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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
s3rius/FastAPI-template | asyncio | 207 | Pre Commits Hooks fails when installed with Python-3.12.1 | Hello,
`Flake8`, `add-trailing-comma`, `pre-commit-hooks`, and `language-formatters-pre-commit-hooks` versions need to be updated. Otherwise fails with following errors on a newly generated project:
``` shell
boilerplate (master) ✗ poetry run pre-commit install
pre-commit installed at .git/hooks/pre-commit
boilerplate (master) ✗ poetry run pre-commit run -a
Check python ast.........................................................Passed
Trim Trailing Whitespace.................................................Passed
Check Toml...............................................................Passed
Fix End of Files.........................................................Passed
Add trailing commas......................................................Failed
- hook id: add-trailing-comma
- exit code: 1
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoqonxr4s2/py_env-python3.12/bin/add-trailing-comma", line 5, in <module>
from add_trailing_comma._main import main
ModuleNotFoundError: No module named 'add_trailing_comma'
Pretty format YAML.......................................................Failed
- hook id: pretty-format-yaml
- exit code: 1
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoh6sog_z7/py_env-python3.12/bin/pretty-format-yaml", line 5, in <module>
from language_formatters_pre_commit_hooks.pretty_format_yaml import pretty_format_yaml
File "/Users/m4hi2/.cache/pre-commit/repoh6sog_z7/py_env-python3.12/lib/python3.12/site-packages/language_formatters_pre_commit_hooks/__init__.py", line 8, in <module>
import pkg_resources
ModuleNotFoundError: No module named 'pkg_resources'
Traceback (most recent call last):
File "/Users/m4hi2/.cache/pre-commit/repoh6sog_z7/py_env-python3.12/bin/pretty-format-yaml", line 5, in <module>
from language_formatters_pre_commit_hooks.pretty_format_yaml import pretty_format_yaml
File "/Users/m4hi2/.cache/pre-commit/repoh6sog_z7/py_env-python3.12/lib/python3.12/site-packages/language_formatters_pre_commit_hooks/__init__.py", line 8, in <module>
import pkg_resources
ModuleNotFoundError: No module named 'pkg_resources'
autoflake................................................................Passed
Format with Black........................................................Passed
isort....................................................................Passed
Check with Flake8........................................................Failed
- hook id: flake8
- exit code: 1
Traceback (most recent call last):
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/bin/flake8", line 8, in <module>
sys.exit(main())
^^^^^^
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/main/cli.py", line 22, in main
app.run(argv)
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/main/application.py", line 375, in run
self._run(argv)
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/main/application.py", line 363, in _run
self.initialize(argv)
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/main/application.py", line 343, in initialize
self.find_plugins(config_finder)
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/main/application.py", line 157, in find_plugins
self.check_plugins = plugin_manager.Checkers(local_plugins.extension)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/plugins/manager.py", line 363, in __init__
self.manager = PluginManager(
^^^^^^^^^^^^^^
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/plugins/manager.py", line 243, in __init__
self._load_entrypoint_plugins()
File "/Users/m4hi2/Library/Caches/pypoetry/virtualenvs/boilerplate-eD2Xgr7s-py3.12/lib/python3.12/site-packages/flake8/plugins/manager.py", line 261, in _load_entrypoint_plugins
eps = importlib_metadata.entry_points().get(self.namespace, ())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'EntryPoints' object has no attribute 'get'
Validate types with MyPy.................................................Passed
```
Workaround, update Flake8 on `pyproject.toml` file, run `poetry update` and to update pre-commit hooks run:
``` shell
poetry run pre-commit autoupdate
``` | open | 2024-04-11T00:09:47Z | 2024-07-12T08:16:00Z | https://github.com/s3rius/FastAPI-template/issues/207 | [] | m4hi2 | 2 |
wger-project/wger | django | 1,307 | Light or dark mode according to system settings, Fedora KDE Spin | ## Use case
Missing setting options for the appearance.
<!--
Please tell us the problem you are running into that led to you wanting
a new feature.
Is your feature request related to a problem? Please give a clear and
concise description of what the problem is.
-->
## Proposal
Light or dark mode according to system settings.
<!--
Briefly but precisely describe what you would like wger to be able to do.
Consider attaching images showing what you are imagining.
-->
| closed | 2023-04-24T09:49:36Z | 2023-04-24T18:22:10Z | https://github.com/wger-project/wger/issues/1307 | [] | midwinters-fall | 3 |
holoviz/panel | plotly | 7,733 | Error when displaying the same `chat.ChatFeed` multiple times in Panel 1.6.1 | Hi team, thanks for the amazing work you're doing!
I encountered an issue while trying to update to a newer version of Panel. I believe it was introduced by [this commit](https://github.com/holoviz/panel/commit/b245bbb8d64bd5becc2d28574945bbb63c3b1872) as `old_objects` may be empty in
https://github.com/holoviz/panel/blob/b245bbb8d64bd5becc2d28574945bbb63c3b1872/panel/layout/feed.py#L162
#### ALL software version info
<details>
<summary>Software Version Info</summary>
Python==3.11 in [Colab](https://colab.research.google.com/drive/1MV4UtSYRGM4Dx5sGqtCoGVk7vKIMsRDL?usp=sharing), VScode and other Jupyter notebooks
```plaintext
panel==1.6.1
bokeh==3.6.3
jupyter_bokeh==4.0.5
```
</details>
#### Description of expected behavior and the observed behavior
The `chat.ChatFeed` should display correctly without errors, even when displayed multiple times.
#### Complete, minimal, self-contained example code that reproduces the issue
```python
import panel as pn
from IPython import display
pn.extension()
feed = pn.chat.ChatFeed(objects=[pn.chat.ChatMessage("Hello")])
display.display(feed)
display.display(feed)
```
#### Stack traceback and/or browser JavaScript console output
```
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[/usr/local/lib/python3.11/dist-packages/IPython/core/formatters.py](https://localhost:8080/#) in __call__(self, obj, include, exclude)
968
969 if method is not None:
--> 970 return method(include=include, exclude=exclude)
971 return None
972 else:
11 frames
[/usr/local/lib/python3.11/dist-packages/panel/layout/feed.py](https://localhost:8080/#) in <genexpr>(.0)
160 self._last_synced and
161 'visible_range' not in events and
--> 162 not any(isIn(old_objects[i], self.objects) for i in range(*self._last_synced))
163 ):
164 with edit_readonly(self):
IndexError: list index out of range
ChatFeed(_placeholder=ChatMessage, sizing_mode='stretch_width')
[0] ChatMessage(object='Hello', user='User', reactions=[])
```
#### Screenshots or screencasts of the bug in action
Here is [a notebook](https://colab.research.google.com/drive/1MV4UtSYRGM4Dx5sGqtCoGVk7vKIMsRDL?usp=sharing)
<img width="653" alt="Image" src="https://github.com/user-attachments/assets/96f54774-686d-4d01-8cd9-8e7a45a77df1" />
<img width="993" alt="Image" src="https://github.com/user-attachments/assets/6d843a83-24fd-4608-846c-b4018855ce04" />
- [x] I may be interested in making a pull request to address this
| open | 2025-02-25T13:09:04Z | 2025-02-25T15:58:22Z | https://github.com/holoviz/panel/issues/7733 | [] | s-alexey | 0 |
pandas-dev/pandas | python | 61,159 | BUG: Faulty DatetimeIndex union | ### Pandas version checks
- [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [x] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
def test_pandas_datetime_index_union(self):
"""
Demonstrates a suspected bug in pandas 2.2.3, where unions of DatetimeIndexes (and therefore pd.concats of dataframes with DatetimeIndexes)
are returning unexpected values.
My actual usecase (concatenating two dataframes with these DatetimeIndexes, from which I extracted these date ranges) works in
pandas 1.5.3, but not 2.2.3.
Interestingly, this passes in both versions if you change the dtype to datetime64[ns].
"""
dti1 = DatetimeIndex(
['2021-10-05 17:30:00',
'2021-10-05 18:00:00',
'2021-10-05 18:30:00',
'2021-10-05 19:00:00',
'2021-10-05 19:30:00'],
dtype='datetime64[us]', name='DATETIME', freq='30min'
)
dti2 = DatetimeIndex(
['2021-10-05 17:30:00',
'2021-10-05 18:00:00',
'2021-10-05 18:30:00',
'2021-10-05 19:00:00',
'2021-10-05 19:30:00',
'2021-10-05 20:00:00'], # <-- Extra datetime
dtype='datetime64[us]', name='DATETIME', freq='30min'
)
union = set(dti1.union(dti2))
expected = set(dti1) | set(dti2)
print(f"{union=}")
print(f"{expected=}")
assert len(union) == len(expected), "Should have all the rows from the concatenated dataframes"
def test_range_index_equality(self):
""" This (presumably) faulty equality check appears to be the root cause of the datetimeindex union bug above
Note that the two stop values are different, so the RangeIndexes should not be equal.
Interestingly, this fails in both pandas 1.5.3 and 2.2.3.
"""
a = RangeIndex(start=1633455000000000, stop=1635262200000000, step=1800000000000)
b = RangeIndex(start=1633455000000000, stop=1635264000000000, step=1800000000000)
assert not a.equals(b)
```
### Issue Description
These tests above (details in the function doc) demonstrate the issue and what I think is the root cause.
Basically we get back what appears to be an incorrect result when taking the union of two DatetimeIndexes with different ranges.
I traced this as far as the RangeIndex equality check in the second test, which appears to be faulty, returning True for two different `stop` values.
### Expected Behavior
Out from first test should be (as in pandas 1.5.3):
```
union={Timestamp('2021-10-05 18:30:00'), Timestamp('2021-10-05 19:00:00'), Timestamp('2021-10-05 17:30:00'), Timestamp('2021-10-05 20:00:00'), Timestamp('2021-10-05 19:30:00'), Timestamp('2021-10-05 18:00:00')}
expected={Timestamp('2021-10-05 18:30:00'), Timestamp('2021-10-05 19:00:00'), Timestamp('2021-10-05 17:30:00'), Timestamp('2021-10-05 20:00:00'), Timestamp('2021-10-05 19:30:00'), Timestamp('2021-10-05 18:00:00')}
```
But the actual output in pandas 2.2.3 is (incorrectly):
```
union={Timestamp('2021-10-05 17:30:00'), Timestamp('2021-10-26 13:30:00')}
expected={Timestamp('2021-10-05 19:30:00'), Timestamp('2021-10-05 20:00:00'), Timestamp('2021-10-05 17:30:00'), Timestamp('2021-10-05 19:00:00'), Timestamp('2021-10-05 18:00:00'), Timestamp('2021-10-05 18:30:00')}
```
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.12.6
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : Intel64 Family 6 Model 183 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_Australia.1252
pandas : 2.2.3
numpy : 2.2.4
pytz : 2025.1
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : 4.5.0
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.13.3
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2025.3.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : None
matplotlib : 3.10.1
numba : None
numexpr : None
odfpy : None
openpyxl : 3.1.5
pandas_gbq : None
psycopg2 : 2.9.10
pymysql : None
pyarrow : 19.0.1
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : 2025.3.0
scipy : 1.15.2
sqlalchemy : 2.0.39
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.1
qtpy : None
pyqt5 : None
</details>
| closed | 2025-03-21T05:11:25Z | 2025-03-23T00:59:02Z | https://github.com/pandas-dev/pandas/issues/61159 | [
"Bug",
"setops",
"Non-Nano"
] | SamRWest | 3 |
tflearn/tflearn | data-science | 630 | what is tflearn equivalent of eval in tensorflow for evaluating labels? | Trying to predict labels, so tensorflow has eval() what is equivalent in tflean?
| closed | 2017-02-25T12:13:33Z | 2017-03-04T17:14:26Z | https://github.com/tflearn/tflearn/issues/630 | [] | jdvala | 1 |
serengil/deepface | deep-learning | 837 | pupile-alignment | Im Wondering how many degrees (pupile-angle) can "correct" some wrapped face detectors('retinaface','mediapipe','yolov8','yunet'). So i rotate an image that contain a (detected)face by 90 degrees and for example 'yunet' fails to detect with this rotation.
pd: sorry for badwritting | closed | 2023-09-04T21:31:44Z | 2023-09-05T10:12:18Z | https://github.com/serengil/deepface/issues/837 | [
"question"
] | diego0718 | 1 |
fbdesignpro/sweetviz | data-visualization | 77 | No visualization for columns 2501+ | I have a dataset that contains 3.8k columns and 300 to 900k rows. When I run the report and save to html, only the first 2501 columns are visualized. The rest is represented by empty tiles (attachment).
