repo_name stringlengths 9 75 | topic stringclasses 30
values | issue_number int64 1 203k | title stringlengths 1 976 | body stringlengths 0 254k | state stringclasses 2
values | created_at stringlengths 20 20 | updated_at stringlengths 20 20 | url stringlengths 38 105 | labels listlengths 0 9 | user_login stringlengths 1 39 | comments_count int64 0 452 |
|---|---|---|---|---|---|---|---|---|---|---|---|
pydantic/pydantic | pydantic | 10,563 | Add 'validate_assignment' context manager to BaseModel for on demand validation of assignments | ### Initial Checks
- [X] I have searched Google & GitHub for similar requests and couldn't find anything
- [X] I have read and followed [the docs](https://docs.pydantic.dev) and still think this feature is missing
### Description
It's possible that models are stored in separate library, potentially auto generated (hence, we try to avoid adding manual code to models), shared across multiple services. In that case, if you're in need of assignments' validation it'd require you to set the flag globally, which potentially could break other services, or override config locally, which is not "beautiful" solution. It's possible to add context manager to temporarily switch `'validate_assignment'` to true, perform assignments that require validation, and switch back.
```python
from contextlib import contextmanager
from typing import TYPE_CHECKING
from pydantic import BaseModel, ValidationError
if TYPE_CHECKING:
from typing import ContextManager
class MyModel(BaseModel):
this_is_integer: int
@contextmanager
def validate_assign(self) -> 'ContextManager[MyModel]':
old_value = self.model_config.get('validate_assignment', False)
self.model_config['validate_assignment'] = True
yield self
self.model_config['validate_assignment'] = old_value
my_model = MyModel(
this_is_integer=1
)
my_model.this_is_integer = 'a'
# No raise
with my_model.validate_assign():
try:
my_model.this_is_integer = 'b'
except ValidationError:
pass
# pydantic_core._pydantic_core.ValidationError: 1 validation error for MyModel
# this_is_integer
# Input should be a valid integer, unable to parse string as an integer
# [type=int_parsing, input_value='not integer', input_type=str]
my_model.this_is_integer = 'c'
# No raise
```
Could be helpful for https://github.com/pydantic/pydantic/issues/1864#issuecomment-2145539155
### Affected Components
- [ ] [Compatibility between releases](https://docs.pydantic.dev/changelog/)
- [X] [Data validation/parsing](https://docs.pydantic.dev/concepts/models/#basic-model-usage)
- [ ] [Data serialization](https://docs.pydantic.dev/concepts/serialization/) - `.model_dump()` and `.model_dump_json()`
- [ ] [JSON Schema](https://docs.pydantic.dev/concepts/json_schema/)
- [ ] [Dataclasses](https://docs.pydantic.dev/concepts/dataclasses/)
- [x] [Model Config](https://docs.pydantic.dev/concepts/config/)
- [ ] [Field Types](https://docs.pydantic.dev/api/types/) - adding or changing a particular data type
- [ ] [Function validation decorator](https://docs.pydantic.dev/concepts/validation_decorator/)
- [ ] [Generic Models](https://docs.pydantic.dev/concepts/models/#generic-models)
- [ ] [Other Model behaviour](https://docs.pydantic.dev/concepts/models/) - `model_construct()`, pickling, private attributes, ORM mode
- [ ] [Plugins](https://docs.pydantic.dev/) and integration with other tools - mypy, FastAPI, python-devtools, Hypothesis, VS Code, PyCharm, etc. | closed | 2024-10-07T15:27:44Z | 2024-10-07T15:56:06Z | https://github.com/pydantic/pydantic/issues/10563 | [
"feature request"
] | mee7ya | 1 |
viewflow/viewflow | django | 253 | [PRO] Multiple flows within the same app causes url not unique via @rest.register | Say I have an app called 'MRT`, inside the app I have 2 flow definitions.
`Flow_A` and `Flow_B`
both registered via `@rest.register` as below:
```python
@frontend.register
@rest.register
class Flow_A(Flow):
....
@frontend.register
@rest.register
class Flow_B(Flow):
....
```
When I `runserver`, it raises warning: `?: (urls.W005) URL namespace 'viewflow_rest:mrt' isn't unique. You may not be able to reverse all URLs in this namespace`
The consequences of this is that causing tasks of one of the flow(depends on which one registered last) cannot be url reversed. eg. `Reverse for 'check_size__detail' not found. 'check_size__detail' is not a valid view function or pattern name.`
originally posted on [Stackoverflow](https://stackoverflow.com/questions/58757046/django-viewflow-multiple-flows-within-the-same-app-causes-url-not-unique-via-r) | closed | 2019-11-10T20:35:52Z | 2019-11-15T11:10:15Z | https://github.com/viewflow/viewflow/issues/253 | [
"request/bug",
"dev/flow"
] | variable | 1 |
rthalley/dnspython | asyncio | 316 | Question about NotImplementedError vs socket.gaierror usage in the resolver _getaddrinfo routine | Hello,
I've arrived here via https://github.com/gevent/gevent/issues/1258 and have a question
about this area of code: https://github.com/rthalley/dnspython/pull/300/files#diff-72a478e3c437bb488c16c63a12c66bbeR1158
I'm wondering why a `NotImplementedError` is raised there instead of a `socket.gaierror`.
Were there some scenarios where a `socket.gaierror` could not / should not be returned in that situation?
I've found at least one library, `ldap3` ( https://github.com/cannatag/ldap3/ ), that uses the `socket.gaierror` error type to determine when it should retry a `socket.getaddrinfo`
call when some `flags` were not supported:
https://github.com/cannatag/ldap3/blob/85a3ce01a1c994b1742dd5de6c8fd8c883088c77/ldap3/core/server.py#L248
I'd like to be able to use dnspython with some projects (via gevent's dnspython resolver) and I'm currently trying to determine the proper spots to incorporate refinements in order for it all to work with some other dependencies like `ldap3`
Thank you | closed | 2018-07-16T19:07:49Z | 2019-01-07T13:36:47Z | https://github.com/rthalley/dnspython/issues/316 | [
"Enhancement Request"
] | corbinbs | 4 |
jonaswinkler/paperless-ng | django | 1,359 | [BUG] Unable to add documents anymore | <!---
=> Before opening an issue, please check the documentation and see if it helps you resolve your issue: https://paperless-ng.readthedocs.io/en/latest/troubleshooting.html
=> Please also make sure that you followed the installation instructions.
=> Please search the issues and look for similar issues before opening a bug report.
=> If you would like to submit a feature request please submit one under https://github.com/jonaswinkler/paperless-ng/discussions/categories/feature-requests
=> If you encounter issues while installing of configuring Paperless-ng, please post that in the "Support" section of the discussions. Remember that Paperless successfully runs on a variety of different systems. If paperless does not start, it's probably an issue with your system, and not an issue of paperless.
=> Don't remove the [BUG] prefix from the title.
-->
**Describe the bug**
All worked fine for a while, but today I am unable to add new documents. Documents in consume directory not consumed and also documents uploaded via GUI not added.
**To Reproduce**
Documents added to the consume directory not consumed
Documents uploaded via "Upload new documents" are not added. Stuck at "Upload complete, waiting..."
**Screenshots**
**Webserver logs**
```
[2021-10-02 14:19:07,879] [DEBUG] [paperless.tasks] Training data unchanged.
[2021-10-02 15:31:15,250] [INFO] [paperless.management.consumer] Adding /usr/src/paperless/src/../consume/BWprinter_004634.pdf to the task queue.
[2021-10-02 15:50:00,994] [INFO] [paperless.management.consumer] Adding /usr/src/paperless/src/../consume/BWprinter_004634.pdf to the task queue.
[2021-10-02 15:50:01,003] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/src/../consume
[2021-10-02 15:51:55,461] [INFO] [paperless.management.consumer] Adding /usr/src/paperless/src/../consume/BWprinter_004635.pdf to the task queue.
[2021-10-02 16:20:09,628] [DEBUG] [paperless.classifier] Gathering data from database...
[2021-10-02 16:20:10,244] [DEBUG] [paperless.tasks] Training data unchanged.
```
```
2021-10-02 12:49:59,570 INFO spawned: 'scheduler' with pid 225
[2021-10-02 15:50:00 +0300] [224] [INFO] Starting gunicorn 20.1.0
[2021-10-02 15:50:00 +0300] [224] [INFO] Listening at: http://0.0.0.0:8000 (224)
[2021-10-02 15:50:00 +0300] [224] [INFO] Using worker: paperless.workers.ConfigurableWorker
[2021-10-02 15:50:00 +0300] [224] [INFO] Server is ready. Spawning workers
[2021-10-02 15:50:00,994] [INFO] [paperless.management.consumer] Adding /usr/src/paperless/src/../consume/BWprinter_004634.pdf to the task queue.
15:50:01 [Q] INFO Enqueued 380
2021-10-02 12:50:00,995 INFO success: consumer entered RUNNING state, process has stayed up for > than 1 seconds (startsecs)
2021-10-02 12:50:00,996 INFO success: gunicorn entered RUNNING state, process has stayed up for > than 1 seconds (startsecs)
2021-10-02 12:50:00,997 INFO success: scheduler entered RUNNING state, process has stayed up for > than 1 seconds (startsecs)
[2021-10-02 15:50:01,003] [INFO] [paperless.management.consumer] Using inotify to watch directory for changes: /usr/src/paperless/src/../consume
15:50:01 [Q] INFO Q Cluster princess-xray-eleven-coffee starting.
15:50:01 [Q] INFO Process-1 guarding cluster princess-xray-eleven-coffee
15:50:01 [Q] INFO Q Cluster princess-xray-eleven-coffee running.
15:50:01 [Q] INFO Process-1:1 ready for work at 253
15:50:01 [Q] INFO Process-1:2 ready for work at 254
15:50:01 [Q] INFO Process-1:3 monitoring at 255
15:50:01 [Q] INFO Process-1:4 pushing tasks at 256
15:50:01 [Q] INFO Process-1:1 processing [jig-network-diet-single]
15:50:01 [Q] INFO Process-1:2 processing [georgia-black-mars-item]
[2021-10-02 15:51:55,461] [INFO] [paperless.management.consumer] Adding /usr/src/paperless/src/../consume/BWprinter_004635.pdf to the task queue.
15:51:55 [Q] INFO Enqueued 374
15:53:31 [Q] INFO Enqueued 375
15:53:31 [Q] INFO Process-1 created a task from schedule [Check all e-mail accounts]
16:03:33 [Q] INFO Enqueued 376
16:03:33 [Q] INFO Process-1 created a task from schedule [Check all e-mail accounts]
16:13:05 [Q] INFO Enqueued 377
16:13:05 [Q] INFO Process-1 created a task from schedule [Check all e-mail accounts]
16:20:07 [Q] WARNING reincarnated worker Process-1:1 after timeout
16:20:07 [Q] INFO Process-1:5 ready for work at 1192
16:20:07 [Q] INFO Process-1:5 processing [mobile-uranus-sodium-alanine]
16:20:08 [Q] WARNING reincarnated worker Process-1:2 after timeout
16:20:08 [Q] INFO Process-1:6 ready for work at 1200
16:20:08 [Q] INFO Process-1:6 processing [mirror-stream-delaware-tango]
16:20:10 [Q] INFO Process-1:5 stopped doing work
16:20:10 [Q] INFO recycled worker Process-1:5
16:20:10 [Q] INFO Process-1:7 ready for work at 1210
16:20:10 [Q] INFO Process-1:7 processing [fish-neptune-paris-mockingbird]
16:20:11 [Q] INFO Processed [mobile-uranus-sodium-alanine]
```
**Relevant information**
- Synology NAS
- Browser Chrome
- Version 1.5.0
- Installation method: docker
```
# docker-compose file for running paperless from the Docker Hub.
# This file contains everything paperless needs to run.
# Paperless supports amd64, arm and arm64 hardware.
#
# All compose files of paperless configure paperless in the following way:
#
# - Paperless is (re)started on system boot, if it was running before shutdown.
# - Docker volumes for storing data are managed by Docker.
# - Folders for importing and exporting files are created in the same directory
# as this file and mounted to the correct folders inside the container.
# - Paperless listens on port 8010.
#
# In addition to that, this docker-compose file adds the following optional
# configurations:
#
# - Instead of SQLite (default), PostgreSQL is used as the database server.
#
# To install and update paperless with this file, do the following:
#
# - Open portainer Stacks list and click 'Add stack'
# - Paste the contents of this file and assign a name, e.g. 'Paperless'
# - Click 'Deploy the stack' and wait for it to be deployed
# - Open the list of containers, select paperless_webserver_1
# - Click 'Console' and then 'Connect' to open the command line inside the container
# - Run 'python3 manage.py createsuperuser' to create a user
# - Exit the console
#
# For more extensive installation and update instructions, refer to the
# documentation.
version: "3.4"
services:
broker:
image: redis:6.0
restart: unless-stopped
db:
image: postgres:13
restart: unless-stopped
volumes:
- /volume1/paperless_data/pgdata:/var/lib/postgresql/data
environment:
POSTGRES_DB: paperless
POSTGRES_USER: paperless
POSTGRES_PASSWORD: paperless
webserver:
image: jonaswinkler/paperless-ng:latest
restart: unless-stopped
depends_on:
- db
- broker
ports:
- 8008:8000
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000"]
interval: 30s
timeout: 10s
retries: 5
volumes:
- /volume1/paperless_data/data:/usr/src/paperless/data
- /volume1/paperless_data/media:/usr/src/paperless/media
- /volume1/paperless_data/export:/usr/src/paperless/export
- /volume1/paperless_data/consume:/usr/src/paperless/consume
environment:
PAPERLESS_REDIS: redis://broker:6379
PAPERLESS_DBHOST: db
# The UID and GID of the user used to run paperless in the container. Set this
# to your UID and GID on the host so that you have write access to the
# consumption directory.
USERMAP_UID: 1034
USERMAP_GID: 100
# Additional languages to install for text recognition, separated by a
# whitespace. Note that this is
# different from PAPERLESS_OCR_LANGUAGE (default=eng), which defines the
# language used for OCR.
# The container installs English, German, Italian, Spanish and French by
# default.
# See https://packages.debian.org/search?keywords=tesseract-ocr-&searchon=names&suite=buster
# for available languages.
PAPERLESS_OCR_LANGUAGES: heb
# Adjust this key if you plan to make paperless available publicly. It should
# be a very long sequence of random characters. You don't need to remember it.
PAPERLESS_SECRET_KEY: ca72910de95145eb97b3f05ff57bd480ee73457e1085397bd4efe242bd87169ed03b07a22fae052b3e860b120dcc963cc1324aad089de01c03ec378ba6c8727ec92870531cd0173be4b679fbf46547b0
# Use this variable to set a timezone for the Paperless Docker containers. If not specified, defaults to UTC.
PAPERLESS_TIME_ZONE: Asia/Jerusalem
# The default language to use for OCR. Set this to the language most of your
# documents are written in.
PAPERLESS_OCR_LANGUAGE: eng+heb
volumes:
data:
media:
pgdata:
``` | open | 2021-10-02T14:15:16Z | 2021-12-13T15:13:08Z | https://github.com/jonaswinkler/paperless-ng/issues/1359 | [] | stepanov1975 | 5 |
PokeAPI/pokeapi | api | 695 | Single-encounter Pokemon are missing | In Generation IV, diamond pearl and platinum, many legendaries can be encountered exactly once. Some appear in static locations, while others will roam across the map once encountered. A third category exists for [Spirtomb's bizarre encounter procedure](https://bulbapedia.bulbagarden.net/wiki/Hallowed_Tower).
Currently, the API does not include any encounter entries for any of these three methods. [Dialga](https://bulbapedia.bulbagarden.net/wiki/Dialga_(Pok%C3%A9mon)#Game_locations), [Palkia](https://bulbapedia.bulbagarden.net/wiki/Palkia_(Pok%C3%A9mon)#Game_locations) and the (unreleased, event-only) Arceus have no data. This seems inconsistent with Generation III, which lists Mew's event-only encounter: https://pokeapi.co/api/v2/pokemon/151/encounters
My current project uses this API to calculate which games are required for which Pokemon. If this feature were not added, then I would have to manually add legendaries. | open | 2022-02-19T19:51:18Z | 2022-07-30T20:52:56Z | https://github.com/PokeAPI/pokeapi/issues/695 | [] | Hyreon | 1 |
coqui-ai/TTS | deep-learning | 3,188 | [Bug] ValueError: Model is not multi-lingual but `language` is provided. | ### Describe the bug
ValueError: Model is not multi-lingual but `language` is provided.
tts = TTS(model_name='xtts-v2', model_path="XTTS-v2/", config_path='XTTS-v2/config.json', gpu=True)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker_wav="data/沈腾_04.MP3",
language="en")
### To Reproduce
tts = TTS(model_name='xtts-v2', model_path="XTTS-v2/", config_path='XTTS-v2/config.json', gpu=True)
# generate speech by cloning a voice using default settings
tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
file_path="output.wav",
speaker_wav="data/沈腾_04.MP3",
language="en")
### Expected behavior
_No response_
### Logs
_No response_
### Environment
```shell
TTS install from git directly newest
```
### Additional context
_No response_ | closed | 2023-11-10T06:12:56Z | 2024-02-22T13:08:44Z | https://github.com/coqui-ai/TTS/issues/3188 | [
"bug"
] | lucasjinreal | 11 |
PeterL1n/BackgroundMattingV2 | computer-vision | 116 | Rotoscope cartoon/anime? | is it possible to do rotoscoping on cartoon or anime using this? | closed | 2021-06-14T06:39:10Z | 2021-09-07T06:19:17Z | https://github.com/PeterL1n/BackgroundMattingV2/issues/116 | [] | krigeta | 3 |
tensorly/tensorly | numpy | 570 | about unfold | I don't understand why the unfold function is different from Kolda's. Is there any benefit? If I make a small change to unfold, such as order='F', it becomes Kolda's and my code gives very different results. Sometimes the result is better with unfold, and sometimes the result is better with Kolda's. I don't understand. I am only operating on the unfolding matrix.
Hope to get your answer, thank you. | closed | 2024-07-31T06:43:16Z | 2024-08-01T09:55:59Z | https://github.com/tensorly/tensorly/issues/570 | [] | wll555 | 7 |
iperov/DeepFaceLab | deep-learning | 920 | Problem saving: "MemoryError" | Starting. Press "Enter" to stop training and save model.
Error:
Traceback (most recent call last):
File "C:\Users\paperspace\Documents\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\mainscripts\Trainer.py", line 186, in trainerThread
model_save()
File "C:\Users\paperspace\Documents\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\mainscripts\Trainer.py", line 68, in model_save
model.save()
File "C:\Users\paperspace\Documents\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\ModelBase.py", line 381, in save
self.onSave()
File "C:\Users\paperspace\Documents\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\Model_SAEHD\Model.py", line 619, in onSave
model.save_weights ( self.get_strpath_storage_for_file(filename) )
File "C:\Users\paperspace\Documents\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Saveable.py", line 59, in save_weights
d_dumped = pickle.dumps (d, 4)
MemoryError
Done. | closed | 2020-10-12T20:50:12Z | 2020-10-12T21:09:36Z | https://github.com/iperov/DeepFaceLab/issues/920 | [] | GuusDeKroon | 1 |
junyanz/pytorch-CycleGAN-and-pix2pix | deep-learning | 1,492 | Pix2PixModel: G_L1 retains lambda_L1 multiplier | When I tune the lambda_L1 hyperparameters, the corresponding G_L1 loss retains the lambda_L1 multiplier. In the image below, the second row (G_L1 loss = 0.1121; lambda_L1 = 10) performs the best (as far as I can tell), but the losses cannot be compared directly.
Defining the Pix2PixModel.get_current_losses() function as shown below may mitigate this problem, but incurs other issues (e.g. losses within a model cannot be compared directly anymore).
```
# Pix2PixModel
def get_current_losses(self):
"""
Cancels out lambda_L1 multiplier for comparing L1 losses across models with different lambda_L1 values
"""
errors_ret = super().get_current_losses()
errors_ret['G_L1'] /= self.opt.lambda_L1
return errors_ret
``` | open | 2022-10-11T21:48:59Z | 2022-10-25T20:59:14Z | https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/1492 | [] | asy51 | 1 |
microsoft/MMdnn | tensorflow | 595 | Pytorch to tensorflow inference results are different | Hi,
I am trying to convert tensorflow [VGGish](https://github.com/tensorflow/models/tree/master/research/audioset) to pytorch.
You can see step by step what I am doing in that [google colab notebook](https://colab.research.google.com/drive/1dBH3-r7IG7xhmL2a-O6ik8P6OUESn6dT#scrollTo=dRpHisuHplqc)
Problems:
- MMdnn handling NWHC order wrong. If I use master branch I got reshape(1,W,H,1), using latest release I got reshape(-1,W,H,1). (on the notebook, I am using latest master).
- When I resolve that problem model expects float32 input and does not do casting itself. (that might be expected)
- My biggest problem is results are different. You can see in the notebook I shared, checksums are different. Is that expected ?
