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mars-project/mars | pandas | 3,216 | [BUG] Ray task backend no progress | <!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
Ray task mode doesn't update progress until whole task finished:
**To Reproduce**
To help us reproducing this bug, please provide information below:
1. Your Python version: 3.7.9
2. The version of Mars you use: master
3. Versions of crucial packages, such as numpy, scipy and pandas
4. Full stack of the error.
5. Minimized code to reproduce the error.
```python
def test_groupby(n=10):
from datetime import datetime
start = datetime.now()
df = md.DataFrame(
mt.random.rand(n * 500, 4, chunk_size=500),
columns=list('abcd'))
# print(df.sum().execute())
result = df.groupby(['a']).apply(lambda pdf: pdf).execute()
duration = datetime.now() - start
return result, duration
mars.new_session(n_worker=10, n_cpu=10*2, backend="ray")
test_groupby(200)
```
**Expected behavior**
A clear and concise description of what you expected to happen.
**Additional context**
Add any other context about the problem here.
| closed | 2022-08-09T11:18:21Z | 2022-11-10T07:24:47Z | https://github.com/mars-project/mars/issues/3216 | []
| chaokunyang | 2 |
biolab/orange3 | scikit-learn | 6,503 | Number of features remains disabled when Suggest features is closed during search | 
How to reproduce:
1. open _Suggest features_ and run
2. close while running
3. choose another mode (circular, LDA or PCA)
4. open _Suggest features_: the _Number of variables_ field is disabled despite no search running
OS: Windows 10 x64
Orange: 3.35.0 | closed | 2023-07-10T10:15:02Z | 2023-09-01T13:40:06Z | https://github.com/biolab/orange3/issues/6503 | [
"bug",
"snack"
]
| processo | 0 |
2noise/ChatTTS | python | 848 | How to detect the noise, breaks and multi-person speak in a audio? | Hello, I am not in the audio field. I would like to ask, for a reference audio, I have removed BGM and reverberation to a certain extent, but the effect of inputting it into the sound cloning is still not good. Is there any better way to detect whether there is noise, distortion, and multiple people speaking in the reference audio? | closed | 2024-12-27T09:35:26Z | 2025-01-07T12:52:45Z | https://github.com/2noise/ChatTTS/issues/848 | [
"wontfix"
]
| TomSuen | 1 |
FactoryBoy/factory_boy | django | 246 | Faker.generate should allow calling without any kwargs | `Faker.generate` currently requires kwargs, but doesn't need them i.e. `Flaker(...).generate({})` will work just fine with most providers. The kwargs attribute should be optional. It would also be good if the kwargs were really kwargs, not just a passed-in dict.
| closed | 2015-11-02T16:19:48Z | 2016-02-11T23:28:33Z | https://github.com/FactoryBoy/factory_boy/issues/246 | []
| iartarisi | 4 |
mwaskom/seaborn | pandas | 3,756 | Use Arrow PyCapsule Interface instead of Dataframe Interchange Protocol | This is something I've chatted with @MarcoGorelli offline about. At the time it was implemented in seaborn, the Dataframe Interchange Protocol was the best option for exchanging dataframe-like data. However, since that was implemented in seaborn, the [PyArrow Capsule Interface](https://arrow.apache.org/docs/format/CDataInterface/PyCapsuleInterface.html) has come along and solved many of the issues that the DataFrame Interchange Protocol left open.
Without knowing the current state of the interchange implementation of seaborn, switching to the PyArrow Capsule Interface should solve at least the following issues:
- It will add support for polars and other dataframe libraries (https://github.com/mwaskom/seaborn/issues/3277 and https://github.com/mwaskom/seaborn/issues/3188)
- It will use the Arrow type system, which supports aggregate types (https://github.com/mwaskom/seaborn/issues/3533)
- The wonkiness of pandas' type system won't be inherited by seaborn (potentially solving https://github.com/mwaskom/seaborn/issues/3519)
The interface has been adopted by a good deal of projects already, some of which are being tracked in https://github.com/apache/arrow/issues/39195 | closed | 2024-08-29T12:25:17Z | 2025-01-28T01:14:13Z | https://github.com/mwaskom/seaborn/issues/3756 | []
| WillAyd | 13 |
SCIR-HI/Huatuo-Llama-Med-Chinese | nlp | 79 | ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`... | 大家好！
我今天试着跑了下这个 Huatuo-Llama-Med-Chinese （整个过程见下文说明），然后遇到了这个错误：
```
$ bash scripts/infer.sh
/usr/lib/python3/dist-packages/requests/__init__.py:87: RequestsDependencyWarning: urllib3 (2.0.4) or chardet (4.0.0) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 5.0
CUDA SETUP: Detected CUDA version 122
/home/tcmai/.local/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!
warn(msg)
CUDA SETUP: Loading binary /home/tcmai/.local/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda122_nocublaslt.so...
/home/tcmai/.local/lib/python3.10/site-packages/bitsandbytes/cextension.py:31: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers and GPU quantization are unavailable.
warn("The installed version of bitsandbytes was compiled without GPU support. "
The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.
The tokenizer class you load from this checkpoint is 'LLaMATokenizer'.
The class this function is called from is 'LlamaTokenizer'.
The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.
Loading checkpoint shards: 100%|█████████████████████████████████████| 33/33 [01:52<00:00, 3.40s/it]
using lora ./lora-llama-med
Traceback (most recent call last):
File "/data/source/medical-llm/Huatuo-Llama-Med-Chinese-git/infer.py", line 125, in <module>
fire.Fire(main)
File "/home/tcmai/.local/lib/python3.10/site-packages/fire/core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/home/tcmai/.local/lib/python3.10/site-packages/fire/core.py", line 475, in _Fire
component, remaining_args = _CallAndUpdateTrace(
File "/home/tcmai/.local/lib/python3.10/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "/data/source/medical-llm/Huatuo-Llama-Med-Chinese-git/infer.py", line 47, in main
model = PeftModel.from_pretrained(
File "/home/tcmai/.local/lib/python3.10/site-packages/peft/peft_model.py", line 181, in from_pretrained
model.load_adapter(model_id, adapter_name, **kwargs)
File "/home/tcmai/.local/lib/python3.10/site-packages/peft/peft_model.py", line 406, in load_adapter
dispatch_model(
File "/home/tcmai/.local/lib/python3.10/site-packages/accelerate/big_modeling.py", line 345, in dispatch_model
raise ValueError(
ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`, the following submodules need to be offloaded: base_model.model.model.layers.4, base_model.model.model.layers.5, base_model.model.model.layers.6, base_model.model.model.layers.7, base_model.model.model.layers.8, base_model.model.model.layers.9, base_model.model.model.layers.10, base_model.model.model.layers.11, base_model.model.model.layers.12, base_model.model.model.layers.13, base_model.model.model.layers.14, base_model.model.model.layers.15, base_model.model.model.layers.16, base_model.model.model.layers.17, base_model.model.model.layers.18, base_model.model.model.layers.19, base_model.model.model.layers.20, base_model.model.model.layers.21, base_model.model.model.layers.22, base_model.model.model.layers.23, base_model.model.model.layers.24, base_model.model.model.layers.25, base_model.model.model.layers.26, base_model.model.model.layers.27, base_model.model.model.layers.28, base_model.model.model.layers.29, base_model.model.model.layers.30, base_model.model.model.layers.31, base_model.model.model.norm, base_model.model.lm_head.
$
```
整个过程大致如下：
（1）clone 这个 Huatuo-Llama-Med-Chinese repo ，下载 readme 中提到的四个模型权重数据，pip 安装好依赖；
（2）运行 ` $ bash scripts/infer.sh `，根据错误提示，手动编译安装 bitsandbytes 支持我显卡的 cuda122 版；
（3）再运行 ` $ bash scripts/infer.sh `，根据错误提示，clone 下载 https://huggingface.co/decapoda-research/llama-7b-hf 中的基础模型数据；
（4）再运行 ` $ bash scripts/infer.sh `，便出现了上面的 "ValueError: We need an `offload_dir` to dispatch this model according to this `device_map`" 错误。
目前就中断在第（4）步里了，不知道这个错误是什么原因。是最近的 llama-7b-hf 数据不兼容，还是？
谢谢！ | open | 2023-07-26T10:28:58Z | 2023-07-26T10:28:58Z | https://github.com/SCIR-HI/Huatuo-Llama-Med-Chinese/issues/79 | []
| Pingmin | 0 |
hbldh/bleak | asyncio | 1,340 | Unreachable, WinRT, GattSessionStatus.CLOSED possible Solution | * bleak version: 0.21.0a1
* bleak-winrt version: 1.2.0
* Python version: 3.8.10
* Operating System: Win 11 22H
### Description
I am working on a program to read Samsung gearVRC (remote controller). Its a device that does not have windows drivers but one can directly read it with a custom program (https://github.com/rdady/gear-vr-controller-linux)
I was able to scan for devices, services and characteristics on the device using the standard service_explorer.py example, however I was getting the "Unreachable" and not connected errors.
I found that a reliable way to solve the problem is by removing the device from the system and starting with a system that has no prior knowledge of the bluetooth device:
```
- Settings
- Bluetooth & devices
- Devices
- Click on 3 dots next to the device name
- Remove Device
- Bluetooth Slider to Off and back to On
- If device reappears, repeat removal (above two steps), after one repetition device should be permanently removed
```
The unreachable problem is likely Windows related as it also occurs with BLEConsole (https://github.com/sensboston/BLEConsole)
Hypothesis is that when bluetooth connection is closed, Windows retains device information that triggers a disconnection (either by Windows or the device itself). Its also possible that the incorrect device information is related to pairing. Since there is no BLE command line tool for pairing in Windows, its difficult to test.
### Request
It would be useful if the documentation section **Troubleshooting** would list **how to completely remove a Bluetooth device from a Windows system**, and suggesting this as **troubleshooting step** before investigating the device with Wireshark.
### What I Did
I made my own program to read the battery status of my device and below, in LOG file section, is the sample output.
One can see that the GattService is getting closed before its possible to read the characteristic. The same behaviour can be seen with examples/service_explorer.py:
```
py -3 .\service_explorer.py --name 'Gear VR Controller(17DB)'
```
I attempted changing options for
```
BleakClient(myDevice, winrt=dict(address_type='public', use_cached_services=True))
```
which did NOT solve the unreachable issue.
I removed battery from the device to reset it. It did NOT solve the issue.
I attempted pairing with:
```
paired = await client.pair(protection_level=2)
print("{} is {}".format(DEVICE_NAME, 'paired' if paired else 'not paired'))
```
regardless of the protection_level, bleak reports device is NOT paired.
I removed the device from the system as described above and it SOLVED the issue. I need to remove the device manually each time before I can use it though.
Therefore, I attempted creating a powershell script for automatic device removal (see below) but so far I have NOT been able to mimic the manual steps listed above.
```
$device = Get-PnpDevice -FriendlyName "Gear VR Controller(17DB)"
pnputil.exe /remove-device $device.InstanceId
Restart-Service -Name bthserv -Force
Update-HostStorageCache
```
### Logs
```
Reading Value...
Gear VR Controller(17DB) is connected
DEBUG:bleak.backends.winrt.client:session_status_changed_event_handler: id: BluetoothLE#BluetoothLEe0:d4:64:23:f6:1c-2c:ba:ba:2e:17:db, error: BluetoothError.SUCCESS, status: GattSessionStatus.CLOSED
DEBUG:bleak.backends.winrt.client:closing requester
DEBUG:bleak.backends.winrt.client:closing session
00002a19-0000-1000-8000-00805f9b34fb (Handle: 10): Battery Level: (['read', 'notify']), Error Could not read characteristic handle 10: Unreachable
```
| open | 2023-06-18T19:19:48Z | 2023-07-19T01:39:39Z | https://github.com/hbldh/bleak/issues/1340 | [
"Backend: WinRT",
"more info required"
]
| uutzinger | 1 |
Guovin/iptv-api | api | 828 | [Bug]: 未找到结果文件，若已启动更新，请耐心等待更新完成... | ### Don't skip these steps | 不要跳过这些步骤
- [X] I understand that I will be **blocked** if I *intentionally* remove or skip any mandatory\* field | 我明白，如果我“故意”删除或跳过任何强制性的\*字段，我将被**封锁**
- [X] I have checked through the search that there are no similar issues that already exist | 我已经通过搜索仔细检查过没有存在已经创建的相似问题
- [X] I will not submit any issues that are not related to this project | 我不会提交任何与本项目无关的问题
### Occurrence environment | 触发环境
- [ ] Workflow | 工作流
- [ ] GUI | 软件
- [X] Docker
- [ ] Command line | 命令行
### Bug description | 具体描述
打开地址出现这个
### Error log | 报错日志
_No response_ | closed | 2025-01-12T23:23:40Z | 2025-02-19T05:51:08Z | https://github.com/Guovin/iptv-api/issues/828 | [
"invalid"
]
| bingbaicai | 8 |
xorbitsai/xorbits | numpy | 104 | DOC: pandas getting started | closed | 2022-12-16T07:44:05Z | 2022-12-16T08:38:09Z | https://github.com/xorbitsai/xorbits/issues/104 | [
"documentation"
]
| UranusSeven | 0 |
|
kizniche/Mycodo | automation | 833 | Install keeps failing on brand new Raspberry Pi 4 | Hello,
I continue to have problems with the install. I even re-imaged my SD card to ensure I was starting out fresh then, tried to install Mycodo immediately thereafter, to no avail. If it means anything, I remember the "ModuleNotFoundError: No module named 'setuptools._distutils'" error coming up before on the install attempts before I wiped the SD card.
Thanks in advance for any help...I've got a brand new Atlas pH probe kit I'm itching to play with ;-)
From the setup log at the first sign of trouble:
Collecting bcrypt==3.1.7
Downloading bcrypt-3.1.7.tar.gz (42 kB)
Installing build dependencies: started
Installing build dependencies: finished with status 'done'
Getting requirements to build wheel: started
Getting requirements to build wheel: finished with status 'done'
ERROR: Exception:
Traceback (most recent call last):
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/cli/base_command.py", line 216, in _main
status = self.run(options, args)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/cli/req_command.py", line 182, in wrapper
return func(self, options, args)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/commands/install.py", line 325, in run
reqs, check_supported_wheels=not options.target_dir
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/resolution/legacy/resolver.py", line 183, in resolve
discovered_reqs.extend(self._resolve_one(requirement_set, req))
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/resolution/legacy/resolver.py", line 388, in _resolve_one
abstract_dist = self._get_abstract_dist_for(req_to_install)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/resolution/legacy/resolver.py", line 340, in _get_abstract_dist_for
abstract_dist = self.preparer.prepare_linked_requirement(req)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/operations/prepare.py", line 483, in prepare_linked_requirement
req, self.req_tracker, self.finder, self.build_isolation,
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/operations/prepare.py", line 91, in _get_prepared_distribution
abstract_dist.prepare_distribution_metadata(finder, build_isolation)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/distributions/sdist.py", line 38, in prepare_distribution_metadata
self._setup_isolation(finder)
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_internal/distributions/sdist.py", line 96, in _setup_isolation
reqs = backend.get_requires_for_build_wheel()
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_vendor/pep517/wrappers.py", line 161, in get_requires_for_build_wheel
'config_settings': config_settings
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_vendor/pep517/wrappers.py", line 265, in _call_hook
raise BackendUnavailable(data.get('traceback', ''))
pip._vendor.pep517.wrappers.BackendUnavailable: Traceback (most recent call last):
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py", line 86, in _build_backend
obj = import_module(mod_path)
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 953, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/usr/lib/python3/dist-packages/setuptools/__init__.py", line 6, in <module>
import distutils.core
File "/home/pi/Mycodo/env/lib/python3.7/site-packages/_distutils_hack/__init__.py", line 83, in create_module
return importlib.import_module('setuptools._distutils')
File "/usr/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
ModuleNotFoundError: No module named 'setuptools._distutils'
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting pylibftdi==0.19.0
Downloading pylibftdi-0.19.0-py2.py3-none-any.whl (32 kB)
Requirement already up-to-date: RPi.GPIO in /usr/lib/python3/dist-packages (from -r /home/pi/Mycodo/install/requirements-rpi.txt (line 3)) (0.7.0)
Collecting smbus2==0.3.0
Downloading https://www.piwheels.org/simple/smbus2/smbus2-0.3.0-py2.py3-none-any.whl (9.1 kB)
Installing collected packages: pylibftdi, smbus2
Successfully installed pylibftdi-0.19.0 smbus2-0.3.0
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting testfixtures==6.14.1
Downloading testfixtures-6.14.1-py2.py3-none-any.whl (89 kB)
Collecting mock==4.0.2
Downloading mock-4.0.2-py3-none-any.whl (28 kB)
Collecting pytest==5.4.3
Downloading pytest-5.4.3-py3-none-any.whl (248 kB)
Collecting factory_boy==2.12.0
Downloading factory_boy-2.12.0-py2.py3-none-any.whl (36 kB)
Collecting webtest==2.0.35
Downloading WebTest-2.0.35-py2.py3-none-any.whl (32 kB)
Collecting pluggy<1.0,>=0.12
Downloading pluggy-0.13.1-py2.py3-none-any.whl (18 kB)
Collecting attrs>=17.4.0
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Collecting more-itertools>=4.0.0
Downloading more_itertools-8.5.0-py3-none-any.whl (44 kB)
Collecting packaging
Downloading packaging-20.4-py2.py3-none-any.whl (37 kB)
Requirement already satisfied, skipping upgrade: importlib-metadata>=0.12; python_version < "3.8" in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->-r /home/pi/Mycodo/install/requirements-testing.txt (line 4)) (1.7.0)
Collecting wcwidth
Downloading wcwidth-0.2.5-py2.py3-none-any.whl (30 kB)
Collecting py>=1.5.0
Downloading py-1.9.0-py2.py3-none-any.whl (99 kB)
Collecting Faker>=0.7.0
Downloading Faker-4.1.2-py3-none-any.whl (1.0 MB)
Collecting WebOb>=1.2
Downloading WebOb-1.8.6-py2.py3-none-any.whl (114 kB)
Collecting waitress>=0.8.5
Downloading waitress-1.4.4-py2.py3-none-any.whl (58 kB)
Requirement already satisfied, skipping upgrade: six in /usr/lib/python3/dist-packages (from webtest==2.0.35->-r /home/pi/Mycodo/install/requirements-testing.txt (line 6)) (1.12.0)
Requirement already satisfied, skipping upgrade: beautifulsoup4 in /usr/lib/python3/dist-packages (from webtest==2.0.35->-r /home/pi/Mycodo/install/requirements-testing.txt (line 6)) (4.7.1)
Collecting pyparsing>=2.0.2
Downloading pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12; python_version < "3.8"->pytest==5.4.3->-r /home/pi/Mycodo/install/requirements-testing.txt (line 4)) (3.1.0)
Collecting python-dateutil>=2.4
Downloading python_dateutil-2.8.1-py2.py3-none-any.whl (227 kB)
Collecting text-unidecode==1.3
Downloading text_unidecode-1.3-py2.py3-none-any.whl (78 kB)
Installing collected packages: testfixtures, mock, pluggy, attrs, more-itertools, pyparsing, packaging, wcwidth, py, pytest, python-dateutil, text-unidecode, Faker, factory-boy, WebOb, waitress, webtest
Successfully installed Faker-4.1.2 WebOb-1.8.6 attrs-20.1.0 factory-boy-2.12.0 mock-4.0.2 more-itertools-8.5.0 packaging-20.4 pluggy-0.13.1 py-1.9.0 pyparsing-2.4.7 pytest-5.4.3 python-dateutil-2.8.1 testfixtures-6.14.1 text-unidecode-1.3 waitress-1.4.4 wcwidth-0.2.5 webtest-2.0.35
--2020-09-02 15:17:41-- https://project-downloads.drogon.net/wiringpi-latest.deb
Resolving project-downloads.drogon.net (project-downloads.drogon.net)... 188.246.205.22, 2a03:9800:10:7b::2
Connecting to project-downloads.drogon.net (project-downloads.drogon.net)|188.246.205.22|:443... connected.