Env: GCP, AI Platform, python 3.7
SweetViz ver: 1.1.2 and 2.0.6
Browsers: Opera 74.0.3911.107, Edge 88.0.705.68
code:
```
features_per_batch = 2500
batches = len(data_source.columns) // features_per_batch + (len(data_source.columns) % features_per_batch > 0)
start_inclusive = 0
end_exclusive = features_per_batch
for batch in range(batches):
print(f'preparing batch {batch + 1}')
report = sv.analyze(source=data_source.iloc[:, start_inclusive:end_exclusive]
, **{k: v for k, v in kwargs.items() if k != 'path'})
report.show_html(kwargs['path'] + f'_{batch + 1}.html')
start_inclusive = end_exclusive
end_exclusive += features_per_batch
```
kwargs contain "path" (output path) and "pairwise_analysis" set to "off".
Unfortunately, I cannot share my dataset, as it`s proprietary. | closed | 2021-02-18T08:38:11Z | 2021-02-19T21:43:45Z | https://github.com/fbdesignpro/sweetviz/issues/77 | [
"bug"
] | smiglidigli | 1 |
pydantic/FastUI | fastapi | 154 | Question: Is there a way to redirect response to an end point or url that's not part of the fastui endpoints? | I tried using RedirectResponse from starlette.responses, like: return RedirectResponse('/logout') or return RedirectResponse('logout.html'). I also tried return [c.FireEvent(event=GoToEvent(url='/logout'))] but it always gives me this error: "Request Error Response not valid JSON". It seems the url is always captured by fastui's /api endpoint. I'd really like to have a way to redirect the page to an external link.
thanks!
| closed | 2024-01-16T10:43:50Z | 2024-02-18T10:50:42Z | https://github.com/pydantic/FastUI/issues/154 | [] | fmrib00 | 2 |
pandas-dev/pandas | python | 60,640 | BUG: memory leak when slice series var assigning to itself | ### Pandas version checks
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.
- [ ] I have confirmed this bug exists on the [main branch](https://pandas.pydata.org/docs/dev/getting_started/install.html#installing-the-development-version-of-pandas) of pandas.
### Reproducible Example
```python
import gc
import pandas as pd
class Test(object):
def __init__(self, ab):
self.__a = ab
a = pd.Series([Test(i) for i in range(100000)])
a = a[-1:]
gc.collect()
```
### Issue Description
The memory allocating in the line `a = pd.Series([Test(i) for i in range(100000)])` does not free when slicing var a and assigning to itself.
### Expected Behavior
Free the memory after slicing like build-in `list` behavior.
### Installed Versions
<details>
INSTALLED VERSIONS
------------------
commit : 0691c5cf90477d3503834d983f69350f250a6ff7
python : 3.12.8
python-bits : 64
OS : Linux
OS-release : 5.4.0-204-generic
Version : #224-Ubuntu SMP Thu Dec 5 13:38:28 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 2.2.3
numpy : 2.2.1
pytz : 2024.2
dateutil : 2.9.0.post0
pip : 24.3.1
Cython : None
sphinx : None
IPython : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : None
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : None
lxml.etree : None
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : None
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2024.2
qtpy : None
pyqt5 : None
</details>
| open | 2025-01-02T03:45:10Z | 2025-01-04T14:52:28Z | https://github.com/pandas-dev/pandas/issues/60640 | [
"Bug",
"Performance",
"Copy / view semantics"
] | hefvcjm | 4 |
cleanlab/cleanlab | data-science | 356 | Jupyter notebook tutorials: Show full plots without scroll-bars | Figure out how to get nbsphinx to display the plots in full-size without scroll bars | closed | 2022-08-23T23:36:17Z | 2024-12-25T19:50:33Z | https://github.com/cleanlab/cleanlab/issues/356 | [
"good first issue"
] | jwmueller | 2 |
allenai/allennlp | pytorch | 4,683 | Model configs should work out of the box | Whenever possible, our model configs should work out of the box with no changes. That means the datasets need to be publicly available, and specified with their URLs. That's impossible for copyrighted datasets, but we should strive to get there for public ones.
Most of our configs indirect datasets with environment variables. We do this so that tests work. We should stop doing this, and make the tests work by using overrides instead. | closed | 2020-09-28T20:27:06Z | 2020-10-26T16:43:40Z | https://github.com/allenai/allennlp/issues/4683 | [] | dirkgr | 0 |
Anjok07/ultimatevocalremovergui | pytorch | 1,619 | abort when use MDX-NET MDX23C-InstVoc-HQ model to cgenerate |
Last Error Received:
Process: MDX-Net
If this error persists, please contact the developers with the error details.
Raw Error Details:
RuntimeError: "Sizes of tensors must match except in dimension 1. Expected size 2 but got size 6 for tensor number 1 in the list."
Traceback Error: "
File "UVR.py", line 6638, in process_start
File "separate.py", line 652, in seperate
File "separate.py", line 759, in demix
"
Error Time Stamp [2024-11-09 12:39:10]
Full Application Settings:
vr_model: Choose Model
aggression_setting: 5
window_size: 512
mdx_segment_size: 256
batch_size: Default
crop_size: 256
is_tta: False
is_output_image: False
is_post_process: False
is_high_end_process: False
post_process_threshold: 0.2
vr_voc_inst_secondary_model: No Model Selected
vr_other_secondary_model: No Model Selected
vr_bass_secondary_model: No Model Selected
vr_drums_secondary_model: No Model Selected
vr_is_secondary_model_activate: False
vr_voc_inst_secondary_model_scale: 0.9
vr_other_secondary_model_scale: 0.7
vr_bass_secondary_model_scale: 0.5
vr_drums_secondary_model_scale: 0.5
demucs_model: v4 | htdemucs_ft
segment: Default
overlap: 0.25
overlap_mdx: Default
overlap_mdx23: 8
shifts: 2
chunks_demucs: Auto
margin_demucs: 44100
is_chunk_demucs: False
is_chunk_mdxnet: False
is_primary_stem_only_Demucs: False
is_secondary_stem_only_Demucs: False
is_split_mode: True
is_demucs_combine_stems: True
is_mdx23_combine_stems: True
demucs_voc_inst_secondary_model: No Model Selected
demucs_other_secondary_model: No Model Selected
demucs_bass_secondary_model: No Model Selected
demucs_drums_secondary_model: No Model Selected
demucs_is_secondary_model_activate: False
demucs_voc_inst_secondary_model_scale: 0.9
demucs_other_secondary_model_scale: 0.7
demucs_bass_secondary_model_scale: 0.5
demucs_drums_secondary_model_scale: 0.5
demucs_pre_proc_model: No Model Selected
is_demucs_pre_proc_model_activate: False
is_demucs_pre_proc_model_inst_mix: False
mdx_net_model: MDX23C-InstVoc HQ
chunks: Auto
margin: 44100
compensate: Auto
denoise_option: None
is_match_frequency_pitch: True
phase_option: Automatic
phase_shifts: None
is_save_align: False
is_match_silence: True
is_spec_match: False
is_mdx_c_seg_def: False
is_invert_spec: False
is_deverb_vocals: False
deverb_vocal_opt: Main Vocals Only
voc_split_save_opt: Lead Only
is_mixer_mode: False
mdx_batch_size: Default
mdx_voc_inst_secondary_model: No Model Selected
mdx_other_secondary_model: No Model Selected
mdx_bass_secondary_model: No Model Selected
mdx_drums_secondary_model: No Model Selected
mdx_is_secondary_model_activate: False
mdx_voc_inst_secondary_model_scale: 0.9
mdx_other_secondary_model_scale: 0.7
mdx_bass_secondary_model_scale: 0.5
mdx_drums_secondary_model_scale: 0.5
is_save_all_outputs_ensemble: True
is_append_ensemble_name: False
chosen_audio_tool: Change Pitch
choose_algorithm: Min Spec
time_stretch_rate: 2.0
pitch_rate: 3.0
is_time_correction: True
is_gpu_conversion: True
is_primary_stem_only: False
is_secondary_stem_only: True
is_testing_audio: False
is_auto_update_model_params: True
is_add_model_name: False
is_accept_any_input: True
is_task_complete: False
is_normalization: False
is_use_opencl: False
is_wav_ensemble: False
is_create_model_folder: False
mp3_bit_set: 320k
semitone_shift: 0
save_format: WAV
wav_type_set: PCM_32
device_set: NVIDIA GeForce RTX 3060:0
help_hints_var: True
set_vocal_splitter: No Model Selected
is_set_vocal_splitter: False
is_save_inst_set_vocal_splitter: False
model_sample_mode: False
model_sample_mode_duration: 30
demucs_stems: All Stems
mdx_stems: Vocals | open | 2024-11-09T04:45:31Z | 2024-11-09T04:45:31Z | https://github.com/Anjok07/ultimatevocalremovergui/issues/1619 | [] | madasan1974 | 0 |
voila-dashboards/voila | jupyter | 755 | Preview extension: switch to LabIcon for the Voila icon | Similar to https://github.com/voila-dashboards/voila/issues/620, but for the Voila icons:
The `@jupyter-voila/jupyterlab-preview` could define the Voila icon as a `LabIcon` and export it, so other plugins could reuse it.
We would need to check whether `LabIcon` supports two versions for an icon (if we want to keep both the light as dark versions as is), or if that would mean keeping only one (potentially modified) and let lab handle the theming. | closed | 2020-10-30T13:39:13Z | 2021-01-21T14:26:21Z | https://github.com/voila-dashboards/voila/issues/755 | [
"jupyterlab-preview"
] | jtpio | 1 |
Yorko/mlcourse.ai | matplotlib | 692 | Issue on page /book/topic07/topic7_pca_clustering.html | Fix links in the article outline | closed | 2021-12-22T18:02:48Z | 2021-12-23T16:31:44Z | https://github.com/Yorko/mlcourse.ai/issues/692 | [
"articles"
] | festline | 0 |
marimo-team/marimo | data-visualization | 3,640 | SQL Engines break base duckdb from df | ### Describe the bug
Hmm, ran into a new issue with engines when trying to update the md docs.
````md
```sql {.marimo}
SELECT GREATEST(a, b), SQRT(c) from {random_numbers}
```
```sql {.marimo query="random_numbers"}
SELECT i AS id,
random() AS a,
random() AS b,
random() AS c
FROM range(1,101) i;
```
````
Fails with
```txt
Traceback (most recent call last):
File "/home/dylan/src/marimo/marimo/_runtime/executor.py", line 141, in execute_cell
exec(cell.body, glbls)
Cell marimo:///home/dylan/src/marimo/marimo/_tutorials/markdown_format.md#cell=cell-21
, line 1, in <module>
_df = mo.sql(
^^^^^^^
File "/home/dylan/src/marimo/marimo/_sql/sql.py", line 73, in sql
df = _execute_query(query, sql_engine)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/dylan/src/marimo/marimo/_sql/sql.py", line 153, in _execute_query
return eval(
^^^^^
File "<string>", line 1, in <module>
File "/home/dylan/src/marimo/marimo/_sql/engines.py", line 43, in execute
relation = duckdb.sql(query)
^^^^^^^^^^^^^^^^^
duckdb.duckdb.ParserException: Parser Error: syntax error at or near ":"
```
If we actually look, `query` has already been evaluated ( I just threw a print on line 42)
```
SELECT GREATEST(a, b), SQRT(c) from shape: (100, 4)
┌───────────┬──────────┬──────────┬──────────┐
│ id ┆ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- ┆ --- │
│ struct[1] ┆ f64 ┆ f64 ┆ f64 │
╞═══════════╪══════════╪══════════╪══════════╡
│ {1} ┆ 0.969375 ┆ 0.31721 ┆ 0.189477 │
│ {2} ┆ 0.264934 ┆ 0.960304 ┆ 0.000486 │
│ {3} ┆ 0.305962 ┆ 0.908223 ┆ 0.386767 │
│ {4} ┆ 0.994923 ┆ 0.291543 ┆ 0.060228 │
│ {5} ┆ 0.113762 ┆ 0.009761 ┆ 0.962222 │
│ … ┆ … ┆ … ┆ … │
│ {96} ┆ 0.53154 ┆ 0.88564 ┆ 0.377939 │
│ {97} ┆ 0.516885 ┆ 0.567692 ┆ 0.845767 │
│ {98} ┆ 0.284095 ┆ 0.379701 ┆ 0.769615 │
│ {99} ┆ 0.183121 ┆ 0.361992 ┆ 0.125663 │
│ {100} ┆ 0.877826 ┆ 0.284728 ┆ 0.061547 │
└───────────┴──────────┴──────────┴──────────┘
```
I doubt this is particular to md, only place I've tried it yet so far though
### Environment
Head
### Code to reproduce
````md
```sql {.marimo}
SELECT GREATEST(a, b), SQRT(c) from {random_numbers}
```
```sql {.marimo query="random_numbers"}
SELECT i AS id,
random() AS a,
random() AS b,
random() AS c
FROM range(1,101) i;
```
````
@Light2Dark @mscolnick
But super cool :eyes: excited to try it with sqlalchemy | closed | 2025-01-31T19:04:34Z | 2025-01-31T22:36:47Z | https://github.com/marimo-team/marimo/issues/3640 | [
"bug"
] | dmadisetti | 3 |
kennethreitz/responder | flask | 438 | api.jinja_values_base removed? | Although the docs says `api.jinja_values_base` is [valid](https://responder.kennethreitz.org/en/latest/api.html#responder.API.template), it seems `api.jinja_values_base` has removed from code.