I am using google Colab:
Platform (like ubuntu 16.04/win10):
Ubuntu version: "17.10 (Artful Aardvark)"
Python version:
3.6.3
Source framework with version (like Tensorflow 1.4.1 with GPU):
Tensorflow 1.13.0-rc1 with GPU
Destination framework with version (like CNTK 2.3 with GPU):
Pytorch 1.0.1 with GPU
Pre-trained model path (webpath or webdisk path):
https://github.com/tensorflow/models/tree/master/research/audioset
https://storage.googleapis.com/audioset/vggish_model.ckpt
Running scripts:
[colab research google](https://colab.research.google.com/drive/1dBH3-r7IG7xhmL2a-O6ik8P6OUESn6dT#scrollTo=dRpHisuHplqc)
[mirror on github](https://github.com/EnisBerk/speech_audio_understanding/blob/master/tensorflow2pytorch_master.ipynb) | closed | 2019-02-25T02:37:04Z | 2019-03-04T06:04:30Z | https://github.com/microsoft/MMdnn/issues/595 | [] | EnisBerk | 1 |
ContextLab/hypertools | data-visualization | 121 | tools.reduce: matrix rank affecting the dimensions of the result | `import hypertools as hyp`
`import numpy as np`
`print hyp.tools.reduce(np.random.normal(0,1,[5,100]),ndims=10).shape`
This is the code I tried which was supposed to give me a 5x10 dimension matrix. However, because the rank of the matrix is 5, PCA is unable to generate 10 dimensions from the data. Therefore the resulting matrix I got was a 5x5 matrix.
I talked about this issue with Professor Manning today, and he suggested a fix to this problem:
if the number of dimensions to reduce to is greater than the rank of the matrix, then pad the matrix with rows of 0s to increase the rank, do PCA then eliminate the 0 rows.
Could you look into this issue and let me know what you think? Thanks! | closed | 2017-05-24T21:14:14Z | 2017-05-30T15:42:33Z | https://github.com/ContextLab/hypertools/issues/121 | [
"bug",
"easy(ish)"
] | TomHaoChang | 6 |
PaddlePaddle/PaddleHub | nlp | 2,047 | InvalidArgumentError: The number of input's channels should be equal to filter's channels * groups for Op(Conv) | # Qustion
links: https://www.paddlepaddle.org.cn/hubdetail?name=animegan_v2_hayao_99&en_category=GANs
I imitated the link to re engage, but there was an error, and the online query was unsuccessful
I don’t know where there is a problem
# animegan_v3 already updated, How do I import a local model? (onnx model)
# Program
* `win11`
* `Python 3.9.6`
* `paddlepaddle==2.3.2`
* `paddlehub==2.3.0`
# Code
``` python
import cv2
import paddlehub as hub
model = hub.Module(name='animegan_v2_hayao_99')
result = model.style_transfer(
images=[cv2.imread('./data/2.jpg')],
output_dir='output',
visualization=True
)
```
# Error
``` log
E:\PythonEnv\paddle\lib\site-packages\paddlenlp\transformers\image_utils.py:213: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
resample=Image.BILINEAR,
E:\PythonEnv\paddle\lib\site-packages\paddlenlp\transformers\image_utils.py:379: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
resample=Image.NEAREST,
E:\PythonEnv\paddle\lib\site-packages\paddlenlp\transformers\ernie_vil\feature_extraction.py:65: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
resample=Image.BICUBIC,
E:\PythonEnv\paddle\lib\site-packages\paddlenlp\transformers\clip\feature_extraction.py:64: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
resample=Image.BICUBIC,
Traceback (most recent call last):
File "G:\level-2\Python\PaddleHub\demo.py", line 6, in <module>
result = model.style_transfer(
File "E:\PythonEnv\paddle\lib\site-packages\paddlehub\compat\paddle_utils.py", line 221, in runner
return func(*args, **kwargs)
File "C:\Users\26522\.paddlehub\modules\animegan_v2_hayao_99\module.py", line 40, in style_transfer
outputs = self.model.predict(processor.input_datas)
File "C:\Users\26522\.paddlehub\modules\animegan_v2_hayao_99\model.py", line 68, in predict
self.predictor.run()
ValueError: In user code:
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\paddle\fluid\framework.py", line 2610, in append_op
attrs=kwargs.get("attrs", None))
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\paddle\fluid\layer_helper.py", line 43, in append_op
return self.main_program.current_block().append_op(*args, **kwargs)
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\paddle\fluid\layers\nn.py", line 2938, in conv2d
"data_format": data_format,
File "Hayao-99\model_with_code\x2paddle_model.py", line 86, in x2paddle_net
generator_G_MODEL_A_Conv_Conv2D = fluid.layers.conv2d(input=conv2d_transpose_0, bias_attr=False, param_attr='generator_G_MODEL_A_Conv_weights', num_filters=32, filter_size=[7, 7], stride=[1, 1], dilation=[1, 1], padding='VALID')
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\x2paddle\core\program.py", line 290, in gen_model
inputs, outputs = x2paddle_model.x2paddle_net()
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\x2paddle\convert.py", line 137, in tf2paddle
program.gen_model(save_dir)
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\site-packages\x2paddle\convert.py", line 291, in main
define_input_shape, params_merge)
File "C:\Users\Xpk22\AppData\Local\Programs\Python\Python37\Scripts\x2paddle.exe\__main__.py", line 7, in <module>
sys.exit(main())
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "c:\users\xpk22\appdata\local\programs\python\python37\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
InvalidArgumentError: The number of input's channels should be equal to filter's channels * groups for Op(Conv). But received: the input's channels is 1030, the input's shape is [1, 1030, 582, 3]; the filter's channels is 3, the filter's shape is [32, 3, 7, 7]; the groups is 1, the data_format is NCHW. The error may come from wrong data_format setting.
[Hint: Expected input_channels == filter_dims[1] * groups, but received input_channels:1030 != filter_dims[1] * groups:3.] (at C:\home\workspace\Paddle_release\paddle/fluid/operators/conv_op.cc:116)
[operator < conv2d > error]
``` | open | 2022-09-27T06:08:05Z | 2022-09-28T03:53:41Z | https://github.com/PaddlePaddle/PaddleHub/issues/2047 | [] | imohuan | 6 |
microsoft/nni | data-science | 5,709 | Adding back the sensitivity analysis tool to v3.0 | <!-- Please only use this template for submitting enhancement requests -->
**What would you like to be added**:
Are there any plans to add back the sensitivty analysis tool to v3.0 ?
**Why is this needed**:
It would be great to have this tool back as a debug tool, to target better the layers that are not sensible to pruning. This tool was available in the previous versions (until [v2.6](https://nni.readthedocs.io/en/v2.6/Compression/CompressionUtils.html#sensitivity-analysis)) but was removed later on.
**Without this feature, how does current nni work**：
Without this tool, it's more difficult to identify which layers can be pruned further instead of using a uniform sparsity rate for the whole network without goint into the tedious process of trial & error. Which can be a big overhead for big models with long evaluation processes.
**Components that may involve changes**:
the utils package
**Brief description of your proposal if any**:
| open | 2023-11-08T15:01:19Z | 2023-11-08T15:01:19Z | https://github.com/microsoft/nni/issues/5709 | [] | mehdi-nait | 0 |
babysor/MockingBird | deep-learning | 472 | 在cmd中输入python demo_toolbox.py后没有反应 | **Summary[问题简述（一句话）]**
A clear and concise description of what the issue is.
在所有东西安装完成并且没有报错之后输入python demo_toolbox.py没有任何反应，普通环境和虚拟环境都没有反应，会直接跳回输入命令的地方
**Env & To Reproduce[复现与环境]**
描述你用的环境、代码版本、模型
**Screenshots[截图（如有）]**
If applicable, add screenshots to help
| closed | 2022-03-23T15:43:59Z | 2022-03-25T07:36:23Z | https://github.com/babysor/MockingBird/issues/472 | [] | perfectwomanaimi | 0 |
iperov/DeepFaceLab | machine-learning | 5,236 | Can't load saved model | ## Expected behavior
When running `6) train SAEHD.bat` again after having ran it once and saved the model, I would expect it to continue training.
## Actual behavior
Throws the following error:
```
Running trainer.
Choose one of saved models, or enter a name to create a new model.
[r] : rename
[d] : delete
[0] : AubreyGillian - latest
:
0
Loading AubreyGillian_SAEHD model...
Choose one or several GPU idxs (separated by comma).
[CPU] : CPU
[0] : GeForce RTX 2060
[0] Which GPU indexes to choose? :
0
Initializing models: 100%|###############################################################| 5/5 [00:12<00:00, 2.43s/it]
Loading samples: 100%|############################################################| 1045/1045 [00:08<00:00, 123.68it/s]
Loading samples: 100%|###############################################################| 617/617 [00:16<00:00, 38.24it/s]
================ Model Summary =================
== ==
== Model name: AubreyGillian_SAEHD ==
== ==
== Current iteration: 10368 ==
== ==
==-------------- Model Options ---------------==
== ==
== resolution: 128 ==
== face_type: f ==
== models_opt_on_gpu: True ==
== archi: liae-ud ==
== ae_dims: 256 ==
== e_dims: 64 ==
== d_dims: 64 ==
== d_mask_dims: 22 ==
== masked_training: True ==
== eyes_mouth_prio: False ==
== uniform_yaw: False ==
== adabelief: True ==
== lr_dropout: n ==
== random_warp: True ==
== true_face_power: 0.0 ==
== face_style_power: 0.0 ==
== bg_style_power: 0.0 ==
== ct_mode: none ==
== clipgrad: True ==
== pretrain: False ==
== autobackup_hour: 1 ==
== write_preview_history: False ==
== target_iter: 0 ==
== random_flip: True ==
== batch_size: 20 ==
== gan_power: 0.0 ==
== gan_patch_size: 16 ==
== gan_dims: 16 ==
== ==
==---------------- Running On ----------------==
== ==
== Device index: 0 ==
== Name: GeForce RTX 2060 ==
== VRAM: 6.00GB ==
== ==
================================================
Starting. Press "Enter" to stop training and save model.
Error: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node Conv2D_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:99) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[concat_39/concat/_809]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
(1) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node Conv2D_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:99) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
0 successful operations.
0 derived errors ignored.
Errors may have originated from an input operation.
Input Source operations connected to node Conv2D_12:
Pad_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:97)
decoder/res0/conv1/weight/read (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:76)
Input Source operations connected to node Conv2D_12:
Pad_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:97)
decoder/res0/conv1/weight/read (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:76)
Original stack trace for 'Conv2D_12':
File "threading.py", line 884, in _bootstrap
File "threading.py", line 916, in _bootstrap_inner
File "threading.py", line 864, in run
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\mainscripts\Trainer.py", line 58, in trainerThread
debug=debug,
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\ModelBase.py", line 189, in __init__
self.on_initialize()
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\Model_SAEHD\Model.py", line 381, in on_initialize
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\models\ModelBase.py", line 117, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\archis\DeepFakeArchi.py", line 154, in forward
x = self.res0(x)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\models\ModelBase.py", line 117, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\archis\DeepFakeArchi.py", line 68, in forward
x = self.conv1(inp)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\LayerBase.py", line 14, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py", line 99, in forward
x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\util\dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2279, in conv2d
name=name)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 972, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 750, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py", line 3536, in _create_op_internal
op_def=op_def)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py", line 1990, in __init__
self._traceback = tf_stack.extract_stack()
Traceback (most recent call last):
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1375, in _do_call
return fn(*args)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1360, in _run_fn
target_list, run_metadata)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1453, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node Conv2D_12}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[concat_39/concat/_809]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
(1) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node Conv2D_12}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
0 successful operations.
0 derived errors ignored.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\mainscripts\Trainer.py", line 130, in trainerThread
iter, iter_time = model.train_one_iter()
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\ModelBase.py", line 462, in train_one_iter
losses = self.onTrainOneIter()
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\Model_SAEHD\Model.py", line 678, in onTrainOneIter
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\Model_SAEHD\Model.py", line 538, in src_dst_train
self.target_dstm_em:target_dstm_em,
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 968, in run
run_metadata_ptr)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1191, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1369, in _do_run
run_metadata)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\client\session.py", line 1394, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: 2 root error(s) found.
(0) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node Conv2D_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:99) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[concat_39/concat/_809]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
(1) Resource exhausted: OOM when allocating tensor with shape[512,512,3,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node Conv2D_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:99) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
0 successful operations.
0 derived errors ignored.
Errors may have originated from an input operation.
Input Source operations connected to node Conv2D_12:
Pad_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:97)
decoder/res0/conv1/weight/read (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:76)
Input Source operations connected to node Conv2D_12:
Pad_12 (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:97)
decoder/res0/conv1/weight/read (defined at C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py:76)
Original stack trace for 'Conv2D_12':
File "threading.py", line 884, in _bootstrap
File "threading.py", line 916, in _bootstrap_inner
File "threading.py", line 864, in run
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\mainscripts\Trainer.py", line 58, in trainerThread
debug=debug,
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\ModelBase.py", line 189, in __init__
self.on_initialize()
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\models\Model_SAEHD\Model.py", line 381, in on_initialize
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder(gpu_src_code)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\models\ModelBase.py", line 117, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\archis\DeepFakeArchi.py", line 154, in forward
x = self.res0(x)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\models\ModelBase.py", line 117, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\archis\DeepFakeArchi.py", line 68, in forward
x = self.conv1(inp)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\LayerBase.py", line 14, in __call__
return self.forward(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\DeepFaceLab\core\leras\layers\Conv2D.py", line 99, in forward
x = tf.nn.conv2d(x, weight, self.strides, 'VALID', dilations=self.dilations, data_format=nn.data_format)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\util\dispatch.py", line 201, in wrapper
return target(*args, **kwargs)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 2279, in conv2d
name=name)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 972, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 750, in _apply_op_helper
attrs=attr_protos, op_def=op_def)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py", line 3536, in _create_op_internal
op_def=op_def)
File "C:\Programs\DeepFaceLab_NVIDIA\_internal\python-3.6.8\lib\site-packages\tensorflow\python\framework\ops.py", line 1990, in __init__
self._traceback = tf_stack.extract_stack()
```
## Steps to reproduce
Run `6) train SAEHD.bat` once, then hit enter to stop and save, run `6) train SAEHD.bat` again.
## Other relevant information
- **Command lined used (if not specified in steps to reproduce)**: The content of `6) train SAEHD.bat`:
```
@echo off
call _internal\setenv.bat
"%PYTHON_EXECUTABLE%" "%DFL_ROOT%\main.py" train ^
--training-data-src-dir "%WORKSPACE%\data_src\aligned" ^
--training-data-dst-dir "%WORKSPACE%\data_dst\aligned" ^
--pretraining-data-dir "%INTERNAL%\pretrain_CelebA" ^
--model-dir "%WORKSPACE%\model" ^
--model SAEHD
pause
```
- **Operating system and version:** Windows 10 Home [64-bit] (10.0.19042)
- **Python version:** 3.9.1 | open | 2021-01-08T15:37:14Z | 2023-06-10T08:46:56Z | https://github.com/iperov/DeepFaceLab/issues/5236 | [] | SonOfDiablo | 2 |
tortoise/tortoise-orm | asyncio | 919 | Model.all().limit(None) fails | Prior to latest release( 0.17.5) `Model.all().limit(None)` works as it should, but now it fails with
```
queryset.py 422 limit
if limit < 0:
TypeError:
'<' not supported between instances of 'NoneType' and 'int'
```
Besides, as stated in docs it should `Raises ParamsError – Limit should be non-negative number.`
I think it should be non-negative number or None.
Because it's convenient to have an optional limit on your queries. And it's very annoying to write some weird dances like:
```
query = Model.all()
if limit:
query.limit(limit)
```
| open | 2021-09-21T14:24:13Z | 2021-09-21T16:15:37Z | https://github.com/tortoise/tortoise-orm/issues/919 | [] | slavaGanzin | 3 |
xinntao/Real-ESRGAN | pytorch | 384 | when i finished finetune,which weights should i use to inference? | closed | 2022-07-11T02:47:48Z | 2023-06-29T00:55:31Z | https://github.com/xinntao/Real-ESRGAN/issues/384 | [] | JerryHao-art | 1 | |
bmoscon/cryptofeed | asyncio | 621 | Hit the limit when subscribe all pairs on trades channel on KuCoin | **Describe the bug**
Get the error message when trying to subscribe on trades channel with all pairs
{'id': '1', 'type': 'error', 'code': 509, 'data': 'exceed max subscription count limitation of 100 per time'}
**To Reproduce**
Steps to reproduce the behavior:
```
from cryptofeed.defines import TRADES
from cryptofeed.exchanges import KuCoin
from cryptofeed.feedhandler import FeedHandler
from cryptofeed.callback import TradeCallback
async def trade(*args):
print(args)
pairs = KuCoin.info()['symbols']
print(len(pairs))
f = FeedHandler()
f.add_feed(
KuCoin(
symbols=pairs,
channels=[TRADES],
callbacks={TRADES: TradeCallback(trade)}
)
)
f.run()
```
* Provide any tracebacks, if applicable
```
2021-09-02 12:18:08,375 : ERROR : KUCOIN.ws.1: encountered an exception, reconnecting in 1.0 seconds
Traceback (most recent call last):
File "/home/user/crfd/venv/lib/python3.8/site-packages/cryptofeed/connection_handler.py", line 68, in _create_connection
await self._handler(connection, self.handler)
File "/home/user/crfd/venv/lib/python3.8/site-packages/cryptofeed/connection_handler.py", line 117, in _handler
await handler(message, connection, self.conn.last_message)
File "/home/user/crfd/venv/lib/python3.8/site-packages/cryptofeed/exchanges/kucoin.py", line 223, in message_handler
topic, symbol = msg['topic'].split(":", 1)
KeyError: 'topic'
```
**Expected behavior**
Subscribe to trades channel for all pairs
**Operating System:**
macOS, linux
**Cryptofeed Version**
1.9.3 from pypi
| closed | 2021-09-02T10:23:54Z | 2021-10-18T22:39:47Z | https://github.com/bmoscon/cryptofeed/issues/621 | [
"bug"
] | kosmodromov | 2 |
apify/crawlee-python | automation | 809 | Extract state persistence into a mixin-style base class | As of now, only the `Statistics` class saves state on `persistState` events. We should make it possible to implement this behavior in other classes by simply extending some mixin and passing it a pydantic model that would validate the serialized state from the key-value store.
EDIT: `SessionPool` also supports persistence | open | 2024-12-11T16:03:51Z | 2024-12-16T16:55:50Z | https://github.com/apify/crawlee-python/issues/809 | [
"enhancement",
"t-tooling"
] | janbuchar | 1 |
matplotlib/matplotlib | data-science | 29,043 | [Bug]: Script never returns when using `qt6-main >=6.8` | ### Bug summary
In an environment containing `qt6-main` version `6.8.0`: plotting scripts will successfully run all lines, but will never return an exit code. In an environment containing version `6.7.3`: plotting scripts successfully return `0`.
### Code for reproduction
```Python
import matplotlib
import matplotlib.pyplot as plt
print(matplotlib.get_backend())
plt.plot(1, 1, "ro")
plt.savefig("tmp.png")
# The same symptoms occur when using plt.show()
print("Script finished.")
```
### Actual outcome
```
qtagg
Script finished
```
No exit code returned
### Expected outcome
```
qtagg
Script finished
```
Returns exit code `0`
### Additional information
- Occurs in environments including `qt6-main >=6.8`
- Does not occur in environments including `qt6-main <6.8`
These appear to be the same symptoms as #28907, but I am running from the command line with no IDE involvement.
### Operating system
RHEL7
### Matplotlib Version
3.9.2
### Matplotlib Backend
qtagg
### Python version
3.12, 3.13
### Jupyter version
N/A
### Installation
conda | closed | 2024-10-30T18:27:24Z | 2024-11-06T08:58:14Z | https://github.com/matplotlib/matplotlib/issues/29043 | [] | trexfeathers | 10 |
pyeve/eve | flask | 682 | is argument "updates" meant to be "updated" for on_updated call | In patch.py, "updates" is used when calling on_updated callbacks. Is it meant to be "updated" instead? "update" has done its work and won't be used anymore in building the response.
```
getattr(app, "on_updated")(resource, updates, original)
getattr(app, "on_updated_%s" % resource)(updates, original)
```
| closed | 2015-08-11T14:53:48Z | 2020-04-22T23:19:56Z | https://github.com/pyeve/eve/issues/682 | [
"enhancement"
] | guonsoft | 4 |
fastapi/sqlmodel | fastapi | 210 | I18N Internationalization | ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options 👆
### Example Code
```python
def get_locale():
return 'fi'
translation_hybrid = TranslationHybrid(
current_locale=get_locale,
default_locale='en'
)
class TeacherBase(SQLModel):
ar_name: Optional[Any] = Field(sa_column=Column(HSTORE))
name: str = Field(sa_column=translation_hybrid(ar_name))
```
### Description
translate key in the database
### Wanted Solution
I want to translate filed in data base using store pg extension or any other way if it's possibledatabase
### Wanted Code
```python
don't know!
