kwargs:
raise TypeError('missing dsn and no parameters')
dsn = _ext.make_dsn(dsn, **kwargs)
> conn = _connect(dsn, connection_factory=connection_factory, **kwasync)
E django.db.utils.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory
E Is the server running locally and accepting connections on that socket?
/usr/local/lib/python3.8/site-packages/psycopg2/__init__.py:127: OperationalError
--------------------------------------------------------------------- Captured stderr setup ----------------------------------------------------------------------
/usr/local/lib/python3.8/site-packages/django/db/backends/postgresql/base.py:304: RuntimeWarning: Normally Django will use a connection to the 'postgres' database to avoid running initialization queries against the production database when it's not needed (for example, when running tests). Django was unable to create a connection to the 'postgres' database and will use the first PostgreSQL database instead.
warnings.warn(
==================================================================== short test summary info =====================================================================
ERROR apps/service/tests/test_api.py::TestCreate::test_new - django.db.utils.OperationalError: connection to server on socket "/var/run/postgresql/.s.PGSQL.5432" failed: No such file or directory
======================================================================== 1 error in 5.25s ========================================================================
```
This is actually true, there is no such file in django container, it's inside the psql container, but I don't understand what to do with this error. I checked that test database created.
I can fix this error if add this fixture in `conftest.py`
```python
import pytest
@pytest.fixture()
def django_db_setup():
pass
```
But using this approach it using my actual database, not the "test_*" one.
I don't think that this is docker-compose problem because other than tests it works fine. | open | 2023-09-15T03:39:25Z | 2023-09-15T05:19:47Z | https://github.com/pytest-dev/pytest-django/issues/1073 | [] | karambaq | 1 |
vitalik/django-ninja | django | 1,306 | [BUG] JSON payload not parsed when using upload with extra fields | **Describe the bug**
Ninja fails to parse the request body correctly and throws a validation error, contrary to the instructions under [Upload files with extra fields](https://django-ninja.dev/guides/input/file-params/).
**Versions (please complete the following information):**
- Python version: 3.11.7
- Django version: 5.1.1
- Django-Ninja version: 1.3.0
- Pydantic version: 2.9.2
Relevant schema
```python
class FileMetadata(Schema):
posts: List[int] = []
visibility: str = "public"
```
API Code
```python
@files_router.post(
"/", response={200: FileDetails}, tags=["files"], auth=JWTAuth(permissions=StaffOnly)
)
def create_file(request: HttpRequest, metadata: FileMetadata, upload: NinjaFile[UploadedFile]):
"""
Creates a file with or without post associations.
"""
try:
if metadata.visibility == "public":
stored_name = PublicStorage().save(upload.name, upload.file)
url = PublicStorage().url(stored_name)
else:
stored_name = PrivateStorage().save(upload.name, upload.file)
url = PrivateStorage().url(stored_name)
upload = File.objects.create(
location=url,
name=stored_name,
content_type=upload.content_type,
charset=upload.charset,
size=upload.size,
visibility=metadata.visibility,
)
if metadata:
upload.posts.set(metadata.posts)
return upload
except Exception as err:
logger.error("Error creating file", error=err)
raise HttpError(500, "Fail to create file") from err
```
Request body
```
-----------------------------291645760626718691221248293984
Content-Disposition: form-data; name="upload"; filename="business card front.pdf"
Content-Type: application/pdf
file stuff
%%EOF
-----------------------------291645760626718691221248293984
Content-Disposition: form-data; name="metadata"; filename="blob"
Content-Type: application/json
{"posts":[2,4],"visibility":"public"}
-----------------------------291645760626718691221248293984--
```
`Content-Type` on the request is set to `multipart/form-data; boundary=---------------------------291645760626718691221248293984`
Response
```json
{
"detail": [
{
"type": "missing",
"loc": [
"body",
"metadata"
],
"msg": "Field required"
}
]
}
```
| open | 2024-09-27T21:17:32Z | 2024-10-03T13:30:05Z | https://github.com/vitalik/django-ninja/issues/1306 | [] | matt0x6F | 8 |
explosion/spaCy | data-science | 13,422 | Converting into exe file through pyinstaller-> spacy cannot find factory for 'curated transformer' | ```
import spacy
import spacy_curated_transformers
# import spacy_transformers
import curated_transformers
import spacy_alignments
import spacy_legacy
import spacy_loggers
import spacy_pkuseg
import os
nlp = spacy.load(os.getcwd()+'\\en_core_web_trf-3.7.3')
x= input()
doc= nlp(x)
result =[]
for sent in doc.sents:
result.append(sent.text)
print(result)
```
I wanted to turn the above code into exe file.
However, [valueerror: [e002] can't find factory for 'curated transformer' for language english (en)] error occurs...
I used pyinstaller to convert it into exe file. In the pyinstaller, I included spacy, spacy_curated_transformers, curated_transformers into the hidden import.
I wonder how to make this executable file configure the curated transformer factory...
Please help me.
## My Environment
* Operating System: Windows 11
* Python Version Used: 3.11.8
* spaCy Version Used: 3.7.4
* Environment Information:
| closed | 2024-04-09T05:19:53Z | 2024-04-09T10:08:54Z | https://github.com/explosion/spaCy/issues/13422 | [
"install",
"feat / transformer"
] | estherkim083 | 1 |
aws/aws-sdk-pandas | pandas | 2,435 | RedshiftDataApi: Support temporary credentials auth via IAM | **Is your idea related to a problem? Please describe.**
I love the convenience of using `wr.data_api.redshift.read_sql_query()` to fetch data from a redshift cluster using temporary credentials, without having to worry about VPCs and network accessibility.
Currently, the authentication methods accepted in the `wr.data_api.redshift.RedshiftDataApi` class are restricted to either an explicit db user name or a link to the secrets manager, and [fails if neither is passed](https://github.com/aws/aws-sdk-pandas/blob/906b4d2/awswrangler/data_api/redshift.py#L96) by the user.
The underlying redshift-data -> executeStatement api call however [falls back to IAM](https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html
) if neither is given, which i'd like to make use of in the wrangler calls as well. A direct mapping to IAM users allows us to easier implement role based access control, as database users would be directly related to the roles already set up for the specific teams.
**Describe the solution you'd like**
If neither a `db_user` nor `secret_arn` are given, the `RedshiftDataApi` class does not throw an error, but pass on neither, which causes the `executeStatement` api call to use `getTemporaryCredentialsWithIAM` instead of `getTemporaryCredentials`.
Alternatively, a `use_iam` flag (or similar) could be implemented if that's preferable.