Is this removed completely? Do you have any plan to provide other means to set default values to pass to jinja2? | closed | 2020-07-27T17:14:39Z | 2024-03-31T00:57:33Z | https://github.com/kennethreitz/responder/issues/438 | [] | aiotter | 2 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,237 | Tutorial on How to Use Audio Files | Hello everyone,
I've been using the software for a few days now, and I've grasped the concept. I've realized that by adding multiple recordings of the same voice, it improves the voice for text dictation. I have also successfully extracted audio files using Audacity and converted them to MP4 or FLAC formats, importing and using them correctly. However, I'm trying to figure out how I can create a folder with 3 or 4 audio files of the same voice so that I can work on them properly.
Thank you. | open | 2023-07-26T07:59:02Z | 2023-07-26T07:59:02Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1237 | [] | lettreabc | 0 |
abhiTronix/vidgear | dash | 29 | Dropped support for Python 2.7 legacy | Hello everyone,
VidGear as of now supports both python 3 and python 2.7 legacies. But the **support for Python 2.7 will be dropped in the upcoming major release i.e. v0.1.6** as most of the vidgear critical dependency's are already been migrated or in process of migrating their source-code to Python3. Therefore, **This issue is a reminder of everyone using vidgear to start migrating into Python 3 as soon as possible.**
| closed | 2019-07-08T02:41:54Z | 2020-01-18T09:56:06Z | https://github.com/abhiTronix/vidgear/issues/29 | [
"ENHANCEMENT :zap:",
"SOLVED :checkered_flag:",
"MAINTENANCE :building_construction:",
"ANNOUNCEMENT :loudspeaker:"
] | abhiTronix | 5 |
2noise/ChatTTS | python | 310 | ChatTTS技术交流群，一起学习探究 | 
| closed | 2024-06-12T11:10:27Z | 2024-06-18T08:49:11Z | https://github.com/2noise/ChatTTS/issues/310 | [
"invalid"
] | rffsok | 3 |
roboflow/supervision | tensorflow | 809 | No module name 'supervision', installed supervision==0.18.0 and imported supervision as sv | ### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar bug report.
### Bug
Traceback (most recent call last):
File "/home/shivakrishnakarnati/Documents/Programming/ROS/ros_supervision_obj/install/object_det/lib/object_det/object_det_node", line 33, in <module>
sys.exit(load_entry_point('object-det==0.0.0', 'console_scripts', 'object_det_node')())
File "/home/shivakrishnakarnati/Documents/Programming/ROS/ros_supervision_obj/install/object_det/lib/object_det/object_det_node", line 25, in importlib_load_entry_point
return next(matches).load()
File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 171, in load
module = import_module(match.group('module'))
File "/usr/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 "/home/shivakrishnakarnati/Documents/Programming/ROS/ros_supervision_obj/install/object_det/lib/python3.10/site-packages/object_det/object_det_node.py", line 19, in <module>
import supervision as sv
ModuleNotFoundError: No module named 'supervision'
[ros2run]: Process exited with failure 1
### Environment
- supervision 0.18.0
### Minimal Reproducible Example
import supervision as sv
### Additional
_No response_
### Are you willing to submit a PR?
- [X] Yes I'd like to help by submitting a PR! | closed | 2024-01-29T14:29:43Z | 2024-01-29T15:21:54Z | https://github.com/roboflow/supervision/issues/809 | [
"bug"
] | shivakarnati | 7 |
dpgaspar/Flask-AppBuilder | rest-api | 2,078 | sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError) (2014, 'Command Out of Sync') | Can error handling
`sqlalchemy.exc.OperationalError: (pymysql.err.OperationalError) (2014, 'Command Out of Sync')`
try except error ? | closed | 2023-07-09T21:08:54Z | 2023-07-10T13:40:23Z | https://github.com/dpgaspar/Flask-AppBuilder/issues/2078 | [] | echochio-tw | 1 |
amisadmin/fastapi-amis-admin | sqlalchemy | 69 | Post array data to ModelAdmin.create, get wrong response | using ModelAdmin Example, as default enable_bulk_create is False
```
class CategoryAdmin(admin.ModelAdmin):
page_schema = "Category"
model = Category
#enable_bulk_create = True
```
post to `/admin/CategoryAdmin/item`
use data `[{"name":"1","description":"1"},{"name":"2","description":"2"}]`, get correct response value_error.jsondecode.
but use data `[{"name":"1","description":"1"}]`, get wrong response `msg : success` `"data"` : {"name" : "description", "description" : ""}`
change enable_bulk_create = True
```
class CategoryAdmin(admin.ModelAdmin):
page_schema = "Category"
model = Category
enable_bulk_create = True
```
post to `/admin/CategoryAdmin/item`
use data `[{"name":"1","description":"1"}]` or `[{"name":"1","description":"1"},{"name":"2","description":"2"}]`, get same wrong response `msg : success` `"data"` : {"name" : "description", "description" : ""}` | closed | 2022-12-02T03:56:49Z | 2023-09-17T08:51:41Z | https://github.com/amisadmin/fastapi-amis-admin/issues/69 | [] | albertix | 0 |
cvat-ai/cvat | tensorflow | 8,914 | SDK methods that list child objects don't always work when the parent object is in an organization | ### 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
1. Create two accounts, `user1` and `user2`.
1. Create an organization as `user1`, and add `user2` as a maintainer.
1. Create a project in the organization as `user1`.
1. Create a task in the project as `user1`.
1. Create the following script:
```python
import os
import sys
from cvat_sdk import make_client
def main():
proj_id = int(sys.argv[1])
with make_client(
host="http://localhost:8080",
credentials=(os.environ["CVAT_AI_USER"], os.environ["CVAT_AI_PASS"])
) as client:
project = client.projects.retrieve(proj_id)
for task in project.get_tasks():
print(task.id)
main()
```
1. Run the script with `user2`'s credentials, passing the ID of the project. It prints nothing.
### Expected Behavior
The script should print the ID of the task.
By comparison, if you run the script with `user1`'s credentials, this does happen.
### Possible Solution
We should probably make the SDK override the current organization while making the "list tasks" request, if the project is in an organization.
Alternatively, we could alter the server's response to list all tasks that the user has access to, regardless of what organization they are in.
### Context
I believe this affects all SDK methods that list child objects of some other objects, although I haven't tested them all. These methods are:
```
Issue.get_comments
Job.get_labels
Job.get_issues
Project.get_tasks
Project.get_labels
Task.get_jobs
Task.get_labels
```
### Environment
Reproducible with commit b594b1cbc7118d64621a78bb4f937ebd44c451f1.
| open | 2025-01-09T12:06:23Z | 2025-01-13T11:20:33Z | https://github.com/cvat-ai/cvat/issues/8914 | [
"bug"
] | SpecLad | 1 |
xonsh/xonsh | data-science | 5,612 | Windows: Comapre typing speed: xonsh 0.16.0 vs 0.18.2 | ## Current Behavior
Was excited to see that v18 was supposed to reduce input lag, but it didn't seem to have helped. Am I missing some extra knobs that could help achieve that stated goal?
With `xonsh --no-rc --no-env`
https://github.com/user-attachments/assets/b75930c8-4e08-4710-aa99-37781f2ab7ee
(with actual config it's more noticably slower)
another empty shell for comparison
https://github.com/user-attachments/assets/6c161006-c82a-494b-98c2-bd03cefb0754
## Expected Behavior
Input lag getting smaller
## xonfig
<details>
```xsh
# Please paste the output of the `xonfig` command here.
+-----------------------------+-----------------+
| xonsh | 0.18.2 |
| Python | 3.12.0 |
| PLY | 3.11 |
| have readline | False |
| prompt toolkit | 3.0.47 |
| shell type | prompt_toolkit |
| history backend | json |
| pygments | 2.18.0 |
| on posix | False |
| on linux | False |
| on darwin | False |
| on windows | True |
| on cygwin | False |
| on msys2 | False |
| is superuser | False |
| default encoding | utf-8 |
| xonsh encoding | utf-8 |
| encoding errors | surrogateescape |
| xontrib | [] |
| RC file | [] |
| UPDATE_OS_ENVIRON | False |
| XONSH_CAPTURE_ALWAYS | False |
| XONSH_SUBPROC_OUTPUT_FORMAT | stream_lines |
| THREAD_SUBPROCS | True |
| XONSH_CACHE_SCRIPTS | True |
+-----------------------------+-----------------+
```
</details>
## For community
⬇️ **Please click the 👍 reaction instead of leaving a `+1` or 👍 comment**
| closed | 2024-07-18T20:36:24Z | 2024-07-22T12:16:57Z | https://github.com/xonsh/xonsh/issues/5612 | [
"windows",
"speed"
] | eugenesvk | 29 |
tflearn/tflearn | data-science | 686 | Question for Reshape Weights? | I'm trying to use already trained network for different size inputs. For example, training shape is (-1, 20, 20, 3) and prediction shape is (-1, 500, 500, 3) and this is the network:
network = conv_2d(x, 16, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
if isTrain:
network = fully_connected(network, 64, activation='relu', name='fc1')
network = dropout(network, 0.5)
network = fully_connected(network, 64, activation='relu', name='fc2')
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax', name='fc3')
else:
network = conv_2d(norm2, 64, 4, activation='relu', name='fc1')
network = conv_2d(network, 64, 1, activation='relu', name='fc2')
network = conv_2d(network, 2, 1, activation='softmax', name='fc3')
x = regression(network, optimizer='adam', loss='categorical_crossentropy')
while it is not train, it's giving a shape match error which is normal. My question is how I can reshape the weight of "fc1", "fc2", "fc3" according to the given size of an input?
| open | 2017-03-28T17:50:31Z | 2017-04-02T17:14:59Z | https://github.com/tflearn/tflearn/issues/686 | [] | iandecks45 | 1 |
MaxHalford/prince | scikit-learn | 82 | utils.validation.check_is_fitted(self) is giving errors | I am using 0.21.0 version of sklearn.
I am following the tutorial on Readme for PCA exactly, but it is giving me the following error.
>>> X, y = datasets.load_iris(return_X_y=True)
>>> X = pd.DataFrame(data=X, columns=['Sepal length', 'Sepal width', 'Petal length', 'Petal width'])
>>> y = pd.Series(y).map({0: 'Setosa', 1: 'Versicolor', 2: 'Virginica'})
>>> X.head()
Sepal length Sepal width Petal length Petal width
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
>>>
>>>
>>> X
Sepal length Sepal width Petal length Petal width
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
.. ... ... ... ...