HTTP request sent, awaiting response... 200 OK
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Saving to: ‘wiringpi-latest.deb’
0K .......... .......... .......... .......... .......... 97% 201K 0s
50K . 100% 1974G=0.2s
2020-09-02 15:17:42 (205 KB/s) - ‘wiringpi-latest.deb’ saved [52260/52260]
(Reading database ... 93509 files and directories currently installed.)
Preparing to unpack wiringpi-latest.deb ...
Unpacking wiringpi (2.52) over (2.50) ...
Setting up wiringpi (2.52) ...
Processing triggers for man-db (2.8.5-2) ...
#### Ensuring compatible version of influxdb is installed ####
#### Incorrect InfluxDB version (v(none)) installed. Installing v1.8.0-1...
Selecting previously unselected package influxdb.
(Reading database ... 93509 files and directories currently installed.)
Preparing to unpack influxdb_1.8.0_armhf.deb ...
Unpacking influxdb (1.8.0-1) ...
Setting up influxdb (1.8.0-1) ...
Created symlink /etc/systemd/system/influxd.service → /lib/systemd/system/influxdb.service.
Created symlink /etc/systemd/system/multi-user.target.wants/influxdb.service → /lib/systemd/system/influxdb.service.
Processing triggers for man-db (2.8.5-2) ...
#### Creating InfluxDB database and user
#### Attempting to connect...
#### Could not connect to Influxdb. Waiting 30 seconds then trying again...
#### Attempting to connect...
#### Influxdb database and user successfully created
#### Running initialization
#### Creating mycodo user
Adding user `mycodo' to group `adm' ...
Adding user mycodo to group adm
Done.
Adding user `mycodo' to group `dialout' ...
Adding user mycodo to group dialout
Done.
Adding user `mycodo' to group `gpio' ...
Adding user mycodo to group gpio
Done.
Adding user `mycodo' to group `i2c' ...
Adding user mycodo to group i2c
Done.
Adding user `mycodo' to group `kmem' ...
Adding user mycodo to group kmem
Done.
Adding user `mycodo' to group `video' ...
Adding user mycodo to group video
Done.
Adding user `pi' to group `mycodo' ...
Adding user pi to group mycodo
Done.
Adding user `mycodo' to group `pi' ...
Adding user mycodo to group pi
Done.
#### Compiling mycodo_wrapper
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c: In function ‘upgrade_commands’:
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c:17:5: warning: ‘strncat’ specified bound 255 equals destination size [-Wstringop-overflow=]
strncat(full_cmd, "/upgrade_commands.sh ", sizeof(full_cmd));
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c: In function ‘main’:
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c:35:4: warning: ‘strncat’ specified bound 255 equals destination size [-Wstringop-overflow=]
strncat(restoreScript, "/upgrade_commands.sh backup-create", sizeof(restoreScript));
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#### Creating symlinks to Mycodo executables
#### Creating files and directories
#### Setting permissions
#### Installing logrotate scripts
logrotate execution moved from cron.daily to cron.hourly
Mycodo logrotate script installed
#### Generating SSL certificates at /home/pi/Mycodo/mycodo/mycodo_flask/ssl_certs (replace with your own if desired)
Generating RSA private key, 4096 bit long modulus (2 primes)
...........................++++
............................................++++
e is 65537 (0x010001)
writing RSA key
Signature ok
subject=O = mycodo, OU = mycodo, CN = mycodo
Getting Private key
#### Disabling installed mycodo startup script
Failed to disable unit: Unit file mycodo.service does not exist.
#### Enabling current mycodo startup script
Created symlink /etc/systemd/system/mycodo.service → /home/pi/Mycodo/install/mycodo.service.
Created symlink /etc/systemd/system/multi-user.target.wants/mycodo.service → /home/pi/Mycodo/install/mycodo.service.
#### Compiling Translations
/home/pi/Mycodo/mycodo/scripts/upgrade_commands.sh: line 115: /home/pi/Mycodo/env/bin/pybabel: No such file or directory
#### Generating widget HTML files
Traceback (most recent call last):
File "/home/pi/Mycodo/mycodo/mycodo_client.py", line 31, in <module>
import Pyro5.errors
ModuleNotFoundError: No module named 'Pyro5'
#### Updating Mycodo restart monitor crontab entry
Crontab entry does not exist, nothing to do.
no crontab for root
Appending to crontab:
-----------------------------------------------
@reboot /var/mycodo-root/env/bin/python /var/mycodo-root/mycodo/scripts/restart_daemon.py --continuouscheck &
-----------------------------------------------
To remove previously added crontab entry, run: /home/pi/Mycodo/install/crontab.sh restart_daemon --remove
#### Running initialization
#### Creating mycodo user
useradd: user 'mycodo' already exists
The user `mycodo' is already a member of `adm'.
The user `mycodo' is already a member of `dialout'.
The user `mycodo' is already a member of `gpio'.
The user `mycodo' is already a member of `i2c'.
The user `mycodo' is already a member of `kmem'.
The user `mycodo' is already a member of `video'.
The user `pi' is already a member of `mycodo'.
The user `mycodo' is already a member of `pi'.
#### Compiling mycodo_wrapper
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c: In function ‘upgrade_commands’:
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c:17:5: warning: ‘strncat’ specified bound 255 equals destination size [-Wstringop-overflow=]
strncat(full_cmd, "/upgrade_commands.sh ", sizeof(full_cmd));
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c: In function ‘main’:
/home/pi/Mycodo/mycodo/scripts/mycodo_wrapper.c:35:4: warning: ‘strncat’ specified bound 255 equals destination size [-Wstringop-overflow=]
strncat(restoreScript, "/upgrade_commands.sh backup-create", sizeof(restoreScript));
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#### Creating symlinks to Mycodo executables
#### Creating files and directories
#### Setting permissions
#### Installing and configuring nginx web server
Failed to disable unit: Unit file mycodoflask.service does not exist.
Synchronizing state of nginx.service with SysV service script with /lib/systemd/systemd-sysv-install.
Executing: /lib/systemd/systemd-sysv-install enable nginx
Created symlink /etc/systemd/system/multi-user.target.wants/mycodoflask.service → /home/pi/Mycodo/install/mycodoflask.service.
Created symlink /etc/systemd/system/mycodoflask.service → /home/pi/Mycodo/install/mycodoflask.service.
#### Restarting nginx
#### Restarting mycodoflask
#### Connecting to http://localhost (creates Mycodo database if it doesn't exist)
#### Could not connect to http://localhost. Waiting 60 seconds then trying again (up to 5 times)...
#### Trying again...
#### Could not connect to http://localhost. Waiting 60 seconds then trying again (up to 5 times)...
#### Trying again...
ALSO:
pi@raspberrypi:~ $ sudo journalctl -u mycodoflask | tail -n 50
-- Logs begin at Thu 2019-02-14 04:11:59 CST, end at Wed 2020-09-02 15:23:08 CDT. --
Sep 02 15:20:15 raspberrypi systemd[1]: Started gunicorn daemon for Mycodo flask web user interface.
Sep 02 15:20:15 raspberrypi systemd[3939]: mycodoflask.service: Failed to execute command: No such file or directory
Sep 02 15:20:15 raspberrypi systemd[3939]: mycodoflask.service: Failed at step EXEC spawning /var/mycodo-root/env/bin/gunicorn: No such file or directory
Sep 02 15:20:15 raspberrypi systemd[1]: mycodoflask.service: Main process exited, code=exited, status=203/EXEC
Sep 02 15:20:15 raspberrypi systemd[1]: mycodoflask.service: Failed with result 'exit-code'. | closed | 2020-09-02T20:47:59Z | 2020-09-02T21:17:35Z | https://github.com/kizniche/Mycodo/issues/833 | [
"duplicate"
]
| griffira | 2 |
deepfakes/faceswap | machine-learning | 1,317 | Installing error on windows for latest installer | (check) CPU Supports AVX Instructions
(check) CPU Supports SSE4 Instructions
(check) Completed check for installed applications
(check) Setting up for: nvidia
Downloading Miniconda3...
Installing Miniconda3. This will take a few minutes...
Error Installing Miniconda3
Install Aborted
here is the full error message. Have tried to install miniconda3 separately, but still got the same error. | closed | 2023-04-18T22:21:46Z | 2023-07-16T11:11:33Z | https://github.com/deepfakes/faceswap/issues/1317 | []
| fyxc | 0 |
aimhubio/aim | tensorflow | 3,129 | Export and import figures from one directory to another | ## ❓Question
Hi! I have some experiments that I ran in one computer and I want to move and present them in another computer but when I just move the directory it doesn't work. Any ideas on how to do it right? | open | 2024-03-29T02:43:27Z | 2024-07-18T07:43:49Z | https://github.com/aimhubio/aim/issues/3129 | [
"type / question"
]
| shani-sony | 1 |
jupyterlab/jupyter-ai | jupyter | 304 | Paste code snippet from chat UI into editor with drag and drop | ### Problem
Only ways to put text from chat UI back into editor are manual copy-and-paste and "replace selection" option on message submission
### Proposed Solution
Allow to paste code text / chat messages / code snippets from chat UI into editor with drag and drop.
Figma concept:
| open | 2023-07-28T21:03:38Z | 2025-03-03T20:28:56Z | https://github.com/jupyterlab/jupyter-ai/issues/304 | [
"enhancement",
"scope:chat-ux"
]
| andrii-i | 0 |
explosion/spaCy | nlp | 12,510 | MemoryError while reading a document using NLP.pipe(text, disable=["tagger", "parser"] | In this use case I am trying to create noun chunks by making use of the spacy, and I am doing this for a batch (Batch size can vary from 20 to 100)
Need to process 2.8Million documents overall.
Using Large spacy english model
In a for loop I am doing NLP on each of the document using NLP.pipe(text, disable=["tagger", "parser"].
Most of the times it is working fine but for some batches I start getting MemoryError, what is the reason for this error, is the infrastructure issue like insufficient cpu/RAM/Memory while processing that batch or is there a problem with the way I am using spacy in my code
## How to reproduce the behaviour
texts = python list of Large documents
Note: many of these documents have character length of 5 Million , average size of a document is 1.5 million character.
```
texts = data_df[CONTENTS].to_list()
with multiprocessing.Pool(processes=no_of_cores) as pool:
noun_chunks = create_noun_chunks(texts)
def create_noun_chunks(text: Iterable[str]) -> List[Sequence[str]]:
"""
Create noun chunks for a given text, after remove entities
:param text: text for which noun chunk is required
:return: strings, Each noun chunk in the string is delimited by \n
"""
global NLP
if NLP is None:
NLP = spacy.load(settings.NLP_LIB)
NLP.max_length = 5000000
all_chunks = []
for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
```
It is while loading this _for_ loop line that I get the memory error
Is it because each element in the _texts_ is very large text of million of characters and there are 20 or 100 such elements in this list _texts_ that I am running into memory error?
Trace back:
```
File "/home/user/projects/my_project/10.6.12/src/core/utilities/text_utility.py", line 174, in create_noun_chunks
for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/language.py", line 1583, in pipe
for doc in docs:
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/transition_parser.pyx", line 230, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1560, in minibatch
batch = list(itertools.islice(items, int(batch_size)))
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/pipe.pyx", line 53, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/pipe.pyx", line 53, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/trainable_pipe.pyx", line 79, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1630, in raise_error
raise e
File "spacy/pipeline/trainable_pipe.pyx", line 75, in spacy.pipeline.trainable_pipe.TrainablePipe.pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/pipeline/tok2vec.py", line 125, in predict
tokvecs = self.model.predict(docs)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 315, in predict
return self._func(self, X, is_train=False)[0]
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 40, in forward
return _list_forward(cast(Model[List2d, List2d], model), Xseq, is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 76, in _list_forward
Yf, get_dXf = layer(Xf, is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/residual.py", line 40, in forward
Y, backprop_layer = model.layers[0](X, is_train)
File "/home/`File "/home/user/projects/my_project/10.6.12/src/core/utilities/text_utility.py", line 174, in create_noun_chunks
for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/language.py", line 1583, in pipe
for doc in docs:
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/transition_parser.pyx", line 230, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1560, in minibatch
batch = list(itertools.islice(items, int(batch_size)))
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/pipe.pyx", line 53, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/pipe.pyx", line 53, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
yield from proc.pipe(docs, **kwargs)
File "spacy/pipeline/trainable_pipe.pyx", line 79, in pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1630, in raise_error
raise e
File "spacy/pipeline/trainable_pipe.pyx", line 75, in spacy.pipeline.trainable_pipe.TrainablePipe.pipe
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/pipeline/tok2vec.py", line 125, in predict
tokvecs = self.model.predict(docs)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 315, in predict
return self._func(self, X, is_train=False)[0]
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 40, in forward
return _list_forward(cast(Model[List2d, List2d], model), Xseq, is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 76, in _list_forward
Yf, get_dXf = layer(Xf, is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/residual.py", line 40, in forward
Y, backprop_layer = model.layers[0](X, is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/ptexts = data_df[CONTENTS].to_list()
with multiprocessing.Pool(processes=no_of_cores) as pool:
noun_chunks = create_noun_chunks(texts)ython3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/maxout.py", line 49, in forward
Y = model.ops.gemm(X, W, trans2=True)
File "thinc/backends/numpy_ops.pyx", line 93, in thinc.backends.numpy_ops.NumpyOps.gemm
File "blis/py.pyx", line 72, in blis.py.gemm
MemoryErroruser/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/ptexts = data_df[CONTENTS].to_list()
with multiprocessing.Pool(processes=no_of_cores) as pool:
noun_chunks = create_noun_chunks(texts)ython3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
Y, inc_layer_grad = layer(X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
return self._func(self, X, is_train=is_train)
File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/maxout.py", line 49, in forward
Y = model.ops.gemm(X, W, trans2=True)
File "thinc/backends/numpy_ops.pyx", line 93, in thinc.backends.numpy_ops.NumpyOps.gemm
File "blis/py.pyx", line 72, in blis.py.gemm
MemoryError
```
## Your Environment
<!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
* Operating System: RedHat in case of server and Linux machine for development
* Python Version Used: 3.7.*
* spaCy Version Used:3.3.*
* Environment Information: Conda environment
| closed | 2023-04-07T07:38:19Z | 2023-04-17T11:50:21Z | https://github.com/explosion/spaCy/issues/12510 | [
"usage",
"perf / memory"
]
| kvkarthikvenkateshan | 1 |
django-import-export/django-import-export | django | 1,819 | Lost all the default import and export options after upgrading to V4 | We have lost all the default file import export options in new version:
Below is what was working previously:
Below is the steps to reproduce:
**requirements.txt**
```
django-import-export==4.0.1
django==5.0.6
openpyxl==3.1.2
```
**models.py**
```
from django.db import models
class Division(models.Model):
id = models.AutoField(primary_key=True)
name = models.CharField(max_length=100)
description = models.TextField()
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
def __str__(self):
return self.name
class Meta:
verbose_name_plural = "Divisions"
class Site(models.Model):
division = models.ForeignKey(Division, on_delete=models.CASCADE)
code = models.CharField(max_length=10, primary_key=True)
name = models.CharField(max_length=100)
description = models.TextField()
created_at = models.DateTimeField(auto_now_add=True)
updated_at = models.DateTimeField(auto_now=True)
def __str__(self):
return self.name
class Meta:
verbose_name_plural = "Sites"
```
**admin.py**
```
from django.contrib import admin
from import_export import resources
from import_export.admin import ImportExportMixin
from .models import Division, Site
class DivisionResource(resources.ModelResource):
class Meta:
model = Division
fields = ("id", "name", "description", "created_at", "updated_at")
@admin.register(Division)
class DivisionAdmin(ImportExportMixin, admin.ModelAdmin):
resource_class = DivisionResource
list_display = ("id", "name", "description", "created_at", "updated_at")
search_fields = ("name", "description")
class SiteResource(resources.ModelResource):
class Meta:
model = Site
fields = ("division", "code", "name", "description", "created_at", "updated_at")
import_id_fields = ("code",)
@admin.register(Site)
class SiteAdmin(ImportExportMixin, admin.ModelAdmin):
resource_class = SiteResource
list_display = (
"division",
"code",
"name",
"description",
"created_at",
"updated_at",
)
search_fields = ("code", "name", "description")
```
Now all the options we have is csv, tsv and json. No settings changed.
| closed | 2024-05-09T01:09:54Z | 2024-05-09T22:53:48Z | https://github.com/django-import-export/django-import-export/issues/1819 | [
"bug"
]
| BabuDip | 2 |
Zeyi-Lin/HivisionIDPhotos | fastapi | 232 | 人像抠图有小的发丝如何去除？ | 
上面的图片生成证件照后，会生成下面的图片
想去除边缘那些小的发丝，有人会吗？ | open | 2025-02-10T05:52:49Z | 2025-02-10T05:53:34Z | https://github.com/Zeyi-Lin/HivisionIDPhotos/issues/232 | []
| sunfw2008 | 0 |
lanpa/tensorboardX | numpy | 129 | demo.py No network structure | Who can help me
tensorboardX 1.1
pytorch 0.3.1
tensorflow(cpu) 1.6
In addition to the network structure, others are successful
| closed | 2018-04-26T04:45:07Z | 2018-04-26T12:00:27Z | https://github.com/lanpa/tensorboardX/issues/129 | []
| clwh310 | 2 |
pydata/xarray | numpy | 9,805 | Add `.quantile(foo=quantiles)` API? | ### Is your feature request related to a problem?
Many of our methods allow the equivalent of `.shift(foo=5)`, where `foo` is a dimension name.
But not `.quantile`! That requires `.quantile(dim="foo", [0,0.5,1])`
### Describe the solution you'd like
I guess this is a missing thing rather than a deliberate choice, is that right?
### Describe alternatives you've considered
_No response_
### Additional context
_No response_ | open | 2024-11-21T03:22:29Z | 2024-11-22T21:29:34Z | https://github.com/pydata/xarray/issues/9805 | [
"enhancement"
]
| max-sixty | 1 |
microsoft/JARVIS | pytorch | 213 | System : Failed to execute 'open' on 'XMLHttpRequest': Invalid URL | Hi @tricktreat , Everything works in `CLI` mode.
I tried to chat in web.
```
Human :
Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.
System :
Failed to execute 'open' on 'XMLHttpRequest': Invalid URL
```
Here is my `index.ts` file
```
const HUGGINGGPT_BASE_URL = "http://{LAN_IP_of_the_server}:{port}/" //"http://localhost:8004"
// use ChatGPT: double click on the setting icon
const CHAT_GPT_URL = "https://api.openai.com"
const CHAT_GPT_LLM = "gpt-3.5-turbo" // gpt-3.5-turbo, gpt-4
// Dev: local endpoint
// const CHAT_GPT_URL = "http://localhost:8006"
export {HUGGINGGPT_BASE_URL, CHAT_GPT_URL, CHAT_GPT_LLM}
```
Everything works fine when I double click the setting icon (switch from huggingGPT to ChatGPT) and ask the same question in Web:
```
ChatGPT :
Hi there! I am OpenAI ChatGPT, an AI assistant for you. How can I help you?
Human :
Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.