Would you be willing to accept/merge a PR that changes this behaviour? | closed | 2023-08-18T10:43:55Z | 2023-10-13T10:16:46Z | https://github.com/aws/aws-sdk-pandas/issues/2435 | [
"enhancement"
] | theister | 2 |
pydata/pandas-datareader | pandas | 547 | Unable to get data from google. |
Df= web.DataReader('SPY', data_source='google')
Df=Df[['Open','High','Low','Close']]
/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/google/daily.py:40: UnstableAPIWarning:
The Google Finance API has not been stable since late 2017. Requests seem
to fail at random. Failure is especially common when bulk downloading.
warnings.warn(UNSTABLE_WARNING, UnstableAPIWarning)
Traceback (most recent call last):
File "<ipython-input-2-329433d59ce6>", line 1, in <module>
Df= web.DataReader('SPY', data_source='google')
File "/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/data.py", line 315, in DataReader
session=session).read()
File "/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/base.py", line 206, in read
params=self._get_params(self.symbols))
File "/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/base.py", line 84, in _read_one_data
out = self._read_url_as_StringIO(url, params=params)
File "/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/base.py", line 95, in _read_url_as_StringIO
response = self._get_response(url, params=params)
File "/Users/vikas/anaconda3/lib/python3.6/site-packages/pandas_datareader/base.py", line 155, in _get_response
raise RemoteDataError(msg)
RemoteDataError: Unable to read URL: https://finance.google.com/finance/historical?q=SPY&startdate=Jan+01%2C+2010&enddate=Jun+23%2C+2018&output=csv
Response Text:
b'<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"/><title>Sorry...</title><style> body { font-family: verdana, arial, sans-serif; background-color: #fff; color: #000; }</style></head><body><div><table><tr><td><b><font face=sans-serif size=10><font color=#4285f4>G</font><font color=#ea4335>o</font><font color=#fbbc05>o</font><font color=#4285f4>g</font><font color=#34a853>l</font><font color=#ea4335>e</font></font></b></td><td style="text-align: left; vertical-align: bottom; padding-bottom: 15px; width: 50%"><div style="border-bottom: 1px solid #dfdfdf;">Sorry...</div></td></tr></table></div><div style="margin-left: 4em;"><h1>We\'re sorry...</h1><p>... but your computer or network may be sending automated queries. To protect our users, we can\'t process your request right now.</p></div><div style="margin-left: 4em;">See <a href="https://support.google.com/websearch/answer/86640">Google Help</a> for more information.<br/><br/></div><div style="text-align: center; border-top: 1px solid #dfdfdf;"><a href="https://www.google.com">Google Home</a></div></body></html>'
| closed | 2018-06-23T03:50:12Z | 2018-09-12T07:56:52Z | https://github.com/pydata/pandas-datareader/issues/547 | [] | Vikas125 | 4 |
flaskbb/flaskbb | flask | 384 | Dose gevent WSGI really worked? | make sure gevent effected, only use WSGIServer without any monkey patch | closed | 2017-12-25T15:20:05Z | 2018-04-15T07:47:49Z | https://github.com/flaskbb/flaskbb/issues/384 | [] | KyRionY | 4 |
ets-labs/python-dependency-injector | asyncio | 466 | django.core.exceptions.AppRegistryNotReady: Apps aren't loaded yet. | Hi,
Thank you so much for a wonderful project. I am using dependency injection in a Django side project of mine.
I notice a big problem with the way the container initialize. As you state in the example section for Django, we initiate the container in `__init__.py` at **project level.**
```
from .di_containers import DIContainer
from . import settings
di_container = DIContainer() -> create dependency before other app load
...
```
https://python-dependency-injector.ets-labs.org/examples/django.html
However, if one of your provider (Ex: UserService needs User model) requires model to do some works, django will throw **django.core.exceptions.AppRegistryNotReady: Apps aren't loaded yet.**
I am not so sure how to solve it? I am using Singleton in my container (required). Not sure if it that the reason?
```
class DIContainer(containers.DeclarativeContainer):
user_service = providers.Singleton(UserService)
```
```
from api_core.apps.user.models import User <- error cause by this import
@inject
class UserService:
def __init__(self):
self.test = 'test'
```
**Update**: I solve the problem with local import. But still I have to import locally multiple places in different function. The main root cause still because of initialization of the container in `__init__.py` at project level. Let say we move it to a custom app, which register at the end of INSTALLED_APP list, we still have a problem how to wire them to the other app.
I don't know if you familiar with ReactiveX. I think this package can help solve the problem of wiring https://github.com/ReactiveX/RxPY. We could have an subscriber at each AppConfig in `def ready: ...`. Then when we initiate the container, it will trigger a signal to tell them, ok it is safe for you guys to wire now ? | open | 2021-06-15T04:30:11Z | 2023-04-10T06:02:31Z | https://github.com/ets-labs/python-dependency-injector/issues/466 | [
"bug"
] | vuhi | 4 |
jina-ai/clip-as-service | pytorch | 112 | bert-as-service | run bert-as-service๏ผresponse command not found ๏ผwhy๏ผ | closed | 2018-12-10T07:53:04Z | 2018-12-11T01:50:22Z | https://github.com/jina-ai/clip-as-service/issues/112 | [] | jiezouguihuafu | 1 |
s3rius/FastAPI-template | fastapi | 192 | Taskiq Mypy validation error | After initializing blank project with Taskiq Mypy gives an validation error
```python
Format with Black........................................................Passed
isort....................................................................Passed
Check with Flake8........................................................Passed
Validate types with MyPy.................................................Failed
- hook id: mypy
- exit code: 1
project_name\tkq.py:9: error: Incompatible types in assignment (expression has
type "InMemoryBroker", variable has type "ZeroMQBroker") [assignment]
broker = InMemoryBroker()
^~~~~~~~~~~~~~~~
Found 1 error in 1 file (checked 44 source files)
```
Initial `project_name/tkq.py`
```python
import taskiq_fastapi
from taskiq import InMemoryBroker, ZeroMQBroker
from project_name.settings import settings
broker = ZeroMQBroker()
if settings.environment.lower() == "pytest":
broker = InMemoryBroker()
taskiq_fastapi.init(
broker,
"project_name.web.application:get_app",
)
``` | open | 2023-10-02T12:57:29Z | 2023-10-02T14:17:59Z | https://github.com/s3rius/FastAPI-template/issues/192 | [] | RoyalGoose | 1 |
MycroftAI/mycroft-core | nlp | 2,148 | Can't get pairing code in a VM | On a Dell Vostro 3000 Series I have a freeBSD 13.0 CURRENT host with a Peppermint OS (Debian, 5.0.X) guest, running MyCroft on it. Everything's fine, except that I don't get any pairing code for the device. We already tried the curl command via konsole but it seems MC doesn't recognize the online prescense of the device in the api. Paired, Unpaired, again trying to pair, nothing works. Matheus from the chat told me to open a new issue with this screenshot.
Maybe M. doesn't want freeBSD hosts? What'ya think? | closed | 2019-06-08T09:06:30Z | 2020-09-22T08:00:30Z | https://github.com/MycroftAI/mycroft-core/issues/2148 | [] | vanbreukelingen | 8 |
sergree/matchering | numpy | 17 | Give us a ๐ | Hi, if you find our app and library useful, please give it a ๐ **[here](https://github.com/vinta/awesome-python/pull/1480)**.
https://github.com/vinta/awesome-python/pull/1480
Thanks!