145 6.7 3.0 5.2 2.3
146 6.3 2.5 5.0 1.9
147 6.5 3.0 5.2 2.0
148 6.2 3.4 5.4 2.3
149 5.9 3.0 5.1 1.8
[150 rows x 4 columns]
>>>
>>> pca = prince.PCA(
... n_components=2,
... n_iter=3,
... rescale_with_mean=True,
... rescale_with_std=True,
... copy=True,
... check_input=True,
... engine='auto',
... random_state=42
... )
>>> pca = pca.fit(X)
>>>
>>> pca.transform(X).head()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/mun/anaconda3/envs/ofried/lib/python3.7/site-packages/prince/pca.py", line 86, in transform
utils.validation.check_is_fitted(self)
TypeError: check_is_fitted() missing 1 required positional argument: 'attributes'
| closed | 2020-01-08T20:38:31Z | 2023-02-27T11:48:17Z | https://github.com/MaxHalford/prince/issues/82 | [] | munkim | 12 |
ymcui/Chinese-LLaMA-Alpaca-2 | nlp | 54 | 微调验证损失趋势 | ### 提交前必须检查以下项目
- [X] 请确保使用的是仓库最新代码（git pull），一些问题已被解决和修复。
- [X] 我已阅读[项目文档](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki)和[FAQ章节](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki/常见问题)并且已在Issue中对问题进行了搜索，没有找到相似问题和解决方案
- [X] 第三方插件问题：例如[llama.cpp](https://github.com/ggerganov/llama.cpp)、[text-generation-webui](https://github.com/oobabooga/text-generation-webui)等，同时建议到对应的项目中查找解决方案
### 问题类型
模型训练与精调
### 基础模型
Alpaca-2-7B
### 操作系统
Linux
### 详细描述问题
代码是周二上午拉取的，基座模型是Chinese Alpaca2， 进行lora指令精调的时候，在验证集上的损失都呈现这样的趋势：
1. 一开始可能有小幅度降低（可能没有）
2. 先上升后下降（幅度都不会很大）
3. 大幅度上升
微调两个不同的任务，验证损失都呈现这样的趋势，很好奇这样是否正常？因为关系到微调轮数设置，是否不再降低的时候就可以早停了？
下图中两个任务微调的batchsize分别是：60和120
### 依赖情况（代码类问题务必提供）
peft 0.3.0.dev0
torch 2.0.0
torchvision 0.15.1
transformers 4.31.0
### 运行日志或截图
| closed | 2023-08-03T00:49:21Z | 2023-08-14T05:16:22Z | https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/issues/54 | [
"stale"
] | Daniel-1997 | 2 |
Lightning-AI/pytorch-lightning | deep-learning | 20,220 | Can no longer install versions 1.5.10-1.6.5 | ### Bug description
Hey everyone,
I have been working on the same server for the past few months ( w/ RTX6000) without issue
Recently, I tried to re-install lightning 1.5.10 (new virtual environment, python 3.9.18), and got the error below
I tried versions up to 1.6.5 with the same error
I can't use the newest version, as that will require a torch upgrade (currently using 1.13.1 due to specific versioning issues)
This popped up in the last month, I'm wondering if anyone else is seeing this problem or if it is to be expected for some reason?
Thanks,
Jonathan
### What version are you seeing the problem on?
v1.x
### How to reproduce the bug
```python
Create a virtual environment with python 3.9.18
Activate
pip install pytorch-lightning==1.5.10
```
### Error messages and logs
```
ERROR: Could not find a version that satisfies the requirement pytorch-lightning==1.5.10 (from versions: 0.0.2, 0.2, 0.2.2, 0.2.3, 0.2.4, 0.2.4.1, 0.2.5, 0.2.5.1, 0.2.5.2, 0.2.6, 0.3, 0.3.1, 0.3.2, 0.3.3, 0.3.4, 0.3.4.1, 0.3.5, 0.3.6, 0.3.6.1, 0.3.6.3, 0.3.6.4, 0.3.6.5, 0.3.6.6, 0.3.6.7, 0.3.6.8, 0.3.6.9, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.4.4, 0.4.5, 0.4.6, 0.4.7, 0.4.8, 0.4.9, 0.5.0, 0.5.1, 0.5.1.2, 0.5.1.3, 0.5.2, 0.5.2.1, 0.5.3, 0.5.3.1, 0.5.3.2, 0.5.3.3, 0.6.0, 0.7.1, 0.7.3, 0.7.5, 0.7.6, 0.8.1, 0.8.3, 0.8.4, 0.8.5, 0.9.0, 0.10.0, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.0.6, 1.0.7, 1.0.8, 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.1.6, 1.1.7, 1.1.8, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.2.6, 1.2.7, 1.2.8, 1.2.9, 1.2.10, 1.3.0rc1, 1.3.0rc2, 1.3.0rc3, 1.3.0, 1.3.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.3.6, 1.3.7, 1.3.7.post0, 1.3.8, 1.4.0rc0, 1.4.0rc1, 1.4.0rc2, 1.4.0, 1.4.1, 1.4.2, 1.4.3, 1.4.4, 1.4.5, 1.4.6, 1.4.7, 1.4.8, 1.4.9, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.5.2, 1.5.3, 1.5.4, 1.5.5, 1.5.6, 1.5.7, 1.5.8, 1.5.9, 1.5.10, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.6.1, 1.6.2, 1.6.3, 1.6.4, 1.6.5, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.7.2, 1.7.3, 1.7.4, 1.7.5, 1.7.6, 1.7.7, 1.8.0rc0, 1.8.0rc1, 1.8.0rc2, 1.8.0, 1.8.0.post1, 1.8.1, 1.8.2, 1.8.3, 1.8.3.post0, 1.8.3.post1, 1.8.3.post2, 1.8.4, 1.8.4.post0, 1.8.5, 1.8.5.post0, 1.8.6, 1.9.0rc0, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.9.4, 1.9.5, 2.0.0rc0, 2.0.0, 2.0.1, 2.0.1.post0, 2.0.2, 2.0.3, 2.0.4, 2.0.5, 2.0.6, 2.0.7, 2.0.8, 2.0.9, 2.0.9.post0, 2.1.0rc0, 2.1.0rc1, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0rc0, 2.2.0, 2.2.0.post0, 2.2.1, 2.2.2, 2.2.3, 2.2.4, 2.2.5, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.4.0)
ERROR: No matching distribution found for pytorch-lightning==1.5.10
```
### Environment
<details>
<summary>Current environment</summary>
```
<summary>Current environment</summary>
* CUDA:
- GPU:
- NVIDIA RTX 6000 Ada Generation
- available: True
- version: 11.6
* Lightning:
- pytorch-tabnet: 3.0.0
- torch: 1.13.1+cu116
- torchaudio: 0.13.1+cu116
- torchmetrics: 0.11.0
- torchvision: 0.14.1+cu116
* Packages:
- absl-py: 1.3.0
- aiohttp: 3.8.3
- aiosignal: 1.3.1
- alembic: 1.13.2
- aniso8601: 9.0.1
- antlr4-python3-runtime: 4.9.3
- association-metrics: 0.0.1
- asttokens: 2.2.1
- async-timeout: 4.0.2
- attrs: 22.2.0
- autocommand: 2.2.2
- backcall: 0.2.0
- backports.tarfile: 1.2.0
- brotlipy: 0.7.0
- cachetools: 5.2.0
- category-encoders: 2.2.2
- certifi: 2020.6.20
- cffi: 1.17.0
- charset-normalizer: 2.1.1
- click: 8.1.3
- cloudpickle: 3.0.0
- comm: 0.1.2
- configparser: 5.3.0
- contourpy: 1.0.6
- cycler: 0.11.0
- databricks-cli: 0.17.4
- databricks-sdk: 0.30.0
- datasets: 2.10.1
- debugpy: 1.6.5
- decorator: 5.1.1
- deprecated: 1.2.14
- dill: 0.3.6
- docker: 7.1.0
- docker-pycreds: 0.4.0
- einops: 0.3.0
- entrypoints: 0.4
- executing: 1.2.0
- filelock: 3.9.0
- flask: 2.2.3
- fonttools: 4.38.0
- frozenlist: 1.3.3
- fsspec: 2022.11.0
- future: 0.18.2
- gitdb: 4.0.10
- gitpython: 3.1.30
- google-auth: 2.15.0
- google-auth-oauthlib: 0.4.6
- gputil: 1.4.0
- graphene: 3.3
- graphql-core: 3.2.3
- graphql-relay: 3.2.0
- greenlet: 3.0.3
- grpcio: 1.51.1
- gunicorn: 22.0.0
- huggingface-hub: 0.13.0
- idna: 3.4
- importlib-metadata: 6.0.0
- importlib-resources: 6.4.0
- inflect: 7.3.1
- ipykernel: 6.19.4
- ipython: 8.8.0
- ipywidgets: 8.0.4
- itsdangerous: 2.1.2
- jaraco.context: 5.3.0
- jaraco.functools: 4.0.1
- jaraco.text: 3.12.1
- jedi: 0.18.2
- jinja2: 3.1.4
- joblib: 1.2.0
- jupyter-client: 7.4.8
- jupyter-core: 5.1.2
- jupyterlab-widgets: 3.0.5
- kiwisolver: 1.4.4
- kornia: 0.7.3
- kornia-rs: 0.1.5
- llvmlite: 0.43.0
- mako: 1.3.5
- markdown: 3.4.1
- markupsafe: 2.1.1
- matplotlib: 3.6.2
- matplotlib-inline: 0.1.6
- mlflow: 2.15.1
- mlflow-skinny: 2.15.1
- more-itertools: 10.3.0
- multidict: 6.0.4
- multiprocess: 0.70.14
- nest-asyncio: 1.5.6
- numba: 0.60.0
- numpy: 1.24.2
- oauthlib: 3.2.2
- omegaconf: 2.3.0
- opentelemetry-api: 1.26.0
- opentelemetry-sdk: 1.26.0
- opentelemetry-semantic-conventions: 0.47b0
- ordered-set: 4.1.0
- packaging: 22.0
- pandas: 1.1.5
- parso: 0.8.3
- patsy: 0.5.3
- pexpect: 4.8.0
- pickleshare: 0.7.5
- pillow: 9.4.0
- pip: 24.2
- platformdirs: 2.6.2
- plotly: 4.14.3
- ply: 3.11
- promise: 2.3
- prompt-toolkit: 3.0.36
- protobuf: 3.20.3
- psutil: 5.9.4
- ptyprocess: 0.7.0
- pure-eval: 0.2.2
- pyarrow: 11.0.0
- pyasn1: 0.4.8
- pyasn1-modules: 0.2.8
- pycparser: 2.22
- pydeprecate: 0.3.1
- pygments: 2.14.0
- pyjwt: 2.6.0
- pyparsing: 3.0.9
- pyqt5-sip: 12.11.0
- python-dateutil: 2.8.2
- pytorch-tabnet: 3.0.0
- pytz: 2022.7
- pyyaml: 5.4.1
- pyzmq: 24.0.1
- querystring-parser: 1.2.4
- regex: 2022.10.31
- requests: 2.28.1
- requests-oauthlib: 1.3.1
- responses: 0.18.0
- retrying: 1.3.4
- rsa: 4.9
- scikit-learn: 1.2.0
- scipy: 1.10.0
- seaborn: 0.12.2
- sentry-sdk: 1.12.1
- setuptools: 72.1.0
- shap: 0.45.0
- shortuuid: 1.0.11
- six: 1.16.0
- slicer: 0.0.7
- smmap: 5.0.0
- sqlalchemy: 2.0.32
- sqlparse: 0.5.1
- stack-data: 0.6.2
- statsmodels: 0.13.5
- subprocess32: 3.5.4
- tabulate: 0.9.0
- tensorboard: 2.11.0
- tensorboard-data-server: 0.6.1
- tensorboard-plugin-wit: 1.8.1
- threadpoolctl: 3.1.0
- tokenizers: 0.13.2
- tomli: 2.0.1
- torch: 1.13.1+cu116
- torchaudio: 0.13.1+cu116
- torchmetrics: 0.11.0
- torchvision: 0.14.1+cu116
- tornado: 6.2
- tqdm: 4.64.1
- traitlets: 5.8.0
- transformers: 4.26.1
- typeguard: 4.3.0
- typing-extensions: 4.12.2
- urllib3: 1.26.13
- wandb: 0.10.11
- watchdog: 2.2.1
- wcwidth: 0.2.5
- webencodings: 0.5.1
- werkzeug: 2.2.2
- wheel: 0.43.0
- widgetsnbextension: 4.0.5
- wrapt: 1.16.0
- xxhash: 3.2.0
- yarl: 1.8.2
- zipp: 3.11.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.9.18
- release: 6.2.0-37-generic
- version: #38~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Nov 2 18:01:13 UTC 2
```
</details>
### More info
_No response_ | open | 2024-08-21T20:09:48Z | 2025-02-02T23:52:06Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20220 | [
"bug",
"dependencies"
] | JonathanBhimani-Burrows | 14 |
hbldh/bleak | asyncio | 1,603 | On Raspberry pi bleakscanner scanning not working :BleakDBusError[org.bluez.Error.InProgress] Operation already in progress . | * bleak version: 0.22.2
* Python version:3.11.6
* BlueZ version:5.68
### Description
when i tried to scan the ble devices it gives me this error while running code on Raspberry pi
```
Internal Server Error: /scan-devices/
Traceback (most recent call last):
File "/srv/envs/hermes_device/lib/python3.11/site-packages/django/core/handlers/exception.py", line 55, in inner
response = get_response(request)
^^^^^^^^^^^^^^^^^^^^^
File "/srv/envs/hermes_device/lib/python3.11/site-packages/django/core/handlers/base.py", line 197, in _get_response
response = wrapped_callback(request, *callback_args, **callback_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/srv/envs/hermes_device/lib/python3.11/site-packages/asgiref/sync.py", line 254, in __call__
return call_result.result()
^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
File "/srv/envs/hermes_device/lib/python3.11/site-packages/asgiref/sync.py", line 331, in main_wrap
result = await self.awaitable(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/srv/device_interface/bledevice/views.py", line 22, in scan_devices
devices = await scanner.discover()
^^^^^^^^^^^^^^^^^^^^^^^^
File "/srv/envs/hermes_device/lib/python3.11/site-packages/bleak/__init__.py", line 320, in discover
async with cls(**kwargs) as scanner:
File "/srv/envs/hermes_device/lib/python3.11/site-packages/bleak/__init__.py", line 158, in __aenter__
await self._backend.start()
File "/srv/envs/hermes_device/lib/python3.11/site-packages/bleak/backends/bluezdbus/scanner.py", line 185, in start
self._stop = await manager.active_scan(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/srv/envs/hermes_device/lib/python3.11/site-packages/bleak/backends/bluezdbus/manager.py", line 438, in active_scan
assert_reply(reply)
File "/srv/envs/hermes_device/lib/python3.11/site-packages/bleak/backends/bluezdbus/utils.py", line 20, in assert_reply
raise BleakDBusError(reply.error_name, reply.body)
bleak.exc.BleakDBusError: [org.bluez.Error.InProgress] Operation already in progress
[30/May/2024 10:17:31] "GET /scan-devices/ HTTP/1.1" 500 90195
```
| open | 2024-06-24T05:04:06Z | 2024-09-05T08:17:41Z | https://github.com/hbldh/bleak/issues/1603 | [
"Backend: BlueZ",
"more info required"
] | wahadatjan | 8 |
ResidentMario/missingno | pandas | 76 | Fontsize not applied to y-axis' nor min/max labels in 'matrix' plot | Hi, first thanks for the good work and for sharing it.
