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paperless-ngx/paperless-ngx
|
django
| 9,386
|
[BUG] OIDC group sync doens't work on first login.
|
### Description
While testing the excellent new social account features from #9039 thanks very much for the work on that!
I noticed that on first login with a social account (OIDC in my case) only the default groups set in
PAPERLESS_SOCIAL_ACCOUNT_DEFAULT_GROUPS and PAPERLESS_ACCOUNT_DEFAULT_GROUPS are applied.
On second login the group memberships are updated to those provided by OIDC.
### Steps to reproduce
1. Configure OIDC with group sync
2. Create one or more groups in paperless where the names match existing OIDC groups
3. ensure no accounts matching your OIDC account username already exist
4. authenticate with OIDC - only the default groups are applied
5. As admin, check the group memberships of the newly created account
6. log out
7. log in again with OIDC - now all OIDC groups are applied
8. Verify updated group memberships with admin level account
### Webserver logs
```bash
From first login with OIDC account:
[2025-03-13 11:38:04,402] [DEBUG] [paperless.auth] Adding default social groups to user `dl`: ['test']
From second login with the same OIDC account:
[2025-03-13 11:38:20,498] [DEBUG] [paperless.auth] Syncing groups for user `dl`: ['idm_all_persons@example.com',...
```
### Browser logs
```bash
```
### Paperless-ngx version
dev build 2025-03-12
### Host OS
K8s/amd64
### Installation method
Docker - official image
### System status
```json
```
### Browser
_No response_
### 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
|
2025-03-13T12:00:12Z
|
2025-03-13T14:24:07Z
|
https://github.com/paperless-ngx/paperless-ngx/issues/9386
|
[
"backend"
] |
Serbitor
| 0
|
tfranzel/drf-spectacular
|
rest-api
| 988
|
Nested serializer schema not showing a field
|
**Describe the bug**
Very possible that I am misunderstanding how something should work, but in case I am not:
These serializers
```python
class ResponseNodeSerializer(serializers.Serializer):
type: serializers.ChoiceField(choices=["text"])
content: serializers.CharField(max_length=5000)
class CreatedResponseSerializer(serializers.Serializer):
response_body = ResponseNodeSerializer(many=True)
```
On a function view:
```python
@extend_schema(responses=CreatedResponseSerializer)
@api_view(["POST"])
@permission_classes([])
def responses_view(request: Request) -> Response:
```
Gives completely empty example and swagger definition.
Code | Description | Links
-- | -- | --
200 | No response body
**To Reproduce**
Simple example provided
**Expected behavior**
Expected the schema to have
```json
{
"response_body": [
"type": "text",
"content": "string"
]
}
```
|
closed
|
2023-05-16T21:53:25Z
|
2023-05-17T13:03:56Z
|
https://github.com/tfranzel/drf-spectacular/issues/988
|
[] |
armanckeser
| 2
|
feature-engine/feature_engine
|
scikit-learn
| 14
|
review tests for categorical encoders
|
Are they working with the latest implementation? are they testing every aspect of the class?
|
closed
|
2019-09-04T08:15:44Z
|
2020-04-19T09:53:23Z
|
https://github.com/feature-engine/feature_engine/issues/14
|
[] |
solegalli
| 0
|
NullArray/AutoSploit
|
automation
| 593
|
Unhandled Exception (81a4e7399)
|
Autosploit version: `3.0`
OS information: `Linux-4.17.0-kali1-amd64-x86_64-with-Kali-kali-rolling-kali-rolling`
Running context: `autosploit.py`
Error meesage: `global name 'Except' is not defined`
Error traceback:
```
Traceback (most recent call):
File "/root/AutoSploit/autosploit/main.py", line 113, in main
loaded_exploits = load_exploits(EXPLOIT_FILES_PATH)
File "/root/AutoSploit/lib/jsonize.py", line 61, in load_exploits
except Except:
NameError: global name 'Except' is not defined
```
Metasploit launched: `False`
|
closed
|
2019-03-25T12:03:01Z
|
2019-04-02T20:24:30Z
|
https://github.com/NullArray/AutoSploit/issues/593
|
[] |
AutosploitReporter
| 0
|
wkentaro/labelme
|
deep-learning
| 699
|
[BUG] Cannot open JPEG
|
**Describe the bug**
Labelme v4.5 does not want to load *.jpg or *.heic. I can see them in the picker view, and it does allow me to select it, but it does not want to load.
**To Reproduce**
Steps to reproduce the behavior:
You will find out that you won't see jpg images.
1. Install Labelme 4.5.0 using pip3 install labelme
2. Click on OpenDir/Open
3. Choose Dir or single image in .jpg or .heic format
4. Observe error
**Expected behavior**
The jpg images must be seen.
**Screenshots**
Here is the folder structure with image names and format.
Here is the error I get
List of applicable image formats
**Desktop (please complete the following information):**
- OS: MacOS Catalina v10.15.5
- Labelme Version [v4.5]
**Additional context**
[ERROR ] label_file:load_image_file:39 - Failed opening image file: /Users/sakibkurtic/Desktop/Horizontal_Traffic_Sign_Train_Data/0420.jpg
This is a detailed console error I got.
|
closed
|
2020-06-25T10:26:29Z
|
2021-09-23T15:20:56Z
|
https://github.com/wkentaro/labelme/issues/699
|
[
"issue::bug"
] |
sakibk
| 2
|
sqlalchemy/alembic
|
sqlalchemy
| 968
|
Alembic keeps detecting JSON change
|
**Describe the bug**
MariaDB's JSON type is an alias for LONGTEXT with a JSON_VALID check. When defining a column of type `sqlalchemy.dialects.mysql.JSON`, Alembic keeps generating a migration of `existing_type=mysql.LONGTEXT(charset='utf8mb4', collation='utf8mb4_bin')` to `type_=mysql.JSON()`.
**Expected behavior**
I would expect Alembic to not detect any changes between LONGTEXT with JSON_VALID check and `sqlalchemy.dialects.mysql.JSON`.
**To Reproduce**
- Add an `sqlalchemy.dialects.mysql.JSON` column
- Create a migration & upgrade
- Create another migration. This will cause another migration with an operation like the one below to be created.
```py
op.alter_column('api_users', 'trusted_ip_networks',
existing_type=mysql.LONGTEXT(charset='utf8mb4', collation='utf8mb4_bin'),
type_=mysql.JSON(),
existing_nullable=False,
existing_server_default=sa.text("'[]'")
)
```
**Error**
None.
**Versions.**
- OS: macOS 11.6
- Python: 3.8.9
- Alembic: 1.4.2
- SQLAlchemy: 1.3.16
- Database: MariaDB 10.5.9
- DBAPI: -
**Additional context**
None.
|
closed
|
2021-11-12T22:12:06Z
|
2021-11-18T15:54:45Z
|
https://github.com/sqlalchemy/alembic/issues/968
|
[
"bug",
"autogenerate - detection",
"mysql"
] |
WilliamDEdwards
| 1
|
iperov/DeepFaceLab
|
machine-learning
| 5,201
|
Head extraction size
|
## Expected behavior
Extract head aligned with more tight crop of the head for frontal shots, so we can use lower model resolution and faster training, rather than trying to compensate with higher res.
## Actual behavior
Frontal aligned are extracted at 40% of aligned frame, so there is ~60% of frame resolution wasted.
## Steps to reproduce
extract head (aligned)
|
closed
|
2020-12-15T14:48:11Z
|
2023-06-08T22:39:54Z
|
https://github.com/iperov/DeepFaceLab/issues/5201
|
[] |
zabique
| 5
|
indico/indico
|
sqlalchemy
| 6,731
|
Support text highlighting in Markdown-formatted minutes
|
In Markdown-formatted minutes, it would be very useful to be able to highlight (yellow background) some part of text. For example with `normal and ==highlighted== text` notation (which I understand is not part of the Markdown standard - but is a common Markdown extension).
|
closed
|
2025-02-06T10:50:14Z
|
2025-02-06T14:01:46Z
|
https://github.com/indico/indico/issues/6731
|
[
"enhancement",
"help wanted"
] |
SebastianLopienski
| 0
|
ckan/ckan
|
api
| 7,604
|
Tighten validation for `id` fields
|
Having loose or inconsistent validation on `id` fields can lead to confusing behaviour or security concerns. Let's do a review of the various entities with `id` field in the model and apply the same validation to all of them (if possible). Validators should include:
* "Can only be changed by a sysadmin": custom ids only make sense in the context of specific scenarios like migrations, it make sense that only sysadmins (or plugins that ignore auth) touch them
* "Value is a valid UUID". In the past we've allowed different values here to support potential use cases but it's probably safer to enforce UUIDs for all ids and use custom fields if needed
* "Id does not already exist when creating": to prevent taking over other entities
Whenever possible we should look at enforcing those not only with validators but also at the SQLAlchemy level, eg using inserts instead of upserts when creating records.
Some of these are already applied to some entities, but it would be good to be consistent across all models
* Dataset
* Resource
* Resource View
* Groups (org/groups)
* User
* Activity
* Extras
* Tags (?)
* ...
|
closed
|
2023-05-25T11:08:20Z
|
2024-05-23T13:42:52Z
|
https://github.com/ckan/ckan/issues/7604
|
[
"Good for Contribution"
] |
amercader
| 2
|
microsoft/MMdnn
|
tensorflow
| 359
|
Incorrect input layer name in code when emitting to MXNet.
|
### Hardware Specifications
Platform (like ubuntu 16.04/win10):
Ubuntu 16.04
**Python version:**
Python 3.5.2
**Source framework with version (like Tensorflow 1.4.1 with GPU):**
Tensorflow 1.9.0 compiled with CUDA 9.0
**Destination framework with version (like CNTK 2.3 with GPU):**
MXNet 1.3.0 with CPU
**Pre-trained model path (webpath or webdisk path):**
I am trying to convert Google's Audioset VGGish network trained on their Audioset data. The architecture is very similar to VGG. The network in located on my disk at the paths `export.ckpt.meta`, `export.ckpt.data-0000-of-00001`, `export.ckpt.index`.
**Running scripts:**
```bash
mmtoir -f tensorflow -n export.ckpt.meta -w export.ckpt --inNodeName vggish/input_features --dstNodeName vggish/embedding --inputShape 96,64 -o exportir
mmtocode -f mxnet -n exportir.pb -w exportir.npy -o exporttest.py -ow exporttest.params
```
### Issue
The mmtoir and mmtocode both return successfully. However, the output MXNet python file has an error when run.
```
ValueError: You created Module with Module(..., data_names=['vggish_input_features']) but input with name 'vggish_input_features' is not found in symbol.list_arguments(). Did you mean one of: vggish/input_features
```
It seems to be because in the autogenerated python MXNet network definition, there is one line
```python
model = mx.mod.Module(symbol = vggish_fc2_Relu, context = mx.cpu(), data_names = ['vggish_input_features'])
```
I was able to get everything to work correctly by replacing `data_names = ['vggish_input_features'])` with `data_names = ['vggish/input_features'])`.
The autogenerated code seems to point to the actual python variable name of the first layer rather than the name given. For reference, the autogenerated network definition has:
```python
vggish_input_features = mx.sym.var('vggish/input_features')
```
This seems to only be a problem when the input layer name has a character that causes the layer python name to be different from the given name.
|
closed
|
2018-08-14T07:49:37Z
|
2018-08-15T01:31:19Z
|
https://github.com/microsoft/MMdnn/issues/359
|
[] |
wfus
| 0
|
pydata/xarray
|
pandas
| 9,573
|
Consider renaming DataTree.map_over_subtree (or revising its API)
|
### What is your issue?
We have a bit of a naming inconsistency in DataTree:
- The `subtrees` property is an iterator over all sub-tree nodes as a DataTree objects
- `map_over_subtree` maps a function over all sub-tree nodes as Dataset objects
I think it makes sense for "subtree" to refer strictly to DataTree objects. In that case, perhaps we should rename `map_over_subtree` to `map_over_datasets`?
Alternatively, could also change the interface to iterate over DataTree objects instead, which are easy to convert into datasets (via `.dataset`) and additionally provide the full context of a node's `path` and parents.
|
closed
|
2024-10-03T01:47:53Z
|
2024-10-21T15:55:34Z
|
https://github.com/pydata/xarray/issues/9573
|
[
"topic-DataTree"
] |
shoyer
| 3
|
cobrateam/splinter
|
automation
| 589
|
Is Zope only supported in Python 2?
|
I'm seeing an exception using `Browser('zope.testbrowser')`
```
Traceback (most recent call last):
File "/d01/sandboxes/jgeorgeson/git/Dev-Engineering/gitlab-scripts/python-gitlab/lib64/python3.6/site-packages/splinter/browser.py", line 60, in Browser
driver = _DRIVERS[driver_name]
KeyError: 'zope.testbrowser'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "./scripts/import-project.py", line 5, in <module>
with Browser('zope.testbrowser') as browser:
File "/d01/sandboxes/jgeorgeson/git/Dev-Engineering/gitlab-scripts/python-gitlab/lib64/python3.6/site-packages/splinter/browser.py", line 62, in Browser
raise DriverNotFoundError("No driver for %s" % driver_name)
splinter.exceptions.DriverNotFoundError: No driver for zope.testbrowser
```
And see the sys.version_info check [here](https://github.com/cobrateam/splinter/blob/2eec5aa844f743ad3dd4ffc4a17070f5a5b7d9b5/splinter/browser.py#L25)
```
$ python --version
Python 3.6.3
$ pip3 list splinter[zopetestbrowser]
DEPRECATION: The default format will switch to columns in the future. You can use--format=(legacy|columns) (or define a format=(legacy|columns) in your pip.conf under the [list] section) to disable this warning.
beautifulsoup4 (4.6.0)
certifi (2018.1.18)
chardet (3.0.4)
cssselect (1.0.3)
idna (2.6)
lxml (4.1.1)
pip (9.0.1)
python-gitlab (1.2.0)
pytz (2018.3)
PyYAML (3.12)
requests (2.18.4)
selenium (3.9.0)
setuptools (28.8.0)
six (1.11.0)
splinter (0.7.7)
urllib3 (1.22)
waitress (1.1.0)
WebOb (1.7.4)
WebTest (2.0.29)
WSGIProxy2 (0.4.4)
zope.cachedescriptors (4.3.1)
zope.event (4.3.0)
zope.interface (4.4.3)
zope.schema (4.5.0)
zope.testbrowser (5.2.4)
```
|
closed
|
2018-02-26T21:47:11Z
|
2020-04-03T16:44:25Z
|
https://github.com/cobrateam/splinter/issues/589
|
[] |
jgeorgeson
| 4
|
deepset-ai/haystack
|
nlp
| 8,893
|
[Question]: Usage of Graph Algorithms and Any Slowdowns?
|
Hi there,
I'm interested in understanding if `haystack` depends on any graph algorithms from its usage of NetworkX? If so,
- What algorithms are used for what purpose?
- What graph sizes are they being used with?
- Have users experienced any slowdowns or issues with algorithms provided by NetworkX? (Speed, algorithm availability, etc)
Furthermore, would users be interested in accelerated nx algorithms via a GPU backend? This would involve zero code change.
Any insight into this topic would be greatly appreciated! Thank you.
|
closed
|
2025-02-20T17:39:13Z
|
2025-02-21T07:12:32Z
|
https://github.com/deepset-ai/haystack/issues/8893
|
[] |
nv-rliu
| 1
|
Miserlou/Zappa
|
flask
| 1,547
|
zappa package with slim_handler=true doesn't include Werkzeug package (introduced v0.46.0)
|
## Context
I thought I was running into issue #64 but turns out I found a regression introduced in v0.46 related to how the handler_venv is packaged and uploaded when using slim_handler.
I created a small repo to show the problem: https://github.com/sheats/zappa_issue
If you keep `zappa==0.46.0` (or 0.46.1) in `requirements.txt` and deploy the function the only thing that prints in the logs is:
```
Unable to import module 'handler': No module named 'werkzeug'
```
If you change requirements.txt to `zappa==0.45.1` and update the function the problem goes away.
When I unzipped the handler venv zip after running `zappa package` I only see the following:
```
drwx------ 9 user staff 288 Jun 25 06:27 .
drwxr-xr-x 16 user staff 512 Jun 25 06:27 ..
-rwxr-xr-x 1 user staff 0 Jun 25 2018 __init__.py
-rw-------@ 1 user staff 85824 Dec 31 1979 _sqlite3.so
-rw-r--r-- 1 user staff 605 Sep 13 2016 django_zappa_app.py
-rw------- 1 user staff 23461 Dec 31 1979 handler.py
-rw------- 1 user staff 143 Dec 31 1979 package_info.json
drwxr-xr-x 14 user staff 448 Jun 25 06:27 zappa
-rw-r--r-- 1 user staff 467 Jun 25 2018 zappa_settings.py
```
## Expected Behavior
I expect the logs to show other errors or normal successful logs.
## Actual Behavior
handler.py tries to [import werkzeug on line 17](https://github.com/Miserlou/Zappa/blob/master/zappa/handler.py#L17) but fails because Werkzeug wasn't included in the handler_venv zipped package.
## Possible Fix
Don't know the code well enough to come up with a fix strategy yet.