ChatGPT :
Named entities in the sentence are:
1. Iron Man (superhero)
2. American (related to the comic books being published)
3. Marvel Comics (publishing house)
4. Stan Lee (writer and editor)
5. Larry Lieber (scripter)
6. Don Heck (artist)
7. Jack Kirby (artist)
``` | open | 2023-06-19T12:08:08Z | 2023-06-19T13:14:30Z | https://github.com/microsoft/JARVIS/issues/213 | []
| ForcewithMe66 | 1 |
glumpy/glumpy | numpy | 46 | Examples fail to run, aborting with a raised "Unknown GLSL error format" | I tried a few examples, including https://github.com/glumpy/glumpy/blob/master/examples/gloo-antialias.py and they fail with the following error. I have just noticed that glxinfo is showing that direct rendering is not working. I am not sure if that is causing the problem.
python main.py
[i] Using GLFW (GL 4.4)
[i] Running at 60 frames/second
Traceback (most recent call last):
File "main.py", line 64, in <module>
app.run()
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/**init**.py", line 301, in run
clock = **init**(clock=clock, framerate=framerate, backend=**backend**)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/**init**.py", line 261, in **init**
window.dispatch_event('on_resize', window._width, window._height)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/event.py", line 375, in dispatch_event
if getattr(self, event_type)(_args):
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/window.py", line 197, in on_resize
self.dispatch_event('on_draw', 0.0)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/event.py", line 365, in dispatch_event
if handler(_args):
File "main.py", line 18, in on_draw
program.draw(gl.GL_POINTS)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 499, in draw
self.activate()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/globject.py", line 87, in activate
self._create()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 147, in _create
self._build_shaders(self._handle)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 195, in _build_shaders
shader.activate()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/globject.py", line 97, in activate
self._update()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/shader.py", line 205, in _update
lineno, mesg = self._parse_error(error)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/shader.py", line 242, in _parse_error
raise ValueError('Unknown GLSL error format')
ValueError: Unknown GLSL error format
Compilation exited abnormally with code 1 at Mon Aug 31 15:14:35
| closed | 2015-08-31T19:40:41Z | 2015-08-31T20:35:23Z | https://github.com/glumpy/glumpy/issues/46 | []
| matthew-egbert | 5 |
marcomusy/vedo | numpy | 981 | Link to examples broken | Hi @marcomusy ,
very minor issue: I found the link to the `surfIntersect.py` example broken (it's now called `surf_intersect.py`) - where could I fix this in the repo? If I stumble over these matters in the futures I can then simply send a small PR :)
Best, Johannes | closed | 2023-11-20T12:28:18Z | 2023-11-21T19:39:43Z | https://github.com/marcomusy/vedo/issues/981 | [
"fixed"
]
| jo-mueller | 1 |
slackapi/bolt-python | fastapi | 778 | Say() Function Producing Duplicate Messages in Channel | (Filling out the following details about bugs will help us solve your issue sooner.)
### Reproducible in:
```bash
pip freeze | grep slack
python --version
sw_vers && uname -v # or `ver`
```
#### The `slack_bolt` version
slack-sdk==3.19.5
slack-bolt==1.15.5
(Paste the output of `pip freeze | grep slack`)
#### Python runtime version
3.9.12
(Paste the output of `python --version`)
#### OS info
ProductName: macOS
ProductVersion: 12.6.1
BuildVersion: 21G217
Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64
(Paste the output of `sw_vers && uname -v` on macOS/Linux or `ver` on Windows OS)
#### Steps to reproduce:
(Share the commands to run, source code, and project settings (e.g., setup.py))
1. Set up venv with starter app.py code
```
import os
# Use the package we installed
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
import re
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Initializes your app with your bot token and signing secret
app = App(
token=os.environ["SLACK_BOT_TOKEN"]
)
#autoreply in hw function
@app.event("message")
def auto_reply(event, say):
try:
if 'files' in event: #need to handle events with attachments differently
if event['channel'] == "C019J946UPJ" and ('.pdf' in event['files'][0]['name'] or '.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "An IL scientist will connect with you shortly. Before we do, take this opportunity to double check your key behavior. Is it uncomfortably specific? See the examples in this document to help you refine your thinking. https://docs.google.com/document/d/1EVoyhVnX_jTzNbWBrN8C9Bz8-A7xHO8wN5dM6o-Oves/edit#",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C01972VPPJ5' and ('.pdf' in event['files'][0]['name'] or '.jpg' in event['files'][0]['name'] or '.png' in event['files'][0]['name'] or '.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'):
say(text = "Great work - you have now uploaded your Behavior Diagnosis! An IL scientist will connect with you shortly but before they do it's worth doing a quick double check … Did you group your steps into steps and substeps? See an example HERE. https://docs.google.com/presentation/d/1vqK8EIEYQfr2ZzVIf3YVjHL-w29eh8TfM62UbZHKMfg/edit#slide=id.g10907c59ee3_0_465",
thread_ts = event['ts'])
#print(event)
else:
if event['channel'] == "C019J946UPJ" and ('.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "An IL scientist will connect with you shortly. Before we do, take this opportunity to double check your key behavior. Is it uncomfortably specific? See the examples in this document to help you refine your thinking. https://docs.google.com/document/d/1EVoyhVnX_jTzNbWBrN8C9Bz8-A7xHO8wN5dM6o-Oves/edit#",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C01972VPPJ5' and ('.google' in event['text'] or 'miro' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "Great work - you have now uploaded your Behavior Diagnosis! An IL scientist will connect with you shortly but before they do it's worth doing a quick double check … Did you group your steps into steps and substeps? See an example HERE. https://docs.google.com/presentation/d/1vqK8EIEYQfr2ZzVIf3YVjHL-w29eh8TfM62UbZHKMfg/edit#slide=id.g10907c59ee3_0_465",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C018UDKTULA' and 'parent_user_id' not in str(event): #auto reply to meet & greet post
say(text = "Sweet :lollipop: :chocolate_bar: :doughnut:! Thank you so much for sharing. \nIf you have a sec, take some time to find someone in the #meet_and_greet channel who works in the same industry or geographic area as you do. Say #hi and tell them what you have in common!",
thread_ts = event['ts'])
#print(event)
except Exception as e:
logger.error(f"Error publishing home tab: {e}")
```
3. Use test channel in place of last "elif " statement in the "auto_reply" function. (elif event['channel'] == 'C018UDKTULA' and 'parent_user_id' not in str(event):)
4. Run code with requirements specified above.
### Expected result:
The reply bot replies in the thread once with the automated message.
### Actual result:
The reply occurs successfully, but several hours later the bot posts a message in channel unprompted. I created a screenshot wireframe of what this looks like (I've been deleting them as they come along. If this doesn't convey the message well let me know and I can send over a real example).
(Tell what actually happened with logs, screenshots)
## Requirements
Please read the [Contributing guidelines](https://github.com/slackapi/bolt-python/blob/main/.github/contributing.md) and [Code of Conduct](https://slackhq.github.io/code-of-conduct) before creating this issue or pull request. By submitting, you are agreeing to those rules.
| closed | 2022-12-06T15:40:39Z | 2022-12-07T14:36:32Z | https://github.com/slackapi/bolt-python/issues/778 | [
"question",
"discussion"
]
| irrational-labs | 8 |
coqui-ai/TTS | deep-learning | 3,462 | [Bug] In 0.22.0 load_checkpoint speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth") | ### Describe the bug
When performing this after fine tuning a model:
def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
global XTTS_MODEL
clear_gpu_cache()
if not xtts_checkpoint or not xtts_config or not xtts_vocab:
return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
config = XttsConfig()
config.load_json(xtts_config)
XTTS_MODEL = Xtts.init_from_config(config)
print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
if torch.cuda.is_available():
XTTS_MODEL.cuda()
print("[FINETUNE] Model Loaded!")
return "Model Loaded!"
TTS version 0.21.3 works absolutely perfectly.
TTS version 0.22.0 errors in the following way:
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
File "C:\AI\text-generation-webui\installer_files\env\Lib\site-packages\TTS\tts\models\xtts.py", line 759, in load_checkpoint
speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen ntpath>", line 108, in join
TypeError: expected str, bytes or os.PathLike object, not NoneType
### To Reproduce
I'm using finetuning and loading a model.
### Expected behavior
to load the model.
### Logs
```shell
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
File "C:\AI\text-generation-webui\installer_files\env\Lib\site-packages\TTS\tts\models\xtts.py", line 759, in load_checkpoint
speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen ntpath>", line 108, in join
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
### Environment
```shell
INFO:root:OS Version: Windows 10.0.22621
INFO:root:Note: Windows 11 build is 10.x.22xxx
INFO:root:Python Version: 3.11.5
INFO:root:Torch Version: 2.1.2+cu121
INFO:root:System RAM: 22.37 GB available out of 31.34 GB total
INFO:root:CUDA_HOME: C:\AI\text-generation-webui\installer_files\env
INFO:root:Package Versions:
INFO:root:numpy Required: >= 1.22.0 Installed: 1.24.4
INFO:root:soundfile Required: >= 0.12.1 Installed: 0.12.1
INFO:root:uvicorn Required: >= 0.24.0.post1 Installed: 0.25.0
INFO:root:TTS Required: >= 0.22.0 Installed: 0.22.0
INFO:root:torch Required: >= 2.1.0+cu118 Installed: 2.1.2+cu121
INFO:root:torchaudio Required: >= 2.1.0+cu118 Installed: 2.1.2+cu121
INFO:root:fastapi Required: >= 0.104.1 Installed: 0.105.0
INFO:root:Jinja2 Required: >= 3.1.2 Installed: 3.1.2
INFO:root:requests Required: >= 2.31.0 Installed: 2.31.0
INFO:root:tqdm Required: >= 4.66.1 Installed: 4.66.1
INFO:root:importlib-metadata Required: >= 4.8.1 Installed: 7.0.1
INFO:root:packaging Required: >= 23.2 Installed: 23.2
INFO:root:GPU Information:
Sun Dec 24 11:21:21 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 546.17 Driver Version: 546.17 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4070 WDDM | 00000000:01:00.0 On | N/A |
| 0% 37C P8 11W / 200W | 597MiB / 12282MiB | 1% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1308 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 1952 C+G ...crosoft\Edge\Application\msedge.exe N/A |
| 0 N/A N/A 2900 C+G ...__8wekyb3d8bbwe\Notepad\Notepad.exe N/A |
| 0 N/A N/A 7528 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 7624 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 7628 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 8840 C+G ...nr4m\radeonsoftware\AMDRSSrcExt.exe N/A |
| 0 N/A N/A 9364 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
| 0 N/A N/A 9412 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 9964 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A |
| 0 N/A N/A 12324 C+G ...m\radeonsoftware\RadeonSoftware.exe N/A |
| 0 N/A N/A 14164 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A |
| 0 N/A N/A 14640 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 17072 C+G ...ft Office\root\Office16\OUTLOOK.EXE N/A |
| 0 N/A N/A 17420 C+G ...s (x86)\Razer\Synapse\RzSynapse.exe N/A |
| 0 N/A N/A 19616 C+G ...2.0_x64__cv1g1gvanyjgm\WhatsApp.exe N/A |
| 0 N/A N/A 20624 C+G ...on\120.0.2210.91\msedgewebview2.exe N/A |
| 0 N/A N/A 23484 C+G ...Programs\Microsoft VS Code\Code.exe N/A |
+---------------------------------------------------------------------------------------+
INFO:root:Package Versions:
INFO:root:absl-py>= 2.0.0
INFO:root:accelerate>= 0.25.0
INFO:root:aiofiles>= 23.2.1
INFO:root:aiohttp>= 3.9.1
INFO:root:aiosignal>= 1.3.1
INFO:root:altair>= 5.2.0
INFO:root:annotated-types>= 0.6.0
INFO:root:antlr4-python3-runtime>= 4.9.3
INFO:root:anyascii>= 0.3.2
INFO:root:anyio>= 3.7.1
INFO:root:appdirs>= 1.4.4
INFO:root:asttokens>= 2.4.1
INFO:root:attributedict>= 0.3.0
INFO:root:attrs>= 23.1.0
INFO:root:audioread>= 3.0.1
INFO:root:autoawq>= 0.1.7
INFO:root:auto-gptq>= 0.6.0+cu121
INFO:root:av>= 10.0.0
INFO:root:Babel>= 2.14.0
INFO:root:bangla>= 0.0.2
INFO:root:beautifulsoup4>= 4.12.2
INFO:root:bitsandbytes>= 0.41.1
INFO:root:blessings>= 1.7
INFO:root:blinker>= 1.7.0
INFO:root:blis>= 0.7.11
INFO:root:bnnumerizer>= 0.0.2
INFO:root:bnunicodenormalizer>= 0.1.6
INFO:root:cachetools>= 5.3.2
INFO:root:catalogue>= 2.0.10
INFO:root:certifi>= 2022.12.7
INFO:root:cffi>= 1.16.0
INFO:root:chardet>= 5.2.0
INFO:root:charset-normalizer>= 2.1.1
INFO:root:click>= 8.1.7
INFO:root:cloudpathlib>= 0.16.0
INFO:root:codecov>= 2.1.13
INFO:root:colorama>= 0.4.6
INFO:root:coloredlogs>= 15.0.1
INFO:root:colour-runner>= 0.1.1
INFO:root:confection>= 0.1.4
INFO:root:contourpy>= 1.2.0
INFO:root:coqpit>= 0.0.17
INFO:root:coverage>= 7.3.4
INFO:root:cramjam>= 2.7.0
INFO:root:ctransformers>= 0.2.27+cu121
INFO:root:ctranslate2>= 3.23.0
INFO:root:cycler>= 0.12.1
INFO:root:cymem>= 2.0.8
INFO:root:Cython>= 3.0.7
INFO:root:DataProperty>= 1.0.1
INFO:root:datasets>= 2.16.0
INFO:root:dateparser>= 1.1.8
INFO:root:decorator>= 5.1.1
INFO:root:deepdiff>= 6.7.1
INFO:root:deep-translator>= 1.9.2
INFO:root:dill>= 0.3.7
INFO:root:diskcache>= 5.6.3
INFO:root:distlib>= 0.3.8
INFO:root:docker-pycreds>= 0.4.0
INFO:root:docopt>= 0.6.2
INFO:root:einops>= 0.7.0
INFO:root:encodec>= 0.1.1
INFO:root:evaluate>= 0.4.1
INFO:root:executing>= 2.0.1
INFO:root:exllama>= 0.0.18+cu121
INFO:root:exllamav2>= 0.0.11+cu121
INFO:root:fastapi>= 0.105.0
INFO:root:faster-whisper>= 0.10.0
INFO:root:fastparquet>= 2023.10.1
INFO:root:ffmpeg>= 1.4
INFO:root:ffmpy>= 0.3.1
INFO:root:filelock>= 3.13.1
INFO:root:flash-attn>= 2.3.4
INFO:root:Flask>= 3.0.0
INFO:root:flask-cloudflared>= 0.0.14
INFO:root:flatbuffers>= 23.5.26
INFO:root:fonttools>= 4.47.0
INFO:root:frozenlist>= 1.4.1
INFO:root:fsspec>= 2023.10.0
INFO:root:g2pkk>= 0.1.2
INFO:root:gekko>= 1.0.6
INFO:root:gitdb>= 4.0.11
INFO:root:GitPython>= 3.1.40
INFO:root:google-auth>= 2.25.2
INFO:root:google-auth-oauthlib>= 1.2.0
INFO:root:gptq-for-llama>= 0.1.1+cu121
INFO:root:gradio>= 3.50.2
INFO:root:gradio_client>= 0.6.1
INFO:root:grpcio>= 1.60.0
INFO:root:gruut>= 2.2.3
INFO:root:gruut-ipa>= 0.13.0
INFO:root:gruut-lang-de>= 2.0.0
INFO:root:gruut-lang-en>= 2.0.0
INFO:root:gruut-lang-es>= 2.0.0
INFO:root:gruut-lang-fr>= 2.0.2
INFO:root:h11>= 0.14.0
INFO:root:hangul-romanize>= 0.1.0
INFO:root:hqq>= 0.1.1.post1
INFO:root:httpcore>= 1.0.2
INFO:root:httpx>= 0.26.0
INFO:root:huggingface-hub>= 0.20.1
INFO:root:humanfriendly>= 10.0
INFO:root:idna>= 3.4
INFO:root:importlib-metadata>= 7.0.1
INFO:root:importlib-resources>= 6.1.1
INFO:root:inflect>= 7.0.0
INFO:root:inspecta>= 0.1.3
INFO:root:ipython>= 8.19.0
INFO:root:itsdangerous>= 2.1.2
INFO:root:jamo>= 0.4.1
INFO:root:jedi>= 0.19.1
INFO:root:jieba>= 0.42.1
INFO:root:Jinja2>= 3.1.2
INFO:root:joblib>= 1.3.2
INFO:root:jsonlines>= 1.2.0
INFO:root:jsonschema>= 4.20.0
INFO:root:jsonschema-specifications>= 2023.11.2
INFO:root:kiwisolver>= 1.4.5
INFO:root:langcodes>= 3.3.0
INFO:root:lazy_loader>= 0.3
INFO:root:librosa>= 0.10.1
INFO:root:llama_cpp_python>= 0.2.24+cpuavx2
INFO:root:llama_cpp_python_cuda>= 0.2.24+cu121
INFO:root:llama_cpp_python_cuda_tensorcores>= 0.2.24+cu121
INFO:root:llvmlite>= 0.41.1
INFO:root:lm_eval>= 0.4.0
INFO:root:lxml>= 4.9.4
INFO:root:Markdown>= 3.5.1
INFO:root:markdown-it-py>= 3.0.0
INFO:root:MarkupSafe>= 2.1.3
INFO:root:matplotlib>= 3.8.2
INFO:root:matplotlib-inline>= 0.1.6
INFO:root:mbstrdecoder>= 1.1.3
INFO:root:mdurl>= 0.1.2
INFO:root:more-itertools>= 10.1.0
INFO:root:mpmath>= 1.3.0
INFO:root:msgpack>= 1.0.7
INFO:root:multidict>= 6.0.4
INFO:root:multiprocess>= 0.70.15
INFO:root:murmurhash>= 1.0.10
INFO:root:networkx>= 2.8.8
INFO:root:ngrok>= 0.12.1
INFO:root:ninja>= 1.11.1.1
INFO:root:nltk>= 3.8.1
INFO:root:num2words>= 0.5.13
INFO:root:numba>= 0.58.1
INFO:root:numexpr>= 2.8.8
INFO:root:numpy>= 1.24.4
INFO:root:oauthlib>= 3.2.2
INFO:root:omegaconf>= 2.3.0
INFO:root:onnxruntime>= 1.16.3
INFO:root:openai-whisper>= 20231117
INFO:root:optimum>= 1.16.1
INFO:root:ordered-set>= 4.1.0
INFO:root:orjson>= 3.9.10
INFO:root:packaging>= 23.2
INFO:root:pandas>= 1.5.3
INFO:root:parso>= 0.8.3
INFO:root:pathvalidate>= 3.2.0
INFO:root:peft>= 0.7.1
INFO:root:Pillow>= 10.1.0
INFO:root:pip>= 23.3.1
INFO:root:platformdirs>= 4.1.0
INFO:root:pluggy>= 1.3.0
INFO:root:pooch>= 1.8.0
INFO:root:portalocker>= 2.8.2
INFO:root:preshed>= 3.0.9
INFO:root:prompt-toolkit>= 3.0.43
INFO:root:protobuf>= 4.23.4
INFO:root:psutil>= 5.9.7
INFO:root:pure-eval>= 0.2.2
INFO:root:pyarrow>= 14.0.2
INFO:root:pyarrow-hotfix>= 0.6
INFO:root:pyasn1>= 0.5.1
INFO:root:pyasn1-modules>= 0.3.0
INFO:root:pybind11>= 2.11.1
INFO:root:pycparser>= 2.21
INFO:root:pydantic>= 2.5.3
INFO:root:pydantic_core>= 2.14.6
INFO:root:pydub>= 0.25.1
INFO:root:Pygments>= 2.17.2
INFO:root:pynndescent>= 0.5.11