It appears that the ```matrix()``` function do not make use of its ```fontsize``` argument when creating the ```set_yticklabels``` and min/max annotations. ```fontsize``` for these items is respectively hardcoded to ```20``` and ```14```.
See[ line 113](https://github.com/ResidentMario/missingno/blob/f190bd6f761761831f5d34308d7436566fcbe16f/missingno/missingno.py#L113),[ line 116](https://github.com/ResidentMario/missingno/blob/f190bd6f761761831f5d34308d7436566fcbe16f/missingno/missingno.py#L116), [line 172](https://github.com/ResidentMario/missingno/blob/f190bd6f761761831f5d34308d7436566fcbe16f/missingno/missingno.py#L172) and[ line 178](https://github.com/ResidentMario/missingno/blob/f190bd6f761761831f5d34308d7436566fcbe16f/missingno/missingno.py#L178)
Thank in advance for your review. | closed | 2018-09-08T10:11:21Z | 2019-02-16T05:51:00Z | https://github.com/ResidentMario/missingno/issues/76 | [] | CacahueteNC | 1 |
psf/black | python | 3,800 | Always respect original comment/code-order | **Is your feature request related to a problem? Please describe.**
It would be nice to be able to force black to **never** change the order of code and comments.
for example:
```python
x = (
codeA # commentA
.codeB # commentB
.codeC # commentC
.codeD # commentD
.codeE # commentE
.codeF # commentF
)
```
[gets formatted to](https://black.vercel.app/?version=stable&state=_Td6WFoAAATm1rRGAgAhARYAAAB0L-Wj4AD9AG5dAD2IimZxl1N_WmyLkeQzBfBJMV7c0GsbQlNWsx2vz5jLXwUjME8VwqyJaa1agU_94XQ7KomB6m74PoY-7_lkbYkf7MKmuFo7huOPlD0i-g5GJN0Vcq1MgDFBkB2EnLUa8SkhtEoFa-sRd5UK-BsAAAAADlDA9udCEVUAAYoB_gEAAHDBZSaxxGf7AgAAAAAEWVo=)
```python
x = (
codeA.codeB.codeC.codeD.codeE.codeF # commentA # commentB # commentC # commentD # commentE # commentF
)
```
which even results in breaking the 88 character limit.
**Describe the solution you'd like**
It feels weird that black would even ever mess with the original ordering of code and comments. Imo, the order should always be respected, since comments go together with code. If the AST contains `<codeA><commentA><codeB>`, then the output should be `<formatted-codeA><formatted-commentA><formatted-codeB>`.
| open | 2023-07-18T17:08:42Z | 2023-09-07T14:05:27Z | https://github.com/psf/black/issues/3800 | [
"T: enhancement",
"F: comments"
] | randolf-scholz | 3 |
ymcui/Chinese-LLaMA-Alpaca-2 | nlp | 516 | load_in_8bit 推理耗时比fp16长 | ### 提交前必须检查以下项目
- [X] 请确保使用的是仓库最新代码（git pull），一些问题已被解决和修复。
- [X] 我已阅读[项目文档](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki)和[FAQ章节](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki/常见问题)并且已在Issue中对问题进行了搜索，没有找到相似问题和解决方案。
- [X] 第三方插件问题：例如[llama.cpp](https://github.com/ggerganov/llama.cpp)、[LangChain](https://github.com/hwchase17/langchain)、[text-generation-webui](https://github.com/oobabooga/text-generation-webui)等，同时建议到对应的项目中查找解决方案。
### 问题类型
模型量化和部署
### 基础模型
Chinese-Alpaca-2 (7B/13B)
### 操作系统
Linux
### 详细描述问题
在V100上fp16推理13b模型，显存占用大概30G,耗时3s以内，同样的prompt, 使用int8推理，虽然显存占用明显减少至15G左右，但耗时增加到5s以上
### 依赖情况（代码类问题务必提供）
```
# 请在此处粘贴依赖情况（请粘贴在本代码块里）
```
### 运行日志或截图
```
# 请在此处粘贴运行日志（请粘贴在本代码块里）
``` | closed | 2024-01-30T13:40:49Z | 2024-02-27T00:39:34Z | https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/issues/516 | [
"stale"
] | haoxurt | 3 |
plotly/dash | data-science | 3,128 | Error on hover event of parallel coordinates graph component | Hi all,
I've been getting the following errors when hovering on a parallel coordinates in a plotly dash app
This seems to be related to this unresolved issue https://github.com/plotly/dash-core-components/issues/157 from the dcc archived repo.
The answers to this issue outline the fixes necessary, but I can't seem to find if these fixes were ever implemented.
**Context**
I'm plotting a parallel coordinates graph in a dash app using plotly.
I run on an Ubuntu 20.04 machine as a server and MS Edge 131 on Windows 11 as front.
Here is the dependencies field from my `pyproject.yml` file:
```yaml
dependencies = [
"dash>=2.18.2",
"dash-bootstrap-components>=1.6.0",
"flask-assets>=2.1.0",
"flask>=3.0.3",
"loguru>=0.7.3",
"numpy>=2.2.1",
"pandas>=2.2.3",
]
```
**Describe the bug**
Hovering over line of a parallel coordinates plot causes a "Uncaught TypeError: Cannot read properties of undefined" error.
Cheers! | open | 2025-01-22T09:28:13Z | 2025-01-23T20:25:40Z | https://github.com/plotly/dash/issues/3128 | [
"bug",
"P2"
] | nplinden | 0 |
babysor/MockingBird | deep-learning | 671 | 训练到30k还是无法收敛 | 已经训练30k了还是没收敛，是不是要重新训练了？请问出现这种情况的原因是什么，怎么解决这个问题？训练用的素材都是纯人声的。
| open | 2022-07-22T05:13:09Z | 2022-07-22T13:57:21Z | https://github.com/babysor/MockingBird/issues/671 | [] | showtime12345 | 1 |
keras-team/autokeras | tensorflow | 1,781 | Bug: TimeseriesForecaster predict produce error when increase lookback size | ### Bug Description
TimeseriesForecaster predict function produce error when increase lookback size, even with example code and dataset from https://autokeras.com/tutorial/timeseries_forecaster/-->
### Bug Reproduction
predict_from = 1
predict_until = 1
lookback = 40
clf = ak.TimeseriesForecaster(
lookback=lookback,
predict_from=predict_from,
predict_until=predict_until,
max_trials=1,
objective="val_loss",
)
clf.fit(
x=data_x,
y=data_y,
validation_data=(data_x_val, data_y_val),
batch_size=32,
epochs=10,
callbacks=[EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)]
)
predictions = clf.predict(data_x_test)
print(predictions.shape)
print(clf.evaluate(data_x_val, data_y_val))
Data used by the code:
dataset = tf.keras.utils.get_file(
fname="AirQualityUCI.csv",
origin="https://archive.ics.uci.edu/ml/machine-learning-databases/00360/"
"AirQualityUCI.zip", )
dataset = dataset[dataset.columns[:-2]]
dataset = dataset.dropna()
dataset = dataset.replace(",", ".", regex=True)
dataset
val_split = int(len(dataset) * 0.7)
data_train = dataset[:val_split]
validation_data = dataset[val_split:]
data_x = data_train[
[
"CO(GT)",
"PT08.S1(CO)",
"NMHC(GT)",
]
].astype("float64")
data_x_val = validation_data[
[
"CO(GT)",
"PT08.S1(CO)",
"NMHC(GT)",
]
].astype("float64")
data_x_test = dataset[
[
"CO(GT)",
"PT08.S1(CO)",
"NMHC(GT)",
]
].astype("float64")
data_y = data_train["CO(GT)"].astype("float64")
data_y_val = validation_data["CO(GT)"].astype("float64")
### Expected Behavior
ValueError: in user code:
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1801, in predict_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1790, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1783, in run_step **
outputs = model.predict_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1751, in predict_step
return self(x, training=False)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" is '
ValueError: Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 40, 3), found shape=(None, 1, 3)
### Setup Details
- OS type and version:
- Python: 3.7
- autokeras: 1.0.20
| open | 2022-09-21T08:24:31Z | 2022-09-21T08:26:58Z | https://github.com/keras-team/autokeras/issues/1781 | [
"bug report"
] | SmallCityBoy | 0 |
Lightning-AI/pytorch-lightning | pytorch | 20,434 | Multi-gpu training with slurm times out | ### Bug description
### Bug description
(Note: cross posting from litgpt since I think this may be about pytorch-lightning?)
I was transferring some checkpoints from a cluster that didn't use slurm to one that does use slurm. I trained the checkpoint using multiple gpus/nodes, and I found that I'm able to load and start training it when using an interactive job. However, when I use sbatch to submit my job, the job times out after some time.
I've seen this post: https://lightning.ai/docs/fabric/2.4.0/guide/multi_node/slurm.html and added `srun` to my submission script. However, even though 4 devices seem to be initialized, the model still gets stuck before training and times out.
A debug log and my submission script is linked. My sbatch script is a bit different since it runs another sh script, which does a bunch of stuff and then `litgpt pretrain <...>`, but I'm not sure this would be an issue...