## Steps to Reproduce
```
git clone git@github.com:sheats/zappa_issue.git
make reproduce && make tail
```
## Your Environment
Everything is in the repo I referenced.
|
open
|
2018-06-25T21:09:57Z
|
2018-07-08T16:34:12Z
|
https://github.com/Miserlou/Zappa/issues/1547
|
[] |
sheats
| 8
|
ageitgey/face_recognition
|
python
| 1,152
|
Face recognition problem
|
Hello i have problem with face recognition i run your code on my laptop and works perfect but i try run te same code on my raspebrry pi and i have error :
Traceback (most recent call last):
File "2.py", line 73, in <module>
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
cv2.error: OpenCV(4.1.2) /home/pi/opencv/opencv-4.1.2/modules/imgproc/src/resize.cpp:3720: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
* face_recognition version:
* Python version:
* Operating System:
### Description
Describe what you were trying to get done.
Tell us what happened, what went wrong, and what you expected to happen.
IMPORTANT: If your issue is related to a specific picture, include it so others can reproduce the issue.
### What I Did
```
Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.
```
|
open
|
2020-05-28T22:12:48Z
|
2020-08-03T16:26:07Z
|
https://github.com/ageitgey/face_recognition/issues/1152
|
[] |
rafalrk
| 1
|
sinaptik-ai/pandas-ai
|
data-science
| 1,330
|
Expose pandas API on SmartDataframe
|
### 🚀 The feature
Expose commonly used pandas API on SmartDataframe so that a smart dataframe can be used as if it is a normal pandas dataframe.
### Motivation, pitch
Continuous modification of a pandas dataframe is often needed. Is there a support for modifying an existing SmartDataframe after it has been constructed? If not, one way of enabling this is to expose pandas API on SmartDataframes.
### Alternatives
_No response_
### Additional context
_No response_
|
closed
|
2024-08-19T17:29:23Z
|
2024-11-25T16:07:45Z
|
https://github.com/sinaptik-ai/pandas-ai/issues/1330
|
[
"enhancement"
] |
c3-yiminliu
| 1
|
jwkvam/bowtie
|
plotly
| 35
|
charting component that's faster than plotly for rendering large data
|
- canvasjs
- echarts
ref: http://blog.udacity.com/2016/03/12-best-charting-libraries-for-web-developers.html
|
open
|
2016-11-09T18:28:22Z
|
2018-07-24T01:43:07Z
|
https://github.com/jwkvam/bowtie/issues/35
|
[] |
jwkvam
| 2
|
Lightning-AI/pytorch-lightning
|
data-science
| 19,667
|
test_ddp.py hangs at test_ddp_configure_ddp
|
### Bug description
When I try to run single test on multi-gpu device, it hangs at specific test case `test_ddp_configure_ddp` https://github.com/Lightning-AI/pytorch-lightning/blob/6f6c07dddfd68717f0b765a63d05a937b8508e15/tests/tests_pytorch/strategies/test_ddp.py#L142.
I've found that this case is related to `torch.distributed.init_process_group` . https://github.com/Lightning-AI/pytorch-lightning/blob/6f6c07dddfd68717f0b765a63d05a937b8508e15/src/lightning/fabric/utilities/distributed.py#L258
I have 4 gpus on my device, so the `world_size` send to `init_process_group` is 4, but the way I ran the test seems not include multiprocessing, so `init_process_group` just hangs for device to be ready.
When I set `CUDA_VISIBLE_DEVICES` to single device, it works fine. I'm not sure is this a bug or I've used the wrong command to run test. If so. how should I run this test?
### What version are you seeing the problem on?
v2.1
### How to reproduce the bug
```python
On multi-gpu environment:
pytest -v tests/tests_pytorch/strategies/test_ddp.py -k test_ddp_configure_ddp
```
### Error messages and logs
```
# Error messages and logs here please
no Error message, just hang up for a long time(default args: 1800 seconds)
```
### Environment
<details>
<summary>Current environment</summary>
* CUDA:
- GPU:
- Tesla V100-SXM2-16GB
- Tesla V100-SXM2-16GB
- Tesla V100-SXM2-16GB
- Tesla V100-SXM2-16GB
- available: True
- version: 12.2
* Lightning:
- lightning: 2.1.0
- lightning-utilities: 0.10.1
- pytorch-lightning: 2.2.1
- torch: 2.3.0a0+gitbfa71b5
- torchmetrics: 1.3.1
- torchvision: 0.18.0a0+423a1b0
* Packages:
- aiohttp: 3.9.4rc0
- aiosignal: 1.3.1
- annotated-types: 0.6.0
- astunparse: 1.6.3
- async-timeout: 4.0.3
- attrs: 23.2.0
- audioread: 3.0.1
- certifi: 2023.11.17
- cffi: 1.16.0
- charset-normalizer: 3.3.2
- contourpy: 1.2.0
- cycler: 0.12.1
- decorator: 5.1.1
- dllogger: 1.0.0
- exceptiongroup: 1.2.0
- expecttest: 0.2.1
- filelock: 3.13.1
- fonttools: 4.49.0
- frozenlist: 1.4.1
- fsspec: 2023.12.2
- hypothesis: 6.97.3
- idna: 3.6
- inflect: 7.0.0
- iniconfig: 2.0.0
- jinja2: 3.1.3
- joblib: 1.3.2
- kiwisolver: 1.4.5
- librosa: 0.8.1
- lightning: 2.1.0
- lightning-utilities: 0.10.1
- llvmlite: 0.42.0
- markupsafe: 2.1.4
- matplotlib: 3.8.3
- mpmath: 1.3.0
- multidict: 6.0.5
- networkx: 3.2.1
- numba: 0.59.0
- numpy: 1.23.1
- optree: 0.10.0
- packaging: 23.2
- pillow: 10.2.0
- pip: 22.2.2
- platformdirs: 4.2.0
- pluggy: 1.4.0
- pooch: 1.8.1
- psutil: 5.9.8
- pycparser: 2.21
- pydantic: 2.6.3
- pydantic-core: 2.16.3
- pyparsing: 3.1.2
- pytest: 8.1.1
- python-dateutil: 2.9.0.post0
- pytorch-lightning: 2.2.1
- pyyaml: 6.0.1
- requests: 2.31.0
- resampy: 0.4.3
- scikit-learn: 1.4.1.post1
- scipy: 1.12.0
- setuptools: 63.2.0
- six: 1.16.0
- sortedcontainers: 2.4.0
- soundfile: 0.12.1
- sympy: 1.12
- tabulate: 0.9.0
- threadpoolctl: 3.3.0
- tomli: 2.0.1
- torch: 2.3.0a0+gitbfa71b5
- torchmetrics: 1.3.1
- torchvision: 0.18.0a0+423a1b0
- tqdm: 4.66.2
- types-dataclasses: 0.6.6
- typing-extensions: 4.9.0
- unidecode: 1.3.8
- urllib3: 2.2.0
- wheel: 0.42.0
- yarl: 1.9.4
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.10.8
- release: 5.11.0-27-generic
- version: #29~20.04.1-Ubuntu SMP Wed Aug 11 15:58:17 UTC 2021
</details>
### More info
_No response_
|
open
|
2024-03-18T09:54:09Z
|
2024-03-18T09:54:23Z
|
https://github.com/Lightning-AI/pytorch-lightning/issues/19667
|
[
"bug",
"needs triage",
"ver: 2.1.x"
] |
PHLens
| 0
|
plotly/dash
|
jupyter
| 2,262
|
Method for generating component IDs
|
Hi there,
I wanted to know the method used to auto-generate component ids in Dash. Does it use UUIDs or something else?
Thanks
|
closed
|
2022-10-07T07:21:20Z
|
2022-10-11T14:31:19Z
|
https://github.com/plotly/dash/issues/2262
|
[] |
anu0012
| 1
|
axnsan12/drf-yasg
|
rest-api
| 84
|
Problem adding Model to definitions
|
Using a simplified serializer for a list view with a limited set of fields does not get a model added to definitions. We get this error instead.
> Resolver error at paths./customers-search/.get.responses.200.schema.items.$ref
> Could not resolve reference because of: Could not resolve pointer: /definitions/SimpleCustomerSerializerV1 does not exist in document
>
```
@swagger_auto_schema(
manual_parameters=[search_query_param],
responses={
status.HTTP_200_OK: openapi.Response(
'Customers found.',
serializers.SimpleCustomerSerializerV1(many=True)),
status.HTTP_404_NOT_FOUND: 'No matching customers found.',
status.HTTP_429_TOO_MANY_REQUESTS: 'Rate limit exceeded.',
status.HTTP_500_INTERNAL_SERVER_ERROR: 'Internal error.',
status.HTTP_504_GATEWAY_TIMEOUT: 'Request timed out.',
})
def list(self, request, *args, **kwargs):
```
If we remove the custom response for 200, the default behavior works as expected and does generate the model in definitions. The code is very hard to trace to figure out what is going on here.
|
closed
|
2018-03-12T17:56:39Z
|
2018-03-12T19:32:54Z
|
https://github.com/axnsan12/drf-yasg/issues/84
|
[
"bug"
] |
aarcro
| 2
|
jina-ai/serve
|
fastapi
| 5,545
|
check tests/integration/instrumentation flakyness
|
**Describe the bug**
This test tests/integration/instrumentation looks very flaky and do not let us work with 3.8
|
closed
|
2022-12-21T09:54:06Z
|
2023-01-25T10:42:34Z
|
https://github.com/jina-ai/serve/issues/5545
|
[
"epic/gRPCTransport"
] |
JoanFM
| 10
|
cobrateam/splinter
|
automation
| 783
|
Please release 0.14.0 to PyPI
|
I am unable to run tests without manually patching the fix for browser.py provided in #749
|
closed
|
2020-07-22T04:55:20Z
|
2020-08-19T22:56:32Z
|
https://github.com/cobrateam/splinter/issues/783
|
[] |
JosephKiranBabu
| 2
|
graphql-python/graphene-sqlalchemy
|
sqlalchemy
| 113
|
When is the next release ?
|
@syrusakbary Its been a while since there has been an official release, Wondering when will there be a release, because mainly im interested in using #78
|
closed
|
2018-02-13T23:26:15Z
|
2023-02-25T00:48:30Z
|
https://github.com/graphql-python/graphene-sqlalchemy/issues/113
|
[] |
kavink
| 3
|
ultralytics/ultralytics
|
python
| 19,369
|
yolo11ndetection evaluation numbers
|
### 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
What numbers should be considered optimal to regard a custom object detection model as perfect? In graphs and in after training result ?
### Additional
_No response_
|
open
|
2025-02-22T08:20:56Z
|
2025-03-03T19:17:52Z
|
https://github.com/ultralytics/ultralytics/issues/19369
|
[
"question",
"detect"
] |
shwetakinger37
| 5
|
Zeyi-Lin/HivisionIDPhotos
|
fastapi
| 125
|
显存释放问题，使用API接口运行不停止就不释放显存！
|
使用api接口，作为服务运行在服务器，使用GPU加载birefnet-v1-lite模型以后，显存就一直被占用，一直没有释放
希望优化一下显存回收机制，比如五分钟十分钟内没有调用这个GPU才用的模型就释放掉显存！
|
open
|
2024-09-14T15:23:57Z
|
2024-10-26T03:02:50Z
|
https://github.com/Zeyi-Lin/HivisionIDPhotos/issues/125
|
[] |
simkinhu
| 3
|
fastapi-admin/fastapi-admin
|
fastapi
| 83
|
Docker config isn't very handy
|
The following improvements:
* Install DB and redis with docker-compose in isolation from the host system
* Mount fastapi-admin and example sources as volume (no image rebuild on src changes)
* Autorestart the example application on source changes
are proposed in the PR:
https://github.com/fastapi-admin/fastapi-admin/pull/82
|
open
|
2021-09-09T06:55:49Z
|
2021-12-17T13:18:29Z
|
https://github.com/fastapi-admin/fastapi-admin/issues/83
|
[] |
radiophysicist
| 1
|
Yorko/mlcourse.ai
|
plotly
| 362
|
Docker image - seaborn
|
Looks like the docker image is not connected to seaborn 0.9.0 , your exercise 2 requires catplot .
|
closed
|
2018-10-06T17:36:15Z
|
2018-10-11T13:48:40Z
|
https://github.com/Yorko/mlcourse.ai/issues/362
|
[
"enhancement"
] |
priteshwatch
| 2
|
healthchecks/healthchecks
|
django
| 131
|
Show user-friendly message when Telegram confirmation link is expired
|
... currently we raise a SignatureExpired exception and return 500.
|
closed
|
2017-08-14T12:20:58Z
|
2021-08-27T09:58:30Z
|
https://github.com/healthchecks/healthchecks/issues/131
|
[
"bug"
] |
cuu508
| 1
|
d2l-ai/d2l-en
|
machine-learning
| 2,218
|
Bahdanau attention (attention mechanisms) tensorflow notebook build fails in Turkish version.
|
Currently, PR-63 (Fixing more typos) builds fail only for tensorflow notebook. I could not figure out the reason. @AnirudhDagar , I would be glad if you could take a look.
|
closed
|
2022-07-25T20:59:30Z
|
2022-08-01T05:08:26Z
|
https://github.com/d2l-ai/d2l-en/issues/2218
|
[] |
semercim
| 1
|
TheKevJames/coveralls-python
|
pytest
| 235
|
coveralls --finish not working as expected in Circle-CI
|
Thanks for this nice client.
It looks like since the github api was introduced, the Circle-CI version seems broken. When I try putting having a run that finishes up a parallel go, the wrong build number is being used to notify the service
```
(venv) circleci@b3cd35dd8048:~/project$ coveralls debug -v --finish
Missing .coveralls.yml file. Using only env variables.
Testing coveralls-python...
{"source_files": [], "git": {"branch": "coveralls", "head": {"id": "fad6a2cc6ab84ef45d06bf2150a4d103c16e1a76", "author_name": "Stephen Roller", "author_email": "roller@fb.com", "committer_name": "Stephen Roller", "committer_email": "roller@fb.com", "message": "Hm."}, "remotes": [{"name": "origin", "url": "git@github.
com:facebookresearch/ParlAI.git"}]}, "config_file": ".coveragerc", "parallel": true, "repo_token": "[secure]", "service_name": "circle-ci", "service_job_id": "48529", "service_pull_request": "2950"}
==
Reporting 0 files
==
```
Here you see that the CIRCLE_BUILD_NUM value has been put into "service_job_id" field of the config. In
However, the call to finish uses the "service_number" field instead:
https://github.com/coveralls-clients/coveralls-python/blob/30e4815169b3db2616981939d55d2f4495816821/coveralls/api.py#L217-L218
As such, when you try calling `coveralls --finish` you get the following error:
```
$ coveralls -v --finish
Missing .coveralls.yml file. Using only env variables.
Finishing parallel jobs...
Parallel finish failed: No build matching CI build number found
Traceback (most recent call last):
File "/home/circleci/venv/lib/python3.7/site-packages/coveralls/cli.py", line 80, in main
coverallz.parallel_finish()
File "/home/circleci/venv/lib/python3.7/site-packages/coveralls/api.py", line 236, in parallel_finish
raise CoverallsException('Parallel finish failed: {}'.format(e))
coveralls.exception.CoverallsException: Parallel finish failed: No build matching CI build number found
```
Note that Circle always puts None into the service_number field:
https://github.com/coveralls-clients/coveralls-python/blob/30e4815169b3db2616981939d55d2f4495816821/coveralls/api.py#L87
https://github.com/coveralls-clients/coveralls-python/blob/30e4815169b3db2616981939d55d2f4495816821/coveralls/api.py#L52-L59
|
closed
|
2020-08-07T17:10:05Z
|
2021-01-12T02:50:08Z
|
https://github.com/TheKevJames/coveralls-python/issues/235
|
[] |
stephenroller
| 12
|
keras-team/keras
|
machine-learning
| 20,098
|
module 'keras.utils' has no attribute 'PyDataset'
|
I have correctly installed version 3.0.5 of keras and used pytorch for the backend, but it always prompts module 'keras. utils' has no attribute' PyDataset '. How can I solve this problem?
|
closed
|
2024-08-08T08:32:19Z
|
2024-08-13T06:58:42Z
|
https://github.com/keras-team/keras/issues/20098
|
[
"stat:awaiting response from contributor",
"type:Bug"
] |
Sticcolet
| 5
|
allenai/allennlp
|
nlp
| 4,798
|
PretrainedTransformerTokenizer doesn't work with seq2seq dataset reader
|
<!--
Please fill this template entirely and do not erase any of it.
We reserve the right to close without a response bug reports which are incomplete.
If you have a question rather than a bug, please ask on [Stack Overflow](https://stackoverflow.com/questions/tagged/allennlp) rather than posting an issue here.
-->
## Checklist
<!-- To check an item on the list replace [ ] with [x]. -->
- [X] I have verified that the issue exists against the `master` branch of AllenNLP.
- [X] I have read the relevant section in the [contribution guide](https://github.com/allenai/allennlp/blob/master/CONTRIBUTING.md#bug-fixes-and-new-features) on reporting bugs.
- [X] I have checked the [issues list](https://github.com/allenai/allennlp/issues) for similar or identical bug reports.
- [X] I have checked the [pull requests list](https://github.com/allenai/allennlp/pulls) for existing proposed fixes.
- [X] I have checked the [CHANGELOG](https://github.com/allenai/allennlp/blob/master/CHANGELOG.md) and the [commit log](https://github.com/allenai/allennlp/commits/master) to find out if the bug was already fixed in the master branch.
- [X] I have included in the "Description" section below a traceback from any exceptions related to this bug.
- [X] I have included in the "Related issues or possible duplicates" section below all related issues and possible duplicate issues (If there are none, check this box anyway).
- [X] I have included in the "Environment" section below the name of the operating system and Python version that I was using when I discovered this bug.