| closed | 2020-02-14T10:54:09Z | 2020-04-08T12:59:03Z | https://github.com/sergree/matchering/issues/17 | [
"help wanted"
] | sergree | 1 |
jumpserver/jumpserver | django | 15,052 | [Bug] docker้จ็ฝฒ๏ผๅ
็ฝ็ฉฟ้่ฎฟ้ฎ๏ผ่ฎฐๅฝ็็ปๅฝๅๅธ้ฝๆฏๅฑๅ็ฝ | ### ไบงๅ็ๆฌ
v4.6.0
### ็ๆฌ็ฑปๅ
- [x] ็คพๅบ็
- [ ] ไผไธ็
- [ ] ไผไธ่ฏ็จ็
### ๅฎ่ฃ
ๆนๅผ
- [ ] ๅจ็บฟๅฎ่ฃ
(ไธ้ฎๅฝไปคๅฎ่ฃ
)
- [ ] ็ฆป็บฟๅ
ๅฎ่ฃ
- [ ] All-in-One
- [x] 1Panel
- [ ] Kubernetes
- [ ] ๆบ็ ๅฎ่ฃ
### ็ฏๅขไฟกๆฏ
ubuntu๏ผ1panel๏ผdocker
### ๐ ็ผบ้ทๆ่ฟฐ
้่ฟ1panel็docker้จ็ฝฒ๏ผๅ
็ฝ็ฉฟ้่ฎฟ้ฎ๏ผ่ฎฐๅฝ็็ปๅฝๅๅธ้ฝๆฏๅฑๅ็ฝ๏ผ่ฎฐๅฝ็็ปๅฝๅฐๅ้ฝๆฏๅฑๅ็ฝ๏ผๆ ๆณ่ฟฝๆบฏ
### ๅค็ฐๆญฅ้ชค
่ฎฟ้ฎ
### ๆๆ็ปๆ
_No response_
### ่กฅๅ
ไฟกๆฏ
_No response_
### ๅฐ่ฏ่ฟ็่งฃๅณๆนๆก
_No response_ | open | 2025-03-17T14:44:00Z | 2025-03-21T10:46:27Z | https://github.com/jumpserver/jumpserver/issues/15052 | [
"๐ Bug",
"โณ Pending feedback"
] | yzl321905 | 1 |
matplotlib/mplfinance | matplotlib | 314 | `tz_localize=True` fails to localize datetimes in `tlines` and `alines` specifications (and in the future also in xlim specification?) | Hi, I tried to implement the tlines using mplfinance, and generate the trendline data as per the example, however, I still get this error:
ValueError:
tlines date pair (2021-01-11 09:35:00-05:00,2021-01-11 10:20:00-05:00) too close, or wrong order, or out of range!
df date range: [2021-01-11 09:35:00+00:00 , 2021-01-11 10:20:00+00:00]
Please, if anyone can advise, It will be much appreciated.
Regards.
| open | 2021-01-12T13:04:49Z | 2021-01-12T16:53:46Z | https://github.com/matplotlib/mplfinance/issues/314 | [
"bug",
"question"
] | ventek | 4 |
pydata/xarray | numpy | 9,179 | Difference in time coordinate values in xarray tutorial dataset loaded with numpy v2 | ### What happened?
Time coordinate values are significantly different (second precision) if numpy v2 in the environment for the xarray "air temperature" tutorial dataset. This leads to discrepancies and errors in selection by date strings.
Numpy v2.0.0
```
array(['2013-01-01T00:02:06.757437440', '2013-01-01T05:59:27.234179072',
'2013-01-01T11:56:47.710920704', ...,
'2014-12-31T05:58:10.831327232', '2014-12-31T11:55:31.308068864',
'2014-12-31T18:02:01.540624384'], dtype='datetime64[ns]')
```
Numpy v1.26.4
```
array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
'2013-01-01T12:00:00.000000000', ...,
'2014-12-31T06:00:00.000000000', '2014-12-31T12:00:00.000000000',
'2014-12-31T18:00:00.000000000'], dtype='datetime64[ns]')
```
### What did you expect to happen?
I expect time coordinates to be identical for different numpy versions
### Minimal Complete Verifiable Example
```Python
#mamba create -n xarray2024.6.0 xarray ipython pooch netCDF4 numpy>2
import xarray as xr
ds = xr.tutorial.load_dataset("air_temperature")
print(ds.time.values)
dates = ['2013-07-09', '2013-10-11', '2013-12-24']
ds.sel(time=dates)
```
### MVCE confirmation
- [X] Minimal example โ the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [X] Complete example โ the example is self-contained, including all data and the text of any traceback.
- [X] Verifiable example โ the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [X] New issue โ a search of GitHub Issues suggests this is not a duplicate.
- [X] Recent environment โ the issue occurs with the latest version of xarray and its dependencies.
### Relevant log output
```Python
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[21], line 2
1 dates = ['2013-07-09', '2013-10-11', '2013-12-24']
----> 2 ds.sel(time=dates)
File ~/miniforge3/envs/xarray2024.6.0/lib/python3.12/site-packages/xarray/core/dataset.py:3126, in Dataset.sel(self, indexers, method, tolerance, drop, **indexers_kwargs)
3058 """Returns a new dataset with each array indexed by tick labels
3059 along the specified dimension(s).
3060
(...)
3123
3124 """
3125 indexers = either_dict_or_kwargs(indexers, indexers_kwargs, "sel")
-> 3126 query_results = map_index_queries(
3127 self, indexers=indexers, method=method, tolerance=tolerance
3128 )
3130 if drop:
3131 no_scalar_variables = {}
File ~/miniforge3/envs/xarray2024.6.0/lib/python3.12/site-packages/xarray/core/indexing.py:192, in map_index_queries(obj, indexers, method, tolerance, **indexers_kwargs)
190 results.append(IndexSelResult(labels))
191 else:
--> 192 results.append(index.sel(labels, **options))
194 merged = merge_sel_results(results)
196 # drop dimension coordinates found in dimension indexers
197 # (also drop multi-index if any)
198 # (.sel() already ensures alignment)
File ~/miniforge3/envs/xarray2024.6.0/lib/python3.12/site-packages/xarray/core/indexes.py:801, in PandasIndex.sel(self, labels, method, tolerance)
799 indexer = get_indexer_nd(self.index, label_array, method, tolerance)
800 if np.any(indexer < 0):
--> 801 raise KeyError(f"not all values found in index {coord_name!r}")
803 # attach dimension names and/or coordinates to positional indexer
804 if isinstance(label, Variable):
KeyError: "not all values found in index 'time'"
```
### Anything else we need to know?
https://github.com/xarray-contrib/xarray-tutorial/issues/271
### Environment
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.12.4 | packaged by conda-forge | (main, Jun 17 2024, 10:13:44) [Clang 16.0.6 ]
python-bits: 64
OS: Darwin
OS-release: 23.5.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2
xarray: 2024.6.0
pandas: 2.2.2
numpy: 2.0.0
scipy: None
netCDF4: 1.7.1
pydap: None
h5netcdf: None
h5py: None
zarr: None
cftime: 1.6.4
nc_time_axis: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 70.1.1
pip: 24.0
conda: None
pytest: None
mypy: None
IPython: 8.25.0
sphinx: None
</details>
| closed | 2024-06-26T20:10:12Z | 2024-06-28T08:18:56Z | https://github.com/pydata/xarray/issues/9179 | [
"bug"
] | scottyhq | 2 |
plotly/dash | dash | 2,588 | enable termination of 'outdated' long callbacks when page changes | **Is your feature request related to a problem? Please describe.**
Long callbacks run for all Figures on all of my application's pages. When I switch between pages quickly the tasks in the backend Celery queue execute in-order regardless of old requests being outdated.
Ex: If I'm on page 1, and I navigate to page 2, then quickly to page 3 then page 4, the long callbacks for figures corresponding to pages 2, then 3, then 4 will run in order, slowing the delivery of Figures pertinent to page 4.
However, this isn't the case if I refresh a single page multiple times. If I'm on page 2 and I refresh the page 5 times, the long callbacks for the Figures associated with page 2 don't execute 5 times in order- the first 4 'render requests' are 'revoked' in the Celery queue, with the final request completing.
**Describe the solution you'd like**
I'd like a page switch to trigger the termination of newly 'outdated' long callbacks in the backend.