INFO:root:pyparsing>= 3.1.1
INFO:root:pypinyin>= 0.50.0
INFO:root:pyproject-api>= 1.6.1
INFO:root:pyreadline3>= 3.4.1
INFO:root:pysbd>= 0.3.4
INFO:root:pytablewriter>= 1.2.0
INFO:root:python-crfsuite>= 0.9.10
INFO:root:python-dateutil>= 2.8.2
INFO:root:python-multipart>= 0.0.6
INFO:root:pytz>= 2023.3.post1
INFO:root:pywin32>= 306
INFO:root:PyYAML>= 6.0.1
INFO:root:py-cpuinfo>= 9.0.0
INFO:root:referencing>= 0.32.0
INFO:root:regex>= 2023.12.25
INFO:root:requests>= 2.31.0
INFO:root:requests-oauthlib>= 1.3.1
INFO:root:responses>= 0.18.0
INFO:root:rich>= 13.7.0
INFO:root:rootpath>= 0.1.1
INFO:root:rouge>= 1.0.1
INFO:root:rouge-score>= 0.1.2
INFO:root:rpds-py>= 0.15.2
INFO:root:rsa>= 4.9
INFO:root:sacrebleu>= 2.4.0
INFO:root:safetensors>= 0.4.1
INFO:root:scikit-learn>= 1.3.2
INFO:root:scipy>= 1.11.4
INFO:root:semantic-version>= 2.10.0
INFO:root:sentencepiece>= 0.1.99
INFO:root:sentry-sdk>= 1.39.1
INFO:root:setproctitle>= 1.3.3
INFO:root:setuptools>= 68.2.2
INFO:root:six>= 1.16.0
INFO:root:smart-open>= 6.4.0
INFO:root:smmap>= 5.0.1
INFO:root:sniffio>= 1.3.0
INFO:root:soundfile>= 0.12.1
INFO:root:soupsieve>= 2.5
INFO:root:soxr>= 0.3.7
INFO:root:spacy>= 3.7.2
INFO:root:spacy-legacy>= 3.0.12
INFO:root:spacy-loggers>= 1.0.5
INFO:root:SpeechRecognition>= 3.10.0
INFO:root:sqlitedict>= 2.1.0
INFO:root:srsly>= 2.4.8
INFO:root:sse-starlette>= 1.6.5
INFO:root:stack-data>= 0.6.3
INFO:root:starlette>= 0.27.0
INFO:root:SudachiDict-core>= 20230927
INFO:root:SudachiPy>= 0.6.8
INFO:root:sympy>= 1.12
INFO:root:tabledata>= 1.3.3
INFO:root:tabulate>= 0.9.0
INFO:root:tcolorpy>= 0.1.4
INFO:root:tensorboard>= 2.15.1
INFO:root:tensorboard-data-server>= 0.7.2
INFO:root:termcolor>= 2.4.0
INFO:root:texttable>= 1.7.0
INFO:root:thinc>= 8.2.2
INFO:root:threadpoolctl>= 3.2.0
INFO:root:tiktoken>= 0.5.2
INFO:root:timm>= 0.9.12
INFO:root:tokenizers>= 0.15.0
INFO:root:toml>= 0.10.2
INFO:root:toolz>= 0.12.0
INFO:root:torch>= 2.1.2+cu121
INFO:root:torchaudio>= 2.1.2+cu121
INFO:root:torchvision>= 0.16.2+cu121
INFO:root:tox>= 4.11.4
INFO:root:tqdm>= 4.66.1
INFO:root:tqdm-multiprocess>= 0.0.11
INFO:root:trainer>= 0.0.36
INFO:root:traitlets>= 5.14.0
INFO:root:transformers>= 4.36.2
INFO:root:TTS>= 0.22.0
INFO:root:typepy>= 1.3.2
INFO:root:typer>= 0.9.0
INFO:root:typing_extensions>= 4.9.0
INFO:root:tzdata>= 2023.3
INFO:root:tzlocal>= 5.2
INFO:root:umap-learn>= 0.5.5
INFO:root:Unidecode>= 1.3.7
INFO:root:urllib3>= 1.26.13
INFO:root:uvicorn>= 0.25.0
INFO:root:virtualenv>= 20.25.0
INFO:root:wandb>= 0.16.1
INFO:root:wasabi>= 1.1.2
INFO:root:wcwidth>= 0.2.12
INFO:root:weasel>= 0.3.4
INFO:root:websockets>= 11.0.3
INFO:root:Werkzeug>= 3.0.1
INFO:root:wheel>= 0.41.2
INFO:root:xxhash>= 3.4.1
INFO:root:yarl>= 1.9.4
INFO:root:zipp>= 3.17.0
INFO:root:zstandard>= 0.22.0
```
### Additional context
No problems on TTS 0.21.3 as mentioned. Only on TTS 0.22.0 | closed | 2023-12-24T11:23:29Z | 2024-02-04T23:03:59Z | https://github.com/coqui-ai/TTS/issues/3462 | [
"bug",
"wontfix"
]
| erew123 | 8 |
huggingface/datasets | pytorch | 6,534 | How to configure multiple folders in the same zip package | How should I write "config" in readme when all the data, such as train test, is in a zip file
train floder and test floder in data.zip | open | 2023-12-26T03:56:20Z | 2023-12-26T06:31:16Z | https://github.com/huggingface/datasets/issues/6534 | []
| d710055071 | 1 |
neuml/txtai | nlp | 186 | Add support for user defined column aliases | Currently, user defined aliases aren't correctly processed. Fix this and add full alias support. | closed | 2021-12-26T23:07:12Z | 2021-12-27T01:06:42Z | https://github.com/neuml/txtai/issues/186 | []
| davidmezzetti | 0 |
twopirllc/pandas-ta | pandas | 125 | Good First Issue >> return NaN when valid data points < length parameter | hi thanks a lot of the great project, been helping me a lot. there is only one thing that has been bothering me here:
- for indicators like rsi(length = 14), seems the program still carry out the calculation even when total non NA datapoints are less than 14, so the result would be misleading. Is there anyway the program can return NaN when there is less than 14 valid datapoints passed.
Thank you!
Michael | closed | 2020-09-13T15:47:21Z | 2021-04-11T16:26:03Z | https://github.com/twopirllc/pandas-ta/issues/125 | [
"enhancement",
"good first issue"
]
| lqmike | 9 |
keras-team/keras | data-science | 20,487 | Add parameter axis to tversky loss | Add parameter axis to tversky loss similar to dice loss.
I can implement that if someone give me green light :) | closed | 2024-11-12T20:37:45Z | 2024-12-01T08:43:06Z | https://github.com/keras-team/keras/issues/20487 | [
"type:feature"
]
| jakubxy08 | 4 |
holoviz/panel | matplotlib | 7,515 | Make Panel Extensions visible | Now that Panel extensions start to pop up I believe we should maintain a list somewhere. I thought about `panel-extensions` org. But that is not visible enough.
I think we should have a page similar to our Panel Components page or integrated with the Panel Components page (like Streamlit does).
At first we could just list, describe and link to them. Later we could add thumbnails if we believe it creates value.
----
```markdown
## 📚 Discover Extensions
Here we maintain a list of Panel Extensions maintained inside and outside this organisation.
### Widgets
#### [`panel-material-ui`](https://github.com/panel-extensions/panel-material-ui)
An extension to bring MaterialUI components to Panel.
### Visualizations
#### [`panel-graphic-walker`](https://github.com/panel-extensions/panel-graphic-walker)
A Tableau like *Graphic Walker* pane for use with HoloViz Panel.
### Jupyter Extensions
#### [`jupyter_bokeh`](https://github.com/bokeh/jupyter_bokeh)
A Jupyter extension for rendering Bokeh and Panel content within Jupyter environments.
#### [`ipywidgets_bokeh`](https://github.com/bokeh/ipywidgets_bokeh)
Support for using Ipywidgets within Bokeh and Panel.
### Templates
``` | open | 2024-11-23T13:23:22Z | 2025-01-20T21:48:10Z | https://github.com/holoviz/panel/issues/7515 | [
"type: docs"
]
| MarcSkovMadsen | 1 |
paperless-ngx/paperless-ngx | machine-learning | 7,230 | [BUG] WebUI login only for admin users | ### Description
After new installation of paperless-ngx 2.10.2 (docker) and import of documents via document_importer WebUI user login is only possible for users with admin rights
### Steps to reproduce
1. Delete browsing data
2. Enter https://localhost:8000
### Webserver logs
```bash
[2024-07-12 15:05:00,207] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 15:05:00,666] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 15:05:00,668] [DEBUG] [paperless.tasks] Training data unchanged.
[2024-07-12 16:05:00,208] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 16:05:00,666] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 16:05:00,668] [DEBUG] [paperless.tasks] Training data unchanged.
[2024-07-12 17:05:00,206] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 17:05:00,676] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 17:05:00,678] [DEBUG] [paperless.tasks] Training data unchanged.
```
### Browser logs
_No response_
### Paperless-ngx version
2.10.2
### Host OS
Ubuntu 22.04
### Installation method
Docker - official image
### System status
_No response_
### Browser
Chrome
### Configuration changes
add in docker-compose.env PAPERLESS_FILENAME_FORMAT={owner_username}/{correspondent}/{created_year}/{created_month}/{title}
### 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] 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 | 2024-07-12T15:20:03Z | 2024-08-12T03:04:26Z | https://github.com/paperless-ngx/paperless-ngx/issues/7230 | [
"not a bug"
]
| midago | 2 |
python-restx/flask-restx | flask | 415 | On fields if `pattern` parameter is added swagger execute request button when clicked has no effect | Here is the minimum code required to replay the error:
```python
from flask import Flask, request
from flask_restx import Resource, Api, fields
app = Flask(__name__)
api = Api(app)
simple_email_model = api.model("email_model", dict(
email=fields.String(required=True, pattern=".+@.+")
))
@api.route('/email')
class TestEmail(Resource):
@api.expect(simple_email_model, validate=True)
def post(self):
return request.json
if __name__ == '__main__':
app.run(debug=True)
```
I expect if pattern doesn't match the validation should fail.
But, when I go to swagger-ui, complete the field and click on `Execute` button - nothing happens (the request is not made, button is freezed).
### **Environment**
- Python version - 3.8
- Flask version - 2.0.3
- Flask-RESTX version - 0.5.1
| open | 2022-02-24T07:29:42Z | 2022-02-24T07:29:42Z | https://github.com/python-restx/flask-restx/issues/415 | [
"bug"
]
| ClimenteA | 0 |
onnx/onnx | scikit-learn | 6,703 | Exported quantized model wasn’t reduced in model size | # Ask a Question
I have a model from sentence- transformers/all-mini-L6-v2 and I have applied dynamic quantization on it that reduces the model size by 75%
Initially 90 MB
After Quantization: 25 MB
But when I convert the model to onnx format using torch.onnx.export it is giving same size as the non quantized model in onnx format
### Question
<!-- Explain your question here. -->
Does onnx format effectively represents quantization
### Further information
- Relevant Area: <!--e.g., model usage, backend, best practices, converters, shape_inference, version_converter, training, test, operators, IR, ONNX Hub, data preprocessing, CI pipelines. -->
- Is this issue related to a specific model?
**Model name**: <!-- *e.g. mnist* --> sentence- transformers/ all-mini-L6-v2
**Model opset**: <!-- *e.g. 17* --> 14
### Notes
<!-- Any additional information, code snippets. -->
| open | 2025-02-14T02:49:16Z | 2025-02-19T06:50:53Z | https://github.com/onnx/onnx/issues/6703 | [
"question"
]
| harshakhmk | 0 |
deepfakes/faceswap | deep-learning | 1,240 | Issue when execute on AMD | **Crash reports MUST be included when reporting bugs.**
**Describe the bug**
Just executed on an AMD PC with python version higher than 3.8
**To Reproduce**
Run the cmd or gui version
**Expected behavior**
Show this message
The AMD version of Faceswap cannot run on versions of Python higher than 3.8
The solution is to cast the device.get() to int (line 109)
| closed | 2022-06-18T09:32:02Z | 2022-06-19T10:41:42Z | https://github.com/deepfakes/faceswap/issues/1240 | []
| RojasAB | 2 |
scikit-learn/scikit-learn | data-science | 30,961 | ⚠️ CI failed on Linux_free_threaded.pylatest_free_threaded (last failure: Mar 10, 2025) ⚠️ | **CI is still failing on [Linux_free_threaded.pylatest_free_threaded](https://dev.azure.com/scikit-learn/scikit-learn/_build/results?buildId=74605&view=logs&j=c10228e9-6cf7-5c29-593f-d74f893ca1bd)** (Mar 10, 2025)
- test_multiclass_plot_max_class_cmap_kwarg | closed | 2025-03-08T02:50:23Z | 2025-03-11T14:49:50Z | https://github.com/scikit-learn/scikit-learn/issues/30961 | [
"Needs Triage"
]
| scikit-learn-bot | 1 |
HumanSignal/labelImg | deep-learning | 564 | Question: How to fix ImportError: DLL load failed: The specified procedure could not be found. Windows + Anaconda | **Here is what I have done so far to setup LabelImg**
**Windows + Anaconda**
Install Python, PyQt5 and install lxml
git clone https://github.com/tzutalin/labelImg
I then had to moved to the **labelImg** directory using cd labelImg
Next, pyrcc5 -o libs/resources.py resources.qrc
**Here come the error response**
`(base) C:\Users\8470p\labelImg>pyrcc5 -o libs/resources.py resources.qrc
Traceback (most recent call last):
File "C:\Users\8470p\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\8470p\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\8470p\Anaconda3\lib\site-packages\PyQt5\pyrcc_main.py", line 23, in <module>
from .pyrcc import *
ImportError: DLL load failed: The specified procedure could not be found.
` | open | 2020-03-17T15:28:31Z | 2020-11-10T18:54:04Z | https://github.com/HumanSignal/labelImg/issues/564 | []
| okoliechykwuka | 1 |
developmentseed/lonboard | data-visualization | 500 | WidgetControl: display other widgets on the same map | See https://github.com/jupyter-widgets/ipyleaflet/blob/1a8e57375ee79ee08a76049ca33a61211b827b83/python/jupyter_leaflet/src/controls/WidgetControl.ts#L10
 | open | 2024-05-03T19:19:31Z | 2024-05-03T19:19:31Z | https://github.com/developmentseed/lonboard/issues/500 | []
| kylebarron | 0 |
ultralytics/yolov5 | pytorch | 12,442 | Exporting yolov5 model as engine format never finishes | ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
### YOLOv5 Component
Export
### Bug
This is what happens when exporting with onnx format:
everything works as normal.
And this is what happens when exporting with engine format:
It fails and just stops with no further information
I have seen another user @SeanSon2005 having the same issue in yolov8 but he wasn't clear in how he solved it and there are no similar issues on yolov5. Also neither yolov8's nor yolov5's export files have been updated to resolve the problem.
### Environment
- YOLOv5 2023-11-22 Python-3.11.6 torch-2.1.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4070, 12282MiB)
- Windows 11
- Python 3.11.6
- tensorrt 8.6.1
### Minimal Reproducible Example
Just the export.py file from the yolov5 ultralitics repo
### Additional
@SeanSon2005 who was having the same problem on yolov8 says that he fixed the problem by going into the Yolo export.py and moving the import tensorrt as trt code segment after getting the onnx export but I wasnt able to replicate that so if anyone follow up on that or elaborate on where I need to move the import tensorrt as trt code segment I would appreciate it even if it doesnt solve the issue.
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | closed | 2023-11-29T13:07:37Z | 2024-01-14T11:30:54Z | https://github.com/ultralytics/yolov5/issues/12442 | [
"bug"
]
| OdysseasTselepidis | 2 |
exaloop/codon | numpy | 340 | Use codon write python module instead of C? | Can I use codon write python module like C?
If it is, where can I find examples? | closed | 2023-04-08T03:57:39Z | 2023-04-10T15:27:34Z | https://github.com/exaloop/codon/issues/340 | []
| lededev | 1 |
cupy/cupy | numpy | 9,046 | `None` RawKernel argument is turned into 0xffffffff instead of NULL pointer | ### Description
I spent this evening chasing a memory corruption bug. As it turns out, the cause was that a `None` argument passed to a `cupy.RawKernel` turned into a pointer with the address `0xffffffff` (i.e. `-1`). I would have expected the address `0`, or at least an address equal to `NULL` or maybe `nullptr`, but `0xffffffff` caught me by surprise. What is going on there?
I found that I can pass `0` from Python to get a `NULL` pointer in the kernel, but it feels a bit dirty, because `0` is of type `int`, but the usual arguments is of type `cupy.ndarray`. Is it okay to just pass `0` when one wants `NULL`, or is there a "more proper" way to obtain a `NULL` value in Python?
### To Reproduce
```python
import cupy
# This prints
#0xffffffff (nil)
#0xffffffff (nil)
source = r"""
extern "C" __global__
void kernel(const float *ptr){
printf("%p %p\n", ptr, NULL);
}
"""
grid_dim = (1,)
block_dim = (2,)
args = (None,)
cupy.RawKernel(source, "kernel")(grid_dim, block_dim, args)
```
### Installation
Wheel (`pip install cupy-***`)
### Environment
```
OS : Linux-6.8.0-55-generic-x86_64-with-glibc2.39
Python Version : 3.12.4
CuPy Version : 13.3.0
CuPy Platform : NVIDIA CUDA
NumPy Version : 1.26.3
SciPy Version : 1.14.1
Cython Build Version : 0.29.36
Cython Runtime Version : None
CUDA Root : /usr/local/cuda
nvcc PATH : /usr/local/cuda/bin/nvcc
CUDA Build Version : 12060
CUDA Driver Version : 12060
CUDA Runtime Version : 12060 (linked to CuPy) / 12060 (locally installed)
CUDA Extra Include Dirs : []
cuBLAS Version : 120604
cuFFT Version : 11300
cuRAND Version : 10307
cuSOLVER Version : (11, 7, 1)
cuSPARSE Version : 12504
NVRTC Version : (12, 6)
Thrust Version : 200600
CUB Build Version : 200600
Jitify Build Version : <unknown>
cuDNN Build Version : None
cuDNN Version : None
NCCL Build Version : None
NCCL Runtime Version : None
cuTENSOR Version : None
cuSPARSELt Build Version : None
Device 0 Name : NVIDIA GeForce RTX 3060 Laptop GPU
Device 0 Compute Capability : 86
Device 0 PCI Bus ID : 0000:01:00.0
```
### Additional Information
_No response_ | open | 2025-03-19T21:15:43Z | 2025-03-21T05:54:05Z | https://github.com/cupy/cupy/issues/9046 | [
"cat:enhancement",
"prio:medium"
]
| 99991 | 1 |
OpenBB-finance/OpenBB | machine-learning | 6,767 | [🕹️]Write a Article Comparing OpenBB and Other Financial Tools | ### What side quest or challenge are you solving?
Write a Article Comparing OpenBB and Other Financial Tools
### Points
300
### Description
Wrote a article on medium comparing OpenBB and other financial tools
### Provide proof that you've completed the task
13-oct-2024 by suryadev pandey >> Link to Article : https://medium.com/@pandeysuryadev00/openbb-a-new-era-of-investment-research-dc81de518243 | closed | 2024-10-13T15:39:17Z | 2024-10-20T13:35:44Z | https://github.com/OpenBB-finance/OpenBB/issues/6767 | []
| Suryadev78 | 14 |
dask/dask | scikit-learn | 11,595 | Supporting inconsistent schemas in read_json | If you have two (jsonl) files where one contains columns `{"id", "text"}` and the other contains `{"text", "id", "meta"}` and you wish to read the two files using `dd.read_json([file1.jsonl, file2.jsonl], lines=True)` we run into an error
```
Metadata mismatch found in `from_delayed`.