- [X] I have included in the "Environment" section below the output of `pip freeze`.
- [X] I have included in the "Steps to reproduce" section below a minimally reproducible example.
## Description
<!-- Please provide a clear and concise description of what the bug is here. -->
As far as I can tell, `PretrainedTransformerTokenizer` is not compatible with the `seq2seq` dataset reader of [`allennlp-models`](https://github.com/allenai/allennlp-models) when it is used as the `source_tokenizer`. The same error, contained in this try/except block [here](https://github.com/allenai/allennlp-models/blob/236034ff54ac3197ec4d710438cebdfa919c5a45/allennlp_models/generation/dataset_readers/seq2seq.py#L94-L102) is triggered in multiple cases.
1. When `allennlp.common.util.START_SYMBOL` and `allennlp.common.util.END_SYMBOL` are not in the pretrained transformers vocabulary. I was able to solve this in the config as follows:
```json
"dataset_reader": {
"type": "copynet_seq2seq",
"target_namespace": "target_tokens",
"source_tokenizer": {
"type": "pretrained_transformer",
"model_name": "distilroberta-base",
"tokenizer_kwargs": {
"additional_special_tokens": {
"allennlp_start_symbol": "@start@",
"allennlp_end_symbol": "@end@",
},
}
},
```
2. If `PretrainedTransformerTokenizer.add_special_tokens` is `True` (the default) for wordpiece-based tokenizers.
3. For any BPE-based tokenizer I tried.
The error arises because there are more than two tokens in the list returned by `self._source_tokeniser.tokenizer` in the [try/except block](https://github.com/allenai/allennlp-models/blob/236034ff54ac3197ec4d710438cebdfa919c5a45/allennlp_models/generation/dataset_readers/seq2seq.py#L94-L102) for all cases listed above:
```python
try:
self._start_token, self._end_token = self._source_tokenizer.tokenize(
start_symbol + " " + end_symbol
)
except ValueError:
raise ValueError(
f"Bad start or end symbol ('{start_symbol}', '{end_symbol}') "
f"for tokenizer {self._source_tokenizer}"
)
```
<details>
<summary><b>Python traceback:</b></summary>
<p>
<!-- Paste the traceback from any exception (if there was one) in between the next two lines below -->
```
2020-11-16 16:53:24,760 - CRITICAL - root - Uncaught exception
Traceback (most recent call last):
File "/project/6006286/johnmg/allennlp-models/allennlp_models/generation/dataset_readers/seq2seq.py", line 98, in __init__
self._start_token, self._end_token = self._source_tokenizer.tokenize(
ValueError: too many values to unpack (expected 2)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/johnmg/seq2rel/bin/allennlp", line 33, in <module>
sys.exit(load_entry_point('allennlp', 'console_scripts', 'allennlp')())
File "/project/6006286/johnmg/allennlp/allennlp/__main__.py", line 34, in run
main(prog="allennlp")
File "/project/6006286/johnmg/allennlp/allennlp/commands/__init__.py", line 118, in main
args.func(args)
File "/project/6006286/johnmg/allennlp/allennlp/commands/train.py", line 110, in train_model_from_args
train_model_from_file(
File "/project/6006286/johnmg/allennlp/allennlp/commands/train.py", line 170, in train_model_from_file
return train_model(
File "/project/6006286/johnmg/allennlp/allennlp/commands/train.py", line 236, in train_model
model = _train_worker(
File "/project/6006286/johnmg/allennlp/allennlp/commands/train.py", line 453, in _train_worker
train_loop = TrainModel.from_params(
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 595, in from_params
return retyped_subclass.from_params(
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 627, in from_params
kwargs = create_kwargs(constructor_to_inspect, cls, params, **extras)
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 198, in create_kwargs
constructed_arg = pop_and_construct_arg(
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 305, in pop_and_construct_arg
return construct_arg(class_name, name, popped_params, annotation, default, **extras)
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 339, in construct_arg
return annotation.from_params(params=popped_params, **subextras)
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 595, in from_params
return retyped_subclass.from_params(
File "/project/6006286/johnmg/allennlp/allennlp/common/from_params.py", line 629, in from_params
return constructor_to_call(**kwargs) # type: ignore
File "/project/6006286/johnmg/allennlp-models/allennlp_models/generation/dataset_readers/seq2seq.py", line 102, in __init__
raise ValueError(
ValueError: Bad start or end symbol ('@start@', '@end@') for tokenizer <allennlp.data.tokenizers.pretrained_transformer_tokenizer.PretrainedTransformerTokenizer object at 0x2b6503248970>
```
</p>
</details>
## Related issues or possible duplicates
- None
## Environment
<!-- Provide the name of operating system below (e.g. OS X, Linux) -->
OS: Linux
<!-- Provide the Python version you were using (e.g. 3.7.1) -->
Python version: 3.8.0
<details>
<summary><b>Output of <code>pip freeze</code>:</b></summary>
<p>
<!-- Paste the output of `pip freeze` in between the next two lines below -->
```bash
-f /cvmfs/soft.computecanada.ca/custom/python/wheelhouse/nix/avx2
-f /cvmfs/soft.computecanada.ca/custom/python/wheelhouse/nix/generic
-f /cvmfs/soft.computecanada.ca/custom/python/wheelhouse/generic
-e git+https://github.com/allenai/allennlp.git@0d8873cfef628eaf0457bee02422bbf8dae475a2#egg=allennlp
-e git+https://github.com/allenai/allennlp-models.git@236034ff54ac3197ec4d710438cebdfa919c5a45#egg=allennlp_models
attrs==20.2.0
blis==0.4.1
boto3==1.16.2
botocore==1.19.2
certifi==2020.6.20
chardet==3.0.4
click==7.1.2
conllu==4.2.1
cymem==2.0.2
filelock==3.0.12
ftfy==5.5.1
future==0.18.2
h5py==2.10.0
idna==2.10
importlib-metadata==2.0.0
iniconfig==1.0.1
jmespath==0.10.0
joblib==0.17.0
jsonnet==0.14.0
jsonpickle==1.4.1
more-itertools==8.5.0
murmurhash==1.0.2
nltk==3.5
numpy==1.19.1
overrides==3.1.0
packaging==20.4
plac==1.1.3
pluggy==0.13.1
preshed==3.0.2
protobuf==3.13.0
py==1.9.0
py-rouge==1.1
pyparsing==2.4.7
pytest==6.0.1
python-dateutil==2.8.1
regex==2019.11.1
requests==2.24.0
s3transfer==0.3.3
sacremoses==0.0.43
scikit-learn==0.23.0
scipy==1.5.2
sentencepiece==0.1.91
six==1.15.0
spacy==2.2.2
srsly==0.2.0
tensorboardX==2.1
thinc==7.3.1
threadpoolctl==2.1.0
tokenizers==0.9.3
toml==0.10.1
torch==1.7.0
tqdm==4.51.0
transformers==3.5.1
typing-extensions==3.7.4.3
urllib3==1.25.10
wasabi==0.6.0
wcwidth==0.2.5
word2number==1.1
zipp==3.4.0
```
</p>
</details>
## Steps to reproduce
<details>
<summary><b>Example source:</b></summary>
<p>
The proximate cause of the error can be reproduced as follows:
<!-- Add a fully runnable example in between the next two lines below that will reproduce the bug -->
```python
from allennlp.data.tokenizers import PretrainedTransformerTokenizer
from allennlp.common.util import START_SYMBOL, END_SYMBOL
tokenizer_kwargs = {"additional_special_tokens": [START_SYMBOL, END_SYMBOL]}
# Case 1, don't add AllenNLPs start/end symbols to vocabulary
tokenizer = PretrainedTransformerTokenizer("bert-base-uncased")
start_token, end_token = tokenizer.tokenize(START_SYMBOL + " " + END_SYMBOL)
# Case 2, set add_special_tokens=True (the default) in PretrainedTransformerTokenizer for a wordpiece based tokenizer
# this WON'T fail
tokenizer = PretrainedTransformerTokenizer("bert-base-uncased", tokenizer_kwargs=tokenizer_kwargs, add_special_tokens=False)
start_token, end_token = tokenizer.tokenize(START_SYMBOL + " " + END_SYMBOL)
# this WILL fail
tokenizer = PretrainedTransformerTokenizer("bert-base-uncased", tokenizer_kwargs=tokenizer_kwargs, add_special_tokens=True)
start_token, end_token = tokenizer.tokenize(START_SYMBOL + " " + END_SYMBOL)
# Case 3, BPE-based tokenizers fail regardless
# this WILL fail
tokenizer = PretrainedTransformerTokenizer("distilroberta-base", tokenizer_kwargs=tokenizer_kwargs, add_special_tokens=False)
start_token, end_token = tokenizer.tokenize(START_SYMBOL + " " + END_SYMBOL)
# this WILL fail
tokenizer = PretrainedTransformerTokenizer("distilroberta-base", tokenizer_kwargs=tokenizer_kwargs, add_special_tokens=True)
start_token, end_token = tokenizer.tokenize(START_SYMBOL + " " + END_SYMBOL)
```
</p>
</details>
|
closed
|
2020-11-17T01:31:42Z
|
2020-11-18T16:09:58Z
|
https://github.com/allenai/allennlp/issues/4798
|
[
"bug"
] |
JohnGiorgi
| 8
|
pywinauto/pywinauto
|
automation
| 1,321
|
The popup menu items are not identified via pywinauto
|
## Expected Behavior
The popup menu info should be available in the output of `app.dialog.print_control_identifiers()`
## Actual Behavior
The popup menu items are not listed in the app.dialog.print_control_identifiers() output, though the popup menu is visible on screen
## Steps to Reproduce the Problem
1. Right click on **TreeItem** control type
2. print **app.dialog.print_control_identifiers()**
3. check the output
4. popup menu items are not listed in the output
## Short Example of Code to Demonstrate the Problem
**Sample UI layout**
## Specifications
- Pywinauto version:0.6.8
- Python version and bitness:3.11 / x64
- Platform and OS: win 22h2
|
open
|
2023-08-11T05:41:17Z
|
2023-08-28T05:36:19Z
|
https://github.com/pywinauto/pywinauto/issues/1321
|
[] |
vikramjitSingh
| 5
|
miguelgrinberg/python-socketio
|
asyncio
| 461
|
multiple namespace, race condition in asyncio, no individual sid
|
## Problem
1. `sio.connect()` first connects to namespace `/` before connecting to other namespaces. Subsequent `emit()` commands are allowed before all namespaces are connected to - causing messages to disappear (due to namespace not connected yet).
Adding `asyncio.sleep(1)` before the first `emit()` seems to fix the problem,
2. in the documentation https://python-socketio.readthedocs.io/en/latest/server.html#namespaces, 2nd paragraph suggests that
> Each namespace is handled independently from the others, with separate session IDs (sids), ...
it seems in my snippet below the same `sid` is used for all namespaces
am I missing something?
## Code
Client code:
```python
import time
import asyncio
import socketio
import logging
logging.basicConfig(level='DEBUG')
loop = asyncio.get_event_loop()
sio = socketio.AsyncClient()
@sio.event
async def message(data):
print(data)
@sio.event(namespace='/abc')
def message(data):
print('/abc', data)
@sio.event
async def connect():
print('connection established', time.time())
@sio.event(namespace='/abc')
async def connect():
print("I'm connected to the /abc namespace!", time.time())
async def start_client():
await sio.connect('http://localhost:8080', transports=['websocket'],
namespaces=['/', '/abc'])
# await asyncio.sleep(2)
await sio.emit('echo', '12345')
await sio.emit('echo', '12345', namespace='/abc')
await sio.wait()
if __name__ == '__main__':
loop.run_until_complete(start_client())
```
server code:
```python
import socketio
import time
from aiohttp import web
sio = socketio.AsyncServer(async_mode = 'aiohttp')
app = web.Application()
sio.attach(app)
redis = None
@sio.event
async def connect(sid, environ):
print("connected", sid, time.time())
@sio.event(namespace='/abc')
async def connect(sid, environ):
print("connected /abc", sid, time.time())
@sio.event(namespace='/abc')
async def echo(sid, msg):
print('abc', sid, msg)
await sio.emit('message', msg, to=sid, namespace='/abc')
@sio.event
async def echo(sid, msg):
print(sid, msg)
await sio.emit('message', msg, to=sid)
if __name__ == '__main__':
web.run_app(app)
```
|
closed
|
2020-04-14T02:00:58Z
|
2023-09-21T11:19:07Z
|
https://github.com/miguelgrinberg/python-socketio/issues/461
|
[
"documentation"
] |
simingy
| 16
|
long2ice/fastapi-cache
|
fastapi
| 417
|
Trouble with Poetry Operations for a Project Dependent on FastAPI-Cache (commit # 8f0920d , dependency redis)
|
I'm working on a project and have specified a dependency in the `pyproject.toml` file as follows:
```toml
[tool.poetry.dependencies]
fastapi-cache2 = {extras = ["redis"], git = "https://github.com/long2ice/fastapi-cache.git", rev = "8f0920d"}
```
When I run `poetry install`, it completes successfully. However, attempting to run `poetry lock`, `poetry add "fastapi-cache2[redis]@git+https://github.com/long2ice/fastapi-cache.git#8f0920d"`, or `poetry update <any-other-dependency>` results in failures. The error message indicates a `CalledProcessError`, mentioning that a subprocess command returned a non-zero exit status:
```plaintext
CalledProcessError
Command '['/path/to/.venv/bin/python', '-']' returned non-zero exit status 1.
at /path/to/python3.11/subprocess.py:569 in run
```
Further details from the error suggest an `EnvCommandError`, indicating an issue with executing a command in the environment:
```plaintext
EnvCommandError
Command ['/path/to/.venv/bin/python', '-'] errored with the following return code 1
Output:
Traceback (most recent call last):
...
raise BuildException(f'Source {srcdir} does not appear to be a Python project: no pyproject.toml or setup.py')
build.BuildException: Source /path/to/cache/pypoetry/virtualenvs/project-py3.11/src/fastapi-cache does not appear to be a Python project: no pyproject.toml or setup.py
```
I attempted to replicate this in a fresh poetry project using the `poetry add "fastapi-cache2[redis]@git+https://github.com/long2ice/fastapi-cache.git#8f0920d"` command, but encountered a different issue:
```plaintext
Failed to clone https://github.com/long2ice/fastapi-cache.git at '8f0920d', verify ref exists on remote.
```
I've confirmed that the specific commit `8f0920d` indeed contains a `pyproject.toml` file.
Cloning the project and checking out this commit works fine outside of Poetry, which suggests there may be a bug within Poetry or the way it interacts with this specific git dependency.
|
open
|
2024-03-22T12:41:35Z
|
2024-11-09T11:16:04Z
|
https://github.com/long2ice/fastapi-cache/issues/417
|
[
"needs-triage"
] |
SudeshnaBora
| 0
|
521xueweihan/HelloGitHub
|
python
| 2,347
|
Android 系统彩蛋（集合）
|
## 推荐项目
<!-- 这里是 HelloGitHub 月刊推荐项目的入口，欢迎自荐和推荐开源项目，唯一要求：请按照下面的提示介绍项目。-->
<!-- 点击上方 “Preview” 立刻查看提交的内容 -->
<!--仅收录 GitHub 上的开源项目，请填写 GitHub 的项目地址-->
- 项目地址：https://github.com/hushenghao/AndroidEasterEggs
<!--请从中选择（C、C#、C++、CSS、Go、Java、JS、Kotlin、Objective-C、PHP、Python、Ruby、Rust、Swift、其它、书籍、机器学习）-->
- 类别：Java、Kotlin
<!--请用 20 个左右的字描述它是做什么的，类似文章标题让人一目了然 -->
- 项目标题：Android 系统彩蛋（集合）
<!--这是个什么项目、能用来干什么、有什么特点或解决了什么痛点，适用于什么场景、能够让初学者学到什么。长度 32-256 字符-->
- 项目描述：整理兼容了Android系统各正式版的彩蛋，可以在更多设备上运行
<!--令人眼前一亮的点是什么？类比同类型项目有什么特点！-->
- 亮点：项目包含了系统彩蛋完整代码，旨在对系统彩蛋的整理和兼容，以保证大多数设备可以体验到不同版本的彩蛋，不会对系统彩蛋代码做过多修改。部分版本使用了系统新特性，低版本只能使用部分功能。
- 截图：
- 后续更新计划：紧随Android 版本发布周期添加新的彩蛋，及现有彩蛋Bug修复
|
open
|
2022-09-01T06:30:00Z
|
2022-09-23T05:55:22Z
|
https://github.com/521xueweihan/HelloGitHub/issues/2347
|
[
"其它"
] |
hushenghao
| 0
|
plotly/dash
|
data-science
| 2,232
|
[BUG] Callbacks in `def layout()` are not triggering
|
**Describe your context**
```
dash 2.6.1
dash-bootstrap-components 1.2.1
dash-core-components 2.0.0
dash-html-components 2.0.0
dash-table 5.0.0
```
**Describe the bug**
When using the new multi-page support with the layout defined in a function in one of the page (`def layout()`), callbacks that are defined inside this layout function aren't getting triggered.