**Describe alternatives you've considered**
In the universe of Dash objects, I considered implementing a 'Sentry' that watches the URL and triggers a 'cancel' function that's bound to all long callbacks, cancelling them via the 'cancelable' interface of callbacks. In the Dash execution graph, however, I don't think I can guarantee that this callback would always precede the scheduling of new, up-to-date callbacks, so I don't think this is a reasonable solution.
**Additional context**
Here's an idea for a patch:
The dash_renderer frontend is aware of when it's going to try to execute a callback for a job that is still running. If a to-be-called callback's output list matches the output list of the job that the frontend is waiting for, it issues an 'oldJob' ID to the backend via the request headers. [Source](https://github.com/plotly/dash/blob/a7a12d180e16eac0a84b88e2b8dc8f7c7601cbaf/dash/dash-renderer/src/actions/callbacks.ts#L662)
In the backend, receiving these 'oldJob' ID's triggers that job's termination in the Celery backend. [Source](https://github.com/plotly/dash/blob/a7a12d180e16eac0a84b88e2b8dc8f7c7601cbaf/dash/_callback.py#L363)
It's evident that the frontend is doing job bookkeeping, tracking the 'output' list of jobs that have been scheduled in the backend but that haven't returned for the frontend. If the frontend could also track the page that the job is intended for, and compare that to the window.location.pathname when the job cleanup is already happening, the 'oldJob' param could be set and the backend could clean up any currently running long callbacks for the previous page.
A parameter could be set in the Dash python object to enable and disable this feature- it'd generally NOT be mutually exclusive of memoization because cancellations wouldn't always happen but it'd be more conservative, and memoization wouldn't always have an opportunity to happen.
| open | 2023-07-06T22:15:41Z | 2024-08-13T19:35:01Z | https://github.com/plotly/dash/issues/2588 | [
"feature",
"P3"
] | JamesKunstle | 16 |
vaexio/vaex | data-science | 2,125 | [BUG-REPORT] Python application won't shut down after calling Vaex df.sum or df.unique | **Description**
I am building a Python FastApi application that uses Vaex. I noticed that when either function df.sum or df.unique is called I can no longer terminate the application by pressing ctrl + c. Here are examples of how I use them:
```
df.unique(axis,
return_inverse=False,
dropna=True,
dropnan=True,
dropmissing=True,
progress=False,
selection=None,
axis=None,
delay=False,
array_type="python",
)
```
and `x = int(df.sum(f"sid__{s}"))`
How do I fix this so I am able to manually terminate the application without having to kill the console?
**Software information**
- Vaex version (`import vaex; vaex.__version__)`: 4.9.1
- OS: Windows
| open | 2022-07-22T14:12:32Z | 2022-08-04T13:58:10Z | https://github.com/vaexio/vaex/issues/2125 | [] | abf7d | 3 |
xonsh/xonsh | data-science | 5,127 | Unexpected exception while updating completions | <!--- Provide a general summary of the issue in the Title above -->
When I set $UPDATE_COMPLETIONS_ON_KEYPRESS = True and type for instance /usr/bin/ls -a in terminal, following exception is thrown: "Exception [Errno 13] Permission denied: '/usr/bin/ls.json'"
<!--- If you have a question along the lines of "How do I do this Bash command in xonsh"
please first look over the Bash to Xonsh translation guide: https://xon.sh/bash_to_xsh.html
If you don't find an answer there, please do open an issue! -->
## xonfig
<details>
```
+------------------+----------------------+
| xonsh | 0.13.4 |
| Python | 3.8.10 |
| PLY | 3.11 |
| have readline | True |
| prompt toolkit | 3.0.36 |
| shell type | prompt_toolkit |
| history backend | json |
| pygments | 2.14.0 |
| on posix | True |
| on linux | True |
| distro | ubuntu |
| on wsl | False |
| on darwin | False |
| on windows | False |
| on cygwin | False |
| on msys2 | False |
| is superuser | False |
| default encoding | utf-8 |
| xonsh encoding | utf-8 |
| encoding errors | surrogateescape |
| xontrib | [] |
| RC file 1 | /home/ralis/.xonshrc |
+------------------+----------------------+
```
</details>
## Expected Behavior
<!--- Tell us what should happen -->
The warning should be either more subtle or no completion suggestions should be shown.
## Current Behavior
<!--- Tell us what happens instead of the expected behavior -->
Huge multi-line error is printed.
<!--- If part of your bug report is a traceback, please first enter debug mode before triggering the error
To enter debug mode, set the environment variable `XONSH_DEBUG=1` _before_ starting `xonsh`.
On Linux and OSX, an easy way to to do this is to run `env XONSH_DEBUG=1 xonsh` -->
### Traceback (if applicable)
<details>
```
Unhandled exception in event loop:
File "/home/ralis/.local/lib/python3.8/site-packages/prompt_toolkit/buffer.py", line 1939, in new_coroutine
await coroutine(*a, **kw)
File "/home/ralis/.local/lib/python3.8/site-packages/prompt_toolkit/buffer.py", line 1763, in async_completer
async for completion in async_generator:
File "/home/ralis/.local/lib/python3.8/site-packages/prompt_toolkit/completion/base.py", line 326, in get_completions_async
async for completion in completer.get_completions_async(
File "/home/ralis/.local/lib/python3.8/site-packages/prompt_toolkit/completion/base.py", line 202, in get_completions_async
for item in self.get_completions(document, complete_event):
File "/usr/local/lib/python3.8/dist-packages/xonsh/ptk_shell/completer.py", line 58, in get_completions
completions, plen = self.completer.complete(
File "/usr/local/lib/python3.8/dist-packages/xonsh/completer.py", line 121, in complete
return self.complete_from_context(
File "/usr/local/lib/python3.8/dist-packages/xonsh/completer.py", line 272, in complete_from_context
for comp in self.generate_completions(
File "/usr/local/lib/python3.8/dist-packages/xonsh/completer.py", line 233, in generate_completions
for comp in res:
File "/usr/local/lib/python3.8/dist-packages/xonsh/completers/man.py", line 137, in completions
for desc, opts in _parse_man_page_options(cmd).items():
File "/usr/local/lib/python3.8/dist-packages/xonsh/completers/man.py", line 121, in _parse_man_page_options
path.write_text(json.dumps(options))
File "/usr/lib/python3.8/pathlib.py", line 1255, in write_text
with self.open(mode='w', encoding=encoding, errors=errors) as f:
File "/usr/lib/python3.8/pathlib.py", line 1222, in open
return io.open(self, mode, buffering, encoding, errors, newline,
File "/usr/lib/python3.8/pathlib.py", line 1078, in _opener
return self._accessor.open(self, flags, mode)
Exception [Errno 13] Permission denied: '/usr/bin/ls.json'
```
</details>
## Steps to Reproduce
<!--- Please try to write out a minimal reproducible snippet to trigger the bug, it will help us fix it! -->
```xsh
$UPDATE_COMPLETIONS_ON_KEYPRESS = True
/usr/bin/ls - # exception after typing
```
## For community
โฌ๏ธ **Please click the ๐ reaction instead of leaving a `+1` or ๐ comment**
| closed | 2023-04-24T20:26:34Z | 2024-04-10T05:48:47Z | https://github.com/xonsh/xonsh/issues/5127 | [
"good first issue",
"completion",
"priority-high"
] | ralisv | 3 |
pallets-eco/flask-sqlalchemy | sqlalchemy | 622 | Very poor performance of Pagination.{pages,iter_pages} | The performance of those two methods is really really bad when you have thousands of pages. In my case, with 100k pages, painting the paginator takes over 300 ms.