I also tried setting the fabric initialization to explicitly have the number of nodes, devices, etc like in the example in `pretrain.py` but it didn't make a difference:
```python
fabric = L.Fabric(
accelerator="gpu", devices=4, num_nodes=1, strategy=strategy, precision=precision, loggers=[logger]
)
```
**Details:**
My script:
```bash
#!/bin/bash
#SBATCH --job-name=train_model
#SBATCH --output=slurm_logs/%j.out
#SBATCH --time=2-00:00:00
#SBATCH --nodes=1
#SBATCH --gres=gpu:A6000:4
#SBATCH --ntasks-per-node=4
#SBATCH --mem=50G
#SBATCH --partition=general
#SBATCH --mail-user=<email>
#SBATCH --mail-type=ALL
export CUDA_DEVICE_ORDER=PCI_BUS_ID
export NCCL_DEBUG=INFO
# Check if training type is provided
if [ $# -eq 0 ]; then
echo "Usage: $0 <sequential|mixed> [training args...]"
exit 1
fi
# Get the training type and remove it from args
TRAIN_TYPE=$1
shift
case $TRAIN_TYPE in
sequential)
srun ./pretrain_then_finetune.sh "$@"
;;
mixed)
srun ./mixed_pretraining_fixed.sh "$@"
;;
*)
echo "Invalid training type. Use 'sequential' or 'mixed'"
exit 1
;;
esac
```
Debug example error:
```bash
```
Node information:
```bash
=== Slurm Environment ===
SLURM_NTASKS: 4
SLURM_PROCID: 0
SLURM_LOCALID: 0
SLURM_JOB_ID: 3178163
=== GPU Information ===
Available GPUs:
GPU 0: NVIDIA RTX A6000 (UUID: GPU-b349d8f4-c2a8-bd4b-2ed8-4678cc3093ad)
GPU 1: NVIDIA RTX A6000 (UUID: GPU-6386b3c4-ba07-b55c-a8d8-1d7e38378b83)
GPU 2: NVIDIA RTX A6000 (UUID: GPU-8a6310cf-0811-4754-64db-8c4117d4be50)
GPU 3: NVIDIA RTX A6000 (UUID: GPU-99486ac3-a03a-16fa-0e46-8bade74f121a)
GPU Topology:
[4mGPU0 GPU1 GPU2 GPU3 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID[0m
GPU0 X SYS SYS SYS SYS 1-2,7-8,129-130 0 N/A
GPU1 SYS X NV4 NODE NODE 64-65,67,69 1 N/A
GPU2 SYS NV4 X NODE NODE 64-65,67,69 1 N/A
GPU3 SYS NODE NODE X NODE 64-65,67,69 1 N/A
NIC0 SYS NODE NODE NODE X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: mlx5_0
```
### What operating system are you using?
Linux
### LitGPT Version
```
Version: 0.4.0
```
### What version are you seeing the problem on?
v2.3
### How to reproduce the bug
```python
submission script:
#!/bin/bash
#SBATCH --job-name=train_model
#SBATCH --output=slurm_logs/%j.out
#SBATCH --time=2-00:00:00
#SBATCH --nodes=1
#SBATCH --gres=gpu:A6000:4
#SBATCH --ntasks-per-node=4
#SBATCH --mem=50G
#SBATCH --partition=general
#SBATCH --mail-user=emmy@cmu.edu
#SBATCH --mail-type=ALL
# Get training type
TRAIN_TYPE=$1
shift
export CUDA_DEVICE_ORDER=PCI_BUS_ID
export NCCL_DEBUG=INFO
# Run training script
case $TRAIN_TYPE in
sequential)
srun ./pretrain_then_finetune.sh "$@"
;;
mixed)
srun ./mixed_pretraining_fixed.sh "$@"
;;
*)
echo "Invalid training type. Use 'sequential' or 'mixed'"
exit 1
;;
esac
inside `pretrain_then_finetune.sh`:
```bash
<conda activate the env>
litgpt pretrain $model_name <...>
```
```
### Error messages and logs
```
[...previous stuff]
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/4
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/4
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/4
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/4
[rank: 0] Seed set to 42
[rank: 0] Seed set to 42
[rank: 0] Seed set to 42
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 4 processes
----------------------------------------------------------------------------------------------------
[rank: 0] Seed set to 42
babel-0-31:2324237:2324237 [0] NCCL INFO Bootstrap : Using ibs8:172.16.1.17<0>
babel-0-31:2324237:2324237 [0] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)
babel-0-31:2324237:2324237 [0] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so
babel-0-31:2324237:2324237 [0] NCCL INFO NET/Plugin: Using internal network plugin.
babel-0-31:2324237:2324237 [0] NCCL INFO cudaDriverVersion 12060
NCCL version 2.21.5+cuda12.4
/home/mengyan3/.local/lib/python3.9/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py:690: FutureWarning: FSDP.state_dict_type() and FSDP.set_state_dict_type() are being deprecated. Please use APIs, get_state_dict() and set_state_dict(), which can support different parallelisms, FSDP1, FSDP2, DDP. API doc: https://pytorch.org/docs/stable/distributed.checkpoint.html#torch.distributed.checkpoint.state_dict.get_state_dict .Tutorial: https://pytorch.org/tutorials/recipes/distributed_checkpoint_recipe.html .
warnings.warn(
babel-0-31:2324240:2324240 [1] NCCL INFO cudaDriverVersion 12060
babel-0-31:2324240:2324240 [1] NCCL INFO Bootstrap : Using ibs8:172.16.1.17<0>
babel-0-31:2324240:2324240 [1] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)
babel-0-31:2324240:2324240 [1] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so
babel-0-31:2324240:2324240 [1] NCCL INFO NET/Plugin: Using internal network plugin.
babel-0-31:2324240:2324524 [1] NCCL INFO NET/IB : Using [0]mlx5_0:1/IB [RO]; OOB ibs8:172.16.1.17<0>
babel-0-31:2324240:2324524 [1] NCCL INFO Using non-device net plugin version 0
babel-0-31:2324240:2324524 [1] NCCL INFO Using network IB
babel-0-31:2324240:2324524 [1] NCCL INFO ncclCommInitRank comm 0x555d6dc227b0 rank 1 nranks 4 cudaDev 1 nvmlDev 1 busId 81000 commId 0xb886029f29f1a815 - Init START
babel-0-31:2324240:2324524 [1] NCCL INFO Setting affinity for GPU 1 to 2b,00000000,00000000,00000000,0000002b,00000000,00000000
babel-0-31:2324240:2324524 [1] NCCL INFO NVLS multicast support is not available on dev 1
babel-0-31:2324240:2324524 [1] NCCL INFO comm 0x555d6dc227b0 rank 1 nRanks 4 nNodes 1 localRanks 4 localRank 1 MNNVL 0
babel-0-31:2324240:2324524 [1] NCCL INFO Trees [0] 2/-1/-1->1->0 [1] 2/-1/-1->1->0
babel-0-31:2324240:2324524 [1] NCCL INFO P2P Chunksize set to 524288
babel-0-31:2324240:2324524 [1] NCCL INFO Channel 00/0 : 1[1] -> 2[2] via P2P/CUMEM
babel-0-31:2324240:2324524 [1] NCCL INFO Channel 01/0 : 1[1] -> 2[2] via P2P/CUMEM
babel-0-31:2324240:2324524 [1] NCCL INFO Connected all rings
babel-0-31:2324240:2324524 [1] NCCL INFO Channel 00/0 : 1[1] -> 0[0] via P2P/CUMEM
babel-0-31:2324240:2324524 [1] NCCL INFO Channel 01/0 : 1[1] -> 0[0] via P2P/CUMEM
babel-0-31:2324240:2324524 [1] NCCL INFO Connected all trees
babel-0-31:2324240:2324524 [1] NCCL INFO threadThresholds 8/8/64 | 32/8/64 | 512 | 512
babel-0-31:2324240:2324524 [1] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
babel-0-31:2324240:2324524 [1] NCCL INFO TUNER/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so
babel-0-31:2324240:2324524 [1] NCCL INFO TUNER/Plugin: Using internal tuner plugin.
babel-0-31:2324240:2324524 [1] NCCL INFO ncclCommInitRank comm 0x555d6dc227b0 rank 1 nranks 4 cudaDev 1 nvmlDev 1 busId 81000 commId 0xb886029f29f1a815 - Init COMPLETE
[rank1]:[E1119 13:21:12.512786305 ProcessGroupNCCL.cpp:616] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800000 milliseconds before timing out.
babel-0-31:2324239:2324239 [3] NCCL INFO cudaDriverVersion 12060
babel-0-31:2324239:2324239 [3] NCCL INFO Bootstrap : Using ibs8:172.16.1.17<0>
babel-0-31:2324239:2324239 [3] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)
babel-0-31:2324239:2324239 [3] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so
babel-0-31:2324239:2324239 [3] NCCL INFO NET/Plugin: Using internal network plugin.
babel-0-31:2324239:2324522 [3] NCCL INFO NET/IB : Using [0]mlx5_0:1/IB [RO]; OOB ibs8:172.16.1.17<0>
babel-0-31:2324239:2324522 [3] NCCL INFO Using non-device net plugin version 0
babel-0-31:2324239:2324522 [3] NCCL INFO Using network IB
babel-0-31:2324239:2324522 [3] NCCL INFO ncclCommInitRank comm 0x5584021f39f0 rank 3 nranks 4 cudaDev 3 nvmlDev 3 busId e1000 commId 0xb886029f29f1a815 - Init START
babel-0-31:2324239:2324522 [3] NCCL INFO Setting affinity for GPU 3 to 2b,00000000,00000000,00000000,0000002b,00000000,00000000
babel-0-31:2324239:2324522 [3] NCCL INFO NVLS multicast support is not available on dev 3
babel-0-31:2324239:2324522 [3] NCCL INFO comm 0x5584021f39f0 rank 3 nRanks 4 nNodes 1 localRanks 4 localRank 3 MNNVL 0
babel-0-31:2324239:2324522 [3] NCCL INFO Trees [0] -1/-1/-1->3->2 [1] -1/-1/-1->3->2
babel-0-31:2324239:2324522 [3] NCCL INFO P2P Chunksize set to 524288
babel-0-31:2324239:2324522 [3] NCCL INFO Channel 00/0 : 3[3] -> 0[0] via P2P/CUMEM
babel-0-31:2324239:2324522 [3] NCCL INFO Channel 01/0 : 3[3] -> 0[0] via P2P/CUMEM
babel-0-31:2324239:2324522 [3] NCCL INFO Connected all rings
babel-0-31:2324239:2324522 [3] NCCL INFO Channel 00/0 : 3[3] -> 2[2] via P2P/CUMEM
babel-0-31:2324239:2324522 [3] NCCL INFO Channel 01/0 : 3[3] -> 2[2] via P2P/CUMEM
babel-0-31:2324239:2324522 [3] NCCL INFO Connected all trees
babel-0-31:2324239:2324522 [3] NCCL INFO threadThresholds 8/8/64 | 32/8/64 | 512 | 512
babel-0-31:2324239:2324522 [3] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
babel-0-31:2324239:2324522 [3] NCCL INFO TUNER/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so
babel-0-31:2324239:2324522 [3] NCCL INFO TUNER/Plugin: Using internal tuner plugin.
babel-0-31:2324239:2324522 [3] NCCL INFO ncclCommInitRank comm 0x5584021f39f0 rank 3 nranks 4 cudaDev 3 nvmlDev 3 busId e1000 commId 0xb886029f29f1a815 - Init COMPLETE
[rank3]:[E1119 13:21:12.512781555 ProcessGroupNCCL.cpp:616] [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800000 milliseconds before timing out.
babel-0-31:2324238:2324238 [2] NCCL INFO cudaDriverVersion 12060
babel-0-31:2324238:2324238 [2] NCCL INFO Bootstrap : Using ibs8:172.16.1.17<0>
babel-0-31:2324238:2324238 [2] NCCL INFO NET/Plugin: No plugin found (libnccl-net.so)
babel-0-31:2324238:2324238 [2] NCCL INFO NET/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-net.so
babel-0-31:2324238:2324238 [2] NCCL INFO NET/Plugin: Using internal network plugin.
babel-0-31:2324238:2324523 [2] NCCL INFO NET/IB : Using [0]mlx5_0:1/IB [RO]; OOB ibs8:172.16.1.17<0>
babel-0-31:2324238:2324523 [2] NCCL INFO Using non-device net plugin version 0
babel-0-31:2324238:2324523 [2] NCCL INFO Using network IB
babel-0-31:2324238:2324523 [2] NCCL INFO ncclCommInitRank comm 0x55880160e670 rank 2 nranks 4 cudaDev 2 nvmlDev 2 busId a1000 commId 0xb886029f29f1a815 - Init START
babel-0-31:2324238:2324523 [2] NCCL INFO Setting affinity for GPU 2 to 2b,00000000,00000000,00000000,0000002b,00000000,00000000
babel-0-31:2324238:2324523 [2] NCCL INFO NVLS multicast support is not available on dev 2
babel-0-31:2324238:2324523 [2] NCCL INFO comm 0x55880160e670 rank 2 nRanks 4 nNodes 1 localRanks 4 localRank 2 MNNVL 0
babel-0-31:2324238:2324523 [2] NCCL INFO Trees [0] 3/-1/-1->2->1 [1] 3/-1/-1->2->1
babel-0-31:2324238:2324523 [2] NCCL INFO P2P Chunksize set to 524288
babel-0-31:2324238:2324523 [2] NCCL INFO Channel 00/0 : 2[2] -> 3[3] via P2P/CUMEM
babel-0-31:2324238:2324523 [2] NCCL INFO Channel 01/0 : 2[2] -> 3[3] via P2P/CUMEM
babel-0-31:2324238:2324523 [2] NCCL INFO Connected all rings
babel-0-31:2324238:2324523 [2] NCCL INFO Channel 00/0 : 2[2] -> 1[1] via P2P/CUMEM
babel-0-31:2324238:2324523 [2] NCCL INFO Channel 01/0 : 2[2] -> 1[1] via P2P/CUMEM
babel-0-31:2324238:2324523 [2] NCCL INFO Connected all trees
babel-0-31:2324238:2324523 [2] NCCL INFO threadThresholds 8/8/64 | 32/8/64 | 512 | 512
babel-0-31:2324238:2324523 [2] NCCL INFO 2 coll channels, 2 collnet channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
babel-0-31:2324238:2324523 [2] NCCL INFO TUNER/Plugin: Plugin load returned 2 : libnccl-net.so: cannot open shared object file: No such file or directory : when loading libnccl-tuner.so
babel-0-31:2324238:2324523 [2] NCCL INFO TUNER/Plugin: Using internal tuner plugin.
babel-0-31:2324238:2324523 [2] NCCL INFO ncclCommInitRank comm 0x55880160e670 rank 2 nranks 4 cudaDev 2 nvmlDev 2 busId a1000 commId 0xb886029f29f1a815 - Init COMPLETE
[rank2]:[E1119 13:21:12.525244336 ProcessGroupNCCL.cpp:616] [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800013 milliseconds before timing out.