```
### Alternatives
_No response_
### Operating System
Windows
### Operating System Details
_No response_
### SQLModel Version
0.0.6
### Python Version
Python 3.9.7
### Additional Context
_No response_ | closed | 2021-12-30T19:28:33Z | 2023-02-08T15:50:45Z | https://github.com/fastapi/sqlmodel/issues/210 | [
"feature"
] | masreplay | 3 |
drivendataorg/cookiecutter-data-science | data-science | 122 | Makefile target `show-help` is hyphen separated | The other targets are snake cases, but only `show-help` is separated by a hyphen.
It should be rename to `show_help` or just `help` ?
How do you think about it? | closed | 2018-06-19T06:16:05Z | 2018-06-21T05:04:40Z | https://github.com/drivendataorg/cookiecutter-data-science/issues/122 | [] | ikuo-suyama | 1 |
lukas-blecher/LaTeX-OCR | pytorch | 107 | Preprocess dark captured image | Images with white text and black background cannot be recognize well，maybe can Preprocess dark captured images | closed | 2022-03-28T18:49:25Z | 2022-03-29T12:25:10Z | https://github.com/lukas-blecher/LaTeX-OCR/issues/107 | [] | trotsky1997 | 1 |
gradio-app/gradio | data-visualization | 9,948 | gr.Dropdown has a fake initial value when using value=None | ### Describe the bug
The dropdown appears to have a value but the function does not receive a value. Also when trying to use the value of a dropdown, it suddenly shows correctly that it is empty. But whenever the page is opened/refreshed it shows the 'mirage' of a value.
### Have you searched existing issues? 🔎
- [X] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
with gr.Blocks() as demo:
gr.Dropdown(
choices=["a", "b", "c"],
value=None,
)
```
### Screenshot
### Logs
_No response_
### System Info
```shell
gradio==5.1.0
```
### Severity
I can work around it | closed | 2024-11-12T22:03:42Z | 2024-11-13T13:35:19Z | https://github.com/gradio-app/gradio/issues/9948 | [
"bug"
] | rsxdalv | 2 |
CorentinJ/Real-Time-Voice-Cloning | pytorch | 823 | from tensorflow.contrib.rseq2seq import Helper ModuleNotFoundError: No module named 'tensorflow.contrib.rseq2seq' | solve help | closed | 2021-08-19T03:36:22Z | 2021-08-25T08:28:47Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/823 | [] | wangkewk | 2 |
explosion/spaCy | data-science | 13,533 | [W030] Some entities could not be aligned in the text | Hi!
I tried training a custom Named Entity Recognition model using spaCy, but despite multiple trials, I get a warning telling me that there are misaligned entities in the training data that I had created.
```
import spacy
from spacy.training import Example
import random
nlp=spacy.load('en_core_web_sm')
training_data=[
("Hello from India", {""entities"": [(11, 15, ""GPE"")]})
]
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']
nlp.disable_pipes(*other_pipes)
optimizer=nlp.create_optimizer()
losses={}
for i in range(10): #10 is the epoch value
random.shuffle(training_data)
for text, annotation in training_data:
doc = nlp.make_doc(text)
example = Example.from_dict(doc, annotation)
nlp.update([example], sgd = optimizer, losses=losses)
```
And the error generated is this. :
```
Warning (from warnings module):
File "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/spacy/training/iob_utils.py", line 141
warnings.warn(
UserWarning: [W030] Some entities could not be aligned in the text "Hello from India" with entities "[(11, 15, 'GPE')]". Use `spacy.training.offsets_to_biluo_tags(nlp.make_doc(text), entities)` to check the alignment. Misaligned entities ('-') will be ignored during training.
```
The entity "India" starts from index 11 and ends at 15, yet spaCy doesn't recognise that it's a token. Any help is appreciated.
* Operating System: MacOS Ventura
* Python Version Used: 3.9
* spaCy Version Used: 3.1.0
| closed | 2024-06-19T07:08:40Z | 2024-06-19T09:15:37Z | https://github.com/explosion/spaCy/issues/13533 | [
"usage"
] | NitGS | 1 |
graphql-python/graphene-django | graphql | 1,293 | Broken with graphql_relay >= 3.1.1 due to wrong imports | * **What is the current behavior?**
`graphql_relay` introduced some internal changes to module names which are not compatible with `graphene-django`.
This is mainly due to renaming of modules and graphene-django not importing from the to level module but deeper in the package. The Readme of `graphql_relay` states that everything should be imported from the top module if possible.
The current beta branch is therefore not working anymore.
* **If the current behavior is a bug, please provide the steps to reproduce and if possible a minimal demo of the problem** via
a github repo, https://repl.it or similar (you can use this template as a starting point: https://repl.it/@jkimbo/Graphene-Django-Example).
1. Install graphene-django (tested with 3.0.0b7) into django with current dependencies (namely graphql-relay > 3.1.1)
2. Run django.
3. App crashes during import of `fields.py`
* **Proposed change**
Change imports in `fields.py` from `graphql-relay` to top level module. All needed methods are available there.
```python
from graphql_relay import (
connection_from_array_slice,
cursor_to_offset,
get_offset_with_default,
offset_to_cursor,
)
```
* **Please tell us about your environment:**
- Version: v3.0.0b7
- Platform: linux x64 | closed | 2022-01-26T16:31:03Z | 2022-01-26T16:40:05Z | https://github.com/graphql-python/graphene-django/issues/1293 | [
"🐛bug"
] | oleo65 | 2 |
InstaPy/InstaPy | automation | 6,790 | import imp ModuleNotFoundError: No module named 'imp' |
from instapy import InstaPy
session = InstaPy(username="user",password="pass")
session.login()
this code provides with error
$ C:/Users/DELL/AppData/Local/Programs/Python/Python312/python.exe c:/Users/DELL/python/insta.py
Traceback (most recent call last):
File "c:\Users\DELL\python\insta.py", line 1, in <module>
from instapy import InstaPy
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\instapy\__init__.py", line 6, in <module>
from .instapy import InstaPy
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\instapy\instapy.py", line 21, in <module>
from .clarifai_util import check_image
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\instapy\clarifai_util.py", line 3, in <module>
from clarifai.rest import ClarifaiApp
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\clarifai\rest\__init__.py", line 3, in <module>
from clarifai.rest.client import ApiClient
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\clarifai\rest\client.py", line 22, in <module>
from future.moves.urllib.parse import urlparse
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\future\moves\__init__.py", line 5, in <module>
from future.standard_library import import_top_level_modules
File "C:\Users\DELL\AppData\Local\Programs\Python\Python312\Lib\site-packages\future\standard_library\__init__.py", line 65, in <module>
import imp
ModuleNotFoundError: No module named 'imp'
im using Python 3.12.0
| closed | 2024-02-03T17:54:33Z | 2024-02-07T17:46:17Z | https://github.com/InstaPy/InstaPy/issues/6790 | [] | cloudglides | 2 |
clovaai/donut | nlp | 290 | custom json schema - ASAP | is it possible to train the model to generate structured output with custom json schema? Pleases help me ASAP | open | 2024-02-20T13:59:37Z | 2024-02-21T10:14:58Z | https://github.com/clovaai/donut/issues/290 | [] | xdevfaheem | 2 |
robotframework/robotframework | automation | 4,934 | Enhance performance of visiting parsing model | When Robot parses data files, it produces a model that both Robot itself and external tools use. Going through the model is easiest by using [visitors](https://robot-framework.readthedocs.io/en/stable/autodoc/robot.parsing.model.html#robot.parsing.model.visitor.ModelVisitor). That is typically pretty fast, but it is possible to enhance the performance, for example, by caching visitor methods. These enhancements are especially important for external tools such as editor plugins that need to process lot of data.
We already have PR #4911 by @d-biehl that adds visitor method caching. This issue exists mainly for tracking purposes, but it also covers mentioning caching in visitor documentation. Possible additional performance enhancements can be linked to this same issue as well. | closed | 2023-11-07T15:16:42Z | 2023-11-10T13:04:28Z | https://github.com/robotframework/robotframework/issues/4934 | [
"priority: medium",
"alpha 1",
"acknowledge",
"effort: small"
] | pekkaklarck | 1 |
ResidentMario/missingno | pandas | 115 | no attribute 'matrix' | When I run my code, theres an error, my code is below:
`import missingno as msno`
`import pandas as pd`
`data = pd.read_csv('xxx.csv')`
`msno.matrix(data, labels=True)`
And then I got this error:
**AttributeError: module 'missingno' has no attribute 'matrix'**
but I won't find the solution on google. can anyone give me some advice that how to fix it, and why has this error | closed | 2020-07-15T09:37:48Z | 2021-07-03T18:50:06Z | https://github.com/ResidentMario/missingno/issues/115 | [] | linlin860320 | 3 |
aleju/imgaug | deep-learning | 738 | AddToHue _LUT_CACHE is NoneType | When running the moco code [here](https://github.com/facebookresearch/moco) with imgaug augmentations, using the following guide to making them compatible with pytorch transforms [here](https://colab.research.google.com/drive/109vu3F1LTzD1gdVV6cho9fKGx7lzbFll#scrollTo=rQ6DFPNvVD8s) I get the following error (see below).
It looks to me like the `_LUT_CACHE` of `AddToHue` gets truncated by the pickeling of the spawn.
I managed to make the error go away by changing the `AddToHue` backend away from cv2.
```
class ImgAugTransform:
def __init__(self):
# augmentation
self.aug = iaa.Sequential([
...
iaa.AddToHue((-10, 10)),
], random_order=True)
self.aug[-1].backend = "numpy"
```
```
Traceback (most recent call last):
File "main_moco.py", line 437, in <module>
main()
File "main_moco.py", line 132, in main
mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 200, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 158, in start_processes
while not context.join():
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 119, in join
raise Exception(msg)
Exception:
-- Process 3 terminated with the following error:
Traceback (most recent call last):
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 20, in _wrap
fn(i, *args)
File "XXX/main_moco.py", line 301, in main_worker
train(train_loader, model, criterion, optimizer, epoch, args)
File "XXX/main_moco.py", line 328, in train
for i, (images, _) in enumerate(train_loader):
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 363, in __next__
data = self._next_data()
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 989, in _next_data
return self._process_data(data)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 1014, in _process_data
data.reraise()
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/_utils.py", line 395, in reraise
raise self.exc_type(msg)
TypeError: Caught TypeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop
data = fetcher.fetch(index)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "XXX/DatasetUnsupervisedExemplar.py", line 204, in __getitem__
sample1 = self.transform(sample1)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 61, in __call__
img = t(img)
File "XXX/DatasetUnsupervisedExemplar.py", line 245, in __call__
return self.aug.augment_image(img)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/meta.py", line 771, in augment_image
return self.augment_images([image], hooks=hooks)[0]
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/meta.py", line 825, in augment_images
hooks=hooks
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/meta.py", line 645, in augment_batch_
hooks=hooks)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/meta.py", line 3127, in _augment_batch_
hooks=hooks
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/meta.py", line 645, in augment_batch_
hooks=hooks)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/color.py", line 2471, in _augment_batch_
image_hsv, hue_i, saturation_i)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/site-packages/imgaug/augmenters/color.py", line 2501, in _transform_image_cv2
table_hue = cls._LUT_CACHE[0]
TypeError: 'NoneType' object is not subscriptable
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/spawn.py", line 115, in _main
self = reduction.pickle.load(from_parent)
_pickle.UnpicklingError: pickle data was truncated
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/spawn.py", line 115, in _main
self = reduction.pickle.load(from_parent)
_pickle.UnpicklingError: pickle data was truncated
XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/semaphore_tracker.py:143: UserWarning: semaphore_tracker: There appear to be 32 leaked semaphores to clean up at shutdown
len(cache))
XXX/miniconda3/envs/fh-ss/lib/python3.6/multiprocessing/semaphore_tracker.py:143: UserWarning: semaphore_tracker: There appear to be 32 leaked semaphores to clean up at shutdown
len(cache))
```
| open | 2020-12-15T16:14:52Z | 2021-09-07T07:39:14Z | https://github.com/aleju/imgaug/issues/738 | [] | BlGene | 1 |
dmlc/gluon-nlp | numpy | 1,372 | fine-tune bert_12_768_12 | If we fine-tune bert_12_768_12 with mxnet/gluon. Can we then fine-tune BERT with Bert embedding of (https://github.com/imgarylai/bert-embedding)?. Can we integrate fine-BERT from huggingface wtih gulon.? | open | 2020-09-23T14:15:43Z | 2020-09-23T20:50:42Z | https://github.com/dmlc/gluon-nlp/issues/1372 | [] | Shafi2016 | 2 |
ivy-llc/ivy | tensorflow | 28,094 | Fix Frontend Failing Test: paddle - comparison_ops.torch.equal | To-do List: https://github.com/unifyai/ivy/issues/27500
| closed | 2024-01-28T10:58:45Z | 2024-01-29T13:07:38Z | https://github.com/ivy-llc/ivy/issues/28094 | [
"Sub Task"
] | Sai-Suraj-27 | 0 |
deeppavlov/DeepPavlov | nlp | 1,438 | Error while building Knowledge Base Question Answering (KBQA) model: ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject | Want to contribute to DeepPavlov? Please read the [contributing guideline](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html) first.
Please enter all the information below, otherwise your issue may be closed without a warning.
**DeepPavlov version** (you can look it up by running `pip show deeppavlov`): Version: 0.14.0
**Python version**: Python 3.6.13
**Operating system** (ubuntu linux, windows, ...): Windows 10 Pro
**Issue**: Error with numpy while building Knowledge Base Question Answering (KBQA) model.
**Content or a name of a configuration file**:
```
deeppavlov.configs.kbqa.kbqa_cq
```
**Command that led to error**:
```
from deeppavlov import configs, build_model
kbqa_model = build_model(configs.kbqa.kbqa_cq)
```
**Error (including full traceback)**:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-10-e19717954803> in <module>
1 from deeppavlov import configs, build_model
2
----> 3 kbqa_model = build_model(configs.kbqa.kbqa_cq)
~\AppData\Roaming\Python\Python38\site-packages\deeppavlov\core\commands\infer.py in build_model(config, mode, load_trained, download, serialized)
60 component_serialized = None
61
---> 62 component = from_params(component_config, mode=mode, serialized=component_serialized)
63
64 if 'id' in component_config:
~\AppData\Roaming\Python\Python38\site-packages\deeppavlov\core\common\params.py in from_params(params, mode, serialized, **kwargs)
93 log.exception(e)
94 raise e
---> 95 obj = get_model(cls_name)
96
97 if inspect.isclass(obj):
~\AppData\Roaming\Python\Python38\site-packages\deeppavlov\core\common\registry.py in get_model(name)
70 raise ConfigError("Model {} is not registered.".format(name))
71 return cls_from_str(name)
---> 72 return cls_from_str(_REGISTRY[name])
73
74
~\AppData\Roaming\Python\Python38\site-packages\deeppavlov\core\common\registry.py in cls_from_str(name)
38 .format(name))
39
---> 40 return getattr(importlib.import_module(module_name), cls_name)
41
42
~\anaconda3\lib\importlib\__init__.py in import_module(name, package)
125 break
126 level += 1
--> 127 return _bootstrap._gcd_import(name[level:], package, level)
128
129
~\anaconda3\lib\importlib\_bootstrap.py in _gcd_import(name, package, level)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load_unlocked(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~\anaconda3\lib\importlib\_bootstrap.py in _gcd_import(name, package, level)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load_unlocked(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~\anaconda3\lib\importlib\_bootstrap.py in _gcd_import(name, package, level)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _find_and_load_unlocked(name, import_)
~\anaconda3\lib\importlib\_bootstrap.py in _load_unlocked(spec)
~\anaconda3\lib\importlib\_bootstrap_external.py in exec_module(self, module)
~\anaconda3\lib\importlib\_bootstrap.py in _call_with_frames_removed(f, *args, **kwds)
~\AppData\Roaming\Python\Python38\site-packages\deeppavlov\models\__init__.py in <module>
15 import os
16
---> 17 import nltk
18
19 from deeppavlov.core.common.prints import RedirectedPrints
~\AppData\Roaming\Python\Python38\site-packages\nltk\__init__.py in <module>
141 ###########################################################
142
--> 143 from nltk.chunk import *
144 from nltk.classify import *
145 from nltk.inference import *
~\AppData\Roaming\Python\Python38\site-packages\nltk\chunk\__init__.py in <module>
155 from nltk.data import load
156
--> 157 from nltk.chunk.api import ChunkParserI
158 from nltk.chunk.util import (
159 ChunkScore,
~\AppData\Roaming\Python\Python38\site-packages\nltk\chunk\api.py in <module>
11 ##//////////////////////////////////////////////////////
12
---> 13 from nltk.parse import ParserI
14
15 from nltk.chunk.util import ChunkScore
~\AppData\Roaming\Python\Python38\site-packages\nltk\parse\__init__.py in <module>
98 from nltk.parse.malt import MaltParser
99 from nltk.parse.evaluate import DependencyEvaluator
--> 100 from nltk.parse.transitionparser import TransitionParser
101 from nltk.parse.bllip import BllipParser
102 from nltk.parse.corenlp import CoreNLPParser, CoreNLPDependencyParser
~\AppData\Roaming\Python\Python38\site-packages\nltk\parse\transitionparser.py in <module>
20 from numpy import array
21 from scipy import sparse
---> 22 from sklearn.datasets import load_svmlight_file
23 from sklearn import svm
24 except ImportError:
~\anaconda3\lib\site-packages\sklearn\__init__.py in <module>
74 else:
75 from . import __check_build
---> 76 from .base import clone
77 from .utils._show_versions import show_versions
78
~\anaconda3\lib\site-packages\sklearn\base.py in <module>
14
15 from . import __version__
---> 16 from .utils import _IS_32BIT
17
18 _DEFAULT_TAGS = {
~\anaconda3\lib\site-packages\sklearn\utils\__init__.py in <module>
13 from scipy.sparse import issparse
14
---> 15 from .murmurhash import murmurhash3_32
16 from .class_weight import compute_class_weight, compute_sample_weight
17 from . import _joblib
__init__.pxd in init sklearn.utils.murmurhash()
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
```
| closed | 2021-05-06T22:43:30Z | 2021-05-09T14:45:21Z | https://github.com/deeppavlov/DeepPavlov/issues/1438 | [
"bug"
] | Aryagm | 3 |
hzwer/ECCV2022-RIFE | computer-vision | 307 | 将fastmode设置为false后图像的插帧效果不佳 | 我对两张图像进行插帧，在fastmode设置为false后，生成的结果不佳，请问是什么原因导致的。
同时在output中保存的原始图像img0与img1质量有些退化，是什么原因导致的。 | open | 2023-04-11T06:18:32Z | 2023-04-11T10:36:18Z | https://github.com/hzwer/ECCV2022-RIFE/issues/307 | [] | Liming-belief | 3 |
chiphuyen/stanford-tensorflow-tutorials | nlp | 136 | `style_transfer.py` content loss computing issue | closed | 2018-10-05T03:00:02Z | 2018-10-05T03:02:43Z | https://github.com/chiphuyen/stanford-tensorflow-tutorials/issues/136 | [] | gpengzhi | 0 | |
microsoft/MMdnn | tensorflow | 943 | pytorch to onnx | Platform (like ubuntu 16.04/win10):
Python version:
Source framework with version (like Tensorflow 1.4.1 with GPU):
Destination framework with version (like CNTK 2.3 with GPU):
Pre-trained model path (webpath or webdisk path):
Running scripts:
| closed | 2023-07-01T21:05:53Z | 2023-07-01T21:08:43Z | https://github.com/microsoft/MMdnn/issues/943 | [] | MadeByKit | 0 |
oegedijk/explainerdashboard | dash | 198 | String categorical values from Lightgbm | Hello,
great tool and library! Wonder if you can point me in the right direction to solve an issue?
- with LightGbm we can list the categorical column and it is ok if the values are strings values
- once in ExplainerDashboard the code crashes as it cannot handle strings values (at leat my understanding)
what can be a solution to this case?
thanks
| open | 2022-03-27T16:00:36Z | 2023-07-13T14:02:33Z | https://github.com/oegedijk/explainerdashboard/issues/198 | [] | Guidosalimbeni | 7 |
modin-project/modin | data-science | 7,093 | BUG: idxmax and idxmin do not allow string columns when numeric_only=False | ### Modin version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest released version of Modin.
- [X] I have confirmed this bug exists on the main branch of Modin. (In order to do this you can follow [this guide](https://modin.readthedocs.io/en/stable/getting_started/installation.html#installing-from-the-github-master-branch).)
### Reproducible Example
```python
import modin.pandas as pd
df = pd.DataFrame([['a', 'b']])
df.idxmax(axis=0)
df.idxmax(axis=1)
df.idxmin(axis=0)
df.idxmin(axis=1)
```
### Issue Description
Need to only check the types if `numeric_only` [here](https://github.com/modin-project/modin/blob/c75343687799e3b69fc04a99b8a9d11eab1fd984/modin/pandas/base.py#L1868) and [here](https://github.com/modin-project/modin/blob/c75343687799e3b69fc04a99b8a9d11eab1fd984/modin/pandas/base.py#L1881).