`Pagination.iter_pages` is slow by itself. `Pagination.pages` is not that slow but it's called once for every page from `Pagination.iter_pages` when you consume the iterator, and calling it 100k times really adds up. Python is a very slow language and calling the same method so many times is undesirable even if [the method seems simple](https://github.com/mitsuhiko/flask-sqlalchemy/blob/381653489af8ee8aa26340a7c3fe0a0dced64258/flask_sqlalchemy/__init__.py#L325-L332).
The low-hanging fruit here is caching the result of `Pagination.pages`. That fixes more than half of the problem (only the call to `Pagination.iter_pages` remains). I did so with this subclass, which might be helpful to someone. (I'm far from an expert in Python, so this may hurt your eyes)
```python
class MyPagination(Pagination):
def __init__(self, query, page, per_page, total, items):
super().__init__(query, page, per_page, total, items)
self._pages = super().pages
@property
def pages(self):
return self._pages
```
But of course a better solution is desirable. | closed | 2018-05-16T14:33:47Z | 2022-10-03T00:21:59Z | https://github.com/pallets-eco/flask-sqlalchemy/issues/622 | [
"pagination"
] | wodim | 2 |
fbdesignpro/sweetviz | data-visualization | 138 | How to show chinese in ASSOCIATIONS | Hi,I have just tried to use sweetviz to do EDA. And I have a problem that in ASSOCIATIONS it doesn't support chinese, and I got this:
![Uploading image.pngโฆ]()
Is there anything I can do to solve it?Thanks! | closed | 2023-03-27T10:18:45Z | 2023-08-20T12:23:13Z | https://github.com/fbdesignpro/sweetviz/issues/138 | [] | nealcha | 2 |
jupyterlab/jupyter-ai | jupyter | 1,263 | Enforce strict version matches between `jupyter-ai` and `jupyter-ai-magics` | ### Problem
We currently lack an automated way to enforce a strict version match between `jupyter-ai-magics` and `jupyter-ai` when installed via `pip`. Fixes in `jupyter-ai` sometimes require downstream changes in `jupyter-ai-magics`, so users may need to update both packages to receive a bug fix. However, this is not done automatically by `pip`, which is really confusing to the end user.
This has caused several bugs:
- #1172
- #1253
- At least one more. Please feel free to comment below if you find another.
### Proposed Solution
- Update `bump-version.sh` to somehow bump the version pin of `jupyter-ai-magics`.
- Assert this in the release process somehow to prevent broken releases if this script gets broken.
### Additional context
We do enforce this in our Conda Forge releases because that process is manual anyways. However, we should be doing this for PyPI releases too. | closed | 2025-02-26T19:08:53Z | 2025-03-20T21:27:17Z | https://github.com/jupyterlab/jupyter-ai/issues/1263 | [
"enhancement",
"scope:settings",
"scope:releaser"
] | dlqqq | 3 |
floodsung/Deep-Learning-Papers-Reading-Roadmap | deep-learning | 74 | Paper [36] link broken | The link for the paper "Sequence to sequence learning with neural networks" [36] appears to be broken. Can you fix. Thanks. | open | 2017-10-16T20:56:00Z | 2017-10-23T12:24:43Z | https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/issues/74 | [] | d-henderson | 2 |
MilesCranmer/PySR | scikit-learn | 448 | [BUG]: Julia interface fails on conda environments | ### What happened?
I get the following error with a fresh install in a fresh conda environment with pysr installed with pip.
```python
ImportError: cannot import name 'Main' from 'julia' (C:\Users\ilyao\miniforge3\envs\pysr_std\Lib\site-packages\julia\__init__.py)
```
### Version
0.16.3
### Operating System
Windows
### Package Manager
Other (specify below)
### Interface
IPython Terminal
### Relevant log output
```shell
>>> model.fit(X, y)
Compiling Julia backend...
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "C:\Users\ilyao\miniforge3\envs\pysr_std\Lib\site-packages\pysr\sr.py", line 1970, in fit
self._run(X, y, mutated_params, weights=weights, seed=seed)
File "C:\Users\ilyao\miniforge3\envs\pysr_std\Lib\site-packages\pysr\sr.py", line 1625, in _run
Main = init_julia(self.julia_project, julia_kwargs=julia_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\ilyao\miniforge3\envs\pysr_std\Lib\site-packages\pysr\julia_helpers.py", line 216, in init_julia
from julia import Main as _Main
ImportError: cannot import name 'Main' from 'julia' (C:\Users\ilyao\miniforge3\envs\pysr_std\Lib\site-packages\julia\__init__.py)
```
### Extra Info
My julia version is 1.9.3, installed with juliaup.
In the fresh environment I can get pyjulia working but only with the lower level interface
```python
import julia
julia.core.Julia() # works
from julia import Main #fails
``` | closed | 2023-10-26T16:50:26Z | 2023-10-30T23:54:54Z | https://github.com/MilesCranmer/PySR/issues/448 | [
"bug"
] | IlyaOrson | 12 |
deepfakes/faceswap | machine-learning | 1,207 | ImportError: numpy.core.multiarray failed to import | D:\faceswap-master>python faceswap.py extract -i D:\faceswap-master\src\han_li.mp4 -o D:\faceswap-master\faces
Setting Faceswap backend to AMD
No GPU detected. Switching to CPU mode
01/29/2022 22:23:02 INFO Log level set to: INFO
01/29/2022 22:23:02 WARNING No GPU detected. Switching to CPU mode
01/29/2022 22:23:02 INFO Switching backend to CPU. Using Tensorflow for CPU operations.