[rank1]:[E1119 13:21:13.938073877 ProcessGroupNCCL.cpp:1785] [PG ID 0 PG GUID 0(default_pg) Rank 1] Exception (either an error or timeout) detected by watchdog at work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank1]:[E1119 13:21:13.938095107 ProcessGroupNCCL.cpp:1834] [PG ID 0 PG GUID 0(default_pg) Rank 1] Timeout at NCCL work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank1]:[E1119 13:21:13.938100947 ProcessGroupNCCL.cpp:630] [Rank 1] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[rank1]:[E1119 13:21:13.938104817 ProcessGroupNCCL.cpp:636] [Rank 1] To avoid data inconsistency, we are taking the entire process down.
[rank2]:[E1119 13:21:13.938073737 ProcessGroupNCCL.cpp:1785] [PG ID 0 PG GUID 0(default_pg) Rank 2] Exception (either an error or timeout) detected by watchdog at work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank2]:[E1119 13:21:13.938094977 ProcessGroupNCCL.cpp:1834] [PG ID 0 PG GUID 0(default_pg) Rank 2] Timeout at NCCL work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank2]:[E1119 13:21:13.938100577 ProcessGroupNCCL.cpp:630] [Rank 2] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[rank2]:[E1119 13:21:13.938104557 ProcessGroupNCCL.cpp:636] [Rank 2] To avoid data inconsistency, we are taking the entire process down.
[rank3]:[E1119 13:21:13.938073817 ProcessGroupNCCL.cpp:1785] [PG ID 0 PG GUID 0(default_pg) Rank 3] Exception (either an error or timeout) detected by watchdog at work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank3]:[E1119 13:21:13.938094667 ProcessGroupNCCL.cpp:1834] [PG ID 0 PG GUID 0(default_pg) Rank 3] Timeout at NCCL work: 1, last enqueued NCCL work: 1, last completed NCCL work: -1.
[rank3]:[E1119 13:21:13.938100907 ProcessGroupNCCL.cpp:630] [Rank 3] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data.
[rank3]:[E1119 13:21:13.938104757 ProcessGroupNCCL.cpp:636] [Rank 3] To avoid data inconsistency, we are taking the entire process down.
[rank2]:[E1119 13:21:13.092845528 ProcessGroupNCCL.cpp:1595] [PG ID 0 PG GUID 0(default_pg) Rank 2] Process group watchdog thread terminated with exception: [Rank 2] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800013 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:618 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7fdf89a24446 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x282 (0x7fdf8ad37772 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x233 (0x7fdf8ad3ebb3 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7fdf8ad4061d in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0x145c0 (0x7fdfd36cd5c0 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch.so)
frame #5: <unknown function> + 0x89c02 (0x7fdfe2c89c02 in /lib64/libc.so.6)
frame #6: <unknown function> + 0x10ec40 (0x7fdfe2d0ec40 in /lib64/libc.so.6)
[rank1]:[E1119 13:21:13.092997168 ProcessGroupNCCL.cpp:1595] [PG ID 0 PG GUID 0(default_pg) Rank 1] Process group watchdog thread terminated with exception: [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800000 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:618 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f3b4e1d4446 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x282 (0x7f3b4f4e7772 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x233 (0x7f3b4f4eebb3 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7f3b4f4f061d in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0x145c0 (0x7f3b97e7d5c0 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch.so)
frame #5: <unknown function> + 0x89c02 (0x7f3ba7489c02 in /lib64/libc.so.6)
frame #6: <unknown function> + 0x10ec40 (0x7f3ba750ec40 in /lib64/libc.so.6)
[rank3]:[E1119 13:21:13.092988978 ProcessGroupNCCL.cpp:1595] [PG ID 0 PG GUID 0(default_pg) Rank 3] Process group watchdog thread terminated with exception: [Rank 3] Watchdog caught collective operation timeout: WorkNCCL(SeqNum=1, OpType=BROADCAST, NumelIn=1, NumelOut=1, Timeout(ms)=1800000) ran for 1800000 milliseconds before timing out.
Exception raised from checkTimeout at ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:618 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7fe0c139c446 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libc10.so)
frame #1: c10d::ProcessGroupNCCL::WorkNCCL::checkTimeout(std::optional<std::chrono::duration<long, std::ratio<1l, 1000l> > >) + 0x282 (0x7fe0c26af772 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #2: c10d::ProcessGroupNCCL::watchdogHandler() + 0x233 (0x7fe0c26b6bb3 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #3: c10d::ProcessGroupNCCL::ncclCommWatchdog() + 0x14d (0x7fe0c26b861d in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch_cuda.so)
frame #4: <unknown function> + 0x145c0 (0x7fe10b0455c0 in /home/mengyan3/.local/lib/python3.9/site-packages/torch/lib/libtorch.so)
frame #5: <unknown function> + 0x89c02 (0x7fe11a689c02 in /lib64/libc.so.6)
frame #6: <unknown function> + 0x10ec40 (0x7fe11a70ec40 in /lib64/libc.so.6)
/data/tir/projects/tir3/users/mengyan3/all_in_one_pretraining/./pretrain_then_finetune.sh: line 184: 2324239 Aborted (core dumped) litgpt pretrain $model_name --resume "${checkpoint_dir}/step${step}/lit_model.pth" --tokenizer_dir "${checkpoint_dir}/step${step}" --data FineWebDataset --data.data_path $pretraining_data_dir --data.val_data_path /data/datasets/hf_cache/data/fineweb/sample-350BT/val/0 --data.num_workers $SLURM_GPUS_ON_NODE --train.micro_batch_size $micro_batch_size --train.max_seq_len $max_seq_len --train.min_lr 1e-6 --train.max_iters ${max_iters} --train.max_additional_steps $max_additional_steps --train.save_interval 500 --train.log_interval $log_interval --train.lr_warmup_fraction 0.01 --train.lr_scheduler $lr_scheduler --eval.interval 1000 --out_dir $out_dir --logs_dir $out_dir --logger_name tensorboard
/data/tir/projects/tir3/users/mengyan3/all_in_one_pretraining/./pretrain_then_finetune.sh: line 184: 2324238 Aborted (core dumped) litgpt pretrain $model_name --resume "${checkpoint_dir}/step${step}/lit_model.pth" --tokenizer_dir "${checkpoint_dir}/step${step}" --data FineWebDataset --data.data_path $pretraining_data_dir --data.val_data_path /data/datasets/hf_cache/data/fineweb/sample-350BT/val/0 --data.num_workers $SLURM_GPUS_ON_NODE --train.micro_batch_size $micro_batch_size --train.max_seq_len $max_seq_len --train.min_lr 1e-6 --train.max_iters ${max_iters} --train.max_additional_steps $max_additional_steps --train.save_interval 500 --train.log_interval $log_interval --train.lr_warmup_fraction 0.01 --train.lr_scheduler $lr_scheduler --eval.interval 1000 --out_dir $out_dir --logs_dir $out_dir --logger_name tensorboard
/data/tir/projects/tir3/users/mengyan3/all_in_one_pretraining/./pretrain_then_finetune.sh: line 184: 2324240 Aborted (core dumped) litgpt pretrain $model_name --resume "${checkpoint_dir}/step${step}/lit_model.pth" --tokenizer_dir "${checkpoint_dir}/step${step}" --data FineWebDataset --data.data_path $pretraining_data_dir --data.val_data_path /data/datasets/hf_cache/data/fineweb/sample-350BT/val/0 --data.num_workers $SLURM_GPUS_ON_NODE --train.micro_batch_size $micro_batch_size --train.max_seq_len $max_seq_len --train.min_lr 1e-6 --train.max_iters ${max_iters} --train.max_additional_steps $max_additional_steps --train.save_interval 500 --train.log_interval $log_interval --train.lr_warmup_fraction 0.01 --train.lr_scheduler $lr_scheduler --eval.interval 1000 --out_dir $out_dir --logs_dir $out_dir --logger_name tensorboard
srun: First task exited 60s ago
srun: StepId=3178163.0 task 0: running
srun: StepId=3178163.0 tasks 1-3: exited
srun: Terminating StepId=3178163.0
slurmstepd: error: *** STEP 3178163.0 ON babel-0-31 CANCELLED AT 2024-11-19T13:24:07 ***
srun: Job step aborted: Waiting up to 122 seconds for job step to finish.