### Expected Behavior
like in pandas, idxmax and idxmin should allow string columns in both dimensions.
### Error Logs
<details>
```python-traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 df.idxmin(axis=1)
File ~/sources/modin/modin/logging/logger_decorator.py:125, in enable_logging.<locals>.decorator.<locals>.run_and_log(*args, **kwargs)
110 """
111 Compute function with logging if Modin logging is enabled.
112
(...)
122 Any
123 """
124 if LogMode.get() == "disable":
--> 125 return obj(*args, **kwargs)
127 logger = get_logger()
128 logger.log(log_level, start_line)
File ~/sources/modin/modin/pandas/base.py:1882, in BasePandasDataset.idxmin(self, axis, skipna, numeric_only)
1878 """
1879 Return index of first occurrence of minimum over requested axis.
1880 """
1881 if not all(d != np.dtype("O") for d in self._get_dtypes()):
-> 1882 raise TypeError("reduce operation 'argmin' not allowed for this dtype")
1883 axis = self._get_axis_number(axis)
1884 return self._reduce_dimension(
1885 self._query_compiler.idxmin(
1886 axis=axis, skipna=skipna, numeric_only=numeric_only
1887 )
1888 )
TypeError: reduce operation 'argmin' not allowed for this dtype
```
</details>
### Installed Versions
<details>
```
UserWarning: Setuptools is replacing distutils.
INSTALLED VERSIONS
------------------
commit : c75343687799e3b69fc04a99b8a9d11eab1fd984
python : 3.9.18.final.0
python-bits : 64
OS : Darwin
OS-release : 23.4.0
Version : Darwin Kernel Version 23.4.0: Wed Feb 21 21:45:49 PST 2024; root:xnu-10063.101.15~2/RELEASE_ARM64_T6020
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
Modin dependencies
------------------
modin : 0.28.0+23.gc7534368
ray : 2.8.0
dask : None
distributed : None
hdk : None
pandas dependencies
-------------------
pandas : 2.2.1
numpy : 1.26.1
pytz : 2023.3.post1
dateutil : 2.8.2
setuptools : 68.0.0
pip : 23.3
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.17.2
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.10.0
gcsfs : None
matplotlib : 3.8.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : 1.11.3
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None
```
</details>
| closed | 2024-03-14T18:13:27Z | 2024-04-17T12:34:54Z | https://github.com/modin-project/modin/issues/7093 | [
"bug 🦗",
"P2",
"Enable plugin"
] | mvashishtha | 0 |
tensorpack/tensorpack | tensorflow | 1,253 | Training speed is faster than TestDataSpeed() | I wrote a dataflow which can process 24 images/sec( 1.5 it/s * batch=16) by using TestDataSpeed()
But training speed(Using QueueInput() with default arguments) is 52 images/sec(3.2 it/s * batch=16) and the queue size in tensorboard is always full, so does GPU utilization rate.
How could training run faster than image preprocessing speed?
| closed | 2019-07-04T03:19:30Z | 2019-07-11T16:13:57Z | https://github.com/tensorpack/tensorpack/issues/1253 | [
"performance"
] | ArtificialNotImbecile | 2 |
cvat-ai/cvat | pytorch | 8,513 | Can't build UI from DockerFile.UI | ### 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
On Ubuntu 22.04 ran:
```
DOCKER_BUILDKIT=1 docker compose -f docker-compose.yml -f docker-compose.dev.yml -f components/serverless/docker-compose.serverless.yml -f build
```
Follow error occurs:
```
=> [cvat_ui stage-1 2/3] COPY cvat-ui/react_nginx.conf /etc/nginx/conf.d/default.conf 0.1s
------
> [cvat_ui cvat-ui 10/16] RUN DISABLE_HUSKY=1 yarn --frozen-lockfile:
4.919 yarn install v1.22.15
6.044 [1/4] Resolving packages...
8.594 [2/4] Fetching packages...
8.632 warning Pattern ["cvat-core@link:./cvat-core"] is trying to unpack in the same destination "/usr/local/share/.cache/yarn/v6/npm-cvat-core-15.2.0/node_modules/cvat-core" as pattern ["cvat-core@15.2.0","cvat-core@15.2.0"]. This could result in non-deterministic behavior, skipping.
8.632 warning Pattern ["cvat-data@link:./cvat-data"] is trying to unpack in the same destination "/usr/local/share/.cache/yarn/v6/npm-cvat-data-2.1.0/node_modules/cvat-data" as pattern ["cvat-data@2.1.0","cvat-data@2.1.0"]. This could result in non-deterministic behavior, skipping.
8.632 warning Pattern ["cvat-canvas3d@link:./cvat-canvas3d"] is trying to unpack in the same destination "/usr/local/share/.cache/yarn/v6/npm-cvat-canvas3d-0.0.10/node_modules/cvat-canvas3d" as pattern ["cvat-canvas3d@0.0.10","cvat-canvas3d@0.0.10"]. This could result in non-deterministic behavior, skipping.
8.632 warning Pattern ["cvat-canvas@link:./cvat-canvas"] is trying to unpack in the same destination "/usr/local/share/.cache/yarn/v6/npm-cvat-canvas-2.20.9/node_modules/cvat-canvas" as pattern ["cvat-canvas@2.20.9","cvat-canvas@2.20.9"]. This could result in non-deterministic behavior, skipping.
92.17 info fsevents@2.3.3: The platform "linux" is incompatible with this module.
92.17 info "fsevents@2.3.3" is an optional dependency and failed compatibility check. Excluding it from installation.
92.23 error lint-staged@13.3.0: The engine "node" is incompatible with this module. Expected version "^16.14.0 || >=18.0.0". Got "16.13.0"
92.27 error Found incompatible module.
92.27 info Visit https://yarnpkg.com/en/docs/cli/install for documentation about this command.
------
failed to solve: process "/bin/sh -c DISABLE_HUSKY=1 yarn --frozen-lockfile" did not complete successfully: exit code: 1
```
### Expected Behavior
Build was successful
### Possible Solution
Change the target node version in Dockerfile.UI to current one (``node:current-slim``) or fixed one to satisfy lint-staged node requirements (``"^16.14.0 || >=18.0.0"``)
### Context
_No response_
### Environment
```Markdown
Git hash: commit 941f5c01deb65dead27202aedab72cc3a24d7cf9 (HEAD, tag: v2.20.0, origin/master)
Docker version: Docker version 27.3.1, build ce12230
```
| closed | 2024-10-05T14:26:35Z | 2024-10-07T05:24:55Z | https://github.com/cvat-ai/cvat/issues/8513 | [
"bug"
] | egortech | 2 |
sunscrapers/djoser | rest-api | 60 | Some url names clash with default Django url names. | For instance, login and logout clash with standard Django registration urls.
| closed | 2015-07-13T21:47:26Z | 2016-03-01T11:51:11Z | https://github.com/sunscrapers/djoser/issues/60 | [] | mcastle | 3 |
dask/dask | scikit-learn | 11,730 | cupy: `__setitem__` with int array index fails when chunked | `x.__setitem__(idx, )`, where x is a dask array with cupy meta and idx is a dask array of integers, returns incorrect results when there are multiple chunks:
```python
def test_setitem_1d():
x = cupy.arange(10)
idx = cupy.asarray([2, 3])
dx = da.from_array(x.copy(), chunks=(5,))
x[idx,] = -1
dx[da.from_array(idx)] = -1
assert_eq(x, dx)
```
```
AssertionError: found values in 'a' and 'b' which differ by more than the allowed amount
E assert False
E + where False = allclose(array([ 0, 1, -1, -1, 4, 5, 6, 7, 8, 9]), array([ 0, 1, -1, -1, 4, -1, 6, 7, 8, 9]), equal_nan=True, **{})
```
the issue disappear if I set `chunks=-1`.
Note that this is both upstream and downstream of #11728. | open | 2025-02-07T11:46:23Z | 2025-03-17T02:05:02Z | https://github.com/dask/dask/issues/11730 | [
"needs attention",
"needs triage"
] | crusaderky | 1 |
gee-community/geemap | streamlit | 982 | problem in convert GEE javascript to python for phenology analysis | I want to convert the following Javascript code into python and show the chart in Jupyter Notebook, but i am unable to do, if anyone can share the equivalent python code it would be really appreciated.
---code java scripts---
var geometry = ee.Geometry.Polygon( [[[-15.327075719833374,16.843462950466662],[-15.325809717178345,16.84301113290545],
[-15.328245162963865,16.836870414568292], [-15.329457521438599,16.83734278458931]]]);
var geometryBuff = geometry.buffer(-10)
Map.addLayer(geometry,{color:'green'},'Border');
Map.addLayer(geometryBuff,{color:'red'},'Buffer');
Map.centerObject(geometry);
var S2 = ee.ImageCollection('COPERNICUS/S2_SR')
.filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than', 20)
.filterDate('2021-06-23', '2021-12-23')
.filterBounds(geometryBuff);
function keepFieldPixel(image) {
var scl = image.select('SCL');
var veg = scl.eq(4);
var soil = scl.eq(5);
var mask = (veg.neq(1)).or(soil.neq(1));
return image.updateMask(mask);
}
var S2 = S2.map(keepFieldPixel);
function maskS2clouds(image) {
var qa = image.select('QA60');
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
.and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask);
}
var addNDVI = function(image) {
return image.addBands(image.normalizedDifference(['B8', 'B4']));
};
var S2 = S2.map(addNDVI);
var evoNDVI = ui.Chart.image.seriesByRegion(
S2,
geometryBuff,
ee.Reducer.mean(),
'nd',
10);
var plotNDVI = evoNDVI
.setChartType('LineChart')
.setSeriesNames(['SCL filter only'])
.setOptions({
interpolateNulls: true,
lineWidth: 1,
pointSize: 3,
title: 'NDVI annual evolution',
hAxis: {title: 'Date'},
vAxis: {title: 'NDVI'}
});
var S2 = S2.map(maskS2clouds);
var plotNDVI = ui.Chart.image.seriesByRegion(
S2,
geometryBuff,
ee.Reducer.mean(),
'nd',10)
.setChartType('LineChart')
.setSeriesNames(['After cloud filter'])
.setOptions({
interpolateNulls: true,
lineWidth: 1,
pointSize: 3,
title: 'NDVI annual evolution',
hAxis: {title: 'Date'},
vAxis: {title: 'NDVI'},
series: {0:{color: 'red'}}
});
print(plotNDVI)
var NDVI = S2.select(['nd']);
var NDVImed = NDVI.median();
var pal = ['#d73027', '#f46d43', '#fdae61', '#fee08b', '#d9ef8b', '#a6d96a'];
Map.addLayer(
NDVImed.clip(geometryBuff),
{min:-0.6, max:0.8, palette: pal},
'NDVI'
)
| closed | 2022-03-16T21:29:39Z | 2022-03-16T21:37:20Z | https://github.com/gee-community/geemap/issues/982 | [
"bug"
] | SIDIBRAFJD | 1 |
dynaconf/dynaconf | django | 1,095 | [docs] Document the new method hooks | Add documentation for the feature added on https://github.com/dynaconf/dynaconf/pull/975
Dynaconf allows changing the wrapped class to allow method hooking.
```python
from dynaconf import Dynaconf
from dynaconf.hooking import Action, Hook, HookableSettings
settings = Dynaconf(_wrapper_class=HookableSettings)
def lookup_for_key_in_cache_or_database(temp_settings, value, key, default):
if key.upper() not in [list of keys I am interested]:
return value.value
print(f"doing things to lookup key {key!r} on cache or db")
new_value = find_in_cache(key) or query_db(key) or value
return new_value
# NOTE: if value is a mergeable data structure, then the temp_settings might be used.
# temp_settings.set(key, new_value)
# return temp_settings.get(key, value.value)
settings["_registered_hooks"][Action.AFTER_GET] = [lookup_for_key_in_cache_or_database]
```
When `settings["_registered_hooks"]` is set as a dictionary, keyed by `Action.AFTER_GET|BEFORE_GET|etc`
at any access to `settings.*` dynaconf after performing its usual get process, will call the hook functions, chained in sequence, passing down the value from one hook to another, this way the latest hook in the list is the final value.
`alter_settings_from_request(lookup_for_key_in_cache_or_database(do_another_thing(value, **kwargs)))`
Each hook function may do a conditional check (that is why we do if key in list_of_interesting_keys) and if the key is not interesting for the hook it may return value to be passed to the next hook unaltered.
If key is the key that the hook wants, then it can alter it and return the altered value (which will be passed to the next hook in the pipeline) or can return a EagerValue(thing) that will avoid repassing to the next and just return as the final value.
In our case, I think we want to maintain a list of enabled hooks, and if there are none registered, we can just bypass its configuration.
This all could be registered on app/settings.py directly, or in special cases using dynaconf_hooks.py | closed | 2024-05-07T16:34:40Z | 2024-07-14T19:00:43Z | https://github.com/dynaconf/dynaconf/issues/1095 | [
"Not a Bug",
"Docs"
] | rochacbruno | 0 |
serengil/deepface | deep-learning | 586 | recorded video to be analysed | Hi first of all thanks for this valuable info!
secondly how can I go about this problem please? I need to record a 10/15 min video which I need to input into DeepFace to analyse it for emotions, so instead of using realtime camera I want to input the video image please | closed | 2022-10-30T22:59:31Z | 2022-10-30T23:15:06Z | https://github.com/serengil/deepface/issues/586 | [
"question"
] | MarouskaVB | 1 |
piskvorky/gensim | data-science | 3,138 | Wrong LDA hyperparameter offset (downweighting factor tau0)? | I observed that the hyperparameter `offset` (introduced as `downweighting` factor in https://github.com/RaRe-Technologies/gensim/commit/aa56561a5c2e49ba699ba0f3468aa762112ad761) which corresponds to tau_0 from [Hoffman et al.](https://papers.neurips.cc/paper/2010/file/71f6278d140af599e06ad9bf1ba03cb0-Paper.pdf) is set differently compared to the [original algorithm](https://github.com/blei-lab/onlineldavb/blob/master/onlineldavb.py#L121) proposed by Hoffman.
self._tau0 = tau0 + 1
When passing `tau0` to his algorithm, `tau0 + 1` is actually used in the calculation of rhot:
rhot = pow(self._tau0 + self._updatect, -self._kappa)
This line is used [here](https://github.com/blei-lab/onlineldavb/blob/dee5dcf9492d2b2870ba5c1fc14ac41cbf83596c/onlineldavb.py#L281-L284) and [here](https://github.com/blei-lab/onlineldavb/blob/dee5dcf9492d2b2870ba5c1fc14ac41cbf83596c/onlineldavb.py#L321-L324).
The commit https://github.com/RaRe-Technologies/gensim/commit/edc3ce5a7f74a2c2844c4d8afb66365596ae52b5 in gensim further changes the computation of `rho` (in order to pay attention to multi-pass algorithm as discussed in #298:
https://github.com/RaRe-Technologies/gensim/blob/351456b4f7d597e5a4522e71acedf785b2128ca1/gensim/models/ldamodel.py#L963-L967
I wonder if there is any rationale behind this decision to deviate from Hoffman's tau0 or if this was unintended? | open | 2021-05-11T16:47:02Z | 2021-05-11T16:49:20Z | https://github.com/piskvorky/gensim/issues/3138 | [] | jonaschn | 1 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,194 | How I can train encoder | How I can train encoder without using VoxCeleb1 and VoxCeleb2,I just don't know where install them. And if you can tell me where it can be downloaded | closed | 2023-04-15T22:55:03Z | 2023-04-17T10:56:14Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1194 | [] | terminatormlp | 1 |
aleju/imgaug | machine-learning | 519 | Drawn bounding box labels | Hi,
When initializing an object using the [BoundingBox](https://imgaug.readthedocs.io/en/latest/_modules/imgaug/augmentables/bbs.html#BoundingBox) class an option to store a `label` parameter is available. However, an option to then draw this label onto an image together with the bounding box, using the method [draw_on_image](https://imgaug.readthedocs.io/en/latest/_modules/imgaug/augmentables/bbs.html#BoundingBox.draw_on_image) is not an option. This presents an issue when drawing bounding boxes onto an image that contains multiple objects.
Is there a way to get these labels drawn onto an image using the `imgaug` library, or is there a plan to add this functionality in the near future.
Thanks! | open | 2019-12-10T07:09:47Z | 2020-02-22T14:33:45Z | https://github.com/aleju/imgaug/issues/519 | [] | marcheseV | 2 |
coqui-ai/TTS | pytorch | 3,280 | [Bug] ValueError: Model is not multi-lingual but `language` is provided. | ### Describe the bug
I got the "ValueError: Model is not multi-lingual but `language` is provided." when i used XTTSv2
### To Reproduce
import torch
from TTS.api import TTS
# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"
# List available 🐸TTS models
print(TTS().list_models())
# Init TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
# Run TTS
# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
# Text to speech list of amplitude values as output
wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
# Text to speech to a file
tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
### Expected behavior
_No response_
### Logs
_No response_
### Environment
```shell
Environment u provided
```
### Additional context
_No response_ | closed | 2023-11-21T09:57:08Z | 2023-12-01T09:56:30Z | https://github.com/coqui-ai/TTS/issues/3280 | [
"bug"
] | JJun-Guo | 5 |
modin-project/modin | pandas | 6,879 | The query_compiler.merge reconstructs the Right dataframe for every partition of Left Dataframe | The query_compiler.merge reconstructs the Right dataframe from its partitions for every partition of Left Dataframe, The concat operation results in higher memory consumption when the size of right dataframe is large.
A possible option is to combine the right Dataframe partitions to a single partition dataframe by calling a remote function. This single partiotion dataframe is then passed to each partition of left dataframe thus avoiding the reconstruction in every worker while doing merge. | closed | 2024-01-25T11:39:01Z | 2024-02-13T12:09:24Z | https://github.com/modin-project/modin/issues/6879 | [
"Internals"
] | arunjose696 | 0 |
google-research/bert | tensorflow | 1,257 | Printing the weights of a pre-trained model | During the generation of the key matrix, query matrix, and value matrix, what are the weights used? How do I print these weights? | open | 2021-08-25T05:26:15Z | 2021-08-25T05:26:15Z | https://github.com/google-research/bert/issues/1257 | [] | nivi1501 | 0 |
encode/databases | asyncio | 171 | [QUESTION] Raw Query IN clause list? | How can I do the following:
```python
# Input list of bar_ids
bar_ids_input = [1, 2, 3, 4]
# Convert to dictionary mapping
bar_ids = [{'bar_id': bar_id} for bar_id in bar_ids]
query = """
SELECT * FROM foo_table WHERE bar_id IN :bar_ids
"""
async with db() as db:
query_result = await db.fetch_all(query, bar_ids)
```
Does encode databases support passing a list of parameters in the IN clause? | closed | 2020-03-06T22:03:48Z | 2020-03-21T01:23:27Z | https://github.com/encode/databases/issues/171 | [] | neilpanchal | 2 |
gradio-app/gradio | deep-learning | 10,108 | Real time logs from subprocesses | The general goal is to develop a UI using Gradio to display subprocess logs in real time. However, the logs aren't updating dynamically. Is it possible to display logs in real time without explicit triggering?
Example (real_time_logs.py):
```
import os
import gradio as gr
import asyncio
import signal
from functools import partial
import time
async def run_child_process(command, processes, logs):
process = await asyncio.create_subprocess_exec(
*command,
stdout=asyncio.subprocess.PIPE,
stderr=asyncio.subprocess.PIPE
)
processes.append(process)
await logs_reader(process, logs)
async def start(processes, commands, logs):
tasks = []
for command in commands:
task = asyncio.create_task(run_child_process(command.split(), processes, logs))
tasks.append(task)
await asyncio.gather(*tasks)
def stop_nodes(processes):
while processes:
p = processes.pop()
os.kill(p.pid, signal.SIGINT)
time.sleep(2)
return 'Node stopped'
async def logs_reader(process, logs):
while True:
line = await process.stdout.readline()
if not line:
break
log_entry = line.decode().strip()
logs.append(log_entry)
return "Logs updated."
def get_logs(logs):
return "\n".join(logs)
with gr.Blocks() as demo:
processes = []
command_text = 'python3 test_script.py'
commands = [command_text]
logs_queue = gr.State([])
with gr.Tab('Simulation'):
with gr.Row(variant='panel'):
with gr.Column():
start_button = gr.Button("Start")
stop_button = gr.Button("Stop")
status_text = gr.Textbox(label="Nodes status")
with gr.Accordion("Logs window", open=False):
logs_text = gr.Textbox(label="Logs", lines=10)
start_button.click(
fn=partial(start, processes, commands),
inputs=logs_queue,
outputs=status_text,
)
stop_button.click(
fn=partial(stop_nodes, processes),
outputs=status_text
)
logs_queue.change(
fn=get_logs,
outputs=logs_text
)
if __name__ == '__main__':
demo.launch()
```
test_script.py
```
if __name__ == '__main__':
for i in range(10):
print(i)
```
| closed | 2024-12-03T14:42:57Z | 2024-12-03T15:47:56Z | https://github.com/gradio-app/gradio/issues/10108 | [] | gzzv | 1 |
huggingface/transformers | pytorch | 36,324 | How can I determine which region of the image the model is focusing on when answering a specific question? | How can I determine which region of the image the model is focusing on when answering a specific question?, Does InternVL use Cross-Attention between images and text? If so, how can I extract the attention between a specific region of the image and a specific word?