Minimal example
app.py
```
import dash
from dash import Dash, html
app = Dash(__name__, use_pages=True)
app.layout = html.Div(
[
html.H1("welcome to my multipage appp"),
html.Ul(
[
html.Li(
html.A("HOME", href="/"),
),
html.Li(
html.A("/cows", href="/cows?x=y"),
),
]
),
dash.page_container,
]
)
app.run_server(
host="0.0.0.0",
debug=True,
)
```
pages/cows.py
```
import dash
from dash import Input, Output, State, callback, dcc, html
def cow(say):
say = f"{say:5s}"
return html.Pre(
rf"""
_______
< {say} >
-------
\ ^__^
\ (oo)\_______
(__)\ )\/\
||----w |
|| ||
"""
)
dash.register_page(__name__)
def layout(**query_parameters):
root = html.Div(
[
cow_saying := dcc.Dropdown(
["MOO", "BAA", "EEK"],
"MOO",
),
cow_holder := html.Div(),
]
)
@callback(
Output(cow_holder, "children"),
Input(cow_saying, "value"),
)
def update_cow(say):
return cow(say)
return root
```
**Expected behavior**
The callback nested in the layout function should get properly triggered.
Nested functions allow us to capture the incoming query parameters in the closure. Based on that we can load data on page load and keep it in memory for callback changes.
I tried a workaround of defining the callbacks in the global (outer) context. However this has following problems:
* query parameters need to be passed to the callbacks via `State`
* not clear how to implement the load-data-on-pageload pattern, since using state could lead to transferring lots of data to the client
* can't use the `Input` and `Output` bindings by passing the component instead of the ID, as given in the example
|
closed
|
2022-09-14T16:31:44Z
|
2023-03-10T21:40:22Z
|
https://github.com/plotly/dash/issues/2232
|
[] |
HennerM
| 2
|
tensorflow/tensor2tensor
|
deep-learning
| 1,191
|
Universal Transformer appears to be buggy and not converging correctly
|
Summary
---------
Universal Transformer appears to be buggy and not converging correctly:
- Universal transformer does not converge on multi_nli as of the latest tensor2tensor master (9729521bc3cd4952c42dcfda53699e14bee7b409). See below for reproduction
- UT does converge on multi_nli as of August 3 2018 commit 5fff1cad2977f063b981e5d8b839bf9d7008e232 (we didn’t run this fully out, but it was making meaningful progress, unlike below, so we terminated it and considered it successful).
- To confirm this was not simply an odd issue with multi_nli, we tried UT with a number of other problems (exact repo not shown below), including ‘lambada_rc’ and ‘stanford_nli’ (run at commit ca628e4fcb04ff42ed21549a4f73e6dfa68a5f7a from around October 16 2018) All of these failed to converge.
Environment information
-------------------------
Docker image based off nvidia/cuda:9.0-devel-ubuntu16.04
Tf version: tensorflow-gpu=1.11.0
T2t version: Tensor2tensor master at commit 9729521bc3cd4952c42dcfda53699e14bee7b409 on Oct 30 2018.
We also saw this failed behavior on tf-nightly-gpu==1.13.0.dev20181022
Reproduce
-----------
Problem: multi_nli
Model: universal_transformer
Hparams_set: universal_transformer_tiny
python3 /usr/src/t2t/tensor2tensor/bin/t2t-trainer \
--data_dir="DATA_DIR" \
--eval_early_stopping_steps="10000" \
--eval_steps="10000" \
--generate_data="True" \
--hparams="" \
--hparams_set="universal_transformer_tiny" \
--iterations_per_loop="2000" \
--keep_checkpoint_max="80" \
--local_eval_frequency="2000" \
--model="universal_transformer" \
--output_dir="OUTPUT_DIR" \
--problem="multi_nli" \
--t2t_usr_dir="T2T_USR_DIR" \
--tmp_dir="T2T_TMP_DIR"
Run was stopped after 50000 steps due to lack of convergence as loss fluctuates between 1.098 and 1.099.
INFO:tensorflow:Saving dict for global step 50000: global_step = 50000, loss = 1.0991247, metrics-multi_nli/targets/accuracy = 0.31821653, metrics-multi_nli/targets/accuracy_per_sequence = 0.31821653, metrics-multi_nli/targets/accuracy_top5 = 1.0, metrics-multi_nli/targets/approx_bleu_score = 0.7479816, metrics-multi_nli/targets/neg_log_perplexity = -1.099124, metrics-multi_nli/targets/rouge_2_fscore = 0.0, metrics-multi_nli/targets/rouge_L_fscore = 0.31869644
|
closed
|
2018-10-31T21:02:06Z
|
2018-11-20T23:37:07Z
|
https://github.com/tensorflow/tensor2tensor/issues/1191
|
[] |
rllin-fathom
| 9
|
ipython/ipython
|
data-science
| 14,410
|
Is there a way to map ctrl+h and backspace to different functions?
|
if I set `'c-h'` only, `backspace` will do same thing as `ctrl+h`, just like below:
```
registry.add_binding('c-h', filter=(HasFocus(DEFAULT_BUFFER) & ViInsertMode()))(nc.backward_char)
```
And `'c-?'` is not allowed to `add_binding`.
Is there a way to make it as many terminal can distinguish `c-h` and `<bs>`.
If for compatibility, is it possible to support bind `c-?`?
Thanks in advance.
```[tasklist]
### Tasks
```
|
open
|
2024-04-19T07:52:39Z
|
2024-06-04T11:33:42Z
|
https://github.com/ipython/ipython/issues/14410
|
[] |
roachsinai
| 0
|
deeppavlov/DeepPavlov
|
nlp
| 1,315
|
multi-threading in NER model ?
|
Sorry for the naive question, I am a newbie of DeepPavlov.
Is a NER model built with build_model multi-threaded (e.g. build_model(configs.ner.ner_ontonotes_bert_mult) ) or is there any parameter/arg to set to have multi-threading?
Thanks for your kind support!
|
closed
|
2020-09-10T15:08:18Z
|
2020-09-10T15:30:13Z
|
https://github.com/deeppavlov/DeepPavlov/issues/1315
|
[
"enhancement"
] |
cattoni
| 1
|
google-research/bert
|
nlp
| 849
|
_
|
closed
|
2019-09-09T04:31:15Z
|
2024-01-30T07:38:36Z
|
https://github.com/google-research/bert/issues/849
|
[] |
garyshincc
| 0
|
|
pallets/flask
|
python
| 4,375
|
flask test client + pytest asyncio: RuntimeError when test and view are both async
|
Hi!
While migrating an application to asyncio, i encountered what I believed to be a bug.
Consider the following scenario:
* There is a view defined with a coroutine
* This function is called via the flask test client
* This test client is used in an async coroutine
Then there is the following exception:
```
Traceback (most recent call last):
File "/home/flo/documents/code/flask-bug-testclient-asyncio/venv/lib/python3.9/site-packages/flask/app.py", line 2073, in wsgi_app
response = self.full_dispatch_request()
File "/home/flo/documents/code/flask-bug-testclient-asyncio/venv/lib/python3.9/site-packages/flask/app.py", line 1518, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/home/flo/documents/code/flask-bug-testclient-asyncio/venv/lib/python3.9/site-packages/flask/app.py", line 1516, in full_dispatch_request
rv = self.dispatch_request()
File "/home/flo/documents/code/flask-bug-testclient-asyncio/venv/lib/python3.9/site-packages/flask/app.py", line 1502, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**req.view_args)
File "/home/flo/documents/code/flask-bug-testclient-asyncio/venv/lib/python3.9/site-packages/asgiref/sync.py", line 160, in __call__
raise RuntimeError(
RuntimeError: You cannot use AsyncToSync in the same thread as an async event loop - just await the async function directly.
```
See a reproducer in attachment.
[flask-bug-testclient-asyncio.tar.gz](https://github.com/pallets/flask/files/7687052/flask-bug-testclient-asyncio.tar.gz)
Steps to reproduce:
```
tar xvf flask-bug-testclient-asyncio.tar.gz
cd flask-bug-testclient-asyncio/
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pytest
```
If you change the view or the test function to not be async, the test pass. Both being not-async also works.
Both being async does not work.
There's probably a conflict due to the way this coroutine is scheduled. Could you have a peek ?
Thanks!
Environment:
- Python version: 3.9.9
- Flask version: 2.0.2
PS: couldn't find anything on the internet, except for [this post](https://stackoverflow.com/q/69431468).
EDIT (2021-12-10): for reference, here's the code sample
```
# App definition
from flask import Flask
app = Flask(__name__)
@app.route("/")
async def hello_world():
return "aaa"
# Test definition
import pytest
@pytest.mark.asyncio
async def test_async_in_async_fail():
with app.test_client() as client:
assert client.get("/").data == b"aaa"
```
|
closed
|
2021-12-09T17:05:31Z
|
2021-12-25T00:03:43Z
|
https://github.com/pallets/flask/issues/4375
|
[] |
0xf10413
| 4
|
chainer/chainer
|
numpy
| 8,100
|
ConvolutionND output on GPU with certain batchsizes is zero
|
```py
> import chainer
> import chainer.links as L
```
CPU batchsize 64
```py
>> c = L.ConvolutionND(3, 3, 64, ksize=3, stride=1, pad=1)
>> d = c.xp.random.randn(64,3,16,64,64).astype('f')
>> c(d).array.var()
0.9107105
>> c(d).array.mean()
-4.8422127e-05
```
Move to GPU
```py
>> c.to_gpu()
<chainer.links.connection.convolution_nd.ConvolutionND object at 0x7f99c0408990>
>> d = c.xp.random.randn(64,3,16,64,64).astype('f')
>> c(d).array.mean()
array(-0.00012456, dtype=float32)
>> c(d).array.var()
array(0.9110826, dtype=float32)
```
GPU batchsize = 32
```py
>> d = c.xp.random.randn(32,3,16,64,64).astype('f')
>> c(d).array.mean()
array(0., dtype=float32)
>> c(d).array.var()
array(0., dtype=float32)
```
GPU batchsize = 64
```py
>> d = c.xp.random.randn(64,3,16,64,64).astype('f')
>> c(d).array.mean()
array(-3.2548433e-06, dtype=float32)
>> c(d).array.var()
array(0.9115594, dtype=float32)
```
GPU batchsize = 32
```py
>> d = c.xp.random.randn(32,3,16,64,64).astype('f')
>> c(d).array.mean()
array(0., dtype=float32)
>> c(d).array.var()
array(0., dtype=float32)
>> c.xp.all(c(d).array == 0)
**array(True)**
```
Runtime info:
```
> chainer.print_runtime_info()
Platform: Linux-4.15.0-47-generic-x86_64-with-debian-buster-sid
Chainer: 7.0.0b3
ChainerX: Not Available
NumPy: 1.17.2
CuPy:
CuPy Version : 7.0.0b3
CUDA Root : /xxx/x/cuda/8.0
CUDA Build Version : 8000
CUDA Driver Version : 10010
CUDA Runtime Version : 8000
cuDNN Build Version : 7102
cuDNN Version : 7102
NCCL Build Version : 2213
NCCL Runtime Version : (unknown)
iDeep: Not Available
```
Default config info
```
> chainer.config.show()
_will_recompute False
autotune False
cudnn_deterministic False
cudnn_fast_batch_normalization False
debug False
dtype float32
enable_backprop True
in_recomputing False
keep_graph_on_report False
lazy_grad_sum False
schedule_func None
train True
type_check True
use_cudnn auto
use_cudnn_tensor_core auto
use_ideep never
use_static_graph True
warn_nondeterministic False
```
|
closed
|
2019-09-10T04:13:57Z
|
2019-09-13T16:29:52Z
|
https://github.com/chainer/chainer/issues/8100
|
[
"prio:high",
"issue-checked"
] |
MannyKayy
| 5
|
tatsu-lab/stanford_alpaca
|
deep-learning
| 73
|
finetuning on 3090, is it possible?
|
Is it possible to finetune the 7B model using 8*3090?
I had set:
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
but still got OOM:
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 194.00 MiB (GPU 0; 23.70 GiB total capacity; 22.21 GiB already allocated; 127.56 MiB free; 22.50 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
my scruptis as follows:
torchrun --nproc_per_node=4 --master_port=12345 train.py \
--model_name_or_path ../llama-7b-hf \
--data_path ./alpaca_data.json \
--bf16 True \
--output_dir ./output \
--num_train_epochs 3 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 2000 \
--save_total_limit 1 \
--learning_rate 2e-5 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--fsdp "full_shard auto_wrap" \
--fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \
--tf32 True
|
open
|
2023-03-17T00:54:23Z
|
2023-03-21T05:59:53Z
|
https://github.com/tatsu-lab/stanford_alpaca/issues/73
|
[] |
yfliao
| 2
|
PokeAPI/pokeapi
|
graphql
| 1,166
|
Multiple issues resoved but are still open and need to be closed.
|
Going through the current open issues to fix problems to find many that have been resolved and need to be closed. Here is some examples.
https://github.com/PokeAPI/pokeapi/issues/786
https://github.com/PokeAPI/pokeapi/issues/882
https://github.com/PokeAPI/pokeapi/issues/865
https://github.com/PokeAPI/pokeapi/issues/901
https://github.com/PokeAPI/pokeapi/issues/1069 fixed by https://github.com/PokeAPI/sprites/pull/156
Was just pointing this out in hoping some of the issue clutter is reduced.
|
closed
|
2024-11-13T15:45:38Z
|
2024-11-14T03:04:08Z
|
https://github.com/PokeAPI/pokeapi/issues/1166
|
[] |
Writey0327
| 3
|
robotframework/robotframework
|
automation
| 4,960
|
Support integer conversion with strings representing whole number floats like `'1.0'` and `'2e10'`
|
Type conversions are a very convenient feature in Robot framework. To make them that extra bit convenient I propose an enhancement.
Currently passing any string representation of a `float` number to a keyword accepting only `int` will fail. In most cases this is justified, but there are situations where floats are convertible to `int` just fine. Examples are `"1.0"`, `"2.00"` or `"1e100"`. Note that these conversions currently are already accepted when passed as type `float` (i.e. `${1.0}` or `${1e100}`. Conversion for numbers for which the decimal part is non-zero should still fail. We are talking about conversion here, not type casting.
|
closed
|
2023-11-27T06:53:43Z
|
2023-12-07T00:15:56Z
|
https://github.com/robotframework/robotframework/issues/4960
|
[
"enhancement",
"priority: medium",
"beta 1",
"effort: small"
] |
JFoederer
| 2
|
pyqtgraph/pyqtgraph
|
numpy
| 2,634
|
When drawing a set of normal data, mark some of the abnormal data points or give them different colors.
|
Hello, I just used this library, and I think it's great, but I'm having some problems using it.
When I plot a set of normal data, I want to mark the abnormal data points or give them a different color.
```python
import pyqtgraph as pg
import numpy as np
w = pg.GraphicsLayoutWidget()
w.show()
x = np.arange(10) # [0 1 2 3 4 5 6 7 8 9]
y = np.arange(10) % 3 # [0 1 2 0 1 2 0 1 2 0]
# =====================================
# [0,0],[1,1],[2,2],[3,0],[4,1],[5,2],[6,0],[7,1],[8,2],[9,0]
# Now I know that some of the data is abnormal data(eg: [3,0],[4,1],[6,0]),
# but how do I plot it? Like giving different colors.
# =====================================
plt = w.addPlot(row=0, col=0)
plt.plot(x, y, symbol='o', pen={'color': 0.8, 'width': 2})
if __name__ == '__main__':
pg.exec()
```
|
closed
|
2023-03-03T07:13:12Z
|
2023-03-03T08:02:43Z
|
https://github.com/pyqtgraph/pyqtgraph/issues/2634
|
[] |
ningwana
| 1
|
modelscope/modelscope
|
nlp
| 972
|
创空间发布配置时，提示 Internal Server Error
|
第一次时，已经授权关联相关帐号。后续想切换到GPU资源，提升上面的错误。
|
closed
|
2024-09-03T09:08:28Z
|
2024-09-09T04:34:41Z
|
https://github.com/modelscope/modelscope/issues/972
|
[] |
Li-Lai
| 2
|
JaidedAI/EasyOCR
|
deep-learning
| 891
|
When I use DBnet, and set like guide, it pop up error.
|
<img width="1051" alt="image" src="https://user-images.githubusercontent.com/25415402/203797617-dede622c-31ce-4f18-87c7-353b00e8c771.png">
reader = easyocr.Reader(['en'],detect_network = 'dbnet18')
"Input type is cpu, but 'deform_conv_cuda.*.so' is not imported successfully."
ENV: Win11;
Python ENV: 3.8.8 in Jupyter
|
open
|
2022-11-24T13:39:05Z
|
2024-10-20T08:47:53Z
|
https://github.com/JaidedAI/EasyOCR/issues/891
|
[] |
CapitaineNemo
| 5
|
mars-project/mars
|
scikit-learn
| 2,952
|
Support Slurm (or other cluster management and job scheduling system)
|
There are lots of school and company which use slurm or other cluster management and job scheduling system.