RuntimeError: module compiled against API version 0xe but this version of numpy is 0xd
01/29/2022 22:23:06 ERROR Got Exception on main handler:
Traceback (most recent call last):
File "D:\faceswap-master\lib\cli\launcher.py", line 180, in execute_script
script = self._import_script()
File "D:\faceswap-master\lib\cli\launcher.py", line 46, in _import_script
module = import_module(mod)
File "C:\Users\44626\AppData\Local\Programs\Python\Python37\lib\importlib\__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "D:\faceswap-master\scripts\extract.py", line 14, in <module>
from scripts.fsmedia import Alignments, PostProcess, finalize
File "D:\faceswap-master\scripts\fsmedia.py", line 18, in <module>
from lib.face_filter import FaceFilter as FilterFunc
File "D:\faceswap-master\lib\face_filter.py", line 7, in <module>
from lib.vgg_face import VGGFace
File "D:\faceswap-master\lib\vgg_face.py", line 15, in <module>
from fastcluster import linkage
File "C:\Users\44626\AppData\Local\Programs\Python\Python37\lib\site-packages\fastcluster.py", line 37, in <module>
from _fastcluster import linkage_wrap, linkage_vector_wrap
ImportError: numpy.core.multiarray failed to import
01/29/2022 22:23:06 CRITICAL An unexpected crash has occurred. Crash report written to 'D:\faceswap-master\crash_report.2022.01.29.222305067754.log'. You MUST provide this file if seeking assistance. Please verify you are running the latest version of faceswap before reporting
-----------------------------------
win10 no gpu
my pip list๏ผ
absl-py 0.15.0
astunparse 1.6.3
cached-property 1.5.2
cachetools 4.2.4
certifi 2021.10.8
cffi 1.15.0
charset-normalizer 2.0.10
clang 5.0
colorama 0.4.4
cycler 0.11.0
enum34 1.1.10
fastcluster 1.2.4
ffmpy 0.2.3
flatbuffers 1.12
gast 0.3.3
google-auth 1.35.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
grpcio 1.43.0
h5py 2.10.0
idna 3.3
imageio 2.14.1
imageio-ffmpeg 0.4.5
importlib-metadata 4.10.1
joblib 1.1.0
Keras 2.2.4
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
kiwisolver 1.3.2
Markdown 3.3.6
matplotlib 3.2.2
numpy 1.19.4
nvidia-ml-py 11.495.46
oauthlib 3.1.1
opencv-python 4.5.5.62
opt-einsum 3.3.0
Pillow 9.0.0
pip 21.3.1
plaidml 0.7.0
plaidml-keras 0.7.0
protobuf 3.19.4
psutil 5.9.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.21
pyparsing 3.0.7
python-dateutil 2.8.2
pywin32 303
PyYAML 6.0
requests 2.27.1
requests-oauthlib 1.3.0
rsa 4.8
scikit-learn 1.0.2
scipy 1.7.3
setuptools 60.5.0
six 1.15.0
tensorboard 2.2.2
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorflow 2.2.3
tensorflow-estimator 2.2.0
termcolor 1.1.0
threadpoolctl 3.0.0
tqdm 4.62.3
typing-extensions 3.7.4.3
urllib3 1.26.8
Werkzeug 2.0.2
wheel 0.37.1
wrapt 1.12.1
zipp 3.7.0
| closed | 2022-01-29T14:30:24Z | 2022-05-15T01:22:24Z | https://github.com/deepfakes/faceswap/issues/1207 | [] | Odimmsun | 3 |
fastapi/sqlmodel | fastapi | 21 | How to make a "timestamp with time zone"? | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options ๐
### Example Code
```python
import datetime
from typing import List, Optional
import sqlmodel
class AuthUser(sqlmodel.SQLModel, table=True):
__tablename__ = 'auth_user'
id: Optional[int] = sqlmodel.Field(default=None, primary_key=True)
password: str = sqlmodel.Field(max_length=128)
last_login: datetime.datetime
```
### Description
I'm trying to make the `last_login` field become a "timestamp with time zone" field in Postgres.
With the above code, it is a "timestamp without time zone".
### Operating System
Linux, macOS
### Operating System Details
_No response_
### SQLModel Version
0.0.3
### Python Version
Python 3.8.1
### Additional Context
_No response_ | closed | 2021-08-25T21:32:00Z | 2021-08-26T19:55:37Z | https://github.com/fastapi/sqlmodel/issues/21 | [
"question"
] | typeshige | 3 |
deepset-ai/haystack | pytorch | 8,086 | Remove references to the removed `DynamicPromptBuilder` and `DynamicChatPromptBuilder` components | Removed in https://github.com/deepset-ai/haystack/pull/8085.
- [x] Docs
- [x] Tutorials
- [x] Cookbooks
- [x] Integrations | closed | 2024-07-25T14:15:50Z | 2024-08-04T21:37:22Z | https://github.com/deepset-ai/haystack/issues/8086 | [
"breaking change",
"type:documentation",
"P1",
"2.x"
] | shadeMe | 0 |
xonsh/xonsh | data-science | 4,756 | distribute wheel files with new releases | <!--- Provide a general summary of the issue in the Title above -->
<!--- If you have a question along the lines of "How do I do this Bash command in xonsh"
please first look over the Bash to Xonsh translation guide: https://xon.sh/bash_to_xsh.html
If you don't find an answer there, please do open an issue! -->
It will help fasten install time during CI for dependant projects.
## Steps to Reproduce
<!--- Please try to write out a minimal reproducible snippet to trigger the bug, it will help us fix it! -->
## For community
โฌ๏ธ **Please click the ๐ reaction instead of leaving a `+1` or ๐ comment**
| closed | 2022-04-15T06:15:04Z | 2022-05-10T15:39:44Z | https://github.com/xonsh/xonsh/issues/4756 | [
"development"
] | jnoortheen | 4 |
holoviz/panel | plotly | 6,850 | Interactivity tutorial returns error when first cell is run. |
Page: https://panel.holoviz.org/tutorials/intermediate/interactivity.html
Result of pressing Play on first cell:
pyodide.ffi.JsException: NetworkError: Failed to execute 'send' on 'XMLHttpRequest': Failed to load 'https://assets.holoviz.org/panel/tutorials/turbines.csv.gz'.
The file downloads fine when the link is entered in a browser.
Content of first cell:
import panel as pn
import pandas as pd
pn.extension("tabulator")
data_url = 'https://assets.holoviz.org/panel/tutorials/turbines.csv.gz'
turbines = pn.cache(pd.read_csv)(data_url)
cols = pn.widgets.MultiChoice(
options=turbines.columns.to_list(), value=['p_name', 't_state', 't_county', 'p_year', 't_manu', 'p_cap'],
width=500, height=100, name='Columns'
)
| closed | 2024-05-18T02:45:25Z | 2024-05-21T14:34:39Z | https://github.com/holoviz/panel/issues/6850 | [
"type: docs",
"more info needed"
] | Coderambling | 3 |
public-apis/public-apis | api | 4,094 | Apis | open | 2024-12-30T07:46:50Z | 2025-02-10T16:13:33Z | https://github.com/public-apis/public-apis/issues/4094 | [] | Bakoorii | 2 |
|
jupyter-incubator/sparkmagic | jupyter | 343 | Can't Connect | Guessing these will turn out to be newbie issues but I've installed sparkmagic and can't get it to connect to Spark. I installed sparkmagic and followed the instructions to enable widgetsnbextension. My environment is:
**Distro**: centos 7
**Python**: 3.6.0
```
$ pip freeze | egrep "jupyter|sparkmagic"
-e git+git@github.com:jupyter-incubator/sparkmagic.git@ef55831fea0a45686f844b4f0efb4d05ea658a26#egg=autovizwidget&subdirectory=autovizwidget
-e git+git@github.com:jupyter-incubator/sparkmagic.git@ef55831fea0a45686f844b4f0efb4d05ea658a26#egg=hdijupyterutils&subdirectory=hdijupyterutils
jupyter==1.0.0
jupyter-client==4.4.0
jupyter-console==5.0.0
jupyter-core==4.2.1
sparkmagic==0.11.2
```
Running the following code produces the error shown:
```
%%configure -f
{"executorMemory": "1000M", "executorCores": 4}
Current session configs: {'executorMemory': '1000M', 'executorCores': 4, 'kind': 'pyspark'}
--output--->
An internal error was encountered.
Please file an issue at https://github.com/jupyter-incubator/sparkmagic
Error:
'>=' not supported between instances of 'NoneType' and 'int'
```
and this, with a PySpark kernel, produces:
```
print ('hi')
x = sc.parallelize ([ 0, 8, 232, 9 ])
โ--output--->
The code failed because of a fatal error:
'>=' not supported between instances of 'NoneType' and 'int'.
Some things to try:
a) Make sure Spark has enough available resources for Jupyter to create a Spark context.
b) Contact your Jupyter administrator to make sure the Spark magics library is configured correctly.
c) Restart the kernel.