Thanks | closed | 2025-02-21T08:05:21Z | 2025-02-21T08:06:12Z | https://github.com/huggingface/transformers/issues/36324 | [] | phongkhanh | 0 |
sgl-project/sglang | pytorch | 4,076 | offline throughput benchmark numbers mismatch with custom benchmark by 3x | Hi I am trying to squeeze out maximum throughput for a 0.5B model with 5000 input and 100 output tokens, but I got quite different throughput from custom benchmarking versus official benchmark script result. This is done on AWS instance with 1 L4 GPU and 16 CPU, my custom got 6.79 req/s, but official got 15.75 req/s, a 3x difference. Is it expected?
custom code:
```
import asyncio
import time
import sglang as sgl
from sglang.test.test_utils import is_in_ci
from transformers import AutoTokenizer
if is_in_ci():
import patch
if __name__ == '__main__':
MODEL = "Qwen/Qwen2.5-0.5B"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
prompts = [
"Repeat after me say Hi, " * 833
] * 1000
print(f"len input tokens: {len(tokenizer(prompts[0])['input_ids'])}")
# raise Exception()
llm = sgl.Engine(model_path=MODEL, disable_radix_cache=True)
# import pdb; pdb.set_trace()
sampling_params = {"temperature": 0, "max_new_tokens": 100}
print("\n=== Testing asynchronous batch generation ===")
async def main():
start_time = time.time() # Start timing
outputs = await llm.async_generate(prompts, sampling_params)
end_time = time.time() # End timing
total_time = end_time - start_time
avg_req_per_sec = len(prompts) / total_time if total_time > 0 else 0
# for prompt, output in zip(prompts, outputs):
# print(f"\nPrompt: {prompt}")
# print(f"Generated text: {output['text']}")
print(f"\nTotal Time Taken: {total_time:.4f} seconds")
print(f"Average Requests Per Second: {avg_req_per_sec:.2f} req/s")
asyncio.run(main())
llm.shutdown()
```
custom output:
```
len input tokens: 4999
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
INFO 03-05 03:05:26 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:05:27,881 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
e image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
sor_class` instead
warnings.warn(
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
warnings.warn(
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:05:36,646 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:05:36,646 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
INFO 03-05 03:05:35 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:05:36,646 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
2025-03-05 03:05:36,646 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 6.99it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 6.98it/s]
0%| | 0/4 [00:00<?, ?it/s]2025-03-05 03:05:38,890 - INFO - flashinfer.jit: Loading JIT ops: batch_prefill_with_kv_cache_dtype_q_bf16_dtype_kv_bf16_dtype_o_bf16_dtype_idx_i32_head_dim_qk_64_head_dim_vo_64_posenc_0_use_swa_False_use_logits_cap_False_f16qk_False
2025-03-05 03:05:38,936 - INFO - flashinfer.jit: Finished loading JIT ops: batch_prefill_with_kv_cache_dtype_q_bf16_dtype_kv_bf16_dtype_o_bf16_dtype_idx_i32_head_dim_qk_64_head_dim_vo_64_posenc_0_use_swa_False_use_logits_cap_False_f16qk_False
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00, 1.57it/s]
=== Testing asynchronous batch generation ===
2025-03-05 03:05:43,943 - INFO - flashinfer.jit: Loading JIT ops: cascade
2025-03-05 03:05:43,962 - INFO - flashinfer.jit: Finished loading JIT ops: cascade
Total Time Taken: 147.2977 seconds
Average Requests Per Second: 6.79 req/s
```
official throughput benchmark:
```
python -m sglang.bench_offline_throughput --disable-radix-cache --model-path Qwen/Qwen2.5-0.5B --dataset-name random --random-input 4999 --random-output 100
```
official output:
```
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
INFO 03-05 03:17:09 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:17:10,957 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
[2025-03-05 03:17:13] server_args=ServerArgs(model_path='Qwen/Qwen2.5-0.5B', tokenizer_path='Qwen/Qwen2.5-0.5B', tokenizer_mode='auto', load_format='auto', trust_remote_code=False, dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, quantization=None, context_length=None, device='cuda', served_model_name='Qwen/Qwen2.5-0.5B', chat_template=None, is_embedding=False, revision=None, skip_tokenizer_init=False, host='127.0.0.1', port=30000, mem_fraction_static=0.88, max_running_requests=None, max_total_tokens=None, chunked_prefill_size=2048, max_prefill_tokens=16384, schedule_policy='lpm', schedule_conservativeness=1.0, cpu_offload_gb=0, prefill_only_one_req=False, tp_size=1, stream_interval=1, stream_output=False, random_seed=987514108, constrained_json_whitespace_pattern=None, watchdog_timeout=300, download_dir=None, base_gpu_id=0, log_level='info', log_level_http=None, log_requests=False, show_time_cost=False, enable_metrics=False, decode_log_interval=40, api_key=None, file_storage_pth='sglang_storage', enable_cache_report=False, dp_size=1, load_balance_method='round_robin', ep_size=1, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', lora_paths=None, max_loras_per_batch=8, lora_backend='triton', attention_backend='flashinfer', sampling_backend='flashinfer', grammar_backend='outlines', speculative_draft_model_path=None, speculative_algorithm=None, speculative_num_steps=5, speculative_num_draft_tokens=64, speculative_eagle_topk=8, enable_double_sparsity=False, ds_channel_config_path=None, ds_heavy_channel_num=32, ds_heavy_token_num=256, ds_heavy_channel_type='qk', ds_sparse_decode_threshold=4096, disable_radix_cache=True, disable_jump_forward=False, disable_cuda_graph=False, disable_cuda_graph_padding=False, enable_nccl_nvls=False, disable_outlines_disk_cache=False, disable_custom_all_reduce=False, disable_mla=False, disable_overlap_schedule=False, enable_mixed_chunk=False, enable_dp_attention=False, enable_ep_moe=False, enable_torch_compile=False, torch_compile_max_bs=32, cuda_graph_max_bs=8, cuda_graph_bs=None, torchao_config='', enable_nan_detection=False, enable_p2p_check=False, triton_attention_reduce_in_fp32=False, triton_attention_num_kv_splits=8, num_continuous_decode_steps=1, delete_ckpt_after_loading=False, enable_memory_saver=False, allow_auto_truncate=False, return_hidden_states=False, enable_custom_logit_processor=False, tool_call_parser=None, enable_hierarchical_cache=False, enable_flashinfer_mla=False)
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py:590: FutureWarning: The image_processor_class argument is deprecated and will be removed in v4.42. Please use `slow_image_processor_class`, or `fast_image_processor_class` instead
warnings.warn(
INFO 03-05 03:17:17 __init__.py:190] Automatically detected platform cuda.
INFO 03-05 03:17:17 __init__.py:190] Automatically detected platform cuda.
2025-03-05 03:17:19,506 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
2025-03-05 03:17:19,507 - INFO - flashinfer.jit: Prebuilt kernels not found, using JIT backend
[2025-03-05 03:17:22 TP0] Init torch distributed begin.
[2025-03-05 03:17:22 TP0] Load weight begin. avail mem=21.98 GB
[2025-03-05 03:17:22 TP0] Using model weights format ['*.safetensors']
[2025-03-05 03:17:23 TP0] No model.safetensors.index.json found in remote.
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 6.80it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 6.79it/s]
[2025-03-05 03:17:23 TP0] Load weight end. type=Qwen2ForCausalLM, dtype=torch.bfloat16, avail mem=20.98 GB
[2025-03-05 03:17:23 TP0] KV Cache is allocated. K size: 9.17 GB, V size: 9.17 GB.
[2025-03-05 03:17:23 TP0] Memory pool end. avail mem=2.10 GB
[2025-03-05 03:17:23 TP0] Capture cuda graph begin. This can take up to several minutes.
0%| | 0/4 [00:00<?, ?it/s]2025-03-05 03:17:24,472 - INFO - flashinfer.jit: Loading JIT ops: batch_prefill_with_kv_cache_dtype_q_bf16_dtype_kv_bf16_dtype_o_bf16_dtype_idx_i32_head_dim_qk_64_head_dim_vo_64_posenc_0_use_swa_False_use_logits_cap_False_f16qk_False
2025-03-05 03:17:24,523 - INFO - flashinfer.jit: Finished loading JIT ops: batch_prefill_with_kv_cache_dtype_q_bf16_dtype_kv_bf16_dtype_o_bf16_dtype_idx_i32_head_dim_qk_64_head_dim_vo_64_posenc_0_use_swa_False_use_logits_cap_False_f16qk_False
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:02<00:00, 1.53it/s]
[2025-03-05 03:17:26 TP0] Capture cuda graph end. Time elapsed: 2.62 s
[2025-03-05 03:17:26 TP0] max_total_num_tokens=1602333, chunked_prefill_size=2048, max_prefill_tokens=16384, max_running_requests=4097, context_len=32768
Downloading from https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json to /tmp/ShareGPT_V3_unfiltered_cleaned_split.json
/tmp/ShareGPT_V3_unfiltered_cleaned_split.json: 100%|███████████████████████████████████████████████████| 642M/642M [00:08<00:00, 75.1MB/s]
...
...
...
====== Offline Throughput Benchmark Result =======
Backend: engine
Successful requests: 1000
Benchmark duration (s): 63.50
Total input tokens: 2505716
Total generated tokens: 50925
Request throughput (req/s): 15.75
Input token throughput (tok/s): 39459.41
Output token throughput (tok/s): 801.95
Total token throughput (tok/s): 40261.37
==================================================
``` | closed | 2025-03-05T03:20:36Z | 2025-03-05T04:13:30Z | https://github.com/sgl-project/sglang/issues/4076 | [] | JunhaoWang | 3 |
lucidrains/vit-pytorch | computer-vision | 38 | About flatening the patch | Hi,
Thanks for sharing this implementation. I got one question when flattening the HxW patch to D dimension, you use a FC layer to map it to a fixed dimension. But in the original paper, they use 224x224 to train and 384x384 to test, which can not be achieved if the flatten layer is fixed. Also, in another repo you shared (https://github.com/rwightman/pytorch-image-models/blob/6f43aeb2526f9fc35cde5262df939ea23a18006c/timm/models/vision_transformer.py#L146), they use 1D conv to avoid resolution mismatch problem. Do you know which one is correct? Thanks! | open | 2020-12-04T04:06:12Z | 2020-12-04T05:31:09Z | https://github.com/lucidrains/vit-pytorch/issues/38 | [] | yifanjiang19 | 2 |
deezer/spleeter | deep-learning | 607 | [Bug] Illegal hardware instruction | - [O ] I didn't find a similar issue already open.
- [O] I read the documentation (README AND Wiki)
- [O] I have installed FFMpeg
- [O] My problem is related to Spleeter only, not a derivative product (such as Webapplication, or GUI provided by others)
## Description
<!-- Give us a clear and concise description of the bug you are reporting. -->
On a windows PC, I was able to successfully run a command using 'spleeter separator test.mp3'. However, on my mac which runs macOS Big Sur with Apple M1, I get the following error when running the same command: zsh: **illegal hardware instruction**
My question is, is spleeter incompatible with Big Sur or Apple M1? I found that this was the case for tensorflow, and I'm curious if there is any way around this to run spleeter on my mac. Thanks.
## Step to reproduce
<!-- Indicates clearly steps to reproduce the behavior: -->
1. Installed using pip with python 3.8
2. Ran as 'spleeter separator test.mp3' on terminal
3. Got 'illegal hardware instruction' error
## Output
When I run the script I get the following error: zsh: illegal hardware instruction
My expectation was that it would run the same as on my PC, with a success message.
## Environment
<!-- Fill the following table -->
| | |
| ----------------- | ------------------------------- |
| OS | macOS Big Sur Version 11.1 |
| Installation type | pip |
| RAM available | 16 GB |
| Hardware spec | - |
## Additional context
<!-- Add any other context about the problem here, references, cites, etc.. -->
| closed | 2021-04-08T17:23:42Z | 2025-02-21T13:17:33Z | https://github.com/deezer/spleeter/issues/607 | [
"bug",
"invalid"
] | e16m | 32 |
Avaiga/taipy | data-visualization | 1,621 | [DOCS] editable__columnname is not documented | ### Issue Description
The proper syntax to make the "Demand" column of a table editable in tgb is:
```python
tgb.table("{demand_table}", on_edit=edit_demand, editable__Demand=True)
```
But this syntax is not documented in the docs:
https://docs.taipy.io/en/develop/manuals/userman/gui/viselements/generic/table/
https://docs.taipy.io/en/develop/tutorials/visuals/2_using_tables/
### Screenshots or Examples (if applicable)
_No response_
### Proposed Solution (optional)
_No response_
### Code of Conduct
- [X] I have checked the [existing issues](https://github.com/Avaiga/taipy/issues?q=is%3Aissue+).
- [ ] I am willing to work on this issue (optional) | closed | 2024-07-31T10:34:27Z | 2024-10-05T21:35:12Z | https://github.com/Avaiga/taipy/issues/1621 | [
"📄 Documentation",
"🖰 GUI",
"🟨 Priority: Medium"
] | AlexandreSajus | 4 |
pytest-dev/pytest-html | pytest | 163 | warnings summary not reflected in html-summary (feature request) | In addition to test failure, pytest captures a warnings summary and reports this at the end of the test summary. It would be great if this could be reflected in the html summary as well. | open | 2018-05-10T05:12:02Z | 2020-12-21T22:47:05Z | https://github.com/pytest-dev/pytest-html/issues/163 | [
"enhancement"
] | nicain | 10 |
mage-ai/mage-ai | data-science | 5,555 | [Feature Request] Vim Motions | **Is your feature request related to a problem? Please describe.**
As a developer used to working with Vim motions in my editor, it is a bit jarring to edit code in Mage and not have support for this, which in turn drives me towards using an external editor instead of working directly in Mage.
**Describe the solution you'd like**
An optional setting for enabling basic Vim functionality, like cursor navigation, yank/delete/paste and so on. This would make Mage a more comfortable tool to work in without needing an external editor, for developers who prefer this style of editing.
| open | 2024-11-12T14:15:50Z | 2024-11-13T10:36:40Z | https://github.com/mage-ai/mage-ai/issues/5555 | [
"feature"
] | oscarlofwenhamn | 3 |
flairNLP/flair | nlp | 2,783 | POS span returns empty list on Colab | Thank you for your dedicated work.
I've been trying to run the POS example from [HuggingFace](https://huggingface.co/flair/pos-english) in a Google Colab. Yet, I cannot retrieve the spans for POS. I've tried various different things, but it always seems to return an empty list and I have used the exact same code as in your tutorial.
I've set up a colab with my issue:
https://colab.research.google.com/drive/1K1NvP8Qg6nM8mVmDr_vNZdE5fQ7aZGdj?usp=sharing
| closed | 2022-05-19T15:11:33Z | 2022-05-19T16:40:03Z | https://github.com/flairNLP/flair/issues/2783 | [
"bug"
] | PhilWicke | 3 |
gradio-app/gradio | data-visualization | 10,752 | Remove scrollers in dataframe | hey all,
trying to remove scrollers from the data frame. Is they a way?
seems they're displaying by default. tried css, styling, max_height... all didn't work out
| closed | 2025-03-07T10:38:50Z | 2025-03-13T07:05:09Z | https://github.com/gradio-app/gradio/issues/10752 | [
"💾 Dataframe"
] | angelica-ignateva | 9 |
plotly/dash | flask | 3,017 | Metadata not generated correctly for Typescript components | With TypeScript components, the `metadata.json` file is not generated correctly in cases where there is a union with a boolean.
For example,
```
persistence?: boolean | string | number;
```
The `metadata.json` file only includes the string and number but not the boolean.
```
"persistence": {
"description": "Used to allow user interactions in this component to be persisted when\nthe component - or the page - is refreshed. If `persisted` is truthy and\nhasn't changed from its previous value, a `value` that the user has\nchanged while using the app will keep that change, as long as\nthe new `value` also matches what was given originally.\nUsed in conjunction with `persistence_type`.",
"required": false,
"type": {
"name": "union",
"value": [
{
"name": "string",
"raw": "string"
},
{
"name": "number",
"raw": "number"
}
],
"raw": "string | number | boolean"
}
},
```
The [docstrings are not generated correctly](https://www.dash-mantine-components.com/components/multiselect#multiselect):
Also, setting the `persistence=True` on the component generates this warning in the console:
| open | 2024-09-29T22:44:41Z | 2024-10-01T18:00:26Z | https://github.com/plotly/dash/issues/3017 | [
"bug",
"P2"
] | AnnMarieW | 1 |
pbugnion/gmaps | jupyter | 233 | AttributeError | Hi,
I followed install instructions but always got this same error:
AttributeError: module 'ipywidgets.widgets.trait_types' has no attribute 'InstanceDict'
How can I fix it?
Thanks in advance, Jaime | closed | 2018-04-02T21:04:44Z | 2018-06-24T14:58:23Z | https://github.com/pbugnion/gmaps/issues/233 | [] | jaimebayes | 20 |
OpenInterpreter/open-interpreter | python | 1,158 | Invalid crash | ### Describe the bug
raise RateLimitError(
litellm.exceptions.RateLimitError: OpenAIException - Error code: 429 - {'error': {'message': 'You exceeded your current quota, please check your plan and billing details. For more information on this error, read the docs: https://platform.openai.com/docs/guides/error-codes/api-errors.', 'type': 'insufficient_quota', 'param': None, 'code': 'insufficient_quota'}}
This crashes but is invalid. I have lots of credits.
### Reproduce
Just open interpreter place openapi key (a newly generated one) and write "all good ?"
### Expected behavior
to not crash
### Screenshots
_No response_
### Open Interpreter version
1st April 2024 latest
### Python version
3.10.13
### Operating System name and version
macos
### Additional context
_No response_ | open | 2024-04-01T09:58:48Z | 2024-04-01T09:58:48Z | https://github.com/OpenInterpreter/open-interpreter/issues/1158 | [] | pligor | 0 |
deezer/spleeter | deep-learning | 781 | [Bug] Model download URL points to wrong location when splitting on Github Actions runner | - [x] I didn't find a similar issue already open.
- [x] I read the documentation (README AND Wiki)
- [x] I have installed FFMpeg
- [x] My problem is related to Spleeter only, not a derivative product (such as Webapplication, or GUI provided by others)
## Description
Any attempt to run `spleeter` on a [Github Actions runner](https://github.com/features/actions) results in a 404 GET failure at the step for downloading the pre-trained model from spleeter's URL (https://github.com/deezer/spleeter/releases).
Instead, the job attempts to fetch the pretrained model from the current Github repo's URL (https://github.com/my-org/myrepo/releases) , which is non-existent, and returns a `404 Not Found`.
## Root Cause
Spleeter uses the [following environment variables(https://github.com/deezer/spleeter/blob/master/spleeter/model/provider/github.py#L88-L90) for building the fetch URL for its pre-trained, published models:
* `GITHUB_HOST`
* `GITHUB_REPOSITORY`
* `GITHUB_RELEASE`
Unfortunately, `GITHUB_REPOSITORY` is [a reserved environment variable](https://docs.github.com/en/actions/learn-github-actions/environment-variables#:~:text=GITHUB_REPOSITORY,Hello%2DWorld.) in the context of Github Actions, and gets picked up at runtime, resulting in an incorrect destination model URL.
## Solution Options
One alternative is to explicitly set `GITHUB_REPOSITORY` at the time of invocation of spleeter.
```
$ GITHUB_REPOSITORY="deezer/spleeter" poetry run python lib/stem_split.py
```
However, this could introduce undesired side-effects in the Github Action by overriding what should be the original expected value that's set by the environment by default.
A more appropriate solution would be to make the internal usage of spleeter-specific Github environment variables even more distinct, than the current, generic, conflicting names.
## Environment
| | |
| ----------------- | ------------------------------- |
| OS | Linux |
| Installation type | poetry |
| RAM available | plenty |
| Hardware spec | CPU |
| open | 2022-08-25T15:56:06Z | 2022-08-25T16:11:57Z | https://github.com/deezer/spleeter/issues/781 | [
"bug",
"invalid"
] | chuttam | 0 |
RobertCraigie/prisma-client-py | pydantic | 711 | Support default values for enum arrays | <!--
Thanks for helping us improve Prisma Client Python! 🙏 Please follow the sections in the template and provide as much information as possible about your problem, e.g. by enabling additional logging output.
See https://prisma-client-py.readthedocs.io/en/stable/reference/logging/ for how to enable additional logging output.
-->
## Bug description
<!-- A clear and concise description of what the bug is. -->
Currently setting a default value for an `enum[]` field will cause the client to crash.