Partition type: `pandas.core.frame.DataFrame`
(or it is Partition type: `cudf.core.dataframe.DataFrame` when backend=='cudf')
+---------+-------+----------+
| Column | Found | Expected |
+---------+-------+----------+
| 'meta1' | - | object |
+---------+-------+----------+
```
For what it's worth this isn't an issue in read_parquet (cpu) and for gpu the fix is in the works https://github.com/rapidsai/cudf/pull/17554/files
## Guessing the rootcause
IIUC in both pandas and cudf, we call `read_json_file` ([here](https://github.com/dask/dask/blob/a9396a913c33de1d5966df9cc1901fd70107c99b/dask/dataframe/io/json.py#L315)).
In the pandas case, even if `dtype` is specified, pandas doesn't prune out the non-specified columns, while cudf does (assuming prune_columns=True). Therefore the pandas case continues to fail, while `cudf` case fails on a column order vs metadata column order mismatch error (since one file has `id, text`, while the other has `text, id`.
One possible hack could be supporting `columns` arg and then performing `engine(.....)[columns]`. Another could be
## MRE
```python
import dask.dataframe as dd
import dask
import tempfile
import pandas as pd
import os
records = [
{"id": 123, "text": "foo"},
{
"text": "bar",
"meta1": [{"field1": "cat"}],
"id": 456,
},
]
columns = ["text", "id"]
with tempfile.TemporaryDirectory() as tmpdir:
file1 = os.path.join(tmpdir, "part.0.jsonl")
file2 = os.path.join(tmpdir, "part.1.jsonl")
pd.DataFrame(records[:1]).to_json(file1, orient="records", lines=True)
pd.DataFrame(records[1:]).to_json(file2, orient="records", lines=True)
for backend in ["pandas", "cudf"]:
read_kwargs = dict()
if backend == "cudf":
read_kwargs["dtype"] = {"id": "str", "text": "str"}
read_kwargs["prune_columns"] = True
print("="*30)
print(f"==== {backend=} ====")
print("="*30)
try:
with dask.config.set({"dataframe.backend": backend}):
df = dd.read_json(
[file1, file2],
lines=True,
**read_kwargs,
)
print(f"{df.columns=}")
print(f"{df.compute().columns=}")
print(f"{type(df.compute())=}")
display((df.compute()))
except Exception as e:
print(f"{backend=} failed due to {e} \n")
```
cc @rjzamora
| open | 2024-12-10T18:24:48Z | 2025-02-24T02:01:24Z | https://github.com/dask/dask/issues/11595 | [
"dataframe",
"needs attention",
"feature"
]
| praateekmahajan | 1 |
kizniche/Mycodo | automation | 1,383 | Introducing a Version of the Enhanced Mycodo Integration for Home Assistant HACS | Hello everyone,
I'm thrilled to announce the release of an enhanced Mycodo integration for Home Assistant, specifically designed for compatibility with HACS.
### Release Information
This is a **beta version** of the integration. I've thoroughly tested it on my own, but I am eager to hear your feedback and to identify any issues you may encounter. Here are the details of this release:
- **GitHub Link:** [Enhanced Mycodo Integration](https://github.com/zachi40/home-assistant-mycodo)
### Adherence to Community Guidelines
I am dedicated to ensuring that this integration complies with all community guidelines and standards. If you notice any issues or believe that my modifications inadvertently breach community rules, please contact me immediately. I am prepared to make necessary changes or remove the content if needed.
I am excited to see how this enhancement can serve the wider Home Assistant and Mycodo communities and am looking forward to your feedback.
Thank you for your support and interest!
| open | 2024-05-19T09:05:09Z | 2024-09-20T16:18:21Z | https://github.com/kizniche/Mycodo/issues/1383 | []
| zachi40 | 4 |
cvat-ai/cvat | tensorflow | 8,512 | Superuser login fails after updating docker desktop | ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
_No response_
### Expected Behavior
_No response_
### Possible Solution
_No response_
### Context
I created a new superuser account with [docker exec -it cvat_server bash -ic 'python3 ~/manage.py createsuperuser'], but the error 'Unable to log in with provided credentials' occurred. If I create a regular account, I can log in with that account.
If there's any change since the last login, it's about [1. downgraded the numpy version 2. updated the version of docker desktop to the latest version], but after you reboot the pc after that update, you've seen pulling the container when you use the command [docker compose -f docker-compose.yml -f components/serverless/docker-compose.yml up -d]. Is there a way to log in as a superuser? I want to back up data such as project/task/annotation.
This is the error message on browser console.
server-proxy.ts:467
POST http://localhost:8080/api/auth/login?org= 400 (Bad Request)
(Anonymous) @ xhr.js:195
xhr @ xhr.js:15
Jo @ dispatchRequest.js:51
Promise.then
_request @ Axios.js:149
request @ Axios.js:40
(Anonymous) @ Axios.js:212
(Anonymous) @ bind.js:5
login @ server-proxy.ts:467
(Anonymous) @ api-implementation.ts:106
apiWrapper @ plugins.ts:43
await in apiWrapper
login @ api.ts:77
(Anonymous) @ auth-actions.ts:102
(Anonymous) @ index.js:16
t.<computed> @ bindActionCreators.js:8
onSubmit @ login-page.tsx:44
onFinish @ login-form.tsx:112
onFinish @ Form.js:73
(Anonymous) @ useForm.js:855
Promise.then
(Anonymous) @ useForm.js:851
onSubmit @ Form.js:130
Re @ react-dom.production.min.js:54
Be @ react-dom.production.min.js:54
(Anonymous) @ react-dom.production.min.js:55
zo @ react-dom.production.min.js:105
$o @ react-dom.production.min.js:106
(Anonymous) @ react-dom.production.min.js:117
uc @ react-dom.production.min.js:274
Pe @ react-dom.production.min.js:52
Vo @ react-dom.production.min.js:109
qn @ react-dom.production.min.js:74
Gn @ react-dom.production.min.js:73
cvat-app.tsx:421
Unable to log in with provided credentials.
o @ cvat-app.tsx:421
showErrors @ cvat-app.tsx:431
componentDidUpdate @ cvat-app.tsx:306
As @ react-dom.production.min.js:261
_s @ react-dom.production.min.js:260
ys @ react-dom.production.min.js:259
(Anonymous) @ react-dom.production.min.js:283
wc @ react-dom.production.min.js:281
cc @ react-dom.production.min.js:273
Wl @ react-dom.production.min.js:127
(Anonymous) @ react-dom.production.min.js:267
XMLHttpRequest.send
(Anonymous) @ xhr.js:195
xhr @ xhr.js:15
Jo @ dispatchRequest.js:51
Promise.then
_request @ Axios.js:149
request @ Axios.js:40
(Anonymous) @ Axios.js:212
(Anonymous) @ bind.js:5
login @ server-proxy.ts:467
(Anonymous) @ api-implementation.ts:106
apiWrapper @ plugins.ts:43
await in apiWrapper
login @ api.ts:77
(Anonymous) @ auth-actions.ts:102
(Anonymous) @ index.js:16
t.<computed> @ bindActionCreators.js:8
onSubmit @ login-page.tsx:44
onFinish @ login-form.tsx:112
onFinish @ Form.js:73
(Anonymous) @ useForm.js:855
Promise.then
(Anonymous) @ useForm.js:851
onSubmit @ Form.js:130
Re @ react-dom.production.min.js:54
Be @ react-dom.production.min.js:54
(Anonymous) @ react-dom.production.min.js:55
zo @ react-dom.production.min.js:105
$o @ react-dom.production.min.js:106
(Anonymous) @ react-dom.production.min.js:117
uc @ react-dom.production.min.js:274
Pe @ react-dom.production.min.js:52
Vo @ react-dom.production.min.js:109
qn @ react-dom.production.min.js:74
Gn @ react-dom.production.min.js:73
### Environment
```Markdown
WSL2 Ubuntu 22.04
Docker desktop 4.33.1>4.34.2(docker engine 27.1.1>27.2.0)
GPU RTX4090
```
| closed | 2024-10-05T07:31:49Z | 2024-10-23T06:26:25Z | https://github.com/cvat-ai/cvat/issues/8512 | [
"bug",
"need info"
]
| prnr | 6 |
miguelgrinberg/Flask-SocketIO | flask | 1,507 | Why does Flask-SocketIO require sticky sessions? (and standard practices) | Hi,
I was wondering why are sticky sessions required for hosting flask-socketio server. From my understanding, sticky sessions are basically used to route a request to the same server where the session originated.
1,) (this might be trivial since I am fairly new to backend development) -- why do you need sticky sessions for multiple workers in the same server. Isn't it possible for them to share session information?
2.) I wanted to confirm - the eventlet flask-socketio server doesn't use multiple workers by default right?
3.) To store the session id's I am using a dictionary in flask app with keys as a random unique id for each client and values as session-id as follows.
```
app = Flask(__name__)
app.clients = {}
# SocketIO
socketio = SocketIO(app)
@socketio.on('connect', namespace='/custom')
def events_connect():
userid = str(uuid.uuid4())
session['userid'] = userid
current_app.clients[userid] = request.sid
emit('userid', {'userid': userid})
```
Is there a suggested/standard approach to store the room information? I believe this should be thread-safe and should work even if I use multiple workers.
Thanks,
| closed | 2021-03-28T08:28:12Z | 2021-03-28T18:14:53Z | https://github.com/miguelgrinberg/Flask-SocketIO/issues/1507 | [
"question"
]
| Naman-ntc | 5 |
ray-project/ray | data-science | 51,060 | [Ray Core] `ray.data.Dataset.repartition` not working consistently with doc/error message | ### What happened + What you expected to happen
```
ds = ds.repartition(num_blocks=x, target_num_rows_per_block=y)
```
Obviously it's conflicting to setting both. Calling this gives the error `ValueError: Either num_blocks or target_num_rows_per_block must be set, but not both.`, as expected.
But this doesn't work `ds = ds.repartition(target_num_rows_per_block=y)`. I got `TypeError: Dataset.repartition() missing 1 required positional argument: 'num_blocks'`
This doesn't seem to make sense to me. According to [doc](https://docs.ray.io/en/latest/data/api/doc/ray.data.Dataset.repartition.html), it should be happy with one of the 2 arguments given.
### Versions / Dependencies
ray 2.43.0
Python 3.11.11
Ubuntu 22.04.3 LTS
### Reproduction script
```
import ray
ds = ray.data.range(100)
ds.repartition(target_num_rows_per_block=10)
```
### Issue Severity
None | closed | 2025-03-04T10:12:28Z | 2025-03-10T22:08:28Z | https://github.com/ray-project/ray/issues/51060 | [
"bug",
"P1",
"triage",
"data"
]
| marcmk6 | 3 |
STVIR/pysot | computer-vision | 123 | SiamRPN vs SiamRPN++ | Hi! The models with the name "siamrpn" in MODEL_ZOO are the SiamRPN++ or the SiamRPN ones? Thank you! | closed | 2019-07-22T07:16:19Z | 2019-07-30T03:22:23Z | https://github.com/STVIR/pysot/issues/123 | []
| BarqueroGerman | 1 |
deepinsight/insightface | pytorch | 1,871 | 2d106track | When will you release the pre-trained model 2d106track? | open | 2021-12-28T13:30:34Z | 2021-12-28T13:30:34Z | https://github.com/deepinsight/insightface/issues/1871 | []
| varshagoalla | 0 |
LAION-AI/Open-Assistant | machine-learning | 3,483 | In search of Swag Designer! | We continue to see the importance of building a high-quality dataset set for fine-tuning. To promote additional support, we want to offer people t-shirts, stickers, and other types of swag for hitting different internal milestones. If you would like to assist, feel free to post a few links to some design ideas you have. | closed | 2023-06-16T11:18:05Z | 2023-06-24T13:25:14Z | https://github.com/LAION-AI/Open-Assistant/issues/3483 | [
"design"
]
| camsdixon1 | 3 |
streamlit/streamlit | python | 10,279 | OSError [Error 2] – Incorrect Path and Missing .DS_Store File in Site-Packages | ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar issues.
- [x] I added a very descriptive title to this issue.
- [x] I have provided sufficient information below to help reproduce this issue.
### Summary
There is an issue with installing the Python package Streamlit in a school computer lab environment running Windows 10. While the package can be successfully installed when logged in as a local administrator, it fails to install for network users (students). Even manual installation attempts do not resolve the problem. The installation process encounters errors, despite other Python packages being installed without any issues. This suggests that the problem is specific to Streamlit, and may be related to user permissions, path configurations, or network-related restrictions that are preventing the package from being installed for non-administrative users. This issue is hindering the ability for students to install the necessary package for their work in the lab.
`ERROR: Could not install packages due to an OSError: [Error 2] No such file or directory:
'I:\\Remote.V2\\AppData\\Python\\Python310\\site-packages\\.DS_Store‘`
### Reproducible Code Example
```Python
```
### Steps To Reproduce
1. Log in as a network user (student) on a Windows 10 machine in the computer lab.
2. Attempt to install Streamlit using pip install streamlit.
3. Observe the installation failure, with no errors for other Python packages.
4. Try manual installation of Streamlit; observe that it still doesn’t work for network users.
5. Log in as a local administrator and install Streamlit successfully.
### Expected Behavior
Streamlit should install successfully for all users (including network users) with the correct installation path configured. The package should be recognized and functional, allowing students to run streamlit run without errors. Other Python packages should also install and work seamlessly for network users, just as they do for local administrators.
### Current Behavior
`
ERROR: Could not install packages due to an OSError: [Error 2] No such file or directory:
'I:\\Remote.V2\\AppData\\Python\\Python310\\site-packages\\.DS_Store‘
`
### Is this a regression?
- [ ] Yes, this used to work in a previous version.
### Debug info
- Streamlit version: 1.41.1
- Python version: 3.10 (https://thonny.org/)
- Operating System: Windows 10
- Browser: Chrome / Edge
### Additional Information
_No response_ | closed | 2025-01-29T08:24:08Z | 2025-02-03T20:10:41Z | https://github.com/streamlit/streamlit/issues/10279 | [
"type:bug",
"status:awaiting-user-response"
]
| Schmitt-A | 3 |
modoboa/modoboa | django | 2,512 | DNS configuration is not displayed | It is not possible to display DNS configuration help for a domain in the new UI. | closed | 2022-05-11T15:54:16Z | 2022-05-11T15:55:09Z | https://github.com/modoboa/modoboa/issues/2512 | [
"bug",
"new-ui"
]
| tonioo | 0 |
gradio-app/gradio | data-science | 10,709 | ChatInterface strips unused tags on display | ### Describe the bug
put
```
test
<test>
test
```
Into
```python
import gradio as gr
def fn(a, b):
print(a)
return a
with gr.Blocks() as demo:
gr.ChatInterface(fn)
demo.launch()
```
The displayed text only shows `test` and not the subsequent two lines
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Severity
I can work around it | closed | 2025-03-02T14:47:38Z | 2025-03-05T11:09:35Z | https://github.com/gradio-app/gradio/issues/10709 | [
"bug"
]
| brycepg | 3 |
google/trax | numpy | 1,661 | Trax Import Error | ### Description
Hello, I am having issues with the tensorflow imports while using trax, I've never experienced the issue before and not sure how to solve it.
### Environment information
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
<ipython-input-2-8b607b832815> in <module>
11 from nltk.tokenize import sent_tokenize, word_tokenize
12 from nltk.stem import WordNetLemmatizer
---> 13 import trax
14 import trax.fastmath.numpy as np
15 from trax import layers as tl
~/opt/anaconda3/lib/python3.7/site-packages/trax/__init__.py in <module>
16 """Trax top level import."""
17
---> 18 from trax import data
19 from trax import fastmath
20 from trax import layers
~/opt/anaconda3/lib/python3.7/site-packages/trax/data/__init__.py in <module>
67 from trax.data.inputs import UniformlySeek
68
---> 69 from trax.data.tf_inputs import add_eos_to_output_features
70 from trax.data.tf_inputs import BertGlueEvalStream
71 from trax.data.tf_inputs import BertGlueTrainStream
~/opt/anaconda3/lib/python3.7/site-packages/trax/data/tf_inputs.py in <module>
30 import numpy as np
31 import scipy
---> 32 import t5.data
33 import tensorflow as tf
34 import tensorflow_datasets as tfds
~/opt/anaconda3/lib/python3.7/site-packages/t5/__init__.py in <module>
15 """Import API modules."""
16
---> 17 import t5.data
18 import t5.evaluation
19 import t5.models
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/__init__.py in <module>
15 """Import data modules."""
16
---> 17 from t5.data.dataset_providers import * # pylint:disable=wildcard-import
18 from t5.data.feature_converters import * # pylint:disable=wildcard-import
19 from t5.data.glue_utils import * # pylint:disable=wildcard-import
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/dataset_providers.py in <module>
30 import dataclasses
31 import gin
---> 32 from t5.data import utils
33 from t5.data.preprocessors import tokenize as tokenize_preprocessor
34 from t5.data.vocabularies import Vocabulary
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/utils.py in <module>
25 import gin
26 import numpy as np
---> 27 from t5.data import vocabularies
28 import tensorflow.compat.v2 as tf
29 import tensorflow_datasets as tfds
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/vocabularies.py in <module>
21
22 import tensorflow.compat.v2 as tf
---> 23 import tensorflow_text as tf_text
24
25 import sentencepiece as sentencepiece_processor
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/__init__.py in <module>
19 # pylint: disable=wildcard-import
20 from tensorflow_text.python import keras
---> 21 from tensorflow_text.python import metrics
22 from tensorflow_text.python.ops import *
23
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/__init__.py in <module>
18
19 # pylint: disable=wildcard-import
---> 20 from tensorflow_text.python.metrics.text_similarity_metric_ops import *
21
22 # Public symbols in the "tensorflow_text.metrics" package.
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/text_similarity_metric_ops.py in <module>
26 from tensorflow.python.framework import load_library
27 from tensorflow.python.platform import resource_loader
---> 28 gen_text_similarity_metric_ops = load_library.load_op_library(resource_loader.get_path_to_datafile('_text_similarity_metric_ops.dylib'))
29
30
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/load_library.py in load_op_library(library_filename)
56 RuntimeError: when unable to load the library or get the python wrappers.
57 """
---> 58 lib_handle = py_tf.TF_LoadLibrary(library_filename)
59 try:
60 wrappers = _pywrap_python_op_gen.GetPythonWrappers(
NotFoundError: dlopen(/Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib, 6): Symbol not found: __ZN10tensorflow15TensorShapeBaseINS_11TensorShapeEEC2EN4absl14lts_2020_02_254SpanIKxEE
Referenced from: /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib
Expected in: /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/../libtensorflow_framework.2.dylib
in /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib
```
OS: 10.15.7
$ pip freeze | grep trax
# your output here
trax==1.3.9
$ pip freeze | grep tensor
# your output here
mesh-tensorflow==0.1.18
tensor2tensor==1.15.7
tensorboard==2.5.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.5.0
tensorflow-addons==0.12.1
tensorflow-datasets==4.2.0
tensorflow-estimator==2.5.0
tensorflow-gan==2.0.0
tensorflow-hub==0.11.0
tensorflow-metadata==0.27.0
tensorflow-probability==0.7.0
tensorflow-text==2.4.3
$ pip freeze | grep jax
# your output here
jax==0.2.9
jaxlib==0.1.59
$ python -V
# Python 3.7.6
```
| open | 2021-07-01T15:44:22Z | 2021-07-01T15:44:22Z | https://github.com/google/trax/issues/1661 | []
| memora0101 | 0 |
pennersr/django-allauth | django | 3,142 | Integration with fusionauth? | Hello,
I'm hoping to use [Inventree](https://inventree.readthedocs.io/en/latest/) for our makerspace and connect it to [FusionAuth](https://fusionauth.io/) via django-allauth (Inventree already uses this library), however I can't see FusionAuth listed as an Auth provider.