It will be great if mars can support it.
|
open
|
2022-04-22T10:10:36Z
|
2022-04-22T16:41:40Z
|
https://github.com/mars-project/mars/issues/2952
|
[
"reso: duplicate"
] |
PeikaiLi
| 1
|
nalepae/pandarallel
|
pandas
| 13
|
Implement docstring for all functions.
|
For example, the docstring of `DataFrame.parallel_apply` should be exactly the same as `Dataframe.apply`.
|
open
|
2019-03-18T09:49:50Z
|
2019-11-11T19:05:39Z
|
https://github.com/nalepae/pandarallel/issues/13
|
[
"enhancement"
] |
nalepae
| 0
|
mckinsey/vizro
|
data-visualization
| 222
|
Integrate a chatbox feature to enhance the capabilities of natural language applications
|
### What's the problem this feature will solve?
Currently, there is no text input feature similar to Streamlit's chatbox in place
### Describe the solution you'd like
Create the custom component to recieve text
### Alternative Solutions
-
### Additional context
This would help teams create natural language applications using vizro
### Code of Conduct
- [X] I agree to follow the [Code of Conduct](https://github.com/mckinsey/vizro/blob/main/CODE_OF_CONDUCT.md).
|
closed
|
2023-12-16T14:53:49Z
|
2024-07-09T15:10:35Z
|
https://github.com/mckinsey/vizro/issues/222
|
[
"Custom Components :rocket:"
] |
matheus695p
| 1
|
KaiyangZhou/deep-person-reid
|
computer-vision
| 172
|
why the network can not convergent with the tripletloss+nasnet. Is there any training tips
|
closed
|
2019-05-10T03:06:26Z
|
2019-10-22T21:40:11Z
|
https://github.com/KaiyangZhou/deep-person-reid/issues/172
|
[] |
yifengW
| 1
|
|
AntonOsika/gpt-engineer
|
python
| 367
|
CODEOWNERS file
|
## Issue Template
A suggestion of creating a `CODEOWNERS` file where some people are mandatory to approve the PRs.
There is a way to put a specific person to be a reviewer for `*.py` or folder for example:
```codeowners
*.py @person1 @person2
*.toml @person1
*.md @person2
.github/* @person3
```
## Expected Behavior
Those people will be mandatory for every PR created with the above-mentioned files.
The branch protection option needs to be adjusted accordingly.
This is an example:
|
closed
|
2023-06-23T16:45:10Z
|
2023-09-09T08:23:08Z
|
https://github.com/AntonOsika/gpt-engineer/issues/367
|
[
"triage"
] |
k1lgor
| 1
|
ray-project/ray
|
data-science
| 51,642
|
[core] Unify `CoreWorker::Exit` and `CoreWorker::Shutdown`
|
### Description
See https://github.com/ray-project/ray/pull/51582#discussion_r2010500080 for more details.
### Use case
_No response_
|
open
|
2025-03-24T16:52:35Z
|
2025-03-24T16:52:44Z
|
https://github.com/ray-project/ray/issues/51642
|
[
"enhancement",
"core"
] |
kevin85421
| 0
|
Johnserf-Seed/TikTokDownload
|
api
| 551
|
[BUG] Status_code=4
|
**描述出现的错误**
请看log
**bug复现**
我不到哇！
更新了最新版的TTD
运行ttd.exe release版
**截图**
**桌面（请填写以下信息）：**
-操作系统：[Win 10 64bit]
-vpn代理：[关闭]
-项目版本：[1.4.2.2]
-py版本：[3.11.5]
-依赖库的版本:
**附文**
log:
[2023-09-15_230606.log](https://github.com/Johnserf-Seed/TikTokDownload/files/12625241/2023-09-15_230606.log)
|
closed
|
2023-09-15T22:17:13Z
|
2023-09-23T21:08:36Z
|
https://github.com/Johnserf-Seed/TikTokDownload/issues/551
|
[
"无效(invalid)"
] |
EsperantoP
| 2
|
fastapi-users/fastapi-users
|
asyncio
| 958
|
cookie + redis -> user_id type error
|
## Describe the bug
www-api-1 | File "/usr/local/lib/python3.10/site-packages/fastapi_users/authentication/authenticator.py", line 136, in current_user_dependency
www-api-1 | user, _ = await self._authenticate(
www-api-1 | │ └ <function Authenticator._authenticate at 0x7fbff974f130>
www-api-1 | └ <fastapi_users.authentication.authenticator.Authenticator object at 0x7fbff930f880>
www-api-1 | File "/usr/local/lib/python3.10/site-packages/fastapi_users/authentication/authenticator.py", line 170, in _authenticate
www-api-1 | user = await strategy.read_token(token, user_manager)
www-api-1 | │ │ │ └ <scripts.database.models.account.manager.user_manager object at 0x7fbff930f220>
www-api-1 | │ │ └ 'KNpgAYJBXSa1d59Ptvd7YCVrPP4gLPH4RhBV5JCz_Ac'
www-api-1 | │ └ <function RedisStrategy.read_token at 0x7fbff96bf400>
www-api-1 | └ <fastapi_users.authentication.strategy.redis.RedisStrategy object at 0x7fbfe290f0a0>
www-api-1 | File "/usr/local/lib/python3.10/site-packages/fastapi_users/authentication/strategy/redis.py", line 28, in read_token
www-api-1 | user_uiid = UUID4(user_id)
www-api-1 | │ └ b'4c357384-df3d-46f4-88b6-6e91d4d308c8'
www-api-1 | └ <class 'pydantic.types.UUID4'>
www-api-1 | File "/usr/local/lib/python3.10/uuid.py", line 174, in __init__
www-api-1 | hex = hex.replace('urn:', '').replace('uuid:', '')
www-api-1 | │ └ <method 'replace' of 'bytes' objects>
www-api-1 | └ b'4c357384-df3d-46f4-88b6-6e91d4d308c8'
www-api-1 |
www-api-1 | TypeError: a bytes-like object is required, not 'str'
## To Reproduce
```python
In [8]: c
Out[8]: Redis<ConnectionPool<Connection<host=cache,port=6379,db=0>>>
In [9]: c.get('test')
Out[9]: <coroutine object Redis.execute_command at 0x7f0a45b6fdf0>
In [10]: await c.set('test','123')
Out[10]: True
In [11]: d = await c.get('test')
In [12]: d
Out[12]: b'123'
```
## Expected behavior
A clear and concise description of what you expected to happen.
## Configuration
- Python version : 3.10.4
- FastAPI version : 0.75.0
- FastAPI Users version : 9.3.0
### FastAPI Users configuration
```py
class user_base(models.BaseUser):
...
class user_create(models.BaseUserCreate):
...
class user_update(models.BaseUserUpdate):
...
class user_read(user_base):
...
class user(user_base, models.BaseUserDB):
...
```
## Additional context
Add any other context about the problem here.
redis: 4.2.2
aioredis: from redis.asyncio import Redis
(Aioredis is now in redis-py 4.2.0rc1+)
`get` method of redis return bytes type object.
I'm using it like this temporarily.
```python
class RedisStrategy_patch(RedisStrategy):
async def read_token(
self, token: str | None, user_manager: BaseUserManager[models.UC, models.UD]
) -> models.UD | None:
if token is None:
return None
user_id = await self.redis.get(token)
if user_id is None:
return None
elif isinstance(user_id, bytes):
user_id = user_id.decode()
try:
user_uiid = UUID4(user_id)
return await user_manager.get(user_uiid)
except ValueError:
return None
except UserNotExists:
return None
```
|
closed
|
2022-04-09T18:08:12Z
|
2022-04-09T18:42:18Z
|
https://github.com/fastapi-users/fastapi-users/issues/958
|
[
"bug"
] |
phi-friday
| 3
|
pywinauto/pywinauto
|
automation
| 424
|
How to start the program which named in Chinese?
|
I have installed Pywinauto and tried to test the client. but I'm failed . I don't know how to use Pywinauto test a client in Chinese。
The details as follows:
Python 2.7.12 (v2.7.12 [MSC v.1500 32 bit (Intel)] on win32)
Pywinauto 0.6.1,
I run the following codes,but the program had no response.
```python
#!/usr/bin/env python
#coding=gbk
from pywinauto.application import Application
app = Application()
app.start("C:\文件\平台.exe".decode('gb2312'))
```
Can someone help me?
|
closed
|
2017-10-18T06:34:37Z
|
2018-11-28T02:32:16Z
|
https://github.com/pywinauto/pywinauto/issues/424
|
[
"question"
] |
tianhaiyao
| 3
|
FactoryBoy/factory_boy
|
sqlalchemy
| 1,046
|
post_generation hook appears to clash with Trait if they override the same attribute and both are called on create().
|
#### Description
When a post_generation hook wraps a field name that is also overriden by a Trait, and both are called on .create(), then none of them appear to have their desired effect.
#### To Reproduce
Should happen just by running the provided code.
##### Model / Factory code
```python
# -------------------------- models.py --------------------------
class Category(models.Model):
name = models.CharField(max_length=255, unique=True)
slug = models.SlugField(max_length=255, unique=True)
available = models.BooleanField(default=True)
class Meta:
verbose_name = 'category'
verbose_name_plural = 'categories'
indexes = [models.Index(fields=['name'])]
def __str__(self):
return self.name
class Product(models.Model):
categories = models.ManyToManyField(
Category,
through='ProductInCategory',
related_name='products'
)
name = models.CharField(max_length=150)
slug = models.SlugField(max_length=255)
description = models.TextField(blank=True)
image = models.ImageField(upload_to='images/products/')
price = models.DecimalField(max_digits=7, decimal_places=2)
is_active = models.BooleanField(default=True)
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
class Meta:
ordering = ('-created_at',)
indexes = [
models.Index(fields=['name']),
]
def __str__(self):
return f'{self.name}'
class ProductInCategory(models.Model):
'''
Intermediate model for many-to-many relationship between Category and Product.
'''
category = models.ForeignKey(Category, null=True, on_delete=models.SET_NULL)
product = models.ForeignKey(Product, null=True, on_delete=models.SET_NULL)
class Meta:
unique_together = ('category', 'product')
# -------------------------- factories.py --------------------------
class CategoryFactory(DjangoModelFactory):
'''
Category model factory.
Allows associated models at build time through the following keywords:
- Product:
- `products:` as an override.
- `with_products:` trait for automatic creation.
'''
class Meta:
model = 'shop.Category'
django_get_or_create = ('name',)
class Params:
with_products = factory.Trait(
products=factory.RelatedFactoryList(
'apps.shop.tests_v2.factories.ProductInCategoryFactory',
'category',
size=lambda: random.randint(1, 3),
))
name = factory.Sequence(lambda n: f'Category {n}')
slug = factory.LazyAttribute(lambda o: slugify(o.name))
available = True
@factory.post_generation
def products(self, create, extracted, **kwargs):
'''
Catch `products` keyword override at build/creation time.
'''
if not (create and extracted):
return
self.products.add(*extracted)
class ProductFactory(DjangoModelFactory):
'''
Product model factory.
'''
class Meta:
model = 'shop.Product'
django_get_or_create = ('name',)
name = factory.Sequence(lambda n: f'Product {n}')
slug = factory.LazyAttribute(lambda o: slugify(o.name))
description = factory.Faker('text')
image = factory.django.ImageField()
price = factory.Faker('pydecimal', left_digits=2, right_digits=2, positive=True)
is_active = True
class ProductInCategoryFactory(DjangoModelFactory):
'''
Product <--> Category relationship intermediate model factory.
'''
class Meta:
model = 'shop.ProductInCategory'
django_get_or_create = ('category', 'product')
category = factory.SubFactory(CategoryFactory)
product = factory.SubFactory(ProductFactory)
```
##### The issue
When used in isolation, `CategoryFactory.create(products=[...])` or `CategoryFactory.create(with_products=True)` work as expected, that is to say: the first one uses the provided products list and sets them to the Category model object, and the second one creates new products for the Category model object. But when used together as `CategoryFactory.create(with_products=True, products=[...])` then the resulting category object has no related products at all. I would understand if one were to override the other and the result was only one of the previous examples, but this seemed like a bug. Am I doing something wrong?
```python
n_products = random.randint(1, 10)
some_products = ProductFactory.create_batch(n_products)
category = CategoryFactory.create(with_products=True, products=some_products)
assert category.products.exists() # Fails.
assert category.products.count() > 0 # Also fails.
```
Edit 1: Apologies, I forgot to add the Category <-> Product intermediate model from models.py, it's there now.
Edit 2: Please note, I don't think it's because of where the M2M field is placed, since I tried it the other way around (setting the post_generation hook and the Trait on ProductFactory) and it happened that way as well.
|
open
|
2023-09-29T23:22:27Z
|
2023-09-29T23:30:14Z
|
https://github.com/FactoryBoy/factory_boy/issues/1046
|
[] |
kvothe9991
| 0
|
pydantic/pydantic
|
pydantic
| 11,454
|
AssertionError for the mypy plugin if type variables are defined before their bound
|
### Initial Checks
- [X] I confirm that I'm using Pydantic V2
### Description
Running `mypy` with the `pydantic.mypy` plugin enabled fails with an error if it encounters a `BaseModel` with a bound `TypeVar`, where the type variable is defined _before_ its `bound`. (See the minimal example below for an illustration) With the `pydantic.mypy` plugin disabled, type checking is successful.
Error details:
```
cat.py:16: error: INTERNAL ERROR -- Please try using mypy master on GitHub:
https://mypy.readthedocs.io/en/stable/common_issues.html#using-a-development-mypy-build
Please report a bug at https://github.com/python/mypy/issues
version: 1.15.0
Traceback (most recent call last):
File "mypy/semanal.py", line 7240, in accept
File "mypy/nodes.py", line 811, in accept
File "mypy/semanal.py", line 907, in visit_func_def
File "mypy/semanal.py", line 952, in analyze_func_def
File "mypy/semanal.py", line 6894, in defer
AssertionError: Must not defer during final iteration
```
In my experience, it is quite common to define type variables at the top of the module, see for example the [mypy docs on Generics](https://mypy.readthedocs.io/en/stable/generics.html).
Issue #11025 describes a different problem, but may be related.
Thank you for your work on Pydantic! :sparkling_heart:
### Example Code
```Python
from typing import Generic, TypeVar
from pydantic import BaseModel
# Defined before Mouse: Causes internal error below
M = TypeVar('M', bound='Mouse')
class Mouse:
pass
# Defined after Mouse: Works without problem
# M = TypeVar('M', bound='Mouse')
class Cat(BaseModel, Generic[M]): # INTERNAL ERROR
pass
```
### Python, Pydantic & OS Version
```Text
pydantic version: 2.10.6
pydantic-core version: 2.27.2
pydantic-core build: profile=release pgo=false
install path: .venv/lib/python3.12/site-packages/pydantic
python version: 3.12.3 (main, Jan 17 2025, 18:03:48) [GCC 13.3.0]
platform: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
related packages: mypy-1.15.0 typing_extensions-4.12.2
commit: unknown
(Additionally I tried to reproduce it with current Pydantic main (aa59d53), with the same results)
```
|
open
|
2025-02-17T23:34:22Z
|
2025-02-18T10:06:57Z
|
https://github.com/pydantic/pydantic/issues/11454
|
[
"bug V2",
"topic-mypy plugin"
] |
noyainrain
| 0
|
huggingface/pytorch-image-models
|
pytorch
| 2,042
|
Columns and DataType Not Explicitly Set on line 22 of generate_csv_results.py
|
Hello!
I found an AI-Specific Code smell in your project.
The smell is called: Columns and DataType Not Explicitly Set
You can find more information about it in this paper: https://dl.acm.org/doi/abs/10.1145/3522664.3528620.
According to the paper, the smell is described as follows:
| **Problem** | If the columns are not selected explicitly, it is not easy for developers to know what to expect in the downstream data schema. If the datatype is not set explicitly, it may silently continue the next step even though the input is unexpected, which may cause errors later. The same applies to other data importing scenarios. |
| ------------- | :------------- |
| **Solution** | **It is recommended to set the columns and DataType explicitly in data processing.** |
| **Impact** | **Readability** |
Example:
```diff
### Pandas Column Selection
import pandas as pd
df = pd.read_csv('data.csv')
+ df = df[['col1', 'col2', 'col3']]
### Pandas Set DataType
import pandas as pd
- df = pd.read_csv('data.csv')
+ df = pd.read_csv('data.csv', dtype={'col1': 'str', 'col2': 'int', 'col3': 'float'})
```
You can find the code related to this smell in this link: https://github.com/rwightman/pytorch-image-models/blob/30bd1746c55974181ed2b24bb68c55ac60c68ce0/results/generate_csv_results.py#L12-L32.
I also found instances of this smell in other files, such as:
File: https://github.com/rwightman/pytorch-image-models/blob/master/inference.py#L321-L331 Line: 326
.
I hope this information is helpful!
|
closed
|
2023-11-21T14:32:18Z
|
2023-11-21T17:49:11Z
|
https://github.com/huggingface/pytorch-image-models/issues/2042
|
[] |
CodeSmileBot
| 0
|
aminalaee/sqladmin
|
asyncio
| 304
|
Error when sorting with the label
|
### Checklist
- [X] The bug is reproducible against the latest release or `master`.
- [X] There are no similar issues or pull requests to fix it yet.
### Describe the bug
When a field is sorted and has a specified label, the following error occurs: `sqlalchemy.exc.CompileError: Can't resolve label reference for ORDER BY / GROUP BY / DISTINCT etc. Textual SQL expression 'Company type' should be explicitly declared as text('Company type')`.
### Steps to reproduce the bug
_No response_
### Expected behavior
_No response_
### Actual behavior
_No response_
### Debugging material
_No response_
### Environment
Python3.10, sqladmin 0.4.0, PostgreSQL 14.1
### Additional context
_No response_
|
closed
|
2022-09-06T10:39:44Z
|
2022-09-06T12:34:01Z
|
https://github.com/aminalaee/sqladmin/issues/304
|
[] |
GitBib
| 1
|
nltk/nltk
|
nlp
| 3,206
|
Bug in nltk.draw.dispersion_plot with nltk 3.8.1, matplotlib-base 3.8.0, matplotlib-inline 0.1.6 and numpy 1.26
|
Following along the exercise in your workbook [chapter 1](https://www.nltk.org/book/ch01.html).