## How to reproduce
<!--
Steps to reproduce the behavior:
1. Go to '...'
2. Change '....'
3. Run '....'
4. See error
-->
https://discord.com/channels/933860922039099444/1080592482850586675/1080592482850586675
## Expected behavior
<!-- A clear and concise description of what you expected to happen. -->
No crash | closed | 2023-03-01T21:47:16Z | 2023-03-01T22:24:57Z | https://github.com/RobertCraigie/prisma-client-py/issues/711 | [
"bug/1-repro-available",
"kind/bug",
"process/candidate",
"priority/high",
"topic: crash"
] | RobertCraigie | 0 |
ionelmc/pytest-benchmark | pytest | 168 | benchmark-compare=NUM option without NUM set, runs as if no benchmark-compare was set | When I try to compare the last saved reference (using `--benchmark-autosave` or `--benchmark-save=some-name` options), with the current run (using `--benchmark-compare=` as explain in the [documentation](https://pytest-benchmark.readthedocs.io/en/latest/usage.html#commandline-options))
```
--benchmark-compare=NUM
Compare the current run against run NUM (or prefix of _id in elasticsearch) or the latest saved run if unspecified.
```
There is no comparison made, only a standard benchmark run.
I have the following tests/test_perf.py file
```python
def fake_print():
print("Fake")
def test_perf_cortex(benchmark):
benchmark(fake_print)
```
When I run `--benchmark-autosave` the file is well generated in .benchmarks/ :
```
$ python3 -m pytest --benchmark-autosave tests
(...)
$ ls -R .benchmarks .benchmarks:
Linux-CPython-3.6-64bit/
.benchmarks/Linux-CPython-3.6-64bit:
0001_unversioned_20200417_145821.json
```
Then running the compare option using the saved **NUM** works fine:
```
$ python3 -m pytest --benchmark-compare=0001 tests/
```
but running without **NUM**, runs pytest as if no `--benchmark-compare` was set
```
$ python3 -m pytest --benchmark-compare= tests/
```
==================
Ubuntu 18.04
```
$ uname -a
Linux xxxxxx 4.15.0-96-generic #97-Ubuntu SMP Wed Apr 1 03:25:46 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
```
Python version and dependencies
```
python=3.6.9
pytest=5.4.1
pytest-benchmark=3.2.3
```
| closed | 2020-04-17T15:16:27Z | 2020-05-11T13:34:19Z | https://github.com/ionelmc/pytest-benchmark/issues/168 | [] | hcuche | 5 |
amidaware/tacticalrmm | django | 1,240 | Capture output of bulk commands | **Is your feature request related to a problem? Please describe.**
Bulk Command and Bulk Script do not have a way to capture the output.
**Describe the solution you'd like**
Ideally, there should be a way to capture the output of the Bulk commands. Bulk Command is useful to gather ad-hoc information. It would be nice if the information could be displayed in a table, or at the very least, email the results.
Bulk Script is useful to take action on a group of machines. Most scripts output something. It would be nice if the information could be displayed in a table, or at the very lest, email the results.
**Describe alternatives you've considered**
N/A
**Additional context**
Other users have mentioned the same on Discord.
| closed | 2022-08-09T16:46:20Z | 2022-08-09T17:04:39Z | https://github.com/amidaware/tacticalrmm/issues/1240 | [] | NiceGuyIT | 2 |
alteryx/featuretools | scikit-learn | 2,427 | Fix warnings encountered during unit tests | Currently when running the full test suite over **17,000** warnings are generated:
```
2417 passed, 23 skipped, 87 xfailed, 17879 warnings
```
Some of these warning may be expected, but some should be addressed. This may require mutiple PRs, but we should investigate these warnings and implement fixes where possible. Of immediate concern are the deprecation warnings that might cause tests to break when new versions of dependencies are released:
```
featuretools/tests/primitive_tests/test_num_consecutive.py::TestNumConsecutiveLessMean::test_inf
/dev/featuretools/featuretools/tests/primitive_tests/test_num_consecutive.py:259: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
x = x.append(pd.Series([np.inf]))
```
This Dask warning also appears to be quite common and perhaps of concern.
```
featuretools/tests/primitive_tests/test_transform_features.py::test_comparisons_with_ordinal_invalid_inputs[dask_es]
/dev/featuretools/env/lib/python3.9/site-packages/dask/dataframe/core.py:4134: UserWarning:
You did not provide metadata, so Dask is running your function on a small dataset to guess output types. It is possible that Dask will guess incorrectly.
To provide an explicit output types or to silence this message, please provide the `meta=` keyword, as described in the map or apply function that you are using.
Before: .apply(func)
After: .apply(func, meta=('priority_level <= ordinal_invalid', 'object'))
warnings.warn(meta_warning(meta))
``` | open | 2022-12-22T18:22:02Z | 2023-06-26T19:15:43Z | https://github.com/alteryx/featuretools/issues/2427 | [] | thehomebrewnerd | 0 |
deepinsight/insightface | pytorch | 1,807 | insightface_paddle 如何进行finetune? | 如题 | open | 2021-11-02T10:31:15Z | 2021-11-02T10:31:15Z | https://github.com/deepinsight/insightface/issues/1807 | [] | Nise-2-meet-U | 0 |
neuml/txtai | nlp | 631 | Txtai | closed | 2024-01-06T09:40:07Z | 2024-01-06T09:41:36Z | https://github.com/neuml/txtai/issues/631 | [] | Gitaymmw | 0 | |
huggingface/transformers | nlp | 35,988 | Nan/0 logits when finetuning ModernBERT with flash attention enabled. | ### System Info
Output without fa:
, attn_implementation="sdpa"
{'loss': 0.3681, 'grad_norm': 5.589271545410156, 'learning_rate': 4e-05, 'epoch': 1.0}
{'eval_loss': 0.2998541593551636, 'eval_runtime': 2.4136, 'eval_samples_per_second': 20.716, 'eval_steps_per_second': 5.386, 'epoch': 1.0}
{'loss': 0.1703, 'grad_norm': 1.1856054067611694, 'learning_rate': 0.0, 'epoch': 2.0}
{'eval_loss': 0.21692198514938354, 'eval_runtime': 1.0645, 'eval_samples_per_second': 46.97, 'eval_steps_per_second': 12.212, 'epoch': 2.0}
{'train_runtime': 86.2749, 'train_samples_per_second': 10.432, 'train_steps_per_second': 2.62, 'train_loss': 0.2692194153777266, 'epoch': 2.0}
100%|████████████████████████████████████████████████████████████████████████████████| 226/226 [01:26<00:00, 2.62it/s]
Model saved successfully.
(loss != nan/0 and resulting model inference works ok)
With fa enabled:
trainer = Trainer(
{'loss': 0.4619, 'grad_norm': nan, 'learning_rate': 4e-05, 'epoch': 1.0}
{'eval_loss': nan, 'eval_runtime': 1.04, 'eval_samples_per_second': 48.079, 'eval_steps_per_second': 12.501, 'epoch': 1.0}
{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0, 'epoch': 2.0}
{'eval_loss': nan, 'eval_runtime': 0.7291, 'eval_samples_per_second': 68.58, 'eval_steps_per_second': 17.831, 'epoch': 2.0}
{'train_runtime': 74.6236, 'train_samples_per_second': 12.061, 'train_steps_per_second': 3.029, 'train_loss': 0.23094445625237658, 'epoch': 2.0}
100%|████████████████████████████████████████████████████████████████████████████████| 226/226 [01:14<00:00, 3.03it/s]
Model saved successfully.
Model produses nans on evals and resulting model is unusable too because of nans during inference.
Speed is improved 10x compared to disabled fa, so i can't train without it.
code snippet:
torch 2.5.1+cu124
transformers 4.48.2 (same problem with latest 4.49 built from git)
triton 3.1.0
flash_attn 2.7.1.post1
windows
```
(ve) C:\Users\Admin\Desktop\bert>nvidia-smi
Fri Jan 31 15:28:53 2025
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 560.94 Driver Version: 560.94 CUDA Version: 12.6 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Driver-Model | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Tesla P40 TCC | 00000000:03:00.0 Off | Off |
| N/A 28C P8 9W / 250W | 9MiB / 24576MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 3090 WDDM | 00000000:04:00.0 Off | N/A |
| 0% 47C P8 30W / 350W | 316MiB / 24576MiB | 2% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
```
(ve) C:\Users\Admin\Desktop\bert>echo %CUDA_VISIBLE_DEVICES%
00000000:04:00.0
(seems like tesla is unused, as it should be)
mirror: https://huggingface.co/answerdotai/ModernBERT-base/discussions/59
### Who can help?
@muellerzr @tomaarsen @ArthurZucker
### Information
- [x] The official example scripts
- [x] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
This is my testing code. Any dataset should be loaded and adjusted a bit (fields) to get nans:
```python
# Split dataset
train_texts, test_texts, train_labels, test_labels = train_test_split(
df_sampled["prompt"].tolist(),
binary_labels,
test_size=0.1,
random_state=42
)
# Create Hugging Face datasets
def create_hf_dataset(texts, labels):
return Dataset.from_dict({
"text": texts,
"labels": labels.astype(np.float32) # Now contains float32 values
})
dataset_train = create_hf_dataset(train_texts, train_labels)
print(dataset_train[0]["labels"]) # Should show [1.0, 0.0, ...] instead of [1, 0, ...]
dataset_test = create_hf_dataset(test_texts, test_labels)
# Load tokenizer
checkpoint = "answerdotai/ModernBERT-large"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# Tokenization function
def preprocess_function(examples):
return tokenizer(examples["text"], truncation=True, padding=True)
# Tokenize datasets
tokenized_train = dataset_train.map(preprocess_function, batched=True)
tokenized_test = dataset_test.map(preprocess_function, batched=True)
# Load model for multi-label classification
model = AutoModelForSequenceClassification.from_pretrained(
checkpoint,
num_labels=len(unique_labels),
id2label={i: label for i, label in enumerate(unique_labels)},
label2id={label: i for i, label in enumerate(unique_labels)},
problem_type="multi_label_classification"
)
train_bsz, val_bsz = 4,4
lr = 8e-5
betas = (0.9, 0.98)
n_epochs = 2
eps = 1e-6
wd = 8e-6
# Training setup (optimized for memory efficiency)
training_args = TrainingArguments(
output_dir="modernbert_finetuned",
learning_rate=lr,
per_device_train_batch_size=train_bsz,
per_device_eval_batch_size=val_bsz,
num_train_epochs=n_epochs,
lr_scheduler_type="linear",
optim="adamw_torch",
adam_beta1=betas[0],
adam_beta2=betas[1],
adam_epsilon=eps,
logging_strategy="epoch",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
bf16=True,
bf16_full_eval=True,
push_to_hub=False,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_test,
tokenizer=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
)
# Train and save the model
trainer.train()
model.save_pretrained("modernbert_finetuned_model")
tokenizer.save_pretrained("modernbert_finetuned_model")
print("Model saved successfully.")
```
Also I have same problems with example code:
```python
# Install necessary libraries
# !pip install transformers datasets accelerate scikit-learn -Uqq
import os
import numpy as np
import pandas as pd
import torch
from functools import partial
import gc
from datasets import load_dataset
from sklearn.metrics import f1_score, accuracy_score, matthews_corrcoef
from scipy.stats import pearsonr, spearmanr
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
DataCollatorWithPadding,
TrainingArguments,
Trainer,
TrainerCallback,
)
# Set environment variables
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_VISIBLE_DEVICES"] = "00000000:04:00.0"
# Define GLUE tasks metadata
glue_tasks = {
"cola": {
"abbr": "CoLA",
"name": "Corpus of Linguistic Acceptability",
"description": "Predict whether a sequence is a grammatical English sentence",
"task_type": "Single-Sentence Task",
"domain": "Misc.",
"size": "8.5k",
"metrics": "Matthews corr.",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence"],
"target": "label",
"metric_funcs": [matthews_corrcoef],
"n_labels": 2,
},
"sst2": {
"abbr": "SST-2",
"name": "Stanford Sentiment Treebank",
"description": "Predict the sentiment of a given sentence",
"task_type": "Single-Sentence Task",
"domain": "Movie reviews",
"size": "67k",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 2,
},
"mrpc": {
"abbr": "MRPC",
"name": "Microsoft Research Paraphrase Corpus",
"description": "Predict whether two sentences are semantically equivalent",
"task_type": "Similarity and Paraphrase Tasks",
"domain": "News",
"size": "3.7k",
"metrics": "F1/Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence1", "sentence2"],
"target": "label",
"metric_funcs": [accuracy_score, f1_score],
"n_labels": 2,
},
"stsb": {
"abbr": "SST-B",
"name": "Semantic Textual Similarity Benchmark",
"description": "Predict the similarity score for two sentences on a scale from 1 to 5",
"task_type": "Similarity and Paraphrase Tasks",
"domain": "Misc.",
"size": "7k",
"metrics": "Pearson/Spearman corr.",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence1", "sentence2"],
"target": "label",
"metric_funcs": [pearsonr, spearmanr],
"n_labels": 1,
},
"qqp": {
"abbr": "QQP",
"name": "Quora question pair",
"description": "Predict if two questions are a paraphrase of one another",
"task_type": "Similarity and Paraphrase Tasks",
"domain": "Social QA questions",
"size": "364k",
"metrics": "F1/Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["question1", "question2"],
"target": "label",
"metric_funcs": [f1_score, accuracy_score],
"n_labels": 2,
},
"mnli-matched": {
"abbr": "MNLI",
"name": "Mulit-Genre Natural Language Inference",
"description": "Predict whether the premise entails, contradicts or is neutral to the hypothesis",
"task_type": "Inference Tasks",
"domain": "Misc.",
"size": "393k",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation_matched", "test": "test_matched"},
"inputs": ["premise", "hypothesis"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 3,
},
"mnli-mismatched": {
"abbr": "MNLI",
"name": "Mulit-Genre Natural Language Inference",
"description": "Predict whether the premise entails, contradicts or is neutral to the hypothesis",
"task_type": "Inference Tasks",
"domain": "Misc.",
"size": "393k",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation_mismatched", "test": "test_mismatched"},
"inputs": ["premise", "hypothesis"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 3,
},
"qnli": {
"abbr": "QNLI",
"name": "Stanford Question Answering Dataset",
"description": "Predict whether the context sentence contains the answer to the question",
"task_type": "Inference Tasks",
"domain": "Wikipedia",
"size": "105k",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["question", "sentence"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 2,
},
"rte": {
"abbr": "RTE",
"name": "Recognize Textual Entailment",
"description": "Predict whether one sentence entails another",
"task_type": "Inference Tasks",
"domain": "News, Wikipedia",
"size": "2.5k",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence1", "sentence2"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 2,
},
"wnli": {
"abbr": "WNLI",
"name": "Winograd Schema Challenge",
"description": "Predict if the sentence with the pronoun substituted is entailed by the original sentence",
"task_type": "Inference Tasks",
"domain": "Fiction books",
"size": "634",
"metrics": "Accuracy",
"dataset_names": {"train": "train", "valid": "validation", "test": "test"},
"inputs": ["sentence1", "sentence2"],
"target": "label",
"metric_funcs": [accuracy_score],
"n_labels": 2,
},
}
# Function to get label maps
def get_label_maps(raw_datasets, train_ds_name):
labels = raw_datasets[train_ds_name].features["label"]
id2label = {idx: name.upper() for idx, name in enumerate(labels.names)} if hasattr(labels, "names") else None
label2id = {name.upper(): idx for idx, name in enumerate(labels.names)} if hasattr(labels, "names") else None
return id2label, label2id
# Function to preprocess data
def preprocess_function(examples, task_inputs, hf_tokenizer):
inps = [examples[inp] for inp in task_inputs]
tokenized = hf_tokenizer(*inps, truncation=True)
return tokenized
# Function to compute metrics
def compute_metrics(eval_pred, task_metrics):
predictions, labels = eval_pred
metrics_d = {}
for metric_func in task_metrics:
metric_name = metric_func.__name__
if metric_name in ["pearsonr", "spearmanr"]:
score = metric_func(labels, np.squeeze(predictions))
else:
score = metric_func(np.argmax(predictions, axis=-1), labels)
if isinstance(score, tuple):
metrics_d[metric_func.__name__] = score[0]
else:
metrics_d[metric_func.__name__] = score
return metrics_d
# Callback to capture training history
class MetricsCallback(TrainerCallback):
def __init__(self):
self.training_history = {"train": [], "eval": []}
def on_log(self, args, state, control, logs=None, **kwargs):
if logs is not None:
if "loss" in logs: # Training logs
self.training_history["train"].append(logs)
elif "eval_loss" in logs: # Evaluation logs
self.training_history["eval"].append(logs)
# Function to fine-tune a GLUE task
def finetune_glue_task(task, checkpoint="answerdotai/ModernBERT-base", train_subset=None, do_cleanup=True):
task_meta = glue_tasks[task]
train_ds_name = task_meta["dataset_names"]["train"]
valid_ds_name = task_meta["dataset_names"]["valid"]
task_inputs = task_meta["inputs"]
n_labels = task_meta["n_labels"]
task_metrics = task_meta["metric_funcs"]
# Load dataset
raw_datasets = load_dataset("glue", task.split("-")[0] if "-" in task else task)
if train_subset is not None and len(raw_datasets["train"]) > train_subset:
raw_datasets["train"] = raw_datasets["train"].shuffle(seed=42).select(range(train_subset))
id2label, label2id = get_label_maps(raw_datasets, train_ds_name)
# Load tokenizer
hf_tokenizer = AutoTokenizer.from_pretrained(checkpoint)
tokenized_datasets = raw_datasets.map(partial(preprocess_function, task_inputs=task_inputs, hf_tokenizer=hf_tokenizer), batched=True)
# Define compute metrics function
task_compute_metrics = partial(compute_metrics, task_metrics=task_metrics)
# Load model
model_additional_kwargs = {"id2label": id2label, "label2id": label2id} if id2label and label2id else {}
hf_model = AutoModelForSequenceClassification.from_pretrained(
checkpoint, num_labels=n_labels, **model_additional_kwargs
)
# Data collator
hf_data_collator = DataCollatorWithPadding(tokenizer=hf_tokenizer)
# Training arguments
training_args = TrainingArguments(
output_dir=f"aai_ModernBERT_{task}_ft",
learning_rate=8e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
num_train_epochs=2,
lr_scheduler_type="linear",
optim="adamw_torch",
adam_beta1=0.9,
adam_beta2=0.98,
adam_epsilon=1e-6,
logging_strategy="epoch",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
bf16=True,
bf16_full_eval=True,
push_to_hub=False,
)
# Trainer
trainer = Trainer(
model=hf_model,
args=training_args,
train_dataset=tokenized_datasets[train_ds_name],
eval_dataset=tokenized_datasets[valid_ds_name],
tokenizer=hf_tokenizer,
data_collator=hf_data_collator,
compute_metrics=task_compute_metrics,
)
# Add callback to trainer
metrics_callback = MetricsCallback()
trainer.add_callback(metrics_callback)
# Train
trainer.train()
# Get training results and hyperparameters
train_history_df = pd.DataFrame(metrics_callback.training_history["train"]).add_prefix("train_")
eval_history_df = pd.DataFrame(metrics_callback.training_history["eval"])
train_res_df = pd.concat([train_history_df, eval_history_df], axis=1)
args_df = pd.DataFrame([training_args.to_dict()])
# Cleanup
if do_cleanup:
cleanup(things_to_delete=[trainer, hf_model, hf_tokenizer, tokenized_datasets, raw_datasets])
return train_res_df, args_df, hf_model, hf_tokenizer
# Function to cleanup GPU memory
def cleanup(things_to_delete=None):
if things_to_delete is not None:
for thing in things_to_delete:
if thing is not None:
del thing
gc.collect()
torch.cuda.empty_cache()
# Fine-tune all GLUE tasks
# Fine-tune all GLUE tasks
for task in glue_tasks.keys():
print(f"----- Finetuning {task} -----")
train_res_df, args_df, hf_model, hf_tokenizer = finetune_glue_task(
task, checkpoint="answerdotai/ModernBERT-base", train_subset=1_000, do_cleanup=True
)
print(":: Results ::")
print(train_res_df) # Print training results DataFrame
print("\n:: Hyperparameters ::")
print(args_df) # Print hyperparameters DataFrame
```
Outputs:
{'eval_loss': nan, 'eval_matthews_corrcoef': 0.0, 'eval_runtime': 5.3788, 'eval_samples_per_second': 193.908, 'eval_steps_per_second': 6.135, 'epoch': 1.0}
{'loss': 0.0, 'grad_norm': nan, 'learning_rate': 0.0, 'epoch': 2.0}
{'eval_loss': nan, 'eval_matthews_corrcoef': 0.0, 'eval_runtime': 1.2161, 'eval_samples_per_second': 857.625, 'eval_steps_per_second': 27.135, 'epoch': 2.0}
### Expected behavior
It should work the same way it works without flash attention enabled (e.g. sdpa). | closed | 2025-01-31T12:35:55Z | 2025-02-10T10:04:46Z | https://github.com/huggingface/transformers/issues/35988 | [
"bug"
] | anunknowperson | 7 |
mars-project/mars | scikit-learn | 3,192 | [BUG] supervisor memory leak | <!--
Thank you for your contribution!
Please review https://github.com/mars-project/mars/blob/master/CONTRIBUTING.rst before opening an issue.