I know that FusionAuth will provide generic OAuth credentials, but I can't see how to configure a generic OAuth provider in the django-allauth config, and FusionAuth is not listed.
Have I missed a step in the documentation somewhere? | closed | 2022-08-18T09:30:40Z | 2022-12-10T21:04:25Z | https://github.com/pennersr/django-allauth/issues/3142 | []
| proffalken | 1 |
microsoft/nni | pytorch | 4,964 | v2.8 | yarn install v1.22.10
[1/4] Resolving packages...
success Already up-to-date.
Done in 0.46s.
yarn run v1.22.10
$ tsc && jupyter labextension build .
Please supply at least one subcommand: check, disable, enable, install, link, list, uninstall, unlink
error Command failed with exit code 1.
info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
Traceback (most recent call last):
File "setup.py", line 300, in <module>
_setup()
File "setup.py", line 142, in _setup
'develop': Develop, | closed | 2022-06-24T06:33:18Z | 2022-06-28T08:45:24Z | https://github.com/microsoft/nni/issues/4964 | [
"installation",
"user raised",
"waiting user confirm",
"support"
]
| luboy522 | 4 |
strawberry-graphql/strawberry | asyncio | 2,874 | Codegen should be able to know the `__typename` for an object | <!--- Provide a general summary of the changes you want in the title above. -->
<!--- This template is entirely optional and can be removed, but is here to help both you and us. -->
<!--- Anything on lines wrapped in comments like these will not show up in the final text. -->
## Feature Request Type
- [ ] Core functionality
- [x] Alteration (**backward compatible enhancement**/optimization) of existing feature(s)
- [ ] New behavior
## Description
The code generator doesn't (currently) know very much about the GraphQL types. e.g. a query like:
```
query OperationName {
__typename
union {
... on Animal {
age
}
... on Person {
name
}
}
}
```
Will generate classes like:
```py
class OperationNameResultUnionAnimal:
# typename: Animal
age: int
class OperationNameResultUnionPerson:
# typename: Person
name: str
OperationNameResultUnion = Union[OperationNameResultUnionAnimal, OperationNameResultUnionPerson]
class OperationNameResult:
union: OperationNameResultUnion
```
In principle, the `__typename` field of the response should tell us whether we are going to get an `Animal` or a `Person`, but it would be very handy for client generators to be able to easily construct the following mapping:
```py
{"Animal": OperationNameResultUnionAnimal, "Person": OperationNameResultUnionPerson}
```
Currently, this is very hard to do with the information exposed to the client generator. Let's plumb that information through so that clients can start to take advantage of it. | closed | 2023-06-21T13:33:27Z | 2025-03-20T15:56:15Z | https://github.com/strawberry-graphql/strawberry/issues/2874 | []
| mgilson | 0 |
15r10nk/inline-snapshot | pytest | 122 | Pytest integration examples are broken in the documentation | All terminal output examples are broken on the [pytest integration page](https://15r10nk.github.io/inline-snapshot/pytest/) in the documentation.
<img width="686" alt="image" src="https://github.com/user-attachments/assets/0fef22e5-5372-4e0f-85df-554f3870e427">
They all fail with the error: `NameError: name 'dirty_equals' is not defined`
Just letting you know in case you are not aware. Thanks for making a great package! 😃 | closed | 2024-10-24T07:49:37Z | 2024-10-24T10:43:42Z | https://github.com/15r10nk/inline-snapshot/issues/122 | []
| pederhan | 2 |
wemake-services/django-test-migrations | pytest | 35 | Support python3.8 | closed | 2020-02-25T13:39:54Z | 2020-02-25T13:40:14Z | https://github.com/wemake-services/django-test-migrations/issues/35 | []
| sobolevn | 0 |
|
pydantic/FastUI | fastapi | 171 | Form Methods | BaseForm only allows POST, GOTO and GET methods, defaults to POST. Any intention to expand this to include DELETE and PATCH?
https://github.com/pydantic/FastUI/blob/cec25c61a7cc5a716d05d21039f95be3e8dac0e8/src/python-fastui/fastui/components/forms.py#L74 | closed | 2024-02-05T09:22:27Z | 2024-02-14T09:50:01Z | https://github.com/pydantic/FastUI/issues/171 | []
| Chops91 | 4 |
babysor/MockingBird | deep-learning | 680 | 奇奇怪怪的报错 | Using model: Tacotron
Using device: cuda
Initialising Tacotron Model...
\Loading the json with %s
{'sample_rate': 16000, 'n_fft': 800, 'num_mels': 80, 'hop_size': 200, 'win_size': 800, 'fmin': 55, 'min_level_db': -100, 'ref_level_db': 20, 'max_abs_value': 4.0, 'preemphasis': 0.97, 'preemphasize': True, 'tts_embed_dims': 512, 'tts_encoder_dims': 256, 'tts_decoder_dims': 128, 'tts_postnet_dims': 512, 'tts_encoder_K': 5, 'tts_lstm_dims': 1024, 'tts_postnet_K': 5, 'tts_num_highways': 4, 'tts_dropout': 0.5, 'tts_cleaner_names': ['basic_cleaners'], 'tts_stop_threshold': -3.4, 'tts_schedule': [[2, 0.001, 10000, 12], [2, 0.0005, 15000, 12], [2, 0.0002, 20000, 12], [2, 0.0001, 30000, 12], [2, 5e-05, 40000, 12], [2, 1e-05, 60000, 12], [2, 5e-06, 160000, 12], [2, 3e-06, 320000, 12], [2, 1e-06, 640000, 12]], 'tts_clip_grad_norm': 1.0, 'tts_eval_interval': 500, 'tts_eval_num_samples': 1, 'tts_finetune_layers': [], 'max_mel_frames': 900, 'rescale': True, 'rescaling_max': 0.9, 'synthesis_batch_size': 16, 'signal_normalization': True, 'power': 1.5, 'griffin_lim_iters': 60, 'fmax': 7600, 'allow_clipping_in_normalization': True, 'clip_mels_length': True, 'use_lws': False, 'symmetric_mels': True, 'trim_silence': True, 'speaker_embedding_size': 256, 'silence_min_duration_split': 0.4, 'utterance_min_duration': 1.6, 'use_gst': True, 'use_ser_for_gst': True}
Trainable Parameters: 0.000M
Loading weights at synthesizer\saved_models\xb\xb.pt
Tacotron weights loaded from step 144
Using inputs from:
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\train.txt
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\mels
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\embeds
Found 284 samples
+----------------+------------+---------------+------------------+
| Steps with r=2 | Batch Size | Learning Rate | Outputs/Step (r) |
+----------------+------------+---------------+------------------+
| 9k Steps | 12 | 0.001 | 2 |
+----------------+------------+---------------+------------------+
Could not load symbol cublasGetSmCountTarget from cublas64_11.dll. Error code 127
Traceback (most recent call last):
File "C:\Users\hyhhy\Desktop\AIless\MockingBird\synthesizer_train.py", line 37, in <module>
train(**vars(args))
File "C:\Users\hyhhy\Desktop\AIless\MockingBird\synthesizer\train.py", line 215, in train
optimizer.step()
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\optimizer.py", line 109, in wrapper
return func(*args, **kwargs)
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 157, in step
adam(params_with_grad,
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 213, in adam
func(params,
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 255, in _single_tensor_adam
assert not step_t.is_cuda, "If capturable=False, state_steps should not be CUDA tensors."
AssertionError: If capturable=False, state_steps should not be CUDA tensors. | open | 2022-07-26T05:48:50Z | 2022-07-27T09:56:29Z | https://github.com/babysor/MockingBird/issues/680 | []
| Secretboy-SMR | 2 |
modelscope/data-juicer | data-visualization | 556 | How to do sentence_dedup | ### Search before continuing 先搜索，再继续
- [x] I have searched the Data-Juicer issues and found no similar feature requests. 我已经搜索了 Data-Juicer 的 issue 列表但是没有发现类似的功能需求。
### Description 描述
请问怎么做句子级的去重，有没有推荐的开源实现？谢谢
### Use case 使用场景
_No response_
### Additional 额外信息
_No response_
### Are you willing to submit a PR for this feature? 您是否乐意为此功能提交一个 PR？
- [ ] Yes I'd like to help by submitting a PR! 是的！我愿意提供帮助并提交一个PR！ | closed | 2025-01-20T01:29:04Z | 2025-02-28T08:38:40Z | https://github.com/modelscope/data-juicer/issues/556 | [
"enhancement"
]
| ftgreat | 2 |
noirbizarre/flask-restplus | api | 481 | How to add Bearer <JWT> in swagger-ui request | Hello,
I have the same issue like : https://github.com/noirbizarre/flask-restplus/issues/315, but I can't find a solution. I a using the flask_restplus 0.11
I've been searching for a long time but I didn't found how to make query with jwt.
My authorisations looks like :
authorizations = {
'apikey': {
'type' : 'apiKey',
'in': 'header',
'name': 'Authorization'
}
}
the resulting query is :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization: 12345"
The api answer :
{
"msg": "Bad Authorization header. Expected value 'Bearer <JWT>'"
}
If I add Bearer in 'authorizations like :
authorizations = {
'apikey': {
'type' : 'apiKey',
'in': 'header',
'name': 'Authorization Bearer'
}
}
the query is :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization Bearer: 12345"
And I want a query like this :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization: Bearer 12345"
Can somebody give me some help ?
Regards.
James
| closed | 2018-06-27T16:54:23Z | 2022-05-16T15:55:38Z | https://github.com/noirbizarre/flask-restplus/issues/481 | []
| jreg2k | 4 |
HIT-SCIR/ltp | nlp | 114 | 分词部分标注数据支持 | closed | 2015-06-10T02:22:52Z | 2015-06-15T09:10:03Z | https://github.com/HIT-SCIR/ltp/issues/114 | [
"feature-request",
"enhancement"
]
| Oneplus | 0 |
|
gradio-app/gradio | data-science | 10,741 | Unable to upload a pdf file of size 2.92MB | ### Describe the bug
`Error
HTTP 413:
413 Request Entity Too Large
nginx/1.18.0`
I am using google colab with to run gradio interface and I am trying to upload a file of size 2.92 MB. I am seeing this error now, never seen this error. I have been using the same code, same gradio interface and same file.
Why am I seeing this error today??
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
#Gradio interface
with gr.Blocks(title=" OCR") as demo:
gr.Markdown("# OCR")
gr.Markdown("Upload a PDF and its ground truth JSON file to convert content to structured JSON with lexicon correction and view performance metrics.")
with gr.Row():
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
gt_json_input = gr.File(label="Upload Ground Truth JSON", file_types=[".json"])
submit_btn = gr.Button("Process")
with gr.Row():
json_output = gr.Textbox(label="Corrected JSON Output", lines=10)
cer_output = gr.Textbox(label="Character Error Rate (CER)", lines=1)
accuracy_output = gr.Textbox(label="Field-Level Accuracy", lines=1)
submit_btn.click(fn=process_pdf, inputs=[pdf_input, gt_json_input], outputs=[json_output, cer_output, accuracy_output])
logging.info("Launching Gradio interface")
demo.launch(share=True)
```
### Screenshot
### Logs
```shell
not getting any errors while debugging
```
### System Info
```shell
Gradio Environment Information:
------------------------------
Operating System: Linux
gradio version: 5.20.0
gradio_client version: 1.7.2
------------------------------------------------
gradio dependencies in your environment:
aiofiles: 23.2.1
anyio: 3.7.1
audioop-lts is not installed.
fastapi: 0.115.11
ffmpy: 0.5.0
gradio-client==1.7.2 is not installed.
groovy: 0.1.2
httpx: 0.28.1
huggingface-hub: 0.28.1
jinja2: 3.1.5
markupsafe: 2.1.5
numpy: 1.26.4
orjson: 3.10.15
packaging: 24.2
pandas: 2.2.2
pillow: 11.1.0
pydantic: 2.10.6
pydub: 0.25.1
python-multipart: 0.0.20
pyyaml: 6.0.2
ruff: 0.9.9
safehttpx: 0.1.6
semantic-version: 2.10.0
starlette: 0.46.0
tomlkit: 0.13.2
typer: 0.15.1
typing-extensions: 4.12.2
urllib3: 2.3.0
uvicorn: 0.34.0
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.
gradio_client dependencies in your environment:
fsspec: 2024.10.0
httpx: 0.28.1
huggingface-hub: 0.28.1
packaging: 24.2
typing-extensions: 4.12.2
websockets: 14.2
```
### Severity
Blocking usage of gradio | closed | 2025-03-06T11:11:51Z | 2025-03-08T00:26:16Z | https://github.com/gradio-app/gradio/issues/10741 | [
"bug"
]
| likith1908 | 3 |
deezer/spleeter | deep-learning | 752 | [Discussion] How to install to High Sierra 10.13.6? | <!-- Please respect the title [Discussion] tag. -->
Hello.
Please help.
I cannot seem to be able to install spleeter to MacOs High Sierra.
Spleeter worked fine for me in Sierra but I now I cannot get it to work.
I have tried every installation method and advice I've search from google without luck.
I've tried to install with "conda" and "pip" without luck.
I'm now on version 2.1.0 (forcing with "==2.1.0 etc suffix) and this is the beginning of the error message atm :
Traceback (most recent call last):
File "/Users/finalcountdown/opt/anaconda3/lib/python3.7/site-packages/soundfile.py", line 143, in <module>
_snd = _ffi.dlopen(_libname)
OSError: cannot load library '/Users/finalcountdown/opt/anaconda3/bin/../lib/libsndfile.dylib': dlopen(/Users/finalcountdown/opt/anaconda3/bin/../lib/libsndfile.dylib, 2): Library not loaded: @rpath/libvorbis.0.4.9.dylib
Referenced from: /Users/finalcountdown/opt/anaconda3/lib/libsndfile.1.0.31.dylib
Reason: image not found
(I've tried other versions too. spleeter 1.5.3 is what "pip" and "conda" installs, but it apparently needs something from TF that TensorFlow 1.15 doesn't have, but downgrading to 1.14 doesn't help either.
This is obviously a compatibility issue, but I dunno what combination of versions I should use? Any version of spleeter is pretty much ok for me in this situation.
-Petri L.
If this request for help is not in right place can you please inform
me where to put my request(?). | open | 2022-04-23T14:59:52Z | 2022-07-29T01:01:01Z | https://github.com/deezer/spleeter/issues/752 | [
"question"
]
| bkmikaelo7 | 2 |
LibrePhotos/librephotos | django | 659 | Search without diacritics | It is possible to implement search without diacritics. For example, to have the same search result when using the word Průhonice as Pruhonice? | open | 2022-10-09T07:06:33Z | 2022-10-09T07:06:33Z | https://github.com/LibrePhotos/librephotos/issues/659 | [
"enhancement"
]
| Martin22 | 0 |
tflearn/tflearn | tensorflow | 1,060 | The problem of accuracy of Dnn_regression model using tflearn | Question 1.
I want to check the accuracy of the regression result window, not binary_acc. Because of this, only the binary_acc is displayed in the tensorboard graph, and it is displayed as 0 only.
Question 2.
I would like to apply the loss function to mean absolute percentage error instead of mse. I would like to ask for advice on the code that should be added to objectives.py.
Question 3.
If there is a theoretical error of deep running in my code, I would appreciate your advice.
Here is my code and the results:
# -*- coding: utf-8 -*-
from xlrd import open_workbook
from sklearn.model_selection import train_test_split
import tflearn
import numpy as np
import random
import tensorflow as tf
class EarlyStoppingCallback(tflearn.callbacks.Callback):
def __init__(self, loss_threshold):
self.loss_threshold = loss_threshold
def on_epoch_end(self, training_state):
if training_state.val_loss is None: return
if training_state.val_loss < self.loss_threshold and training_state.val_loss!=0.:
raise StopIteration
# Loss function
def mean_absolute_percentage_error(y_test, estimation_result):
y_test, estimation_result = np.array(y_test), np.array(estimation_result)
return np.mean(np.abs((y_test - estimation_result) / y_test)) * 100
# control variables
needed_col_numbers_for_data = [2,3,4,5,6,7,8,9,10,11]
num_feat = 9
needed_col_numbers_for_label = [11]
starting_row_index = 4 # this variable can be modified according to the data format in given excel file
# read data from excel file
filename = 'concrete_mix_data_6.xlsx'
workbook = open_workbook(filename)
worksheet = workbook.sheet_by_index(0)
nrows = worksheet.nrows
ncols = worksheet.ncols
# data preparation --> data & label
ending_row_index = nrows
data_and_label = []
for row_num in range(starting_row_index-1, ending_row_index):
data_and_label.append([worksheet.row_values(row_num)[i] for i in needed_col_numbers_for_data])
random.shuffle(data_and_label)
data_and_label = np.array(data_and_label)
X = data_and_label[:,:-1]
Y = np.array(data_and_label[:,-1])
Y = np.reshape(Y, (len(Y),1))
# split data into training and testing group
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
# init
tflearn.init_graph(num_cores=4, gpu_memory_fraction=0.5)
print('======================= DNN regression model ==================')
# Build neural network
net = tflearn.input_data(shape=[None, num_feat])
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer1')
net = tflearn.dropout(net, 0.5, name='Dropout1')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer2')
net = tflearn.dropout(net, 0.5, name='Dropout2')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer3')
net = tflearn.dropout(net, 0.5, name='Dropout3')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer4')
# net = tflearn.dropout(net, 0.5, name='Dropout4')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer5')
# net = tflearn.dropout(net, 0.5, name='Dropout5')
net = tflearn.fully_connected(net, 1, activation='linear')
net = tflearn.regression(net, optimizer='Adam', loss='mean_square', metric='accuracy', learning_rate=0.001)
# Define model
model = tflearn.DNN(net, tensorboard_verbose=3, tensorboard_dir="/tmp/tflearn_logs/test")
# Start training (apply gradient descent algorithm)
# print X
# print Y
earlyStoppingCallback = EarlyStoppingCallback(loss_threshold = 60)
model.fit(X, Y, n_epoch=10000, batch_size=60, show_metric=True, snapshot_epoch=True, validation_set=0.25, callbacks=earlyStoppingCallback, run_id="tensorboard_test")
# Predictive Data('best')
estimation_result = model.predict(x_test)
# Results
result = np.concatenate((estimation_result,y_test),axis=1)
print('======================= DNN regression Results ==================')
print('Estimation Result / Target ')
print(result)
print('==================================================================')
mape = mean_absolute_percentage_error(y_test, estimation_result)
print('Mean absolute percentage error :',mape)
# mse = mean_squared_error(y_test, estimation_result)
# print(mse)
# # Save a model
model.save('tensorboard_test2.tflearn')
Training Step: 7377 | total loss: 133.95444 | time: 0.787s
| Adam | epoch: 1054 | loss: 133.95444 - binary_acc: 0.0000 -- iter: 360/390
Training Step: 7378 | total loss: 132.92976 | time: 1.935s
| Adam | epoch: 1054 | loss: 132.92976 - binary_acc: 0.0000 | val_loss: 76.89469 - val_acc: 0.0000 -- iter: 390/390 | closed | 2018-06-07T09:21:19Z | 2018-06-15T11:33:09Z | https://github.com/tflearn/tflearn/issues/1060 | []
| Rra2 | 0 |
deepinsight/insightface | pytorch | 1,835 | TypeError: 'int' object is not subscriptable | I try to do inference on your pretrained model following: https://github.com/deepinsight/insightface/blob/master/examples/demo_analysis.py
Here's my code:
```
import insightface
from cv2 import cv2
app = insightface.app.FaceAnalysis()
app.prepare(0, det_size=640)
image = cv2.imread("my-picture.jpg")
faces = app.get(image)
assert len(faces)==6
rimg = app.draw_on(image, faces)
cv2.imwrite("output.jpg", rimg)
```
Error:
```
Traceback (most recent call last):
File "E:\Work work\Python\Work\atin\FaceRecog\main.py", line 7, in <module>
faces = app.get(image)
File "E:\Anaconda\envs\insightface\lib\site-packages\insightface\app\face_analysis.py", line 59, in get
bboxes, kpss = self.det_model.detect(img,
File "E:\Anaconda\envs\insightface\lib\site-packages\insightface\model_zoo\retinaface.py", line 212, in detect
model_ratio = float(input_size[1]) / input_size[0]
TypeError: 'int' object is not subscriptable
find model: .insightface\models\buffalo_l\w600k_r50.onnx recognition ['None', 3, 112, 112] 127.5 127.5
set det-size: 640
```
| closed | 2021-11-19T06:48:47Z | 2021-11-19T08:07:15Z | https://github.com/deepinsight/insightface/issues/1835 | []
| iumyx2612 | 2 |
microsoft/nni | machine-learning | 5,670 | NNI install in development mode on macOS M1 | I install nni from soruce code in development mode on apple M1 by following instructions from https://nni.readthedocs.io/en/stable/notes/build_from_source.html
After install, I can import nni successfully.