I do not get the same plot when I use:
```
text4.dispersion_plot(["citizens", "democracy", "freedom", "duties", "America"])
```
After using the words singularly with the function, I believe 'citizens' output has been swapped with 'America' and 'democracy' output has been swapped with duties.
|
closed
|
2023-11-16T19:33:11Z
|
2024-03-05T23:23:42Z
|
https://github.com/nltk/nltk/issues/3206
|
[] |
m-d-grunnill
| 2
|
Johnserf-Seed/TikTokDownload
|
api
| 86
|
[Feature]无名字视频与防重复下载引发的下载不全
|
有些博主发视频时并没有文字，直接是空白的。
那么假设其有10个这样的视频，那么这10个视频都是重名的
因为防重复下载的缘故，结果只下载了一个视频。
希望可以改善这个命名功能
|
closed
|
2022-02-05T13:58:06Z
|
2022-03-02T02:57:29Z
|
https://github.com/Johnserf-Seed/TikTokDownload/issues/86
|
[
"需求建议(enhancement)"
] |
boluohong
| 1
|
apache/airflow
|
automation
| 47,839
|
Rename area:datasets label to area:assets
|
### Body
Obviously we need to change the label name, but also I believe this would also break some automation tooling on the project. So we should have a PR fixing the tools, and merge it once we do the rename in GitHub admin.
### Committer
- [x] I acknowledge that I am a maintainer/committer of the Apache Airflow project.
|
open
|
2025-03-17T05:33:30Z
|
2025-03-23T12:18:24Z
|
https://github.com/apache/airflow/issues/47839
|
[
"kind:meta"
] |
uranusjr
| 5
|
Avaiga/taipy
|
data-visualization
| 1,488
|
[🐛 BUG] Coverage report is failing
|
### What went wrong? 🤔
The cover report does not have the correct authorization to update the PR and failed.
### Code of Conduct
- [X] I have checked the [existing issues](https://github.com/Avaiga/taipy/issues?q=is%3Aissue+).
- [X] I am willing to work on this issue (optional)
|
closed
|
2024-07-08T06:40:38Z
|
2024-07-17T14:44:44Z
|
https://github.com/Avaiga/taipy/issues/1488
|
[
"🧪Testing",
"🖧 Devops",
"💥Malfunction",
"🟧 Priority: High"
] |
florian-vuillemot
| 1
|
thtrieu/darkflow
|
tensorflow
| 1,092
|
Tried to run custom model without success (cfg and weights file do not correspond)
|
I've managed to succefully create a custom weights file based on a custom configuration file. However, when I try to run the new tiny yolo:
`runfile('flow.py', args='--model cfg/tiny-yolo-voc-1c.cfg --load bin/tiny-yolo-voc-1c.weights --demo ../video.mp4')`
it gives me the following error:
```
File "flow.py", line 6, in <module>
cliHandler(sys.argv)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\cli.py", line 26, in cliHandler
tfnet = TFNet(FLAGS)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\net\build.py", line 58, in __init__
darknet = Darknet(FLAGS)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\dark\darknet.py", line 27, in __init__
self.load_weights()
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\dark\darknet.py", line 82, in load_weights
wgts_loader = loader.create_loader(*args)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\utils\loader.py", line 105, in create_loader
return load_type(path, cfg)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\utils\loader.py", line 19, in __init__
self.load(*args)
File "E:\PeopleTrackers\deep_sort_yolov3-master\darkflow-master\darkflow\utils\loader.py", line 77, in load
walker.offset, walker.size)
AssertionError: expect 63082060 bytes, found 63600178
```
|
open
|
2019-11-13T16:30:14Z
|
2021-08-19T09:10:02Z
|
https://github.com/thtrieu/darkflow/issues/1092
|
[] |
marcialbaptista
| 3
|
DistrictDataLabs/yellowbrick
|
matplotlib
| 1,052
|
Ridge' is not a CV regularization model; try ManualAlphaSelection instead
|
**Describe the bug**
I am trying to find the best Alpha for a Ridge model without CV using Yellowbrick ManualAlphaSelection API. My code is pretty basic and it has been taken from the yellowbrick´s documentation. Even though it does not work:
**To Reproduce**
from yellowbrick.regressor import ManualAlphaSelection
from sklearn.linear_model import Ridge
model = ManualAlphaSelection(Ridge(), scoring='neg_mean_squared_error')
model.fit(X_train, y_train)
model.show()
`
**Dataset**
The dataset does not matter because the code itself does not Works. It is a syntax problem.
**Expected behavior**
It was expected that ManualAlphaSelection Works but Python raises the message: 'Ridge' is not a CV regularization model; try ManualAlphaSelection instead. But this message is wrong because the ManualAlphaSelection is already being used
**Traceback**
```
If applicable, add the traceback from the exception.
```
**Desktop (please complete the following information):**
- OS: Windows 10
- Python Version Anaconda3 Python 3
- Yellowbrick Version is the last on because I just installed it on march the 19th. I don´t know how to check this out.
**Additional context** Jupyter notebook 6.0.3
|
closed
|
2020-03-23T22:31:00Z
|
2020-06-10T17:58:32Z
|
https://github.com/DistrictDataLabs/yellowbrick/issues/1052
|
[
"type: bug",
"priority: high"
] |
mmhernandm
| 2
|
DistrictDataLabs/yellowbrick
|
matplotlib
| 464
|
Finish up CVScores visualizer
|
CVScores is a new visualizer under yellowbrick/model_selection. Here are a couple things left to do and small enhancements:
- [x] Add documentation ([done](http://www.scikit-yb.org/en/develop/api/model_selection/cv.html))
- [x] Add test cases
- [x] Add a legend that labels the average cvscore dotted line with the numeric value of `self.cv_scores_mean_`
- [x] Adjust the ylim so that `CVScores` plots will be more easily comparable across different models.
|
closed
|
2018-06-03T00:15:31Z
|
2018-08-20T15:58:32Z
|
https://github.com/DistrictDataLabs/yellowbrick/issues/464
|
[
"priority: high",
"type: technical debt",
"level: intermediate",
"type: documentation"
] |
pdamodaran
| 1
|
healthchecks/healthchecks
|
django
| 95
|
Cancel Account
|
Allow a signed-in account owner to cancel and remove their entire account.
|
closed
|
2016-11-14T11:12:05Z
|
2017-03-16T17:39:39Z
|
https://github.com/healthchecks/healthchecks/issues/95
|
[] |
cuu508
| 0
|
saulpw/visidata
|
pandas
| 1,626
|
save_xlsx: null values become the string "None"
|
**Small description**
Setting `--null-value` in either direction doesn't help, so I suspect it isn't just that `options.null_value` is set to `None`.
I found this during the batch conversion. There's code below.
**Expected result**
An empty string (or the `options.null_value`) is more reasonable than `None`, for this conversion. But I can't set an empty string, with `--null-value`.
**Actual result with screenshot**
In lieu of a screenshot, I have console output.
```console
> vd -f json -b --save-filetype=xlsx -o nones.xlsx <<< '[{"foo":"None","bar":null}]'
opening - as json
saving 1 sheets to nones.xlsx as xlsx
Pay attention.
nones.xlsx save finished
> vd -f xlsx -b --save-filetype=json -o - nones.xlsx +:-:: | jq
opening nones.xlsx as xlsx
Let your best be for your friend.
saving 1 sheets to - as json
[
{
"foo": "None",
"bar": "None"
}
]
```
<details>
<summary>Testing with `--null-value`</summary>
```
> vd -f json -b --save-filetype=xlsx --cmdlog-histfile=vd.log --null-value "None" -o nones.xlsx <<< '[{"foo":"None","bar":null}]'
opening - as json
saving 1 sheets to nones.xlsx as xlsx
Stop this moment, I tell you!
nones.xlsx save finished
> vd -f xlsx -b --save-filetype=json --null-value "" -o - nones.xlsx +:-:: | jq
opening nones.xlsx as xlsx
Listen.
saving 1 sheets to - as json
[
{
"foo": "None",
"bar": "None"
}
]
> vd -f xlsx -b --save-filetype=json --null-value "None" -o - nones.xlsx +:-:: | jq
opening nones.xlsx as xlsx
Was I the same when I got up this morning?
saving 1 sheets to - as json
[
{
"foo": "None",
"bar": "None"
}
]
> vd -f json -b --save-filetype=xlsx --cmdlog-histfile=vd.log --null-value "" -o nones.xlsx <<< '[{"foo":"None","bar":null}]'
opening - as json
saving 1 sheets to nones.xlsx as xlsx
Listen.
nones.xlsx save finished
> vd -f xlsx -b --save-filetype=json --null-value "" -o - nones.xlsx +:-:: | jq
opening nones.xlsx as xlsx
I wonder what they'll do next!
saving 1 sheets to - as json
[
{
"foo": "None",
"bar": "None"
}
]
> vd -f xlsx -b --save-filetype=json --null-value "None" -o - nones.xlsx +:-:: | jq
opening nones.xlsx as xlsx
What are you thinking of?
saving 1 sheets to - as json
[
{
"foo": "None",
"bar": "None"
}
]
```
</details>
**Steps to reproduce with sample data and a .vd**
This was all done within `--batch` mode (and setting `--cmdlog-histfile` resulted in no output).
**Additional context**
I'm pretty sure this is due to naive serialization of the python value.
```python
>>> f"{None}"
'None'
```
Version
```
saul.pw/VisiData v2.9.1
```
As it happens, I'm interested in extending the `save_xlsx` functionality to create Tables (there is support in `openpyxl`). If I get round to that sooner rather than later, I'll look to fix this first.
|
closed
|
2022-12-18T17:04:47Z
|
2022-12-28T23:26:00Z
|
https://github.com/saulpw/visidata/issues/1626
|
[
"bug"
] |
dbaynard
| 1
|
rougier/numpy-100
|
numpy
| 71
|
Jupyter notebook files are not opening
|
When I try to view the .ipynb files on GitHub:
```
100_Numpy_exercises.ipynb
100_Numpy_exercises_with_hint.ipynb
```
I get the error
> Sorry, something went wrong. Reload?
I also tried to view them on https://nbviewer.jupyter.org/ but without success either:
https://nbviewer.jupyter.org/github/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb
https://nbviewer.jupyter.org/github/rougier/numpy-100/blob/master/100_Numpy_exercises_with_hint.ipynb
> Error reading JSON notebook
Same when running locally on my computer.
Note: 100_Numpy_exercises_no_solution.ipynb is opening without problem.
|
closed
|
2018-11-03T06:22:03Z
|
2018-11-05T15:07:54Z
|
https://github.com/rougier/numpy-100/issues/71
|
[
"bug"
] |
kuzand
| 1
|
neuml/txtai
|
nlp
| 567
|
searching with Multiple Parameters (Tabular Data)
|
Thankyou for building such a great application for semantic search,
What i want to know is : how can we create multiple parameter based search engine . like if we have a product table containing different details of individual products namely "manufactured address", "category", "buildType", "shortdescription". then, how to create a more robust search engine involving all the different fields. like a user may come and ask for "x category product having y build material from z place". I hope you got my problem.
Currently, I'm able to create single column based text embeddings.
|
closed
|
2023-09-29T02:13:13Z
|
2023-11-02T13:27:46Z
|
https://github.com/neuml/txtai/issues/567
|
[] |
raaj1v
| 11
|
ray-project/ray
|
python
| 50,681
|
[core] Add a util function to initialize NCCL communicator
|
### Description
https://github.com/ray-project/ray/blob/8f7e30a2beb3003d09392b2f89a6d1a99b7ac338/python/ray/experimental/channel/torch_tensor_nccl_channel.py#L721
1. Should we use the same util function to initialize NCCL communicator for both RayCG and Ray GPU objects?
2. Should I change the ` _init_communicator` function signature?
### Use case
_No response_
|
open
|
2025-02-18T07:40:10Z
|
2025-02-19T01:31:48Z
|
https://github.com/ray-project/ray/issues/50681
|
[
"enhancement",
"core"
] |
kevin85421
| 1
|
lepture/authlib
|
django
| 578
|
Refresh Token using public client with out client secret key is not working
|
**Describe the bug**
A clear and concise description of what the bug is.
When we hit the new token request using refresh_token grant type getting the invalid client error.
**Error Stacks**
```
put error stacks here
```
**To Reproduce**
A minimal example to reproduce the behavior:
**Expected behavior**
when we hit token endpoint using the refresh_token grant type it should return the access token
**Environment:**
- OS:
- Python Version:
- Authlib Version:
**Additional context**
Add any other context about the problem here.
When i go through the code BaseGrant class, There is constant TOKEN_ENDPOINT_AUTH_METHODS, which is allow only client_secret_basic can we update the none options also which will support for public clients
|
closed
|
2023-09-08T12:14:49Z
|
2023-09-11T09:35:04Z
|
https://github.com/lepture/authlib/issues/578
|
[] |
Arokiasamy-tec
| 5
|
Anjok07/ultimatevocalremovergui
|
pytorch
| 1,687
|
PLEASE HELPPPP
|
Last Error Received:
Process: VR Architecture
Missing file error raised. Please address the error and try again.
If this error persists, please contact the developers with the error details.
Raw Error Details:
FileNotFoundError: "[WinError 2] The system cannot find the file specified"
Traceback Error: "
File "UVR.py", line 6638, in process_start
File "separate.py", line 1070, in seperate
File "separate.py", line 382, in final_process
File "separate.py", line 446, in write_audio
File "separate.py", line 419, in save_with_message
File "separate.py", line 393, in save_audio_file
File "separate.py", line 1318, in save_format
File "pydub\audio_segment.py", line 820, in from_wav
File "pydub\audio_segment.py", line 735, in from_file
File "pydub\utils.py", line 274, in mediainfo_json
File "subprocess.py", line 951, in __init__
File "subprocess.py", line 1420, in _execute_child
"
Error Time Stamp [2024-12-28 15:12:00]
Full Application Settings:
vr_model: 1_HP-UVR
aggression_setting: 5
window_size: 320
mdx_segment_size: 256
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: Choose Model
segment: Default
overlap: 0.25
overlap_mdx: Default
overlap_mdx23: 8
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
is_mdx23_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: Choose Model
chunks: Auto
margin: 44100
compensate: Auto
denoise_option: None
is_match_frequency_pitch: True
phase_option: Automatic
phase_shifts: None
is_save_align: False
is_match_silence: True
is_spec_match: False
is_mdx_c_seg_def: False
is_invert_spec: False
is_deverb_vocals: False
deverb_vocal_opt: Main Vocals Only
voc_split_save_opt: Lead Only
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Manual Ensemble
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 2.0
is_time_correction: True
is_gpu_conversion: False
is_primary_stem_only: False
is_secondary_stem_only: False
is_testing_audio: False
is_auto_update_model_params: True
is_add_model_name: False
is_accept_any_input: False
is_task_complete: False
is_normalization: False
is_use_opencl: False
is_wav_ensemble: False
is_create_model_folder: False
mp3_bit_set: 320k
semitone_shift: 0
save_format: MP3
wav_type_set: 32-bit Float
device_set: Default
help_hints_var: True
set_vocal_splitter: No Model Selected
is_set_vocal_splitter: False
is_save_inst_set_vocal_splitter: False
model_sample_mode: False
model_sample_mode_duration: 30
demucs_stems: All Stems
mdx_stems: All Stems
|
open
|
2024-12-28T13:14:38Z
|
2024-12-30T16:33:47Z
|
https://github.com/Anjok07/ultimatevocalremovergui/issues/1687
|
[] |
GOHARSSS
| 1
|
pallets-eco/flask-wtf
|
flask
| 163
|
Use new reCaptcha api
|
Google recently released an update to their reCaptcha service.
The old api remains working, but it would be nice to use the new api instead.
I already did some work on this (#164).
See: https://developers.google.com/recaptcha/docs/
|
closed
|
2014-12-05T12:54:03Z
|
2021-05-29T01:16:00Z
|
https://github.com/pallets-eco/flask-wtf/issues/163
|
[] |
git-commit
| 3
|
httpie/cli
|
python
| 1,547
|
Trim trailing empty lines from CLI output
|
## Checklist
- [x] I've searched for similar feature requests.
---
## Enhancement request
The output from the CLI seems to always include 2 trailing empty lines. Could they be removed?
Example command: `$ https google.com`
Current output:
```
HTTP/1.1 301 Moved Permanently
Alt-Svc: h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
Cache-Control: private, max-age=2592000
Content-Length: 220
Content-Security-Policy-Report-Only: object-src 'none';base-uri 'self';script-src 'nonce-qWBSXxuQK80KB3NcDHBMJw' 'strict-dynamic' 'report-sample' 'unsafe-eval' 'unsafe-inline' https: http:;report-uri https://csp.withgoogle.com/csp/gws/other-hp
Content-Type: text/html; charset=UTF-8
Date: Thu, 21 Dec 2023 11:01:33 GMT
Expires: Thu, 21 Dec 2023 11:01:33 GMT
Location: https://www.google.com/
P3P: CP="This is not a P3P policy! See g.co/p3phelp for more info."
Server: gws
Set-Cookie: CONSENT=PENDING+017; expires=Sat, 20-Dec-2025 11:01:33 GMT; path=/; domain=.google.com; Secure
X-Frame-Options: SAMEORIGIN
X-XSS-Protection: 0
<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>301 Moved</TITLE></HEAD><BODY>
<H1>301 Moved</H1>
The document has moved
<A HREF="https://www.google.com/">here</A>.