```
Output at the command line is:
```
$ jupyter notebook --ip=0.0.0.0
[I 22:36:01.173 NotebookApp] Serving notebooks from local directory: /projects/stars/app/greendatatranslator/src/greentranslator
[I 22:32:50.681 NotebookApp] 0 active kernels
[I 22:32:50.681 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/?token=7e4ac4e27b0b1602ab3c9db94addde895e7b5876b1823a11
[I 22:32:50.682 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[W 22:32:50.682 NotebookApp] No web browser found: could not locate runnable browser.
[C 22:32:50.682 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://0.0.0.0:8888/?token=7e4ac4e27b0b1602ab3c9db94addde895e7b5876b1823a11
[W 22:32:59.283 NotebookApp] 404 GET /api/kernels/b0d889f3-7939-4494-8e85-b4d7a8002174/channels?session_id=8A08DAF5C8EF4ECF96897C3E150F3DD5 (152.54.4.37): Kernel does not exist: b0d889f3-7939-4494-8e85-b4d7a8002174
[W 22:32:59.364 NotebookApp] 404 GET /api/kernels/b0d889f3-7939-4494-8e85-b4d7a8002174/channels?session_id=8A08DAF5C8EF4ECF96897C3E150F3DD5 (152.54.4.37) 104.07ms referer=None
[W 22:33:16.252 NotebookApp] Replacing stale connection: b0d889f3-7939-4494-8e85-b4d7a8002174:8A08DAF5C8EF4ECF96897C3E150F3DD5
[I 22:33:21.725 NotebookApp] Kernel started: db52791d-0d69-407c-9bea-3dbd4310755b
[I 22:35:22.239 NotebookApp] Saving file at /Untitled.ipynb
```
What am I missing?
| closed | 2017-04-11T02:48:13Z | 2017-04-22T00:34:37Z | https://github.com/jupyter-incubator/sparkmagic/issues/343 | [] | stevencox | 2 |
ultralytics/ultralytics | pytorch | 19,424 | Failed to export yolonas-m | ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.
### Ultralytics YOLO Component
_No response_
### Bug
updated ultralytics to latest one, and trying following
from ultralytics import NAS
model = NAS('yolo_nas_s.pt')
model.export(format='onnx')
the export fails
### Environment
>>> from ultralytics import NAS
>>> model = NAS('yolo_nas_s.pt')
The console stream is logged into /home/memryx/sg_logs/console.log
[2025-02-25 11:32:31] INFO - crash_tips_setup.py - Crash tips is enabled. You can set your environment variable to CRASH_HANDLER=FALSE to disable it
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_s.pt to 'yolo_nas_s.pt'...
100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 83.3M/83.3M [00:03<00:00, 26.1MB/s]
>>> model.export(format='onnx')
python3.10/site-packages/torch/nn/modules/module.py", line 1928, in __getattr__
raise AttributeError(
AttributeError: 'YoloNAS_S' object has no attribute 'args'
### Minimal Reproducible Example
from ultralytics import NAS
model = NAS('yolo_nas_s.pt')
model.export(format='onnx')
### Additional
_No response_
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | closed | 2025-02-25T16:33:49Z | 2025-02-26T12:09:50Z | https://github.com/ultralytics/ultralytics/issues/19424 | [
"bug",
"fixed",
"exports"
] | poppyzy | 4 |
davidteather/TikTok-Api | api | 807 | _ The URL signature calculation of signature uses get_ acrawler. Py inside_ get_ Acrawler? | I see a lot of garbled code in it, and the JS code can't work normally | closed | 2022-01-26T15:40:16Z | 2023-08-08T22:21:34Z | https://github.com/davidteather/TikTok-Api/issues/807 | [] | wuliao6688 | 5 |
dask/dask | pandas | 11,123 | New CI failure showing up in fsspec | I am seeing the following consistently in fsspec's downstream CI runs:
```
FAILED ../../../micromamba/envs/test_env/lib/python3.9/site-packages/dask/bytes/tests/test_s3.py::test_parquet_append[pyarrow] - AssertionError: DataFrame are different
DataFrame shape mismatch
[left]: (2000, 4)
[right]: (1000, 4)
```
| closed | 2024-05-15T14:32:54Z | 2024-05-29T21:27:34Z | https://github.com/dask/dask/issues/11123 | [
"needs triage"
] | martindurant | 5 |
deeppavlov/DeepPavlov | tensorflow | 933 | Refactor Bert Classifier to output int classes instead of numpy.Int64 | I have a code with a custom config for Bert Paraphraser:
```
DATA_PATH = BASE_PATH + "data/"
para_config = {
"dataset_reader": {
"class_name": "csv_reader",
"data_path": DATA_PATH,
"do_lower_case": False,
"delimiter": ",",
"train_ds_fname": "micro_train.csv",
"valid_ds_fname": "micro_valid.csv",
"test_ds_fname": "micro_test.csv"
},
"dataset_iterator": {
"class_name": "siamese_iterator",
"seed": 243,
"len_valid": 500
},
"chainer": {
"in": ["text_a", "text_b"],
"in_y": ["y"],
"pipe": [
{
"class_name": "bert_preprocessor",
"vocab_file": "{DOWNLOADS_PATH}/bert_models/rubert_cased_L-12_H-768_A-12_v1/vocab.txt",
"do_lower_case": False,
"max_seq_length": 160,
"in": ["text_a", "text_b"],
"out": ["bert_features"]
},
{
"class_name": "bert_classifier",
"n_classes": 2,
"one_hot_labels": False,
"bert_config_file": "{DOWNLOADS_PATH}/bert_models/rubert_cased_L-12_H-768_A-12_v1/bert_config.json",
"pretrained_bert": "{DOWNLOADS_PATH}/bert_models/rubert_cased_L-12_H-768_A-12_v1/bert_model.ckpt",
"save_path": "{MODELS_PATH}/paraphraser_rubert/model_rubert",
"load_path": "{MODELS_PATH}/paraphraser_rubert/model_rubert",
"keep_prob": 0.5,
"optimizer": "tf.train:AdamOptimizer",
"learning_rate": 2e-05,
"learning_rate_drop_patience": 3,
"learning_rate_drop_div": 2.0,
"in": ["bert_features"],
"in_y": ["y"],
"out": ["predictions"]
}
],
"out": ["predictions"]
},
"train": {
"batch_size": 32,
"train_metrics": ["acc"],
"metrics": ["acc"],
"validation_patience": 7,
"val_every_n_batches": 2,
"log_every_n_batches": 1,
"tensorboard_log_dir": "{MODELS_PATH}/paraphraser_rubert/logs",
"show_examples": True,
},
"metadata": {
"variables": {
"ROOT_PATH": "~/.deeppavlov",
"DOWNLOADS_PATH": "{ROOT_PATH}/downloads",
"MODELS_PATH": "{ROOT_PATH}/models",
"DATA_PATH": DATA_PATH
},
"requirements": [
"{DEEPPAVLOV_PATH}/requirements/tf.txt",
"{DEEPPAVLOV_PATH}/requirements/bert_dp.txt"
],
"download": [
{
"url": "http://files.deeppavlov.ai/deeppavlov_data/bert/rubert_cased_L-12_H-768_A-12_v1.tar.gz",
"subdir": "{DOWNLOADS_PATH}/bert_models"
},
{
"url": "http://files.deeppavlov.ai/deeppavlov_data/classifiers/paraphraser_rubert_v0.tar.gz",
"subdir": "{ROOT_PATH}/models"
}
]
}
}
paraphraser_model = train_model(para_config)
```
The problem with this code is that it fails with error:
```
Traceback (most recent call last):
File "runparaphraser_train.py", line 100, in <module>
paraphraser_model = train_model(para_config)
File "/home/alx/Cloud/dns/.venv3/lib/python3.6/site-packages/deeppavlov-0.4.0-py3.6.egg/deeppavlov/__init__.py", line 31, in train_model
train_evaluate_model_from_config(config, download=download, recursive=recursive)
File "/home/alx/Cloud/dns/.venv3/lib/python3.6/site-packages/deeppavlov-0.4.0-py3.6.egg/deeppavlov/core/commands/train.py", line 121, in train_evaluate_model_from_config
trainer.train(iterator)
File "/home/alx/Cloud/dns/.venv3/lib/python3.6/site-packages/deeppavlov-0.4.0-py3.6.egg/deeppavlov/core/trainers/nn_trainer.py", line 294, in train
self.train_on_batches(iterator)
File "/home/alx/Cloud/dns/.venv3/lib/python3.6/site-packages/deeppavlov-0.4.0-py3.6.egg/deeppavlov/core/trainers/nn_trainer.py", line 234, in train_on_batches
self._validate(iterator)
File "/home/alx/Cloud/dns/.venv3/lib/python3.6/site-packages/deeppavlov-0.4.0-py3.6.egg/deeppavlov/core/trainers/nn_trainer.py", line 178, in _validate
print(json.dumps(report, ensure_ascii=False))
File "/usr/lib/python3.6/json/__init__.py", line 238, in dumps
**kw).encode(obj)
File "/usr/lib/python3.6/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib/python3.6/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/usr/lib/python3.6/json/encoder.py", line 180, in default
o.__class__.__name__)
TypeError: Object of type 'int64' is not JSON serializable
```
The code fails because BertClassifier returns numpy.int64 elements as predictions (https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/models/bert/bert_classifier.py#L242), while in evaluation the code constructs a report as json and then prints it into output....