However, when I try some simple NAS tutorial in here https://nni.readthedocs.io/en/stable/tutorials/hello_nas.html
I fail to import sublibrary retiarii
from nni.retiarii import model_wrapper
How should I solve the missing extra dependencies if I install from source code for M1?
thank you for your help!
| open | 2023-08-24T19:57:07Z | 2023-10-25T14:08:56Z | https://github.com/microsoft/nni/issues/5670 | []
| XiaochunLiu | 1 |
piccolo-orm/piccolo | fastapi | 700 | UniqueViolationError within a transaction (using get_or_create) | I noticed that when multiple concurrent executions of my `add_list` coroutine overlap, occasionally I get `UniqueViolationError`. I initially thought this was protected against by using a transaction. But it seems that the transaction does not block other coroutines from starting another transaction (on same table) as well, is that correct?
For illustration, the `add_list` function looks something like the following, and is started multiple times (concurrently), with almost no break in between.
```
async def add_main(i):
m = await Main.objects().get_or_create(Main.key == f"Main-{i}")
a = await EntityA.objects().get_or_create(EntityA.key == f"EntityA-{i}")
m.foreign_1 = a
await m.save()
async def add_list():
async with Main._meta.db.transaction():
for i in range(100):
await add_main(i)
```
I don't get the `UniqueViolationError` on the "Main" table, but on a related one (e.g. EntityA), and while doing `get_or_create`. I think I will add an `with asyncio.Lock` before the transaction - Or are there better ways to prevent the `UniqueViolationError` from occurring? | open | 2022-12-01T22:38:43Z | 2022-12-02T22:38:35Z | https://github.com/piccolo-orm/piccolo/issues/700 | []
| powellnorma | 2 |
PeterL1n/BackgroundMattingV2 | computer-vision | 136 | Evaluation metrics | Hi,
Great job!
For a fair comparison, is it possible to share the evaluation metrics code in the paper?
Thanks | closed | 2021-07-21T08:06:27Z | 2021-11-15T11:41:24Z | https://github.com/PeterL1n/BackgroundMattingV2/issues/136 | []
| hqqxyy | 7 |
feder-cr/Jobs_Applier_AI_Agent_AIHawk | automation | 232 | Add file_name inside failed.json and success.json | Adding the file name inside `.json` files can help to create a track of generated resume for each job application
| closed | 2024-09-01T21:02:44Z | 2024-09-11T02:00:15Z | https://github.com/feder-cr/Jobs_Applier_AI_Agent_AIHawk/issues/232 | []
| VanshWadhwa | 6 |
jonaswinkler/paperless-ng | django | 1,494 | Server Error 500 after Bare Metal Update DB upgrade 1.4.2 -> 1.5.0 | I had a bare metal install of 1.4.2 running on buster.
Now after upgrade to bullseye I installed 1.5.0 and just copied the paperless.conf file.
Most of the stuff runs fine, but: While trying to access E-Mail accounts , E-Mail rules and planned tasks from the admin menu I get a Server Error 500. My guess is, that this could be due to some db structure incompatibilities, but tbh I do not have a glue.
I do not see anything in any log?
What could I do?
What is a clean upgrade path for bare metal? | closed | 2021-12-17T10:50:57Z | 2021-12-17T11:49:02Z | https://github.com/jonaswinkler/paperless-ng/issues/1494 | []
| steviehs | 1 |
JoeanAmier/TikTokDownloader | api | 256 | 是否可以在批量下载视频的时候将获取到的链接保存到本地txt文件 | 获取到用户视频链接后，将这些链接和视频大小保存一份到本地txt | open | 2024-07-22T02:08:32Z | 2024-07-22T10:23:21Z | https://github.com/JoeanAmier/TikTokDownloader/issues/256 | []
| aliuzzz | 1 |
deepspeedai/DeepSpeed | machine-learning | 6,006 | [BUG] Bad compatibility check by testing the existence of a CHANGELOG.md file which is not always available depending on the way of CUTLASS library installation | In this code, if we have nvidia-cutlass package installed with pip, there will be no CHANGELOG.md file in the installed site-package directory, and hence the test result will be false negative.
https://github.com/microsoft/DeepSpeed/blob/4d4ff0edddb28a4d53d40dffad9ba16e59b83aec/op_builder/evoformer_attn.py#L55C41-L55C53
Please fix this by checking other files that are always available or maybe the best way to check is to see if the following python code return a valid result without throwing an exception:
```python
import os, cutlass
cutlass_path = os.path.dirname(cutlass.__file__)
cutlass_version = cutlass.__version__
```
| closed | 2024-08-16T00:33:29Z | 2024-08-22T17:36:14Z | https://github.com/deepspeedai/DeepSpeed/issues/6006 | []
| zhangwei217245 | 0 |
comfyanonymous/ComfyUI | pytorch | 6,653 | Comfy X Braile | ### Feature Idea
[Cheesecake Johnson-]
Original post https://civitai.com/articles/11100
https://www.youtube.com/watch?v=_p1vo9uwu18
https://www.youtube.com/watch?v=0fIg4rI4cDw&t=15s
I thought of something uncommon!
As Comfy supports TTS, it could also support Braille! Would be so cool... not for me, but for people who are actually visually impaired.
I bet a lot of people would be happy if comfy implemented this and other visually-impaired tools.
Imagine a visually impaired person producing images of what he thinks the world looks like simply by using the interface of comfy, with a different CSS so it displays properly on their braille tablet.
I always been fascinated with adaptive technologies even thought I'm healthy and in a whole single piece.
http://braillespecs.github.io/braille-css/20210129/
The braille tablets that are developed in universities are supposed to be capable of drawing full interfaces and probably also have push buttons and drag and drop but I seen more modern devices for the broader public which have visual light screen behind too for non-blind people to see... This is a domain in perpetual development.
As AIs are mostly developed in renowned universities, I'm persuaded that developers will find colleague universitarian individuals who are impassioned by assistive technologies in their close relatives
While this is niche, this is also essential and while there mightn't be masses of budget in it, it's an important thing do do than to make computers have wide accessibility features, more than just a chore.
It's like the fact of teaching sign language... As there's not tons of money into that, hearing impaired or mute people are at risk of lacking competent teachers to teach them sign language. Sometimes things have to be done wherever the money's at. It's some kind of a chore and it's okay like that.
### Existing Solutions
_No response_
### Other
I propose developers would contact various professionals in the domain of sign language, braille and assistive technologies related institutions and organisms in Asia, America and Europe as the standards varies.
After, they can communicate with people in the blind programmer community which could be of a serious helping hand in order to implement Braille CSS and other TTS assistant cues in the HTML.
Blind programmer community is an actual real thing.
Personally, I won't benefit, but might enjoy seeing the result nonetheless. It's selfless.
seeing the world through the eyes of a visually impaired artist would be a huge move forward for their emancipation. As they don't know colours and are more about shapes, it would be very unique.
With this device, visually impaired could visually see the art they generate using marigold depth in comfy
https://www.hackster.io/news/stanford-university-unveils-pin-art-like-tactile-3d-display-for-visually-impaired-3d-modelers-fdadaf780deb
Visually impaired people making art would be so great!
Then people could describe what they see and it would make them create social bounds!
[-Cheesecake Johnson]
| open | 2025-01-31T01:03:13Z | 2025-01-31T01:04:38Z | https://github.com/comfyanonymous/ComfyUI/issues/6653 | [
"Feature"
]
| shiroanon | 0 |
Kav-K/GPTDiscord | asyncio | 256 | [BUG] | **Describe the bug**
ValueError: ALLOWED_GUILDS is not defined properly in the environment file!Please copy your server's guild ID and put it into ALLOWED_GUILDS in the .env file.For example a line should look like: `ALLOWED_GUILDS="971268468148166697"`
I have a 19 digit ID | closed | 2023-04-05T02:47:20Z | 2023-04-06T06:08:11Z | https://github.com/Kav-K/GPTDiscord/issues/256 | [
"bug"
]
| SnowbambooXsshallot | 1 |
pyg-team/pytorch_geometric | deep-learning | 9,704 | Unclear usage of the PNA model | ### 📚 Describe the documentation issue
https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.PNA.html
It's listed here that the **non-optional** arguments are:
**in_channels, hidden_channels, num_layers, out_channels**
which is consistent with the other models (GIN-GCN) that create the layers under the hood.
However, PNA requires the presence of 'aggregators', 'scaler', 'degree' making them **non-optional** arguments.
```
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.PNAConv`.
```
### Suggest a potential alternative/fix
A simple fix would be:
```
def init_conv(self, in_channels: int, out_channels: int,
**kwargs) -> MessagePassing:
## For the deg argument ##
The init must recieve the deg argument
## Solution-1 for the aggregators/scalers ##
Defined here:
aggregators = ['mean', 'min', 'max', 'std']
scalers = ['identity', 'amplification', 'attenuation']
return PNAConv(in_channels, out_channels, aggregators = aggregators , scalers = scalers )
```
Alternatively, default the constructor of PNAConv to use these lists if nothing is defined.
| open | 2024-10-09T16:19:53Z | 2024-10-09T16:19:53Z | https://github.com/pyg-team/pytorch_geometric/issues/9704 | [
"documentation"
]
| Helaly96 | 0 |
mwouts/itables | jupyter | 22 | Center Align Table | How do I center align the header and the rows within the table ? I have tried many things but nothing seems to be working. | closed | 2021-01-23T15:05:01Z | 2021-02-04T23:13:09Z | https://github.com/mwouts/itables/issues/22 | []
| jatin-sandhuria | 1 |
custom-components/pyscript | jupyter | 236 | List comprehension failing with an empty list | The following very simple list comprehension works fine in the interactive Python interpreter, but fails if used in PyScript:
`[x for x in []]`
I would expect it to return an empty list (`[]`), as it does in the interactive interpreter:
```
>>> [x for x in []]
[]
```
but PyScript gives an error:
```
[x for x in []]
^
KeyError: 'x'
```
It's not a very interesting example, but I've had situations where an incoming iterable may be empty and it's easier to just execute the list comprehension without having to add an `if xxx` to the end to filter empty lists. I've worked around it with a `for` loop, but I didn't see a limitation explicitly called out in the docs and wanted to file an issue for it.
Weirdly, I have seen this construct work as expected sometimes, but I can get to to reliably fail if I write an app like this:
```
@time_trigger('startup')
def startup():
[x for x in []]
``` | closed | 2021-08-28T23:00:31Z | 2023-10-18T02:06:17Z | https://github.com/custom-components/pyscript/issues/236 | []
| rccoleman | 0 |
home-assistant/core | python | 140,395 | SLZB-06M integration - connect through VPN | ### The problem
I have two SLZB-06M: one connected in network directly, other - throught VPN
I try to use component to connect to SLZB-06M witch connected throught VPN. First stage was good - devace was added, but second stage (initializing with counter and etc) was failed.
When search in log i saw that component try to connect to internal IP, not vpn ip
### What version of Home Assistant Core has the issue?
core-2025.3.1
### What was the last working version of Home Assistant Core?
_No response_
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
_No response_
### Link to integration documentation on our website
_No response_
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_ | open | 2025-03-11T16:04:50Z | 2025-03-13T09:10:35Z | https://github.com/home-assistant/core/issues/140395 | [
"integration: smlight"
]
| 000al000 | 4 |
igorbenav/fastcrud | pydantic | 52 | Using multi_joined with response object in object | @igorbenav I have more a question about get multi_joined. When i joined 2 table many to one. I can't not create response object in object. Example: I only create like that
```json
"data": [
{
"id": 0,
"title": "string",
"template_id": 0,
"description": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"template_title": "Example text for encryption",
"template_description": "Example description for encryption template"
}
]
```
This is response that i want
```json
"data": [
{
"id": 0,
"title": "string",
"template_id": 0,
"description": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"template":{
"title": "Example text for encryption",
"description": "Example description for encryption template"
}
}
]
```
Please help me :( | closed | 2024-04-15T03:31:43Z | 2024-04-22T06:45:24Z | https://github.com/igorbenav/fastcrud/issues/52 | [
"enhancement",
"FastCRUD Methods"
]
| vnlebaoduy | 8 |
roboflow/supervision | deep-learning | 1,096 | [DetectionDataset] - expand `from_yolo` to include support for OBB (Oriented Bounding Boxes) | ### Description
In [supervision-0.18.0](https://github.com/roboflow/supervision/releases/tag/0.18.0), we added initial support for OBB; it's time to extend it to include dataset loading.
Make the necessary changes in [sv.DetectionDataset.from_yolo](https://supervision.roboflow.com/0.19.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to enable loading OBB datasets from disk in YOLO format. [Here](https://docs.ultralytics.com/datasets/obb/) you can read more about the YOLO OBB Format. In short, each line of the `.txt` file should have the following format.
```
class_index, x1, y1, x2, y2, x3, y3, x4, y4
```
The [sv.OrientedBoxAnnotator](https://supervision.roboflow.com/0.19.0/annotators/#supervision.annotators.core.OrientedBoxAnnotator.annotate) expects information about oriented bounding boxes to be stored in the `xyxyxyxy` field of [`sv.Detections.data`](https://github.com/roboflow/supervision/blob/4729e20a9408fbe08d342cc4dbae835d808686a5/supervision/detection/core.py#L88). Ensure that the information loaded from the dataset is stored there.
### API
Here's an example of how to use the new API. Roboflow allows for the export of segmentation datasets as OBB. Let's ensure that our support for OBB definitely works with datasets exported from Roboflow.
```python
import random
import roboflow
from roboflow import Roboflow
import supervision as sv
roboflow.login()
rf = Roboflow()
project = rf.workspace("roboflow-jvuqo").project("fashion-assistant")
version = project.version(3)
dataset = version.download("yolov8-obb")
train_ds = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/train/images",
annotations_directory_path=f"{dataset.location}/train/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
)
image_name = random.choice(list(train_ds.images))
image = train_data.images[image_name]
detections = train_data.annotations[image_name]
oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
### Additional
- Note: Please share a Google Colab with minimal code to test the new feature. We know it's additional work, but it will speed up the review process. The reviewer must test each change. Setting up a local environment to do this is time-consuming. Please ensure that Google Colab can be accessed without any issues (make it public). Thank you! 🙏🏻 | closed | 2024-04-05T13:02:11Z | 2024-06-19T09:10:24Z | https://github.com/roboflow/supervision/issues/1096 | [
"enhancement",
"help wanted",
"api:datasets",
"Q2.2024"
]
| pedbrgs | 31 |
alpacahq/alpaca-trade-api-python | rest-api | 135 | cancel_order not working | The cancel_order call in the REST api that calls Alpaca seem to execute fine but the order is still open. I tested this outside regular trading hours. Not sure if it works during normal trading hours. Regardless, it should throw an exception if the order was not cancelled. | closed | 2020-01-11T04:59:42Z | 2020-01-29T04:14:58Z | https://github.com/alpacahq/alpaca-trade-api-python/issues/135 | []
| PrashantJalan | 3 |
jupyter/nbviewer | jupyter | 800 | python: error while loading shared libraries: libpython3.6m.so.1.0: cannot open shared object file: No such file or directory | Tried to do a quick run of nbviewer but got this error.
Can't find any solution after some preliminary research.
My local python version is 3.6.5
Thank you! | closed | 2018-10-10T08:01:14Z | 2018-10-16T08:53:21Z | https://github.com/jupyter/nbviewer/issues/800 | []
| Alive24 | 3 |
shibing624/text2vec | nlp | 109 | 如何开启多卡训练？ | 【v1.2.2版本: 支持多卡训练】
我运行[examples/training_sup_text_matching_model.py]文件，发现是单卡训练，请问如何开启多卡训练？ | closed | 2023-08-07T13:27:46Z | 2023-08-08T02:38:55Z | https://github.com/shibing624/text2vec/issues/109 | [
"question"
]
| sangyongjia | 2 |
bmoscon/cryptofeed | asyncio | 1,023 | Kucoin - Unauthenticated subscription disconnects after 24 hours, fails to refresh token, and then cannot reconnect | **Describe the bug**
Using an unauthenticated subscription to Kucoin's trade feed for a period of time (about 24 hours), Kucoin disconnects the websocket and then cryptofeed is unable to reconnect due to cryptofeed not refreshing the connection token.
**To Reproduce**
Steps to reproduce the behavior:
* Unauthenticated KuCoin feed on TRADE channel.
* Allow to run for 24+ hours.
```
2024-04-05 10:47:36,818 : WARNING : KUCOIN: error from exchange {'id': '660fd6c84d0eab582c237b51', 'type': 'error', 'code': 401, 'data': 'token is expired'}
2024-04-05 10:47:36,819 : WARNING : KUCOIN.ws.3: encountered connection issue no close frame received or sent - reconnecting in 1.0 seconds...
```
**Expected behavior**
Cryptofeed should renew the token, reconnect, and continue the channel subscriptions.
**Operating System:**
- Ubuntu 22.04
**Cryptofeed Version**
- cryptofeed 2.4.0 installed via pip / pypi
I see that the Binance exchange code has functions to refresh the auth token, I'll take a look and see if I can get something similar to work with KuCoin and submit a PR if successful.
| open | 2024-04-05T12:22:47Z | 2024-04-23T13:48:15Z | https://github.com/bmoscon/cryptofeed/issues/1023 | [
"bug"
]
| vincentmele | 1 |
tortoise/tortoise-orm | asyncio | 1,066 | How do ON DUPLICATE KEY UPDATE? | Table have a unique constrain on 2 fields.