-->
**Describe the bug**
A clear and concise description of what the bug is.
The Mars job executed hundreds of tasks, some task has a large graph:
Set environ var `PYTHONTRACEMALLOC=1`, then run the following code:
```python
import tracemalloc
import gc
import mars
import mars.tensor as mt
import mars.dataframe as md
def run_round(extra_config=None):
df = md.DataFrame(
mt.random.rand(1200, 70, chunk_size=70),
columns=[f"col{i}" for i in range(70)])
for i in range(70):
df[f"col{i + 70}"] = df[f"col{i}"].fillna(0)
df[f"col{i + 140}"] = df[f"col{i}"].fillna(0)
for i in range(70):
df[f"col{i}"] = df[f"col{i}"] / 100
df = df.fillna(0)
df.map_chunk(lambda x: x).execute(extra_config=extra_config)
def main():
mars.new_session()
count = 0
run_round(extra_config={"enable_profiling": True})
gc.collect()
s1 = tracemalloc.take_snapshot()
while True:
print(f"==============>run {count}")
count += 1
run_round()
if count == 3:
break
gc.collect()
s2 = tracemalloc.take_snapshot()
diff = s2.compare_to(s1, "lineno")
print("[ Top 10 differences ]")
for stat in diff[:10]:
print(stat)
if __name__ == "__main__":
main()
```
output
``` python
[ Top 10 differences ]
/home/admin/mars/mars/core/base.py:96: size=10.5 MiB (+8098 KiB), count=59034 (+44276), average=187 B
/home/admin/mars/mars/core/operand/core.py:100: size=10200 KiB (+7647 KiB), count=61221 (+45900), average=171 B
/home/admin/mars/mars/core/graph/builder/base.py:72: size=9861 KiB (+7400 KiB), count=85245 (+63956), average=118 B
/home/admin/mars/mars/services/task/analyzer/analyzer.py:250: size=9629 KiB (+7222 KiB), count=91908 (+68931), average=107 B
/home/admin/mars/mars/core/graph/builder/base.py:62: size=9194 KiB (+6895 KiB), count=91812 (+68859), average=103 B
/home/admin/mars/mars/core/base.py:37: size=7590 KiB (+5691 KiB), count=131726 (+98781), average=59 B
/home/admin/mars/mars/core/operand/base.py:266: size=6733 KiB (+5048 KiB), count=132583 (+99408), average=52 B
/home/admin/mars/mars/core/operand/base.py:225: size=5691 KiB (+4268 KiB), count=132393 (+99294), average=44 B
/home/admin/mars/mars/core/entity/core.py:35: size=5180 KiB (+3883 KiB), count=66294 (+49706), average=80 B
/home/admin/mars/mars/core/operand/core.py:121: size=5025 KiB (+3769 KiB), count=30629 (+22971), average=168 B
```
After digging into the code, the memory leak is because the `TaskProcessor` is not removed. The tileable graph, chunk graph and subtask graph are not GC.
**To Reproduce**
To help us reproducing this bug, please provide information below:
1. Your Python version
2. The version of Mars you use Latest 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.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Additional context**
Add any other context about the problem here.
| closed | 2022-07-15T04:03:15Z | 2022-09-14T09:32:44Z | https://github.com/mars-project/mars/issues/3192 | [] | fyrestone | 0 |
scrapy/scrapy | python | 5,841 | Setting a cookie for a different domain does not work | Given a request like:
```python
Request(
url="https://a.example",
cookies=[
{
'name': 'foo',
'value': 'bar',
'domain': 'b.example',
},
]
)
```
The cookie download middleware will discard the cookie because `b.example` does not match¹ `a.example`. The cookie will not only be ignored for the purpose of sending this specific request, which is OK, but it will not be added to the cookie jar either, meaning that if `a.example` redirects to `b.example`, the follow-up request to `b.example` is not going to include this cookie either.
I think we need to make it so that domain-based filtering does not keep a cookie out of the cookie jar, so that we can set a cookie for a different domain on a request with the goal of having that cookie reach the right domain in a redirect scenario.
But we need to make sure that we keep applying the domain filtering to cookies that come in the `Set-Cookie` header in a response, as doing otherwise would be a security issue.
---
¹ Understanding by “match” what the cookie specification understands when it defines how user agents must handle `Set-Cookie` headers. | open | 2023-03-02T12:14:08Z | 2023-03-16T18:01:44Z | https://github.com/scrapy/scrapy/issues/5841 | [
"bug"
] | Gallaecio | 1 |
bmoscon/cryptofeed | asyncio | 938 | Funding rate DyDx | Issue/Feature request: Missing funding rate for dydx.
I am able to use dydx and call other functions: for example
pairs = ['ETH-USD-PERP']
f.add_feed(dYdX(symbols=pairs, channels = [TRADES], callbacks= {TRADES: trade}))
generates ETH/USD data --> exchange: DYDX symbol: ETH-USD-PERP side: sell amount: 2.996 price: 1273.8 id: None type: None timestamp: 1670913339.171
However funding generates: cryptofeed.exceptions.UnsupportedDataFeed: funding is not supported on DYDX
Looking at dydx client there is an option to query funding rate using client.get_perpetual_market so I was wondering if this will be included. Thanks! | open | 2022-12-13T09:09:31Z | 2022-12-13T09:09:31Z | https://github.com/bmoscon/cryptofeed/issues/938 | [
"Feature Request"
] | vijayengineer | 0 |
plotly/dash-bio | dash | 49 | remove styles from app_manhattanplot.py | closed | 2018-11-28T08:23:37Z | 2018-11-30T08:42:01Z | https://github.com/plotly/dash-bio/issues/49 | [] | Bachibouzouk | 0 | |
yt-dlp/yt-dlp | python | 11,954 | ERROR: Unable to download video: [Errno 30] Read-only file system: 'output.json' | ### DO NOT REMOVE OR SKIP THE ISSUE TEMPLATE
- [X] I understand that I will be **blocked** if I *intentionally* remove or skip any mandatory\* field
### Checklist
- [X] I'm asking a question and **not** reporting a bug or requesting a feature
- [X] I've looked through the [README](https://github.com/yt-dlp/yt-dlp#readme)
- [X] I've verified that I have **updated yt-dlp to nightly or master** ([update instructions](https://github.com/yt-dlp/yt-dlp#update-channels))
- [X] I've searched [known issues](https://github.com/yt-dlp/yt-dlp/issues/3766) and the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=) for similar questions **including closed ones**. DO NOT post duplicates
- [X] I've read the [guidelines for opening an issue](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#opening-an-issue)
### Please make sure the question is worded well enough to be understood
```
ydl_opts = { "proxy": "http://username:password@address:port", 'progress_hooks': [hook], 'overwrites': True, "paths": { "temp": "/tmp/", "home": "/tmp/" }, "verbose": True }
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
res = ydl.download([URL])
```
I am running on lambda and trying to change the temporary files dir but `output.json` is still saving to read-only file system. How do I change the path of `output.json` to `/tmp/` or skip downloading it completely?
### Provide verbose output that clearly demonstrates the problem
- [X] Run **your** yt-dlp command with **-vU** flag added (`yt-dlp -vU <your command line>`)
- [X] If using API, add `'verbose': True` to `YoutubeDL` params instead
- [X] Copy the WHOLE output (starting with `[debug] Command-line config`) and insert it below
### Complete Verbose Output
```shell
[debug] Encodings: locale UTF-8, fs utf-8, pref UTF-8, out utf-8 (No ANSI), error utf-8 (No ANSI), screen utf-8 (No ANSI)
[debug] yt-dlp version stable@2024.10.22 from yt-dlp/yt-dlp [67adeb7ba] (pip) API
[debug] params: {'proxy': 'http://username:password@address:port', 'progress_hooks': [<function handler.<locals>.hook at 0x7f3e53dd89d0>], 'overwrites': True, 'paths': {'temp': '/tmp/', 'home': '/tmp/'}, 'verbose': True, 'compat_opts': set(), 'http_headers': {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.45 Safari/537.36', 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', 'Accept-Language': 'en-us,en;q=0.5', 'Sec-Fetch-Mode': 'navigate'}}
[debug] Python 3.10.15 (CPython x86_64 64bit) - Linux-5.10.227-239.884.amzn2.x86_64-x86_64-with-glibc2.26 (OpenSSL 1.1.1x 30 Jan 2024, glibc 2.26)
[debug] exe versions: ffmpeg 7.0.2-static (setts)
[debug] Optional libraries: Cryptodome-3.21.0, certifi-2024.12.14, mutagen-1.47.0, requests-2.32.3, sqlite3-3.7.17, urllib3-2.2.3, websockets-13.1
[debug] Proxy map: {'all': 'http://username:password@address:port'}
[debug] Request Handlers: urllib, requests, websockets
[debug] Loaded 1839 extractors
[youtube] Extracting URL: https://www.youtube.com/watch?v=BCk1p_h-ewk
[youtube] BCk1p_h-ewk: Downloading webpage
[youtube] BCk1p_h-ewk: Downloading ios player API JSON
[youtube] BCk1p_h-ewk: Downloading mweb player API JSON
[debug] [youtube] Extracting signature function js_03dbdfab_107
[youtube] BCk1p_h-ewk: Downloading player 03dbdfab
WARNING: [youtube] BCk1p_h-ewk: Signature extraction failed: Some formats may be missing
[debug] [youtube] Could not find JS function "decodeURIComponent"; please report this issue on https://github.com/yt-dlp/yt-dlp/issues?q= , filling out the appropriate issue template. Confirm you are on the latest version using yt-dlp -U
[youtube] BCk1p_h-ewk: Downloading m3u8 information
[debug] Sort order given by extractor: quality, res, fps, hdr:12, source, vcodec:vp9.2, channels, acodec, lang, proto
[debug] Formats sorted by: hasvid, ie_pref, quality, res, fps, hdr:12(7), source, vcodec:vp9.2(10), channels, acodec, lang, proto, size, br, asr, vext, aext, hasaud, id
[debug] Default format spec: bestvideo*+bestaudio/best
[info] BCk1p_h-ewk: Downloading 1 format(s): 136+251
[debug] Invoking http downloader on "https://rr3---sn-n02xgoxufvg3-2gb6.googlevideo.com/videoplayback?expire=1735602155&ei=i9tyZ9P6B63k6dsP3bH_0As&ip=64.137.42.112&id=o-AEij63_bmZHqLsMxrocV1fvFES8mTFny42e-AIh3-lRt&itag=136&source=youtube&requiressl=yes&xpc=EgVo2aDSNQ%3D%3D&met=1735580555%2C&mh=4t&mm=31%2C29&mn=sn-n02xgoxufvg3-2gb6%2Csn-4g5edns7&ms=au%2Crdu&mv=m&mvi=3&pl=23&rms=au%2Cau&initcwndbps=1202500&bui=AfMhrI8z2YnPWC7msKKysShMhmJPBc3GfkTKu7cMs8Bpc_WMqForhrntoX78fbA_Hir7LcDixmBBBAeE&spc=x-caUDbBkR9bS9FQXi2asRF2Rlghyi0gmdICZH7RoJzo3qUhtw0I&vprv=1&svpuc=1&mime=video%2Fmp4&rqh=1&gir=yes&clen=24234706&dur=199.066&lmt=1706945654208638&mt=1735580010&fvip=2&keepalive=yes&fexp=51326932%2C51335594%2C51371294&c=IOS&txp=6219224&sparams=expire%2Cei%2Cip%2Cid%2Citag%2Csource%2Crequiressl%2Cxpc%2Cbui%2Cspc%2Cvprv%2Csvpuc%2Cmime%2Crqh%2Cgir%2Cclen%2Cdur%2Clmt&sig=AJfQdSswRAIgIXOy3JXbb7KAZWNADQpXr7eUKg58ObZpUbPXpNNeqwcCID4lXm_ADWTtVuqDojRXFy_FszTZjdkxFxl5eg0jDY4Q&lsparams=met%2Cmh%2Cmm%2Cmn%2Cms%2Cmv%2Cmvi%2Cpl%2Crms%2Cinitcwndbps&lsig=AGluJ3MwRQIhAMl-GaSWRgyh8-fmJ24jRJ7wjZEbOX-xTURpXc0v3f-PAiBRfFVZun323CNu-2Uv0IOlRfi1xLD1nMNyCw0uu64X_w%3D%3D"
[download] Destination: /tmp/Sia x David Guetta - Let's Love (Music Video) ft. Sia's Moments ：D [BCk1p_h-ewk].f136.mp4
[download] 0.0% of 23.11MiB at 39.22KiB/s ETA 10:03
[download] 0.0% of 23.11MiB at 114.41KiB/s ETA 03:26
[download] 0.0% of 23.11MiB at 261.95KiB/s ETA 01:30
[download] 0.1% of 23.11MiB at 550.74KiB/s ETA 00:42
[download] 0.1% of 23.11MiB at 257.58KiB/s ETA 01:31
[download] 0.3% of 23.11MiB at 325.16KiB/s ETA 01:12
[download] 0.5% of 23.11MiB at 432.32KiB/s ETA 00:54
[download] 1.1% of 23.11MiB at 662.31KiB/s ETA 00:35
[download] 2.2% of 23.11MiB at 1.04MiB/s ETA 00:21
[download] 4.3% of 23.11MiB at 1.73MiB/s ETA 00:12
[download] 8.6% of 23.11MiB at 2.96MiB/s ETA 00:07
[download] 17.3% of 23.11MiB at 4.94MiB/s ETA 00:03
[download] 34.6% of 23.11MiB at 6.72MiB/s ETA 00:02
[download] 41.9% of 23.11MiB at 7.82MiB/s ETA 00:01
[download] 41.9% of 23.11MiB at Unknown B/s ETA Unknown
[download] 41.9% of 23.11MiB at Unknown B/s ETA Unknown
[download] 41.9% of 23.11MiB at 4.35MiB/s ETA 00:03
[download] 41.9% of 23.11MiB at 6.91MiB/s ETA 00:01
[download] 42.0% of 23.11MiB at 10.55MiB/s ETA 00:01
[download] 42.1% of 23.11MiB at 16.68MiB/s ETA 00:00
[download] 42.4% of 23.11MiB at 28.43MiB/s ETA 00:00
[download] 42.9% of 23.11MiB at 48.04MiB/s ETA 00:00
[download] 44.0% of 23.11MiB at 7.76MiB/s ETA 00:01
[download] 46.2% of 23.11MiB at 13.70MiB/s ETA 00:00
[download] 50.5% of 23.11MiB at 21.89MiB/s ETA 00:00
[download] 59.2% of 23.11MiB at 19.56MiB/s ETA 00:00
[download] 76.5% of 23.11MiB at 14.79MiB/s ETA 00:00
[download] 84.5% of 23.11MiB at 11.11MiB/s ETA 00:00
[download] 84.5% of 23.11MiB at Unknown B/s ETA Unknown
[download] 84.5% of 23.11MiB at Unknown B/s ETA Unknown
[download] 84.5% of 23.11MiB at 4.62MiB/s ETA 00:00
[download] 84.5% of 23.11MiB at 7.11MiB/s ETA 00:00
[download] 84.6% of 23.11MiB at 11.00MiB/s ETA 00:00
[download] 84.7% of 23.11MiB at 17.58MiB/s ETA 00:00
[download] 85.0% of 23.11MiB at 28.79MiB/s ETA 00:00
[download] 85.6% of 23.11MiB at 47.55MiB/s ETA 00:00
[download] 86.6% of 23.11MiB at 11.48MiB/s ETA 00:00
[download] 88.8% of 23.11MiB at 18.98MiB/s ETA 00:00
[download] 93.1% of 23.11MiB at 30.92MiB/s ETA 00:00
[download] 100.0% of 23.11MiB at 22.98MiB/s ETA 00:00
[download] 100% of 23.11MiB in 00:00:03 at 6.91MiB/s ERROR: Unable to download video: [Errno 30] Read-only file system: 'output.json'
Traceback (most recent call last):
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3464, in process_info
partial_success, real_download = self.dl(fname, new_info)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3202, in dl
return fd.download(name, new_info, subtitle)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/downloader/common.py", line 466, in download
ret = self.real_download(filename, info_dict)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/downloader/http.py", line 370, in real_download
return download()
File "/opt/python/lib/python3.10/site-packages/yt_dlp/downloader/http.py", line 356, in download
self._hook_progress({
File "/opt/python/lib/python3.10/site-packages/yt_dlp/downloader/common.py", line 481, in _hook_progress
ph(status)
File "/var/task/index.py", line 25, in hook
with open('output.json', 'w') as f:
OSError: [Errno 30] Read-only file system: 'output.json'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3581, in wrapper
res = func(*args, **kwargs)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1614, in extract_info
return self.__extract_info(url, self.get_info_extractor(key), download, extra_info, process)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1625, in wrapper
return func(self, *args, **kwargs)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1781, in __extract_info
return self.process_ie_result(ie_result, download, extra_info)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1840, in process_ie_result
ie_result = self.process_video_result(ie_result, download=download)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3014, in process_video_result
self.process_info(new_info)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 178, in wrapper
return func(self, *args, **kwargs)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3494, in process_info
raise UnavailableVideoError(err)
yt_dlp.utils.UnavailableVideoError: Unable to download video: [Errno 30] Read-only file system: 'output.json'
[ERROR] DownloadError: ERROR: Unable to download video: [Errno 30] Read-only file system: 'output.json'
Traceback (most recent call last):
File "/var/task/index.py", line 33, in handler
res = ydl.download([body['url']])
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3608, in download
self.__download_wrapper(self.extract_info)(
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 3585, in wrapper
self.report_error(e)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1091, in report_error
self.trouble(f'{self._format_err("ERROR:", self.Styles.ERROR)} {message}', *args, **kwargs)
File "/opt/python/lib/python3.10/site-packages/yt_dlp/YoutubeDL.py", line 1030, in trouble
raise DownloadError(message, exc_info)
```
| closed | 2024-12-30T18:09:57Z | 2024-12-30T18:51:06Z | https://github.com/yt-dlp/yt-dlp/issues/11954 | [
"invalid",
"incomplete"
] | fadone | 2 |
CorentinJ/Real-Time-Voice-Cloning | python | 529 | can't pickle tensorflow | I have tried the main repository, and i get the error that "self" is not defined. There was an issue about this, so i used the branch from that fix. Now i am getting:
**raceback (most recent call last):
File "demo_cli.py", line 93, in <module>
mels = synthesizer.synthesize_spectrograms(texts, embeds)
File "D:\RealTimeDeepFake\voice\Real-Time-Voice-Cloning\Real-Time-Voice-Cloning\synthesizer\inference.py", line 101, in synthesize_spectrograms
[(self.checkpoint_fpath, embeddings, texts, self._seed)])[0]
File "C:\Users\fnafm\AppData\Local\Programs\Python\Python37\lib\site-packages\multiprocess\pool.py", line 276, in starmap
return self._map_async(func, iterable, starmapstar, chunksize).get()
File "C:\Users\fnafm\AppData\Local\Programs\Python\Python37\lib\site-packages\multiprocess\pool.py", line 657, in get
raise self._value
multiprocess.pool.MaybeEncodingError: Error sending result: '<multiprocess.pool.ExceptionWithTraceback object at 0x00000250FDB07DC8>'. Reason: 'TypeError("can't pickle tensorflow_core.python._tf_stack.Stack objects")'**
I have tried using conda environment with python 3.7, and also, plain python, following this procedure : https://poorlydocumented.com/2019/11/installing-corentinjs-real-time-voice-cloning-project-on-windows-10-from-scratch/
My hardware really sucks and i can't run linux. I am stuck with live sessions and windows.
How can i fix the issue? | closed | 2020-09-17T13:33:55Z | 2021-02-26T16:36:13Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/529 | [] | ghost | 4 |
aleju/imgaug | machine-learning | 228 | BUG?: reseed() does nothing for deterministic augmenters when given a RandomState | Hello,
I've just learned about this great library, but I'm having some trouble using it. Will be happy to get your help with the issue below.
When trying to call reseed() on a deterministic augmenter and provide it with a previously constructed RandomState(), reseed() has no effect.
For example, let's construct an augmenter as follows:
`aug = iaa.Pad(percent=(0,0.5), random_state=np.random.RandomState(17), deterministic=True)`
And then plot an augmented image:
`plt.imshow(aug.augment_image(im))`
Now, let's reseed the augmenter (with a different random state):
`aug.reseed(np.random.RandomState(1234))`
The following will plot the same image as before:
`plt.imshow(aug.augment_image(im))`
I believe the reason is this code in Augmenter.reseed():
```
if random_state is None:
random_state = ia.current_random_state()
elif isinstance(random_state, np.random.RandomState):
pass # just use the provided random state without change
else:
random_state = ia.new_random_state(random_state)
```
I guess that `pass` should be replaced with `self.random_state = random_state`.
Am I missing something?
Thank you!
| closed | 2019-01-06T17:24:46Z | 2019-01-06T18:42:10Z | https://github.com/aleju/imgaug/issues/228 | [] | ig177 | 1 |
huggingface/text-generation-inference | nlp | 2,130 | [RFC]Add Auto-Round Support | Hi, here is the INC team from Intel. Thank you for developing this amazing project.