</BODY></HTML>
```
The output I would like:
```
HTTP/1.1 301 Moved Permanently
Alt-Svc: h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
Cache-Control: private, max-age=2592000
Content-Length: 220
Content-Security-Policy-Report-Only: object-src 'none';base-uri 'self';script-src 'nonce-qWBSXxuQK80KB3NcDHBMJw' 'strict-dynamic' 'report-sample' 'unsafe-eval' 'unsafe-inline' https: http:;report-uri https://csp.withgoogle.com/csp/gws/other-hp
Content-Type: text/html; charset=UTF-8
Date: Thu, 21 Dec 2023 11:01:33 GMT
Expires: Thu, 21 Dec 2023 11:01:33 GMT
Location: https://www.google.com/
P3P: CP="This is not a P3P policy! See g.co/p3phelp for more info."
Server: gws
Set-Cookie: CONSENT=PENDING+017; expires=Sat, 20-Dec-2025 11:01:33 GMT; path=/; domain=.google.com; Secure
X-Frame-Options: SAMEORIGIN
X-XSS-Protection: 0
<HTML><HEAD><meta http-equiv="content-type" content="text/html;charset=utf-8">
<TITLE>301 Moved</TITLE></HEAD><BODY>
<H1>301 Moved</H1>
The document has moved
<A HREF="https://www.google.com/">here</A>.
</BODY></HTML>
```
So exactly the same, and still with an empty line for padding between sections like headers and body, but without the 2 trailing empty lines.
---
## Problem it solves
Would give a shorter output. This means there would be a shorter distance to scroll up to some output, and in some cases there would be no need to scroll at all, since more actual output could fit in the window.
---
## Additional information, screenshots, or code examples
Version: 3.2.1
OS: Ubuntu 23.10
|
open
|
2023-12-21T11:13:05Z
|
2024-01-09T10:47:45Z
|
https://github.com/httpie/cli/issues/1547
|
[
"enhancement",
"new"
] |
olatoft
| 1
|
serengil/deepface
|
machine-learning
| 593
|
Deepface.analyze doesn't show Gender Logits
|
Awesome library! Thanks for all your work on this.
I'm having a small problem with gender outputs on DeepFace.analyze()
My Code:
```
demography = DeepFace.analyze(img_path = file_prefix + 'image1.jpeg', actions = ['gender'])
```
My Output:
```
{'gender': 'Man', 'region': {'x': 137, 'y': 79, 'w': 291, 'h': 291}}
```
The output example given in the code is a bit different from my output.
https://github.com/serengil/deepface/blob/master/deepface/DeepFace.py#L294-L321
From what I understand, the gender logits for each class should be returned in the property "gender" along with the "dominant gender" property that assigns a specific label. Right now all that's being returned is the "gender" property and it only provides the dominant gender prediction.
This is relevant for me because I'm trying to test if the model is good at identifying the ambiguity in gender among non-binary individuals. This would only be possible if I am able to access the gender class logits.
Thanks for you help!
Matthew
PS: I installed using `pip install deepface`
UPDATE:
I see the gender logits were added in this commit:
https://github.com/serengil/deepface/commit/3f29c6a606ca5fc0454595521c7c88ccb5aebbfe
So it looks like this problem would be fixed by publishing a new version in pip.
|
closed
|
2022-11-03T20:17:46Z
|
2022-11-03T22:33:45Z
|
https://github.com/serengil/deepface/issues/593
|
[
"enhancement"
] |
liechtym
| 1
|
numba/numba
|
numpy
| 9,573
|
Incrementing array location with rank 0 array
|
On current main I can not increment a location of an array with a rank 0 value:
```python
import numba
@numba.njit
def inc_array(x, val):
x[0] += val
inc_array(np.zeros(5), np.array(2.))
```
<details>
```
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<built-in function iadd>) found for signature:
>>> iadd(float64, array(float64, 0d, C))
There are 18 candidate implementations:
- Of which 16 did not match due to:
Overload of function 'iadd': File: <numerous>: Line N[/A.](http://localhost:7890/A.)
With argument(s): '(float64, array(float64, 0d, C))':
No match.
- Of which 2 did not match due to:
Operator Overload in function 'iadd': File: unknown: Line unknown.
With argument(s): '(float64, array(float64, 0d, C))':
No match for registered cases:
* (int64, int64) -> int64
* (int64, uint64) -> int64
* (uint64, int64) -> int64
* (uint64, uint64) -> uint64
* (float32, float32) -> float32
* (float64, float64) -> float64
* (complex64, complex64) -> complex64
* (complex128, complex128) -> complex128
During: typing of intrinsic-call at [/tmp/ipykernel_359313/84858975.py](http://localhost:7890/tmp/ipykernel_359313/84858975.py) (5)
File "../../../../tmp/ipykernel_359313/84858975.py", line 5:
<source missing, REPL[/exec](http://localhost:7890/exec) in use?>
```
</details>
If I call it with a scalar value instead, it work:
```
inc_array(np.zeros(5), 2.)
```
|
open
|
2024-05-13T20:41:19Z
|
2024-05-24T14:54:47Z
|
https://github.com/numba/numba/issues/9573
|
[
"feature_request",
"bug - typing"
] |
aseyboldt
| 6
|
graphdeco-inria/gaussian-splatting
|
computer-vision
| 228
|
About the instant-ngp version
|
Hello, thanks for your great work! And I have a question about the instant-ngp in your paper, since I have use the program of ngp from https://github.com/NVlabs/instant-ngp and I find that it would finish training in less than 3 mins while in your paper the ngp models took more than 6 mins to complete it.
|
closed
|
2023-09-23T15:58:28Z
|
2023-10-10T20:00:17Z
|
https://github.com/graphdeco-inria/gaussian-splatting/issues/228
|
[] |
tangyubbb
| 1
|
chezou/tabula-py
|
pandas
| 153
|
tabula has no attribute 'read_pdf' - in VSCode.
|
# Summary of your issue
From VSCode, none of the tabula methods and attributes work.
It seems like they work from anaconda prompt, though.
```
Traceback (most recent call last):
File "c:\Users\User\.spyder-py3\JacobPDF\PDFTableToExcel.py", line 11, in <module>
df = tabula.read_pdf("./Import/Ground Floor Finishes Plan.pdf", pages='all')
AttributeError: module 'tabula' has no attribute 'read_pdf'
```
# Environment
Write and check your environment. Please paste outputs of specific commands if required.
- Paste the output of `import tabula; tabula.environment_info()` on Python REPL: ?
```
Python version:
3.7.3 (default, Apr 24 2019, 15:29:51) [MSC v.1915 64 bit (AMD64)]
Java version:
java version "1.8.0_211"
Java(TM) SE Runtime Environment (build 1.8.0_211-b12)
Java HotSpot(TM) Client VM (build 25.211-b12, mixed mode)
tabula-py version: 1.3.1
platform: Windows-10-10.0.17134-SP0
uname:
uname_result(system='Windows', node='LAPTOP', release='10', version='10.0.17134', machine='AMD64', processor='Intel64 Family 6 Model 142 Stepping 10, GenuineIntel')
linux_distribution: ('', '', '')
mac_ver: ('', ('', '', ''), '')
```
# What did you do when you faced the problem?
I pasted in the example code from the docs and tried it.
I checked that I have tabula-py and not tabula.
Interestingly, it appears to work in Anaconda prompt, but it doesn't work in VSCode.
## Example code:
```
import tabula
inPDF = 'Ground Floor Finishes Plan.pdf'
df = tabula.read_pdf("./Import/Ground Floor Finishes Plan.pdf", pages='all')
tabula.convert_into(inPDF, "output.csv", output_format="csv", pages='all')
```
## Output:
```
PS C:\Users\User\.spyder-py3> python -u "c:\Users\User\.spyder-py3\JacobPDF\PDFTableToExcel.py"
Traceback (most recent call last):
File "c:\Users\User\.spyder-py3\JacobPDF\PDFTableToExcel.py", line 11, in <module>
df = tabula.read_pdf("./Import/Ground Floor Finishes Plan.pdf", pages='all')
AttributeError: module 'tabula' has no attribute 'read_pdf'
PS C:\Users\User\.spyder-py3>
```
## What did you intend it to be?
I intended it to create a .csv file.
|
closed
|
2019-06-14T02:41:35Z
|
2019-07-11T11:08:35Z
|
https://github.com/chezou/tabula-py/issues/153
|
[] |
Tobio89
| 7
|
paperless-ngx/paperless-ngx
|
machine-learning
| 7,968
|
[BUG] Newly created custom fields do not appear in the "Show" menu until the page is refreshed
|
### Description
After creating a new custom field, it will not appear in the "Show" menu until after the page is refreshed. Therefore, it's not possible to add a column for it to a document view until the page is refreshed. I think this UX is a bit confusing since I would have expected the custom field to show up right away.
### Steps to reproduce
1. Go to "Custom Fields".
2. Add a new custom field.
3. Go to "Documents".
4. Click the "Show" drop down at the top right corner.
5. Note the new custom field does not appear.
6. Refresh the page and click "Show" again.
7. Note the new custom field now appears.
### Webserver logs
```bash
I think this is not relevant but let me know if this is needed
```
### Browser logs
_No response_
### Paperless-ngx version
2.12.1
### Host OS
Unraid 6.12.11
### Installation method
Docker - official image
### System status
```json
{
"pngx_version": "2.12.1",
"server_os": "Linux-6.1.99-Unraid-x86_64-with-glibc2.36",
"install_type": "docker",
"storage": {
"total": 1000203837440,
"available": 127298310144
},
"database": {
"type": "postgresql",
"url": "paperless",
"status": "OK",
"error": null,
"migration_status": {
"latest_migration": "documents.1052_document_transaction_id",
"unapplied_migrations": []
}
},
"tasks": {
"redis_url": "redis://redis:6379",
"redis_status": "OK",
"redis_error": null,
"celery_status": "OK",
"index_status": "OK",
"index_last_modified": "2024-10-20T13:28:05.964393-04:00",
"index_error": null,
"classifier_status": "OK",
"classifier_last_trained": "2024-10-20T17:05:27.657430Z",
"classifier_error": null
}
}
```
### Browser
LibreWolf 131.0.3-1
### Configuration changes
n/a
### 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
|
2024-10-20T17:34:45Z
|
2024-11-22T03:13:22Z
|
https://github.com/paperless-ngx/paperless-ngx/issues/7968
|
[
"not a bug"
] |
bdr99
| 4
|
deepinsight/insightface
|
pytorch
| 2,606
|
Insightface in google colab SD comfyUI.
|
hey everybody! I encountered the error 'no module named 'insightface.app''. I have tried everything possible from reinstalling to downloading the .whl file, but nothing worked. Can anyone help me? (I'm run it on google colab note book).
`Traceback (most recent call last):
File "/content/drive/MyDrive/ComfyUI/nodes.py", line 1906, in load_custom_node
module_spec.loader.exec_module(module)
File "<frozen importlib._bootstrap_external>", line 879, in exec_module
File "<frozen importlib._bootstrap_external>", line 1016, in get_code
File "<frozen importlib._bootstrap_external>", line 1073, in get_data
FileNotFoundError: [Errno 2] No such file or directory: '/content/drive/MyDrive/ComfyUI/custom_nodes/insightface/__init__.py'
Cannot import /content/drive/MyDrive/ComfyUI/custom_nodes/insightface module for custom nodes: [Errno 2] No such file or directory: '/content/drive/MyDrive/ComfyUI/custom_nodes/insightface/__init__.py'`
|
open
|
2024-07-03T14:03:06Z
|
2024-07-03T14:03:06Z
|
https://github.com/deepinsight/insightface/issues/2606
|
[] |
LiesInTheDark
| 0
|
microsoft/nni
|
machine-learning
| 5,022
|
restserver does not response when using frameworkcontroller
|
**Describe the issue**:
Hi I am running the example using frameworkcontroller, but somehow I always have timeout when making request to the restserver, it seems like it hangs up somewhere (I also tried using Postman to send request to "GET /check-status" and the restserver doesn't seem to response). The web interface even does not show up. Actually sometimes it does but unfortunenately most of the time it doesn't. Does anyone tried this and face the same issue?
**Environment**:
- NNI version: 2.8
- Training service (local|remote|pai|aml|etc): frameworkcontroller on minikube
- Client OS: ubuntu
- Python version: 3.8
**Log message**:
- nnimanager.log:
> [2022-07-26 18:30:09] INFO (main) Start NNI manager
[2022-07-26 18:30:09] DEBUG (SqlDB) Database directory: /home/viet/nni-experiments/2a1e3f6u/db
[2022-07-26 18:30:09] INFO (NNIDataStore) Datastore initialization done
[2022-07-26 18:30:09] INFO (RestServer) Starting REST server at port 8080, URL prefix: "/"
[2022-07-26 18:30:09] WARNING (NNITensorboardManager) Tensorboard may not installed, if you want to use tensorboard, please check if tensorboard installed.
[2022-07-26 18:30:09] INFO (RestServer) REST server started.
[2022-07-26 18:30:09] DEBUG (main) start() returned.
[2022-07-26 18:30:10] DEBUG (NNIRestHandler) GET: /check-status: body: {}
[2022-07-26 18:30:10] DEBUG (NNIRestHandler) POST: /experiment: body: {
searchSpace: {
features: { _type: 'choice', _value: [Array] },
lr: { _type: 'loguniform', _value: [Array] },
momentum: { _type: 'uniform', _value: [Array] }
},
trialCodeDirectory: '/home/viet/Programming/python/ba-hpo/workspace/NNI/experiments/nni-kube-cluster/frameworkcontroller-python',
trialConcurrency: 2,
maxTrialNumber: 10,
nniManagerIp: '192.168.0.101',
useAnnotation: false,
debug: false,
logLevel: 'info',
experimentWorkingDirectory: '/home/viet/nni-experiments',
tuner: { name: 'TPE', classArgs: { optimize_mode: 'maximize' } },
trainingService: {
platform: 'frameworkcontroller',
trialCommand: '',
trialCodeDirectory: '/home/viet/Programming/python/ba-hpo/workspace/NNI/experiments/nni-kube-cluster/frameworkcontroller-python',
nniManagerIp: '192.168.0.101',
debug: false,
storage: {
storageType: 'nfs',
server: '192.168.0.101',
path: '/exports',
storage: 'nfs'
},
serviceAccountName: 'frameworkcontroller',
taskRoles: [ [Object] ],
reuseMode: true,
namespace: 'default'
}
}
[2022-07-26 18:30:10] INFO (NNIManager) Starting experiment: 2a1e3f6u
[2022-07-26 18:30:10] INFO (NNIManager) Setup training service...
[2022-07-26 18:30:10] DEBUG (TrialDispatcher) current folder /home/viet/.local/lib/python3.8/site-packages/nni_node/training_service/reusable
[2022-07-26 18:30:10] INFO (NNIManager) Setup tuner...
[2022-07-26 18:30:10] DEBUG (NNIManager) dispatcher command: /usr/bin/python,-m,nni,--exp_params,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
[2022-07-26 18:30:10] INFO (NNIManager) Change NNIManager status from: INITIALIZED to: RUNNING
- dispatcher.log:
> [2022-07-26 18:30:09] INFO (nni.experiment) Creating experiment, Experiment ID: 2a1e3f6u
[2022-07-26 18:30:09] INFO (nni.experiment) Starting web server...
[2022-07-26 18:30:10] INFO (nni.experiment) Setting up...
[2022-07-26 18:30:30] ERROR (nni.experiment) Create experiment failed
[2022-07-26 18:30:30] INFO (nni.experiment) Stopping experiment, please wait...
**How to reproduce it?**:
I just followed [the tutorial from the doc page ](https://nni.readthedocs.io/en/stable/experiment/training_service/frameworkcontroller.html)
|
closed
|
2022-07-26T17:23:21Z
|
2022-08-12T15:12:15Z
|
https://github.com/microsoft/nni/issues/5022
|
[
"user raised",
"support",
"Framework Support"
] |
hviet2603
| 1
|
google-research/bert
|
nlp
| 583
|
BERT has a non deterministic behaviour
|
I am using the BERT implementation in https://github.com/google-research/bert for feature extracting and I have noticed a weird behaviour which I was not expecting: if I execute the program twice on the same text, I get different results. I need to know if this is normal and why this happens in order to treat this fact in one or another way. Why is the reason for this? Aren't neural networks deterministic algorithms?
|
open
|
2019-04-17T08:15:01Z
|
2021-01-11T23:02:56Z
|
https://github.com/google-research/bert/issues/583
|
[] |
RodSernaPerez
| 6
|
modelscope/modelscope
|
nlp
| 1,086
|
请问支持异步吗？
|
例如：
import asyncio
async def hello():
print("Hello world!")
# 异步调用asyncio.sleep(1):
await asyncio.sleep(1)
print("Hello again!")
asyncio.run(hello())
如果支持，该如何使用呢
|
closed
|
2024-11-18T02:17:11Z
|
2024-12-25T02:00:40Z
|
https://github.com/modelscope/modelscope/issues/1086
|
[
"Stale"
] |
cgq0816
| 4
|
huggingface/datasets
|
tensorflow
| 6,522
|
Loading HF Hub Dataset (private org repo) fails to load all features
|
### Describe the bug
When pushing a `Dataset` with multiple `Features` (`input`, `output`, `tags`) to Huggingface Hub (private org repo), and later downloading the `Dataset`, only `input` and `output` load - I believe the expected behavior is for all `Features` to be loaded by default?
### Steps to reproduce the bug
Pushing the data. `data_concat` is a `list` of `dict`s.