When i try to `await MyModel.create(**dict_of_data)` with exist entry in unique fields, i got error:
`tortoise.exceptions.IntegrityError: (1062, "Duplicate entry...`
Try `await MyModel(**dict_of_data).save(force_update=True)` raise error:
`tortoise.exceptions.IntegrityError: Can't update object that doesn't exist. PK: None`
How can i do `ON DUPLICATE KEY UPDATE` for create new or update exist record without many requests and conditional structures? | open | 2022-02-15T18:49:55Z | 2024-04-09T15:34:53Z | https://github.com/tortoise/tortoise-orm/issues/1066 | []
| 16bc | 3 |
ets-labs/python-dependency-injector | asyncio | 71 | Add docs about Configuration provider | closed | 2015-06-19T09:15:06Z | 2020-06-25T22:14:42Z | https://github.com/ets-labs/python-dependency-injector/issues/71 | [
"docs"
]
| rmk135 | 4 |
|
roboflow/supervision | tensorflow | 1,362 | sv changes bbox shape for object detection with YOLOv8?? | ### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
### Question
I've been using supervision, its tracker, annotators, ... Nice work!! However I've noticed that, doing object detection with yolov8, bboxe shape from ultralytics are changed by supervision even though it refers to the same detection. The following screenshot shows a detected object provided by YOLO, ultralytics.Result (before doing `supervision_tracker.update(results[0])` and after parsing it to `supervision_tracker`.
The bboxes are diferent. I expect they shouldn't...
Can this bbox shape change be removed? I would like to keep original bbox shape.
Thanks!!
### Additional
_No response_ | open | 2024-07-15T10:20:40Z | 2024-07-16T18:01:17Z | https://github.com/roboflow/supervision/issues/1362 | [
"question"
]
| abelBEDOYA | 10 |
iterative/dvc | data-science | 10,194 | pull: "No file hash info found" and checkout fails when pulling frozen imported url that is a directory that has subsequently changed | # Bug Report
## Description
I have used `dvc import-url` to track a GCS directory that is updated daily with incremental new data. [The documentation](https://dvc.org/doc/user-guide/data-management/importing-external-data#how-external-outputs-work) suggests that once the data is imported, it is frozen and DVC should not attempt to download changes from the tracked URL unless `dvc update` is run.
It also says that
> During [dvc push](https://dvc.org/doc/command-reference/push), DVC will upload the version of the data tracked by [the .dvc file] to the [DVC remote](https://dvc.org/doc/user-guide/data-management/remote-storage) so that it is backed up in case you need to recover it.
Instead, when I `dvc push`, the log messages suggest the files were uploaded, but nothing shows up in the remote. y imported data to remote, `dvc pull` from a fresh checkout downloads all the files from the remote url (including updated ones), and fails to check out the frozen version of the data as expected.
### Reproduce
I created a minimal example using a public GCS bucket in my account:
1. mkdir dvc-import-issue & cd dvc-import-issue
2. git init
3. dvc init
4. dvc import-url gs://hasha-ds-public/test_dir/ test_dir
5. dvc remote add origin gs://hasha-ds-public/test_remote/
6. dvc push -r origin
Output from `dvc push` is
```
Collecting |5.00 [00:00, 441entry/s]
Pushing |0.00 [00:01, ?file/s]
Collecting hasha-ds-public/test_remote/ on gs |5.00 [00:01, 3.29entry/s]
4 files pushed
```
However I later noticed that no files had been pushed to the remote at all despite the misleading log message. I was able to correct this by running `dvc import-url gs://hasha-ds-public/test_dir --to-remote`, but this feels like another bug.
8. git add .dvc/config test_dir.dvc .gitignore
9. git commit -m "Committed and pushed"
Now set up a fresh clone of the project
10. cd ..
11. git clone dvc-import-url-pull-issue dvc-import-url-pull-issue2
Add another file to the tracked URL
12. echo "more stuff" > blah.txt & gsutil cp blah.txt gs://hasha-ds-public/test_dir/
Finally, try pulling the frozen data associated with the imported URL from remote into the fresh clone
13. cd dvc-import-issue2
14. dvc pull -r origin
### Result
```
Collecting |5.00 [00:00, 11.3entry/s]
Fetching
Building workspace index |0.00 [00:00, ?entry/s]
Comparing indexes |6.00 [00:00, 14.9entry/s]
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/baz.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/blah.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/bar.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/foo.txt'. It won't be created.
Applying changes |0.00 [00:00, ?file/s]
4 files fetched
ERROR: failed to pull data from the cloud - Checkout failed for following targets:
test_dir
Is your cache up to date?
<https://error.dvc.org/missing-files>
```
Note that it's trying to checkout `blah.txt` in the error message, though that file wasn't present when the directory was imported and test_dir.dvc suggests there should only be 3 files in the directory.*-
I have pushed this repository to https://github.com/ahasha/dvc-import-url-pull-issue . You should be able to reproduce the error message by cloning it and running `dvc pull -r origin`.
### Expected
1. After running `dvc import-url`, `dvc push` should upload the version of the data tracked by the created .dvc file to the remote
2. In a fresh clone of the repository, `dvc pull` should retrieve the version of the data tracked by the .dvc file from the remote, and not download anything new from the tracked URL.
### Environment information
**Output of `dvc doctor`:**
```console
DVC version: 3.36.0 (pip)
-------------------------
Platform: Python 3.11.4 on macOS-14.1.2-x86_64-i386-64bit
Subprojects:
dvc_data = 3.2.0
dvc_objects = 3.0.0
dvc_render = 1.0.0
dvc_task = 0.3.0
scmrepo = 2.0.2
Supports:
gs (gcsfs = 2023.12.2.post1),
http (aiohttp = 3.9.1, aiohttp-retry = 2.8.3),
https (aiohttp = 3.9.1, aiohttp-retry = 2.8.3)
Config:
Global: /Users/alexhasha/Library/Application Support/dvc
System: /Library/Application Support/dvc
Cache types: reflink, hardlink, symlink
Cache directory: apfs on /dev/disk1s3s1
Caches: local
Remotes: gs
Workspace directory: apfs on /dev/disk1s3s1
Repo: dvc, git
Repo.site_cache_dir: /Library/Caches/dvc/repo/a48422a1ed3a65e3efe064e5b18830a7
```
| closed | 2023-12-22T18:09:31Z | 2024-01-03T11:14:18Z | https://github.com/iterative/dvc/issues/10194 | [
"bug",
"regression",
"A: data-sync"
]
| ahasha | 10 |
MagicStack/asyncpg | asyncio | 756 | New release? | It seems there has not been a release since 0.22.0 in Feb 2021, but there have been several fixes.
Can you release a new version? | closed | 2021-05-13T00:02:40Z | 2021-05-17T20:34:52Z | https://github.com/MagicStack/asyncpg/issues/756 | []
| pauldraper | 3 |
AUTOMATIC1111/stable-diffusion-webui | deep-learning | 16,285 | [Bug]: Alias recognition issue in stable-diffusion-webui | ### Checklist
- [X] The issue exists after disabling all extensions
- [x] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
**Description:**
The instructions on the [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Change-model-folder-location) indicate that models can be stored externally and aliases can be created for them. This used to work correctly but no longer does. Despite completely deleting files and reinstalling, the issue persists. The system fails to recognize aliases properly and treats them as incorrect files.
### Steps to reproduce the problem
**Steps to Reproduce:**
Follow the instructions on the wiki to store models externally and create aliases.
Attempt to use the alias in the stable-diffusion-webui.
Observe the errors in the terminal.
**Expected Behavior:**
The system should correctly recognize the alias and load the model without errors.
**Actual Behavior:**
The system fails to recognize the alias and produces the following errors:
```
Running on local URL: http://0.0.0.0:8777
To create a public link, set `share=True` in `launch()`.
Startup time: 0.4s (load scripts: 0.1s, create ui: 0.1s, gradio launch: 0.1s).
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
0%| | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(shidvi47vvgc19f)', <gradio.routes.Request object at 0x3234199c0>, 'one girl', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 74, in f
res = list(func(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 53, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 847, in process_images
res = process_images_inner(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 988, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 1346, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_common.py", line 272, in launch_sampling
return func()
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 249, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 789, in forward
emb = self.time_embed(t_emb)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/devices.py", line 169, in forward_wrapper
result = self.org_forward(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 584, in network_Linear_forward
return originals.Linear_forward(self, input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Placeholder storage has not been allocated on MPS device!
```
### What should have happened?
The system should correctly recognize the alias and load the model without errors.
### What browsers do you use to access the UI ?
Apple Safari
### Sysinfo
[sysinfo-2024-07-28-18-46.json](https://github.com/user-attachments/files/16404890/sysinfo-2024-07-28-18-46.json)
### Console logs
```Shell
on local URL: http://0.0.0.0:8777
To create a public link, set `share=True` in `launch()`.
Startup time: 0.4s (load scripts: 0.1s, create ui: 0.1s, gradio launch: 0.1s).
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
0%| | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(shidvi47vvgc19f)', <gradio.routes.Request object at 0x3234199c0>, 'one girl', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 74, in f
res = list(func(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 53, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 847, in process_images
res = process_images_inner(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 988, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 1346, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_common.py", line 272, in launch_sampling
return func()
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 249, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 789, in forward
emb = self.time_embed(t_emb)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/devices.py", line 169, in forward_wrapper
result = self.org_forward(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 584, in network_Linear_forward
return originals.Linear_forward(self, input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Placeholder storage has not been allocated on MPS device!
```
### Additional information
_No response_ | open | 2024-07-28T18:48:57Z | 2024-08-08T20:19:51Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/16285 | [
"bug-report"
]
| ghost | 8 |
plotly/dash | plotly | 2,391 | Navigation Button with tooltip error when clicking it | **Describe your context**
Please provide us your environment, so we can easily reproduce the issue.
```
dash 2.7.1
dash-auth 1.4.1
dash-bootstrap-components 1.3.0
dash-core-components 2.0.0
dash-daq 0.5.0
dash-extensions 0.1.8
dash-html-components 2.0.0
dash-table 5.0.0
python-dash 0.0.1
```
- if frontend related, tell us your Browser, Version and OS
- OS: Mac OS 12.3.1
- Safari, Chrome
**Describe the bug**
MWE:
```
import dash
from dash import dcc
from dash import html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
app = dash.Dash(__name__, suppress_callback_exceptions=True)
app.layout = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content'),
])
def get_overview_page():
page = html.Div([
dcc.Link(
html.Button('Neu', id='new_button',
style={'margin-left': '10px', 'width': '100px', 'height': '27px',
'fontSize': '16px'}),
href='/new-entry'
),
dbc.Tooltip(
"A.",
target="new_button", placement="top"
)
], style={'width': '30%', 'margin-top': '10px', 'display': 'inline-block', 'text-align': 'left'})
return page
# Update the index
@app.callback(Output('page-content', 'children'),
[Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/new-entry':
return html.Div()
else:
return get_overview_page()
if __name__ == '__main__':
#app.run_server(debug=True, port=8080, host='0.0.0.0')
app.run_server(debug=True, port=8086, host='127.0.0.1')
```
When you press the button and move the mouse you receive:
`An object was provided as children instead of a component, string, or number (or list of those). Check the children property that looks something like: { "1": { "props": { "is_open": false } } }`
When you remove the tooltip. It is working, so it has to do something with it.
**Expected behavior**
No error.
| open | 2023-01-19T17:42:54Z | 2024-08-13T19:25:10Z | https://github.com/plotly/dash/issues/2391 | [
"bug",
"P3"
]
| Birdy3000 | 0 |
eamigo86/graphene-django-extras | graphql | 87 | MultiSelectField returns null during query | Hi
I am using the django-multiselectfield package and when I am making a query it always returns null. Am I missing some configuration because the other fields seems to working fine.
My enum for the Multiselect field
```
DAYS = (
('0', 'Sunday'),
('1', 'Monday'),
('2', 'Tuesday'),
('3', 'Wednesday'),
('4', 'Thursday'),
('5', 'Friday'),
('6', 'Saturday'))
```
My model
```
from multiselectfield import MultiSelectField
class Queue(models.Model):
days = MultiSelectField(choices=DAYS)
```
My schema
```
class QueueListType(DjangoListObjectType):
class Meta:
model = Queue
pagination = LimitOffsetGraphqlPagination(default_limit=25)
class Query(object):
all_queues = DjangoListObjectField(QueueListType)
```
When I am doing the query
```
query {
allQueues{
totalCount
results{
id
days
}
}
}
```
The result is
```
{
"data": {
"allQueues": {
"totalCount": 2,
"results": [
{
"id": "1",
"days": null
},
{
"id": "2",
"days": null
}
]
}
}
}
``` | open | 2018-11-14T10:23:01Z | 2019-05-30T08:55:46Z | https://github.com/eamigo86/graphene-django-extras/issues/87 | []
| bacteria323 | 1 |
OFA-Sys/Chinese-CLIP | computer-vision | 139 | 关于完全在自己的数据集上的训练 | 您好，我想问一下，能不能完全不需要微调，从头训练自己的数据集呢？要是可以的话我该如何设置训练脚本呢？ | open | 2023-06-13T02:21:41Z | 2024-01-24T03:58:54Z | https://github.com/OFA-Sys/Chinese-CLIP/issues/139 | []
| Duyz232 | 6 |
rthalley/dnspython | asyncio | 457 | Examples are written for Python 2 | At the site: http://www.dnspython.org/examples.html
The examples seem to be written for Python 2. They use print statements rather than print() commands. | closed | 2020-04-30T21:22:24Z | 2020-06-20T20:57:03Z | https://github.com/rthalley/dnspython/issues/457 | []
| JohnVillalovos | 2 |
sktime/sktime | data-science | 7,746 | [BUG] Spurious warning about `gluonts` data containers when none is used |
**Describe the bug**
<!--
A clear and concise description of what the bug is.
-->
Getting warning to install 'orjson', 'ujson' while running a script that uses sktime and gluonts.
**To Reproduce**
<!--
Add a Minimal, Complete, and Verifiable example (for more details, see e.g. https://stackoverflow.com/help/mcve
If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
-->
```python
from sktime.clustering.k_means import TimeSeriesKMeansTslearn
from sklearn.datasets import make_blobs
X_train, _ = make_blobs(n_samples=100, centers=3, n_features=10, random_state=42)
model = TimeSeriesKMeansTslearn(n_clusters=3, metric="euclidean")
model.fit(X_train)
y_pred = model.predict(X_train)
print(y_pred)
```
**Expected behavior**
<!--
A clear and concise description of what you expected to happen.
-->
No warning message should be displayed, and the script should run without suggesting additional installations.
**Additional context**
<!--
Add any other context about the problem here.
-->
It says "/sktime/env/lib/python3.12/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.
warnings.warn(
"
**Versions**
<details>
<!--
Please run the following code snippet and paste the output here:
from sktime import show_versions; show_versions()
-->
System:
python: 3.12.3 (main, Jan 17 2025, 18:03:48) [GCC 13.3.0]
executable: /home/kartik/Desktop/sktime/env/bin/python
machine: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
Python dependencies:
pip: 25.0
sktime: 0.35.0
sklearn: 1.5.2
skbase: 0.12.0
numpy: 1.26.4
scipy: 1.15.1
pandas: 2.2.3
matplotlib: 3.10.0
joblib: 1.4.2
numba: 0.60.0
statsmodels: 0.14.4
pmdarima: 2.0.4
statsforecast: 2.0.0
tsfresh: None
tslearn: 0.6.3
torch: None
tensorflow: 2.18.0
</details>
<!-- Thanks for contributing! -->
| closed | 2025-02-03T17:50:11Z | 2025-02-04T13:48:05Z | https://github.com/sktime/sktime/issues/7746 | [
"bug",
"module:datatypes"
]
| kartik-555 | 4 |
plotly/dash | flask | 2,709 | [BUG] `Patch` does not seem to work correctly for data updates of figures created with `make_subplots` | **Describe your context**
Hi, I am Jonas, one of the core developers of [`plotly-resampler`](https://github.com/predict-idlab/plotly-resampler/).
For our library, we created a custom component i.e., [`traceUpdater`](https://github.com/predict-idlab/trace-updater), to perform partial trace updates.
But as `Patch` was released, we think that this `traceUpdater` component can be omitted (since we also got some issues which were easily resolved by utilizing patch) - see https://github.com/predict-idlab/plotly-resampler/issues/271.
**Describe the bug**
However, when updating the `plotly-resampler` codebase to solely utilize `Patch` (PR: https://github.com/predict-idlab/plotly-resampler/pull/281) I remarked that `Patch` does not work when the figure consists of subplots (i.e. made with `make_subplots`). Is this expected behavior? see 📷 ⬇️
**Environment**
- clone the `refactor/remove-trace-updater` plotly-resampler branch and toy around with the example provided in - https://github.com/predict-idlab/plotly-resampler/pull/281
**Examples & Screenshots**
```python
from plotly_resampler import FigureResampler
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np
x = np.arange(2_000_000)
noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000
fig = FigureResampler(
make_subplots(rows=2, cols=2, shared_xaxes="columns", horizontal_spacing=0.03)
)
log = noisy_sin * 0.9999995**x
exp = noisy_sin * 1.000002**x
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=log)
fig.add_trace(go.Scattergl(name="exp"), hf_x=x, hf_y=exp)
fig.add_trace(go.Scattergl(name="-log"), hf_x=x, hf_y=-exp, row=1, col=2)
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=-log, row=2, col=1)
fig.add_trace(go.Scattergl(name="3-exp"), hf_x=x, hf_y=3 - exp, row=2, col=1)
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=log**2, row=2, col=2)
fig.show_dash(mode="inline")
```
| closed | 2023-12-05T11:51:15Z | 2023-12-12T23:31:35Z | https://github.com/plotly/dash/issues/2709 | []
| jonasvdd | 2 |
Nike-Inc/koheesio | pydantic | 169 | [BUG] Redundant fields required when using merge_builder in DeltaTableWriter | When merging data into a Delta table using `DeltaTableWriter` with the `merge_builder` param one needs to provide the source DataFrame and the target table name as arguments to `DeltaTableWriter`, even though they are already passed to the merge builder. Otherwise Koheesio throws an error about missing required fields. So effectively we are passing the same values twice.
## Describe the bug
Take this example of merging data into a Delta table using _merge_builder_:
```
spark = get_active_session()
merge_condition = (
"target.COLUMN_A = source.COLUMN_A AND target.COLUMN_B = source.COLUMN_B"
)
DeltaTableWriter(
df=source_df,
table=target_table_name,
output_mode=BatchOutputMode.MERGE,
output_mode_params={
"merge_builder": (
DeltaTable.forName(sparkSession=spark, tableOrViewName=target_table_name)
.alias("target")
.merge(source=source_df.alias("source"), condition=merge_condition)
.whenNotMatchedInsertAll()
)
},
).write()
```
Even though in the `merge_builder` param we already pass `source_df` and `target_table_name`, we still need to pass the same values as the `df` and `table` arguments respectively to `DeltaTableWriter`. Otherwise Pydantic will throw a Validation Error.
## Steps to Reproduce
Use the above code snippet to merge data into a Delta table using merge builder to trigger the error.
## Expected behavior
When using a merge builder with appropriate params, one should not be forced to additionally pass the `df` and `table` params to `DeltaTableWriter`.
## Environment
Koheesio 0.9.0 | open | 2025-02-21T14:25:22Z | 2025-02-27T15:05:34Z | https://github.com/Nike-Inc/koheesio/issues/169 | [
"bug"
]
| zarembat | 0 |
plotly/dash | plotly | 2,824 | Can callback be set to take effect with the current page or globally | Can callback be set to take effect with the current page or globally? Is this more reasonable? | closed | 2024-03-31T08:31:56Z | 2024-04-02T17:16:57Z | https://github.com/plotly/dash/issues/2824 | []
| jaxonister | 1 |
sigmavirus24/github3.py | rest-api | 601 | github3.models.GitHubError: 401 Requires username/password authentication | ##
| closed | 2016-04-26T21:34:42Z | 2016-12-13T22:54:01Z | https://github.com/sigmavirus24/github3.py/issues/601 | []
| haizaar | 1 |
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