@mu-arkhipov helped me to resolve the problem in my case by adding following lines to BertClassifierModel.__call__ just before return:
```
if pred.ndim == 1:
pred = [int(p) for p in pred]
```
this resolves the problem for my case only. Could anybody offer more general solution?
@dilyararimovna | closed | 2019-07-19T12:11:07Z | 2023-07-07T09:13:06Z | https://github.com/deeppavlov/DeepPavlov/issues/933 | [
"enhancement"
] | acriptis | 2 |
mckinsey/vizro | plotly | 313 | Rename docs pages that include `_` to use `-` | Google doesn't recognise underscores as word separators when it indexes pages. So if we have a page called `first_dashboard` then Google will report that as `firstdashboard` to its algorithm. (If we had `first-dashboard` then it would go into the mix as `first dashboard` which earns more google juice for the keywords "dashboard"). [More explanation here](https://www.woorank.com/en/blog/underscores-in-urls-why-are-they-not-recommended)
As we are at an early stage with Vizro, we can make some changes (and use RTD redirects to ensure we don't break anyone's links) that set the docs up for success later. SEO doesn't seem that important but every little helps.
## Solution
1. Rename pages
2. Set up redirects in readthedocs to redirect to newly hyphenated pages for external users who have bookmarks, and blog posts we can't update etc.
3. Change all existing internal linking within the docs to the new page names | closed | 2024-02-15T12:14:45Z | 2024-02-21T09:56:45Z | https://github.com/mckinsey/vizro/issues/313 | [
"Docs :spiral_notepad:"
] | stichbury | 2 |
tqdm/tqdm | pandas | 827 | Change colorhints | Iโd like to use tqdm to show progress for an iterative algorithm with a max number of iterations. Since being interrupted then implies convergence, I would want to switch colorhints to show green if interrupted and red if it reaches total. Is that possible?
| open | 2019-10-23T06:18:54Z | 2019-10-29T12:18:14Z | https://github.com/tqdm/tqdm/issues/827 | [
"question/docs โฝ",
"p4-enhancement-future ๐งจ",
"submodule-notebook ๐"
] | bdch1234 | 3 |
keras-team/keras | tensorflow | 20,341 | LSTM not supporting channels_first data_format | tensorflow version = 2.12
Keras version = 3.6
This code is working well
```
nbr_frame = 10
img_width = 180
img_height = 150
img_size = (img_height, img_width)
input_shape = img_size + (3,)
tf.keras.backend.set_image_data_format(
'channels_last'
)
full_input_shape = (nbr_frame,) + input_shape
print(full_input_shape)
np.random.seed(1234)
num_classes = 2
#vg19 = tf.keras.applications.vgg19.VGG19
#base_model = vg19(include_top=False,weights='imagenet',input_shape=(img_width, img_height,3))
base_model = tf.keras.applications.MobileNetV2(
include_top=False, weights='imagenet', input_tensor=None,
input_shape = input_shape,
pooling=None,
)
for layer in base_model.layers:
layer.trainable = False
base_model.summary()
cnn = models.Sequential()
cnn.add(base_model)
cnn.add(layers.GlobalAveragePooling2D())
cnn.add(layers.Dropout(0.2))
base_model.trainable = False
# define LSTM model
model = models.Sequential()
print(full_input_shape)
model.add(layers.TimeDistributed(cnn, input_shape=full_input_shape))
model.add(layers.LSTM(nbr_frame, return_sequences=True))
model.add(layers.TimeDistributed(layers.Dense(nbr_frame, activation='relu')))
model.add(layers.Flatten())
model.add(layers.Dense(164, activation='relu', name="filter"))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(24, activation='sigmoid', name="filter2"))
model.add(layers.Dropout(0.1))
model.add(layers.Dense(num_classes, activation="sigmoid", name="last"))
rms = optimizers.RMSprop()
metrics = [tf.keras.metrics.CategoricalAccuracy('accuracy', dtype=tf.float32)]
loss = tf.keras.losses.CategoricalCrossentropy()
model.compile(
loss=loss,
optimizer= rms,
metrics=metrics
)
model.summary()
```
But I am interested to change the set_image_data_format to `channels_first` by modifying :
```
input_shape = (3,) + img_size
tf.keras.backend.set_image_data_format(
'channels_first'
)
```
I am getting this error
```
File ~/miniconda3/envs/ai/lib/python3.11/site-packages/keras/backend.py:6780, in bias_add(x, bias, data_format)
6778 if len(bias_shape) == 1:
6779 if data_format == "channels_first":
-> 6780 return tf.nn.bias_add(x, bias, data_format="NCHW")
6781 return tf.nn.bias_add(x, bias, data_format="NHWC")
6782 if ndim(x) in (3, 4, 5):
ValueError: Exception encountered when calling layer "lstm_9" (type LSTM).
Shape must be at least rank 3 but is rank 2 for '{{node BiasAdd}} = BiasAdd[T=DT_FLOAT, data_format="NCHW"](add, bias)' with input shapes: [?,40], [40].
Call arguments received by layer "lstm_9" (type LSTM):
โข inputs=tf.Tensor(shape=(None, 10, 1280), dtype=float32)
โข mask=None
โข training=None
โข initial_state=None
``` | closed | 2024-10-12T12:41:17Z | 2024-10-12T12:52:20Z | https://github.com/keras-team/keras/issues/20341 | [] | nassimus26 | 1 |
kymatio/kymatio | numpy | 996 | Need for new `test_data_1d.npz` post-#984 | After discussion with @janden et al. today
v0.4 | closed | 2023-02-16T17:40:45Z | 2023-03-04T17:04:18Z | https://github.com/kymatio/kymatio/issues/996 | [
"tests"
] | lostanlen | 1 |
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