### Motivation
Our team has developed a new weight-only quantization algorithm called Auto-Round. It has achieved superior accuracy compared to [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), and [OmniQuant](https://arxiv.org/abs/2308.13137) across 11 tasks, particularly excelling in low-bit quantization (e.g., 2-bits and 3-bits). Auto-Round supports quantization from 2 to 8 bits, involves low tuning costs, and imposes no additional overhead during inference. Key results are summarized below, with detailed information available in our [paper](https://arxiv.org/abs/2309.05516), [GitHub repository](https://github.com/intel/auto-round/blob/main/docs/acc.md), and Hugging Face [low-bit quantization leaderboard](https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard).
We would like to contribute this quantization algorithm to TGI and enable users to:
1. Quantize a floating model using Auto-Round.
2. Perform inference with an AutoRound-quantized model.
### 1. Quantize Floating Model Using Auto-Round
Extend the current `quantize` API and add `method` as a new argument to select different algorithms. Users can utilize it as follows:
```bash
text-generation-server quantize \
--MODEL_ID path/to/float/model/\
--OUTPUT_DIR /path/to/save/quantized/model \
--method autoround # <--- select the different methods, such as `gptq`, `autoround`
```
We propose two options to implement it:
#### Option 1: Adding Auto-Round as a New Python Dependency (Recommended)
Auto-Round is currently released as a pure [Python binary](https://pypi.org/project/auto-round/). The option adds `auto-round` to TGI's [`requirements_xx.txt`](https://github.com/huggingface/text-generation-inference/blob/main/server/requirements_cuda.txt) and calls Auto-Round's API to obtain the quantized model.
Advantages:
- Minimal maintenance effort for TGI. We already integrated it into our [INC](https://github.com/intel/neural-compressor) project, and enabled the integration tests.
- Easy synchronization with new enhancements. As we continually improve the Auto-Round algorithm, updates can be effortlessly incorporated into TGI by updating the package version.
#### Option 2: Porting All Source Code of Auto-Round into TGI
We are also willing to integrate all source code of Auto-Round directly into TGI.
Advantages:
- Not introduce third-party dependency.
- TGI maintainers have better control.
Here is the overall calling flow for the these two options:
```python
# tgi/server/text_generation_server/layers/autoround/quantize.py
def quantize(
model_id: str,
bits: int,
groupsize: int,
output_dir: str,
...
):
# Load model...
model = ...
# Start quantize model using auto-round
# Import autoround from auto-round package for Option 1
# Import autoround from the current folder for Option 2
import autoround
rounder = autoround.AutoRound(model, ...)
rounder.quantize(...)
rounder.save_quantized(output_dir)
```
### 2. Perform Inference with an AutoRound-quantized Model.
We propose extending the current `text-generation-launcher` API to include `autoround` as a new option within `--quantize`. Users can utilize it as follows:
```bash
text-generation-launcher \
--model-id INC/Llama-2-7b-Chat-Autoround \ # Quantized model using auto-round
--trust-remote-code --port 8080 \
--max-input-length 3072 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize autoround # <------ select autoround
```
Your feedback is important. Please feel free to comment on the options mentioned above or suggest additional approaches to ensure the most appropriate way to contribute :). Thank you in advance!
| closed | 2024-06-27T07:49:14Z | 2024-08-08T01:53:35Z | https://github.com/huggingface/text-generation-inference/issues/2130 | [
"Stale"
] | yiliu30 | 19 |
horovod/horovod | deep-learning | 3,829 | Spark tests fail with AttributeError: 'pyarrow.lib.Schema' object has no attribute 'to_arrow_schema' | Spark tests fail with
```
Traceback (most recent call last):
File "/horovod/examples/spark/pytorch/pytorch_spark_mnist.py", line 122, in <module>
torch_model = torch_estimator.fit(train_df).setOutputCols(['label_prob'])
File "/usr/local/lib/python3.8/dist-packages/horovod/spark/common/estimator.py", line 35, in fit
return super(HorovodEstimator, self).fit(df, params)
File "/usr/local/lib/python3.8/dist-packages/pyspark/ml/base.py", line 205, in fit
return self._fit(dataset)
File "/usr/local/lib/python3.8/dist-packages/horovod/spark/common/estimator.py", line 68, in _fit
with util.prepare_data(backend.num_processes(),
File "/usr/lib/python3.8/contextlib.py", line 113, in __enter__
return next(self.gen)
File "/usr/local/lib/python3.8/dist-packages/horovod/spark/common/util.py", line 735, in prepare_data
dataset_idx = _get_or_create_dataset(key, store, df, feature_columns, label_columns,
File "/usr/local/lib/python3.8/dist-packages/horovod/spark/common/util.py", line 672, in _get_or_create_dataset
train_rows, val_rows, pq_metadata, avg_row_size = get_simple_meta_from_parquet(
File "/usr/local/lib/python3.8/dist-packages/horovod/spark/common/util.py", line 495, in get_simple_meta_from_parquet
train_data_schema = train_data.schema.to_arrow_schema()
AttributeError: 'pyarrow.lib.Schema' object has no attribute 'to_arrow_schema'
```
https://github.com/horovod/horovod/actions/runs/4025636527/jobs/6919132641
It's probably related to the 11.0 release of pyarrow. https://pypi.org/project/pyarrow/#history | closed | 2023-01-27T16:33:29Z | 2023-01-28T02:38:02Z | https://github.com/horovod/horovod/issues/3829 | [
"bug"
] | maxhgerlach | 0 |
tiangolo/uvicorn-gunicorn-fastapi-docker | pydantic | 61 | add python 3.9 tag | add python 3.9 tag since it is released. | closed | 2020-10-09T12:04:35Z | 2021-09-17T14:20:53Z | https://github.com/tiangolo/uvicorn-gunicorn-fastapi-docker/issues/61 | [] | wasdee | 10 |
huggingface/datasets | tensorflow | 6,769 | (Willing to PR) Datasets with custom python objects | ### Feature request
Hi thanks for the library! I would like to have a huggingface Dataset, and one of its column is custom (non-serializable) Python objects. For example, a minimal code:
```
class MyClass:
pass
dataset = datasets.Dataset.from_list([
dict(a=MyClass(), b='hello'),
])
```
It gives error:
```
ArrowInvalid: Could not convert <__main__.MyClass object at 0x7a852830d050> with type MyClass: did not recognize Python value type when inferring an Arrow data type
```
I guess it is because Dataset forces to convert everything into arrow format. However, is there any ways to make the scenario work? Thanks!
### Motivation
(see above)
### Your contribution
Yes, I am happy to PR!
Cross-posted: https://discuss.huggingface.co/t/datasets-with-custom-python-objects/79050?u=fzyzcjy
EDIT: possibly related https://github.com/huggingface/datasets/issues/5766 | open | 2024-04-01T13:18:47Z | 2024-04-01T13:36:58Z | https://github.com/huggingface/datasets/issues/6769 | [
"enhancement"
] | fzyzcjy | 0 |
python-gino/gino | sqlalchemy | 664 | Website is severely broken | ## Description
The python-gino.org website is severely broken when you resize the browser window. I thought you might like to know. Happens in both latest Firefox and Chrome. See screenshot.
| closed | 2020-05-03T15:25:52Z | 2020-05-19T04:22:39Z | https://github.com/python-gino/gino/issues/664 | [
"task"
] | retnikt | 6 |
plotly/dash | data-science | 2,250 | [Feature Request] Long Polling / Web Sockets for background / long callbacks | **Is your feature request related to a problem? Please describe.**
Too many requests, when we're using background/long callbacks with simple pooling, may break the page due to low latency between requests and page overload.
**Describe the solution you'd like**
What about using [Long Polling](https://javascript.info/long-polling) (dash-like way), when we will send to the FE only different events supporting reconnections or [WebSocket](https://javascript.info/websocket) (modern way)?
**Alternatives**
We can research [Server Sent Events](https://javascript.info/server-sent-events) | closed | 2022-09-26T00:59:42Z | 2024-07-24T15:10:24Z | https://github.com/plotly/dash/issues/2250 | [] | ArtsiomAntropau | 1 |
smarie/python-pytest-cases | pytest | 349 | Programatically generate test parameters (parameters generators?) | Bonjour,
I just discover this plugin and it looks like it might help me to achieve what I am looking for in terms of tests.
If I try to get a minimal working example of what I need is:
* One of the test could be: for a given column (list of numbers), test that the sum of the all cells but the last equals the last (sum of the parts equals total)
* I have multiple columns to test (e.g. in a dataframe)
* I have multiple dataframes in one file (e.g. Excel files with multiple tabs)
* I have multiple files
The test is always the same for all the columns that are identified, so I guess the test is unique but the parameters are the product of all the components (files x dataframes x columns)
But I cannot find the proper way to "make it happen"...
In the documentation, the part [Case Generator](https://smarie.github.io/python-pytest-cases/#case-generators) looks promising but the list of all values is hard-coded (`@parametrize(who=('you', 'there'))`).
I was wondering if there is a way to programmatically generated the values of the fixtures or cases.
What I would imagine:
```
@fixture
@parametrize(file=list_of_files_from_glob, key=list_of_keys)
def df_from_file(file, key)
return pd.read_excel(file, sheet_name=key)
@fixture
@parametrize(df=df_from_file()) # <-- This is not possible but this is what I am trying to achieve...
def column(df):
filter = get_filter_info(df)
for col in filter_cols(df):
yield df[col] # <-- Generator are not possible but this is the idea
@parametrize(col=column()) # <-- same...
def test_sum_of_parts(col)
# here col are all the cols from all the dfs from all the tabs/keys from all the files...
assert np.isclose(col[;-1].sum(), col[-1])
```
I will keep on reading the documentation to find some hints. The fixtures unions looks like an option but I am lost so far in the way it works.
Thanks in advance for your help. | open | 2024-09-03T16:36:25Z | 2024-09-09T13:21:35Z | https://github.com/smarie/python-pytest-cases/issues/349 | [] | ant1j | 1 |
jupyter-book/jupyter-book | jupyter | 2,033 | Launch button for opening a local notebook? | Is it possible to make a launch button that opens a notebook with your local jupyter setup? e.g. could you have have a local flask app running and a url to it that could launch it? | open | 2023-06-20T19:32:53Z | 2023-06-20T19:32:53Z | https://github.com/jupyter-book/jupyter-book/issues/2033 | [] | jkitchin | 0 |
encode/databases | sqlalchemy | 154 | Transaction rollback in `with transaction` block causes IndexError | Should I be able to manually rollback transaction when using transaction context?
```python3
import asyncio
from databases import Database
def database():
return Database("postgresql://db",
min_size=1,
max_size=4,
user="tiger",
password="hunter2",
database="test")
async def run():
async with database() as db:
async with db.transaction() as tx:
await db.fetch_one("select 1")
await tx.rollback()
if __name__ == "__main__":
asyncio.run(run())
```
When run, it causes `IndexError`:
```
Traceback (most recent call last):
File "/usr/local/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/usr/local/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/app/app/foo.py", line 22, in <module>
asyncio.run(run())
File "/usr/local/lib/python3.7/asyncio/runners.py", line 43, in run
return loop.run_until_complete(main)
File "/usr/local/lib/python3.7/asyncio/base_events.py", line 579, in run_until_complete
return future.result()
File "/app/app/foo.py", line 18, in run
await tx.rollback()
File "/usr/local/lib/python3.7/site-packages/databases/core.py", line 275, in __aexit__
await self.commit()
File "/usr/local/lib/python3.7/site-packages/databases/core.py", line 305, in commit
assert self._connection._transaction_stack[-1] is self
IndexError: list index out of range
```
Using Databases 0.2.5 with Python 3.7.4.
| closed | 2019-10-27T16:35:47Z | 2021-09-01T07:03:03Z | https://github.com/encode/databases/issues/154 | [
"question"
] | jarppe | 1 |
dynaconf/dynaconf | flask | 1,195 | disable casting for env vars | Am looking for a way to load value from an env with quotes. e.g.:
```exoprt DYNACONF_MYVAR='"with_quotes"'```
Is there any way to achieve this? To disable the tomlfy for env vars? | open | 2024-11-14T16:09:56Z | 2024-11-16T13:23:26Z | https://github.com/dynaconf/dynaconf/issues/1195 | [
"question"
] | zalmane | 3 |
pydata/xarray | pandas | 9,642 | Release/support roadmap in documentation | ### Is your feature request related to a problem?
I can not find easily info on the release cycle, python versions support, when they would be dropped, etc.
It's handy to know e.g. how long would python 3.11 be supported by xarray.
### Describe the solution you'd like
We could add a new page in the documentation, similar to [Development roadmap](https://docs.xarray.dev/en/stable/roadmap.html), but targeting users.
Say, "Release Roadmap", with info on:
- release cycle
- when support (=releases) would cease for a given python version
I understand the release cycle isn't on strict schedule, but then we could mention that, or what the project is striving to achieve.
### Describe alternatives you've considered
I have been looking around the project but did not find an overview. If I missed it, my apologies. Let me know :)
### Additional context
Nowadays I work on hardening xarray-based workflows for production.
Some of the target systems are rigid.
When designing new systems, it is important to get the python version/env right the first time.
And for aging in-place systems, to plan the update wisely.
| open | 2024-10-17T14:08:28Z | 2024-10-18T10:11:59Z | https://github.com/pydata/xarray/issues/9642 | [
"topic-documentation",
"enhancement"
] | arongergely | 4 |
ml-tooling/opyrator | pydantic | 63 | More dynamic input modules for audio and video like webcam and 🎤 | <!--
Thanks for requesting a feature 🙌 ❤️
Before opening a new issue, please make sure that we do not have any duplicates already open. You can ensure this by searching the issue list for this repository. If there is a duplicate, please close your issue and add a comment to the existing issue instead. Also, be sure to check our documentation first.
-->
**Feature description:**
<!---
Provide a detailed description of the feature or improvement you are proposing. What specific solution would you like? What is the expected behaviour?
Add any other context, screenshots, or code snippets about the feature request here as well.
-->
**Problem and motivation:**
<!---
Why is this change important to you? What is the problem this feature would solve? How would you use it? How can it benefit other users?
-->
**Is this something you're interested in working on?**
<!--- Yes or No -->
| closed | 2022-10-11T08:23:45Z | 2023-01-25T02:28:27Z | https://github.com/ml-tooling/opyrator/issues/63 | [
"feature",
"stale"
] | aadityaverma | 1 |
scikit-learn/scikit-learn | data-science | 30,707 | Add sample_weight support to QuantileTransformer | ### Describe the workflow you want to enable
Would be good to get sample_weight support for QuantileTransformer for dealing with sparse or imbalanced data, a la [#15601](https://github.com/scikit-learn/scikit-learn/issues/15601).
```
scaler = QuantileTransformer(output_distribution="normal")
scaler.fit(X, sample_weight=w)
```
### Describe your proposed solution
As far as I know it would just require adding the weight argument to the quantiles_ computation in np.nanpercentile.
`KBinsDiscretizer `supports sample_weight and with strategy='quantile', encode='ordinal' this behavior can be achieved but it is much, much slower.
### Describe alternatives you've considered, if relevant
_No response_
### Additional context
_No response_ | open | 2025-01-22T23:07:51Z | 2025-02-03T16:37:49Z | https://github.com/scikit-learn/scikit-learn/issues/30707 | [
"New Feature",
"Moderate"
] | antipisa | 2 |
marcomusy/vedo | numpy | 547 | Density now showing | Hi Marco,
I'm trying to plot `Points().density()` following up on this issue: https://github.com/brainglobe/brainrender/issues/107#issuecomment-981112830
When I run the `vedo` [example](https://github.com/marcomusy/vedo/blob/master/examples/pyplot/plot_density2d.py) the density is not shown:
<img width="619" alt="image" src="https://user-images.githubusercontent.com/17436313/143956904-8048a500-0ac2-45cd-abc1-0c9f4494377e.png">
while in brainrender it shows a `Volume` actor like in the issue mentioned above. Previously it would visualize a nice semi-transparent 'heatamap' of density in 3D. Do you know how I can recover that?
| closed | 2021-11-29T23:09:11Z | 2021-12-09T10:59:58Z | https://github.com/marcomusy/vedo/issues/547 | [] | FedeClaudi | 6 |
xonsh/xonsh | data-science | 4,725 | Failed to load xontrib jedi. | <!--- Provide a general summary of the issue in the Title above -->
<!--- If you have a question along the lines of "How do I do this Bash command in xonsh"
please first look over the Bash to Xonsh translation guide: https://xon.sh/bash_to_xsh.html
If you don't find an answer there, please do open an issue! -->
## xonfig
<details>
```
$ xonfig
+------------------+-------------------------+
| xonsh | 0.11.0 |
| Git SHA | 337cf25a |
| Commit Date | Nov 17 15:37:41 2021 |
| Python | 3.10.2 |
| PLY | 3.11 |
| have readline | True |
| prompt toolkit | 3.0.22 |
| shell type | prompt_toolkit |
| history backend | json |
| pygments | 2.10.0 |
| on posix | True |
| on linux | False |
| on darwin | True |
| on windows | False |
| on cygwin | False |
| on msys2 | False |
| is superuser | False |
| default encoding | utf-8 |
| xonsh encoding | utf-8 |
| encoding errors | surrogateescape |
| on jupyter | False |
| jupyter kernel | None |
| xontrib 1 | argcomplete |
| xontrib 2 | fzf-widgets |
| xontrib 3 | prompt_vi_mode |
| xontrib 4 | z |
| RC file 1 | /Users/jfperez/.xonshrc |
+------------------+-------------------------+
```
</details>
## Expected Behavior
<!--- Tell us what should happen -->
> Use Jedi as xonsh’s python completer.
>
> This xontrib is preinstalled with xonsh.
>
> Usage:
>
> Run the following command to enable (or add it to your [.xonshrc](https://xon.sh/xonshrc.html) file to enable on startup.)
>
> `xontrib load jedi`
Expect behavior to be as described in docs
## Current Behavior
<!--- Tell us what happens instead of the expected behavior -->
<!--- If part of your bug report is a traceback, please first enter debug mode before triggering the error
To enter debug mode, set the environment variable `XONSH_DEBUG=1` _before_ starting `xonsh`.
On Linux and OSX, an easy way to to do this is to run `env XONSH_DEBUG=1 xonsh` -->
jedi is not installed? Module not found
### Traceback (if applicable)
<details>
```
$ xontrib load jedi
<xonsh-code>:1:8 - xontrib load jedi
<xonsh-code>:1:8 + ![xontrib load jedi]
xonsh: To log full traceback to a file set: $XONSH_TRACEBACK_LOGFILE = <filename>
Traceback (most recent call last):
File "/opt/homebrew/Cellar/xonsh/0.11.0/libexec/lib/python3.10/site-packages/xonsh/__amalgam__.py", line 12116, in xontribs_load
update_context(name, ctx=ctx)
File "/opt/homebrew/Cellar/xonsh/0.11.0/libexec/lib/python3.10/site-packages/xonsh/__amalgam__.py", line 12072, in update_context
modctx = xontrib_context(name)
File "/opt/homebrew/Cellar/xonsh/0.11.0/libexec/lib/python3.10/site-packages/xonsh/__amalgam__.py", line 12037, in xontrib_context
m = importlib.import_module(spec.name)
File "/opt/homebrew/Cellar/python@3.10/3.10.2/Frameworks/Python.framework/Versions/3.10/lib/python3.10/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 883, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/opt/homebrew/Cellar/xonsh/0.11.0/libexec/lib/python3.10/site-packages/xontrib/jedi.py", line 19, in <module>
import jedi
ModuleNotFoundError: No module named 'jedi'
Failed to load xontrib jedi.
```
</details>
## Steps to Reproduce
<!--- Please try to write out a minimal reproducible snippet to trigger the bug, it will help us fix it! -->
NOTE: I enabled a few other xontribs but this error has occurred since first installation via homebrew today.
```
brew install xonsh
xonsh
xontrib load jedi
```
## For community
⬇️ **Please click the 👍 reaction instead of leaving a `+1` or 👍 comment**
| closed | 2022-03-19T22:45:50Z | 2022-03-24T03:53:15Z | https://github.com/xonsh/xonsh/issues/4725 | [
"xontrib-jedi"
] | justin-f-perez | 7 |
facebookresearch/fairseq | pytorch | 4,786 | How to get the perplexity of a single sentence for the custom fairseq pretrained language model? | ## ❓ Questions and Help
### Before asking:
1. search the issues.
2. search the docs.
<!-- If you still can't find what you need: -->
#### What is your question?
#### Code
<!-- Please paste a code snippet if your question requires it! -->
#### What have you tried?
#### What's your environment?
- fairseq Version (e.g., 1.0 or main):
- PyTorch Version (e.g., 1.0)
- OS (e.g., Linux):
- How you installed fairseq (`pip`, source):
- Build command you used (if compiling from source):
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- Any other relevant information:
| open | 2022-10-13T09:58:04Z | 2022-10-13T09:58:04Z | https://github.com/facebookresearch/fairseq/issues/4786 | [
"question",
"needs triage"
] | robotsp | 0 |
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