```python
for datum in data_concat:
datum_tags = {d["key"]: d["value"] for d in datum["tags"]}
split_fraction = # some logic that generates a train/test split number
if split_faction < test_fraction:
data_test.append(datum)
else:
data_train.append(datum)
dataset = DatasetDict(
{
"train": Dataset.from_list(data_train),
"test": Dataset.from_list(data_test),
"full": Dataset.from_list(data_concat),
},
)
dataset_shuffled = dataset.shuffle(seed=shuffle_seed)
dataset_shuffled.push_to_hub(
repo_id=hf_repo_id,
private=True,
config_name=m,
revision=revision,
token=hf_token,
)
```
Loading it later:
```python
dataset = datasets.load_dataset(
path=hf_repo_id,
name=name,
token=hf_token,
)
```
Produces:
```
DatasetDict({
train: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
test: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
full: Dataset({
features: ['input', 'output'],
num_rows: <obfuscated>
})
})
```
### Expected behavior
The expected result is below:
```
DatasetDict({
train: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
test: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
full: Dataset({
features: ['input', 'output', 'tags'],
num_rows: <obfuscated>
})
})
```
My workaround is as follows:
```python
dsinfo = datasets.get_dataset_config_info(
path=data_files,
config_name=data_config,
token=hf_token,
)
allfeatures = dsinfo.features.copy()
if "tags" not in allfeatures:
allfeatures["tags"] = [{"key": Value(dtype="string", id=None), "value": Value(dtype="string", id=None)}]
dataset = datasets.load_dataset(
path=data_files,
name=data_config,
features=allfeatures,
token=hf_token,
)
```
Interestingly enough (and perhaps a related bug?), if I don't add the `tags` to `allfeatures` above (i.e. only loading `input` and `output`), it throws an error when executing `load_dataset`:
```
ValueError: Couldn't cast
tags: list<element: struct<key: string, value: string>>
child 0, element: struct<key: string, value: string>
child 0, key: string
child 1, value: string
input: <obfuscated>
output: <obfuscated>
-- schema metadata --
huggingface: '{"info": {"features": {"tags": [{"key": {"dtype": "string",' + 532
to
{'input': <obfuscated>, 'output': <obfuscated>
because column names don't match
```
Traceback for this:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/load.py", line 2152, in load_dataset
builder_instance.download_and_prepare(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 948, in download_and_prepare
self._download_and_prepare(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1043, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1805, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/Users/bt/github/core/.venv/lib/python3.11/site-packages/datasets/builder.py", line 1950, 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
```
### Environment info
- `datasets` version: 2.15.0
- Platform: macOS-14.0-arm64-arm-64bit
- Python version: 3.11.5
- `huggingface_hub` version: 0.19.4
- PyArrow version: 14.0.1
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0
|
open
|
2023-12-21T12:26:35Z
|
2023-12-21T13:24:31Z
|
https://github.com/huggingface/datasets/issues/6522
|
[] |
versipellis
| 0
|
noirbizarre/flask-restplus
|
api
| 606
|
restict representation for a resource
|
My users asked me to generate a RSS feed to their resource. So I added this representation for my API :
```
@api.representation('application/rss+xml')
def rss(data, code, headers=None):
resp = make_response(data.rss(), code)
resp.headers.extends(headers)
return resp
...
@ns.route('/feeds/localization/<string:localization_id>')
class LocalizationFeeds(Resource):
def get(self, localization_id):
feed = createRssFeed(localization_id) # will return a rfeed.Feed instance (https://github.com/svpino/rfeed)
return feed, 200
```
It works fine, I can reach any of my requested resource.
But now, all my endpoints are available for this representation.
I also would like that "rss" representation would be the only one possible for my 'feeds' endpoints. I didn't find how to restrict representations for specific endpoints or set a default representation for
|
open
|
2019-03-18T14:30:39Z
|
2019-03-18T14:30:39Z
|
https://github.com/noirbizarre/flask-restplus/issues/606
|
[] |
zannkukai
| 0
|
google-research/bert
|
tensorflow
| 655
|
Unable to run Squad dataset on BERT -permission issues
|
I was trying to run the BERT models on Squad dataset using BERT models on GS and it gave me permission issues. I looked at similar issue opened here and tried copying the models on to my bucket. Still getting the same exception
InvalidArgumentError (see above for traceback): Unsuccessful TensorSliceReader constructor: Failed to get matching files on gs://bert_model_uncased_12/bert_model.ckpt: Permission denied: Error executing an HTTP request: HTTP response code 403 with body '{
"error": {
"errors": [
{
"domain": "global",
"reason": "forbidden",
"message": "service-495559152420@cloud-tpu.iam.gserviceaccount.com does not have storage.objects.list access to bert_model_uncased_12."
}
],
"code": 403,
"message": "service-495559152420@cloud-tpu.iam.gserviceaccount.com does not have storage.objects.list access to bert_model_uncased_12."
}
}
|
open
|
2019-05-20T03:53:32Z
|
2019-11-17T21:03:56Z
|
https://github.com/google-research/bert/issues/655
|
[] |
pratibha5
| 3
|
vitalik/django-ninja
|
pydantic
| 1,123
|
Path parameters, order matters
|
Is there a way to add something like [this](https://fastapi.tiangolo.com/tutorial/path-params/#order-matters) in the docs?
One of my team mates was faced with this problem and couldn't understand why when making a request to `/stores/verticals` the request was instead being processed by `/stores/{store_id}` (and returning a 422 error because `store_id` should be an `int`)
MRE:
```
from django.urls import path
from ninja import NinjaAPI
api = NinjaAPI()
@api.get("/stores/{store_id}")
def get_store(request, store_id: int):
print(f"Store {store_id}")
@api.get("/stores/verticals")
def get_stores_verticals(request):
print("Stores verticals")
urlpatterns = [
path("api/", api.urls),
]
```
|
open
|
2024-04-08T19:10:18Z
|
2024-04-12T04:15:07Z
|
https://github.com/vitalik/django-ninja/issues/1123
|
[] |
santigandolfo
| 3
|
JaidedAI/EasyOCR
|
machine-learning
| 892
|
How to replace the best_accuracy.pth with arabic.pth ?
|
I finetuned the Arabic model on my dataset :
and this is my config file:
```
number: '1234567890١٢٣٤٥٦٧٨٩٠'
symbol: ''
character: "0123456789!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ٠١٢٣٤٥٦٧٨٩«»؟،؛ءآأؤإئااًبةتثجحخدذرزسشصضطظعغفقكلمنهوىيٱٹپچڈڑژکڭگںھۀہۂۃۆۇۈۋیېےۓە"
experiment_name: 'arabic'
train_data: 'all_data/en_train_filtered'
valid_data: 'all_data/en_val'
manualSeed: 1111
workers: 6
batch_size: 2
num_iter: 15
valInterval: 5
saved_model: '/content/drive/MyDrive/OCR Data/arabic.pth'
FT: False
optim: False # default is Adadelta
lr: 1.
beta1: 0.9
rho: 0.95
eps: 0.00000001
grad_clip: 5
#Data processing
select_data: 'all_data/en_train_filtered' # this is dataset folder in train_data
batch_ratio: '1'
total_data_usage_ratio: 1.0
batch_max_length: 34
imgH: 64
imgW: 600
rgb: False
contrast_adjust: False
sensitive: True
PAD: True
contrast_adjust: 0.0
data_filtering_off: False
# Model Architecture
Transformation: 'None'
FeatureExtraction: 'ResNet'
SequenceModeling: 'BiLSTM'
Prediction: 'CTC'
num_fiducial: 20
input_channel: 1
output_channel: 512
hidden_size: 512
decode: 'greedy'
new_prediction: False
freeze_FeatureFxtraction: False
freeze_SequenceModeling: False
```
I go to the originally downloaded model here and replace it with my model :
when i try to initialize the reader again i got this error:
**What is the correct way to replace the original model with my fine-tuned model?**
|
open
|
2022-11-25T12:01:39Z
|
2024-06-19T08:20:22Z
|
https://github.com/JaidedAI/EasyOCR/issues/892
|
[] |
MohieEldinMuhammad
| 23
|
d2l-ai/d2l-en
|
deep-learning
| 2,595
|
The content is outdated
|
I found the book having very good content for the topics it covers. But the book stopped at GANs. Many not-very-new topics like YOLO, Diffusion were never discussed. I've seen some opened issues mentioned this several years ago but it seems no contents have been added. Will the book continue to be updated or it's archived?
|
open
|
2024-03-31T03:33:11Z
|
2024-12-15T15:41:30Z
|
https://github.com/d2l-ai/d2l-en/issues/2595
|
[] |
hiepdang-ml
| 1
|
gevent/gevent
|
asyncio
| 1,653
|
CLI to patch extra modules (psycogreen)
|
* gevent version: 20.6.2
* Python version: 3.6
* Operating System: Ubuntu 18.04
### Description:
I am trying to use pytest and django manage.py with gevent without changing much to the current code. I have seen that there is a very cool command line to do this. The thing is it doesn't patch psycopg2. It would be nice somehow to ask it in the command line.
### What I've run:
Works well but without psycogreen patch:
```
python3 -m gevent.monkey --verbose --module pytest -vvs
```
Also I have a trouble running django `manage.py runserver` using the command line:
```
python3 -m gevent.monkey --verbose --module manage runserver
```
I get the following error:
```
gevent.monkey.patch_all()
sys.version=3.6.9 (default, Apr 18 2020, 01:56:04) [GCC 8.4.0]
sys.path=['',
'/usr/lib/python36.zip',
'/usr/lib/python3.6',
'/usr/lib/python3.6/lib-dynload',
'/usr/local/lib/python3.6/dist-packages',
'/usr/lib/python3/dist-packages']
sys.modules=['__future__',
'__main__',
'_ast',
'_bootlocale',
'_codecs',
'_collections',
'_collections_abc',
'_cython_3_0a5',
'_frozen_importlib',
'_frozen_importlib_external',
'_functools',
'_heapq',
'_imp',
'_io',
'_locale',
'_opcode',
'_operator',
'_signal',
'_sitebuiltins',
'_sre',
'_stat',
'_sysconfigdata_m_linux_x86_64-linux-gnu',
'_thread',
'_warnings',
'_weakref',
'_weakrefset',
'abc',
'ast',
'builtins',
'codecs',
'collections',
'collections.abc',
'contextlib',
'copyreg',
'cython_runtime',
'deepomatic',
'dis',
'encodings',
'encodings.aliases',
'encodings.latin_1',
'encodings.utf_8',
'enum',
'errno',
'fcntl',
'functools',
'gc',
'genericpath',
'gevent',
'gevent._compat',
'gevent._config',
'gevent._gevent_c_greenlet_primitives',
'gevent._gevent_c_hub_local',
'gevent._gevent_c_hub_primitives',
'gevent._gevent_c_ident',
'gevent._gevent_c_waiter',
'gevent._gevent_cgreenlet',
'gevent._greenlet_primitives',
'gevent._hub_local',
'gevent._hub_primitives',
'gevent._ident',
'gevent._interfaces',
'gevent._tblib',
'gevent._util',
'gevent._waiter',
'gevent.exceptions',
'gevent.greenlet',
'gevent.hub',
'gevent.libev',
'gevent.libev.corecext',
'gevent.os',
'gevent.timeout',
'google',
'google.cloud',
'google.logging',
'greenlet',
'heapq',
'importlib',
'importlib._bootstrap',
'importlib._bootstrap_external',
'importlib.abc',
'importlib.machinery',
'importlib.util',
'inspect',
'io',
'itertools',
'keyword',
'linecache',
'marshal',
'opcode',
'operator',
'os',
'os.path',
'pkgutil',
'posix',
'posixpath',
'pprint',
're',
'reprlib',
'runpy',
'signal',
'site',
'sitecustomize',
'sre_compile',
'sre_constants',
'sre_parse',
'stat',
'sys',
'sysconfig',
'textwrap',
'time',
'token',
'tokenize',
'traceback',
'types',
'warnings',
'weakref',
'zipimport',
'zope',
'zope.interface',
'zope.interface._compat',
'zope.interface._zope_interface_coptimizations',
'zope.interface.advice',
'zope.interface.declarations',
'zope.interface.exceptions',
'zope.interface.interface',
'zope.interface.interfaces',
'zope.interface.ro']
cwd=/app/engine/api/api
/usr/bin/python3: can't open file 'manage': [Errno 2] No such file or directory
```
Note however that replacing `runserver` with `help` or `makemigrations` works. So it seems specific to `runserver`.
|
closed
|
2020-07-02T15:35:16Z
|
2020-12-10T14:23:31Z
|
https://github.com/gevent/gevent/issues/1653
|
[
"Type: Question",
"Status: cantfix"
] |
maingoh
| 6
|
TracecatHQ/tracecat
|
fastapi
| 457
|
Unable to delete non-existent custom repository from the UI
|
**Describe the bug**
Users report that they are unable to delete non-existent custom repository from the UI
**To Reproduce**
This happens on any attempt to delete a repository. I've encountered this before and i suspect it's a bug in the `RegistryRepositories` service layer
**Expected behavior**
A clear and concise description of what you expected to happen.
**Screenshots**
**Environment (please complete the following information):**
- OS: [e.g. Ubuntu 20.03 on WSL2]
- CPU architecture
- Browser version
- Docker version
- Docker compose version
- Are you using Docker desktop?
**Additional context**
Add any other context about the problem here.
|
closed
|
2024-10-23T21:39:19Z
|
2024-11-06T20:04:15Z
|
https://github.com/TracecatHQ/tracecat/issues/457
|
[] |
daryllimyt
| 0
|
frol/flask-restplus-server-example
|
rest-api
| 9
|
Upgrade Flask-RESTplus to 0.9.0
|
0.9.0 release included a lot of breaking changes, including dropping Flask-RESTful dependency and improving namespaces support.
|
closed
|
2016-02-26T05:28:27Z
|
2016-02-26T15:20:10Z
|
https://github.com/frol/flask-restplus-server-example/issues/9
|
[
"enhancement"
] |
frol
| 0
|
JaidedAI/EasyOCR
|
pytorch
| 1,218
|
Does retraining on the new dataset (with some specific characters in it) decrease the accuracy of the ordinary characters detection?
|
Hello.EasyOcr has very good accuracy of the ordinary english characters detection, but i also need to detect some specific characters, such as: Δ. (I usually have ordinary characters and specific characters in one string)
Example:
I am going to retrain EasyOcr on the dataset which has english characters, numbers and my specific characters in it, but i am not completly sure, is it really worh it.
Does retraining on the new dataset (with some specific characters in it) decrease the accuracy of the ordinary characters detection?
If so, how can i solve this problem ?
Can i get the possbibility of specifiс characters detection without ordinary characters detection ruin ?
|
open
|
2024-02-23T17:27:41Z
|
2024-02-23T17:28:33Z
|
https://github.com/JaidedAI/EasyOCR/issues/1218
|
[] |
Aflexg
| 0
|
lk-geimfari/mimesis
|
pandas
| 1,313
|
Reseeding doesn't return consistent data for some providers
|
# Bug report
## What's wrong
`generic.file.file_name()` does not provide the same data when reseeding the provider.
```
In [5]: g = Generic()
In [8]: g.reseed(1)
In [9]: g.file.file_name()
Out[9]: 'conducted.md'
In [10]: g.reseed(1)
In [11]: g.file.file_name()
Out[11]: 'encouraged.md'
```
## How is that should be
When reseeding the values returned should be the same.
It seems that the extension remains the same on each reseed but the filename differs each time.
Not all providers have this issue. So far I've only noticed this happening with `file.file_name`
## System information
mimesis 7.0.0
python 3.9.9
windows 11
|
closed
|
2023-01-31T17:50:56Z
|
2023-04-03T08:48:29Z
|
https://github.com/lk-geimfari/mimesis/issues/1313
|
[
"bug"
] |
Svenito
| 7
|
Aeternalis-Ingenium/FastAPI-Backend-Template
|
pytest
| 23
|
Validation errors on Run
|
Show this errors in console at run
```
pydantic_core._pydantic_core.ValidationError: 11 validation errors for BackendDevSettings
BACKEND_SERVER_HOST
Extra inputs are not permitted [type=extra_forbidden, input_value='127.0.0.1', input_type=str]
For further information visit https://errors.pydantic.dev/2.0.3/v/extra_forbidden
BACKEND_SERVER_PORT
Extra inputs are not permitted [type=extra_forbidden, input_value='8000', input_type=str]
For further information visit https://errors.pydantic.dev/2.0.3/v/extra_forbidden
BACKEND_SERVER_WORKERS
Extra inputs are not permitted [type=extra_forbidden, input_value='4', input_type=str]
For further information visit https://errors.pydantic.dev/2.0.3/v/extra_forbidden
```
|
open
|
2023-07-16T21:44:18Z
|
2023-11-18T15:13:34Z
|
https://github.com/Aeternalis-Ingenium/FastAPI-Backend-Template/issues/23
|
[] |
goyometeojorito
| 2
|
microsoft/nlp-recipes
|
nlp
| 236
|
[FEATURE] Setup script should include conda update
|
### Description
Conda is not up to date in DSVM. Environment create script fails without first updating conda.
### Expected behavior with the suggested feature
We should include conda update command in Setup script.
### Other Comments
|
open
|
2019-08-02T19:46:50Z
|
2019-08-02T19:46:50Z
|
https://github.com/microsoft/nlp-recipes/issues/236
|
[
"enhancement"
] |
dipanjan77
| 0
|
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