slurmstepd: error: --task-epilog failed status=9
```
### Environment
<details>
<summary>Current environment</summary>
* CUDA:
- GPU:
- NVIDIA RTX A6000
- NVIDIA RTX A6000
- NVIDIA RTX A6000
- NVIDIA RTX A6000
- available: True
- version: 12.4
* Lightning:
- botorch: 0.10.0
- gpytorch: 1.11
- lightning: 2.3.0.dev20240428
- lightning-utilities: 0.11.8
- pytorch-lightning: 2.3.1
- torch: 2.5.1
- torchmetrics: 1.4.0.post0
* Packages:
- absl-py: 2.1.0
- accelerate: 0.32.0
- aiohttp: 3.9.5
- aiosignal: 1.3.1
- annotated-types: 0.7.0
- antlr4-python3-runtime: 4.11.0
- anyio: 4.4.0
- argcomplete: 3.5.1
- asttokens: 2.4.1
- async-timeout: 4.0.3
- attrs: 23.2.0
- awscrt: 0.20.11
- beautifulsoup4: 4.12.3
- bitsandbytes: 0.42.0
- boto3: 1.35.63
- botocore: 1.34.138
- botorch: 0.10.0
- bs4: 0.0.2
- build: 1.2.1
- certifi: 2024.6.2
- chardet: 5.2.0
- charset-normalizer: 3.3.2
- click: 8.1.7
- colorama: 0.4.6
- contourpy: 1.2.1
- cycler: 0.12.1
- dataproperty: 1.0.1
- datasets: 2.20.0
- dill: 0.3.8
- distro: 1.9.0
- dnspython: 2.6.1
- docker-pycreds: 0.4.0
- docstring-parser: 0.16
- dotwiz: 0.4.0
- email-validator: 2.2.0
- evaluate: 0.4.2
- exceptiongroup: 1.2.1
- executing: 2.0.1
- exrex: 0.11.0
- fastapi: 0.111.0
- fastapi-cli: 0.0.4
- filelock: 3.16.1
- fonttools: 4.53.1
- frozenlist: 1.4.1
- fsspec: 2024.10.0
- funcy: 2.0
- git-filter-repo: 2.34.0
- gitdb: 4.0.11
- gitpython: 3.1.43
- gpytorch: 1.11
- grpcio: 1.64.1
- h11: 0.14.0
- hf-transfer: 0.1.6
- httpcore: 1.0.5
- httptools: 0.6.1
- httpx: 0.27.0
- huggingface-hub: 0.23.4
- idna: 3.7
- importlib-metadata: 8.0.0
- importlib-resources: 6.4.0
- jaxtyping: 0.2.33
- jinja2: 3.1.4
- jiter: 0.5.0
- jmespath: 1.0.1
- joblib: 1.4.2
- jsonargparse: 4.31.0
- jsonlines: 4.0.0
- kiwisolver: 1.4.5
- lightning: 2.3.0.dev20240428
- lightning-utilities: 0.11.8
- linear-operator: 0.5.1
- litdata: 0.2.30
- litgpt: 0.4.0
- litserve: 0.1.1.dev0
- littleutils: 0.2.4
- lm-eval: 0.4.3
- lxml: 5.2.2
- magicattr: 0.1.6
- markdown: 3.6
- markdown-it-py: 3.0.0
- markupsafe: 2.1.5
- matplotlib: 3.9.1.post1
- mbstrdecoder: 1.1.3
- mdurl: 0.1.2
- more-itertools: 10.3.0
- mpmath: 1.3.0
- multidict: 6.0.5
- multipledispatch: 1.0.0
- multiprocess: 0.70.16
- networkx: 3.2.1
- nltk: 3.8.1
- numexpr: 2.10.1
- numpy: 1.26.4
- nvidia-cublas-cu12: 12.4.5.8
- nvidia-cuda-cupti-cu12: 12.4.127
- nvidia-cuda-nvrtc-cu12: 12.4.127
- nvidia-cuda-runtime-cu12: 12.4.127
- nvidia-cudnn-cu12: 9.1.0.70
- nvidia-cufft-cu12: 11.2.1.3
- nvidia-curand-cu12: 10.3.5.147
- nvidia-cusolver-cu12: 11.6.1.9
- nvidia-cusparse-cu12: 12.3.1.170
- nvidia-nccl-cu12: 2.21.5
- nvidia-nvjitlink-cu12: 12.4.127
- nvidia-nvtx-cu12: 12.4.127
- openai: 1.43.0
- opt-einsum: 3.3.0
- orjson: 3.10.6
- packaging: 24.1
- pandas: 2.2.2
- pathvalidate: 3.2.0
- peft: 0.11.1
- pillow: 10.4.0
- pip: 24.0
- pip-tools: 7.4.1
- platformdirs: 4.2.2
- portalocker: 2.10.0
- protobuf: 4.25.3
- psutil: 6.0.0
- pyarrow: 16.1.0
- pyarrow-hotfix: 0.6
- pybind11: 2.13.1
- pydantic: 2.8.0
- pydantic-core: 2.20.0
- pygments: 2.18.0
- pyheck: 0.1.5
- pyparsing: 3.1.2
- pyproject-hooks: 1.1.0
- pyro-api: 0.1.2
- pyro-ppl: 1.9.1
- pytablewriter: 1.2.0
- python-dateutil: 2.9.0.post0
- python-dotenv: 1.0.1
- python-multipart: 0.0.9
- pytorch-lightning: 2.3.1
- pytz: 2024.1
- pyyaml: 6.0.1
- regex: 2024.5.15
- requests: 2.32.3
- rich: 13.7.1
- rouge-score: 0.1.2
- s3transfer: 0.10.3
- sacrebleu: 2.4.2
- safetensors: 0.4.3
- scikit-learn: 1.5.1
- scipy: 1.13.1
- sentencepiece: 0.2.0
- sentry-sdk: 2.7.1
- setproctitle: 1.3.3
- setuptools: 69.5.1
- shellingham: 1.5.4
- six: 1.16.0
- smmap: 5.0.1
- sniffio: 1.3.1
- sorcery: 0.2.2
- soupsieve: 2.6
- sqlitedict: 2.1.0
- starlette: 0.37.2
- sympy: 1.13.1
- tabledata: 1.3.3
- tabulate: 0.9.0
- tasksource: 0.0.45
- tcolorpy: 0.1.6
- tensorboard: 2.17.0
- tensorboard-data-server: 0.7.2
- threadpoolctl: 3.5.0
- tokenizers: 0.19.1
- tomli: 2.0.1
- tomlkit: 0.13.2
- torch: 2.5.1
- torchmetrics: 1.4.0.post0
- tqdm: 4.66.4
- tqdm-multiprocess: 0.0.11
- transformers: 4.42.3
- triton: 3.1.0
- typeguard: 2.13.3
- typepy: 1.3.2
- typer: 0.12.3
- typeshed-client: 2.5.1
- typing-extensions: 4.12.2
- tzdata: 2024.1
- ujson: 5.10.0
- urllib3: 1.26.19
- uvicorn: 0.30.1
- uvloop: 0.19.0
- wandb: 0.17.4
- watchfiles: 0.22.0
- websockets: 12.0
- werkzeug: 3.0.3
- wheel: 0.43.0
- word2number: 1.1
- wrapt: 1.16.0
- xmltodict: 0.14.2
- xxhash: 3.4.1
- yarl: 1.9.4
- zipp: 3.19.2
- zstandard: 0.22.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.9.0
- release: 5.14.0-427.40.1.el9_4.x86_64
- version: #1 SMP PREEMPT_DYNAMIC Wed Oct 16 07:08:17 EDT 2024
</details>
### More info
_No response_ | open | 2024-11-19T22:53:46Z | 2025-02-11T17:44:40Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20434 | [
"bug",
"needs triage",
"ver: 2.3.x"
] | nightingal3 | 1 |
ijl/orjson | numpy | 390 | Add ARMv8-A to builds | Hello,
I am attempting to integrate orjson in a yocto build. But the problem is that we need to have a build for the ARM Cortex-A53 which runs on ARMv8-A.
Unfortunately, the maturin build system doesn't seem to be supported by the yocto project right now, so using a built wheel seems to be the way to go.
However there doesn't seem to be such a build in the GitHub workflow build matrix.
Would it be kindly possible to add it to the build?
Thanks! | closed | 2023-05-30T21:29:01Z | 2023-06-12T08:01:55Z | https://github.com/ijl/orjson/issues/390 | [
"Stale"
] | TommyDuc | 0 |
aeon-toolkit/aeon | scikit-learn | 2,666 | [MNT] Introduce multithreading to the `examples/` testing workflow | ### Describe the issue
Our examples testing is currently single threaded, not making use of the multiple cores available in GitHub runners. This should be (hopefully) an easy speed up for the CI.
### Suggest a potential alternative/fix
Multithread the examples workflow/script.
### Additional context
_No response_ | open | 2025-03-20T16:28:11Z | 2025-03-20T16:28:11Z | https://github.com/aeon-toolkit/aeon/issues/2666 | [
"maintenance",
"testing",
"examples"
] | MatthewMiddlehurst | 0 |
davidsandberg/facenet | computer-vision | 1,077 | Is the MTCNN model optimized for RGB or BGR image format? | Hello everyone,
I am under the assumption the MTCNN model weights were imported from the original authors' [repository](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and were not trained from scratch. If so, then the MTCNN was trained using the Caffe framework which reads images as BGR since it uses OpenCV ([evidence](https://github.com/BVLC/caffe/wiki/Image-Format:-BGR-not-RGB)).
Then why is the MTCNN performing well enough when having RGB images as input instead of BGR?
EDIT:
The model should be optimized for RGB because the authors' [repository](https://github.com/kpzhang93/MTCNN_face_detection_alignment/blob/master/code/codes/MTCNNv2/demo.m#L42) uses the MATLAB imread() function which uses RGB instead of the caffe.io.load_image() function which uses BGR. | closed | 2019-09-01T10:50:01Z | 2022-06-01T10:53:56Z | https://github.com/davidsandberg/facenet/issues/1077 | [] | tamerthamoqa | 2 |
Lightning-AI/pytorch-lightning | data-science | 20,218 | using deepspeed in pytorch lightning, a bug occurred : RuntimeError: Function ConvolutionBackward0 returned an invalid gradient at index 1 | ### Bug description
Hi, everyone!
I'm using DeepSpeedPlugin to speed up in pytorch lightning 1.4.2, however, a bug occurred during the backward process: RuntimeError: Function ConvolutionBackward0 returned an invalid gradient at index 1 - got [1280, 1280, 3, 3] but expected shape compatible with [0].
This confuse me. Because when i set accerlator to ddp, the model runs successfully. Also, this bug will appear even if pytorch lightning is 2.4.0.
### What version are you seeing the problem on?
v1.x
### How to reproduce the bug
```python
def main() -> None:
parser = ArgumentParser()
parser.add_argument("--config", type=str, default='configs/train.yaml')
args = parser.parse_args()
config = OmegaConf.load(args.config)
pl.seed_everything(config.lightning.seed, workers=True)
data_module = instantiate_from_config(config.data)
model = instantiate_from_config(OmegaConf.load(config.model.config))
# TODO: resume states saved in checkpoint.
if config.model.get("resume"):
load_state_dict(model, torch.load(config.model.resume, map_location="cpu"), strict=True)
callbacks = []
for callback_config in config.lightning.callbacks:
callbacks.append(instantiate_from_config(callback_config))
logger =TensorBoardLogger(save_dir=config.lightning.trainer.default_root_dir,version=1,name="lightning_logs")
trainer = pl.Trainer(callbacks=callbacks, logger=logger, plugins=DeepSpeedPlugin(stage=3), **config.lightning.trainer)
trainer.fit(model, datamodule=data_module)
```
### Error messages and logs
```
Traceback (most recent call last):
File "/data3/sxhong/project/CCSR_deepspeed/train.py", line 36, in <module>
main()
File "/data3/sxhong/project/CCSR_deepspeed/train.py", line 32, in main
trainer.fit(model, datamodule=data_module)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 553, in fit
self._run(model)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 918, in _run
self._dispatch()
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 986, in _dispatch
self.accelerator.start_training(self)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 92, in start_training
self.training_type_plugin.start_training(trainer)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 161, in start_training
self._results = trainer.run_stage()
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 996, in run_stage
return self._run_train()
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1045, in _run_train
self.fit_loop.run()
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 200, in advance
epoch_output = self.epoch_loop.run(train_dataloader)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/training_epoch_loop.py", line 130, in advance
batch_output = self.batch_loop.run(batch, self.iteration_count, self._dataloader_idx)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 101, in run
super().run(batch, batch_idx, dataloader_idx)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/base.py", line 111, in run
self.advance(*args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 148, in advance
result = self._run_optimization(batch_idx, split_batch, opt_idx, optimizer)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 202, in _run_optimization
self._optimizer_step(optimizer, opt_idx, batch_idx, closure)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 396, in _optimizer_step
model_ref.optimizer_step(
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/core/lightning.py", line 1618, in optimizer_step
optimizer.step(closure=optimizer_closure)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py", line 209, in step
self.__optimizer_step(*args, closure=closure, profiler_name=profiler_name, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py", line 129, in __optimizer_step
trainer.accelerator.optimizer_step(optimizer, self._optimizer_idx, lambda_closure=closure, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 292, in optimizer_step
make_optimizer_step = self.precision_plugin.pre_optimizer_step(
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/deepspeed_precision.py", line 47, in pre_optimizer_step
lambda_closure() # DeepSpeed does not support closures
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 236, in _training_step_and_backward_closure
result = self.training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 549, in training_step_and_backward
self.backward(result, optimizer, opt_idx)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/loops/batch/training_batch_loop.py", line 590, in backward
result.closure_loss = self.trainer.accelerator.backward(result.closure_loss, optimizer, *args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/accelerators/accelerator.py", line 276, in backward
self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/deepspeed_precision.py", line 60, in backward
deepspeed_engine.backward(closure_loss, *args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn
ret_val = func(*args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/deepspeed/runtime/engine.py", line 1967, in backward
self.optimizer.backward(loss, retain_graph=retain_graph)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/deepspeed/utils/nvtx.py", line 15, in wrapped_fn
ret_val = func(*args, **kwargs)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/deepspeed/runtime/zero/stage3.py", line 2213, in backward
self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward
scaled_loss.backward(retain_graph=retain_graph)
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/torch/_tensor.py", line 487, in backward
torch.autograd.backward(
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/torch/autograd/__init__.py", line 200, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/torch/autograd/function.py", line 274, in apply
return user_fn(self, *args)
File "/data3/sxhong/project/CCSR_deepspeed/ldm/modules/diffusionmodules/util.py", line 145, in backward
input_grads = torch.autograd.grad(
File "/data3/sxhong/miniconda3/envs/CCSR/lib/python3.9/site-packages/torch/autograd/__init__.py", line 303, in grad
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Function ConvolutionBackward0 returned an invalid gradient at index 1 - got [1280, 1280, 3, 3] but expected shape compatible with [0]
```
### Environment
```
#- PyTorch Lightning Version (e.g., 2.4.0):
#- torch 2.0.1+cu118
#- torchmetrics 0.6.0
#- torchvision 0.15.2+cu118
#- pytorch-lightning 1.4.2
#- deepspeed 0.14.4
```
| open | 2024-08-20T07:35:03Z | 2024-08-20T07:37:54Z | https://github.com/Lightning-AI/pytorch-lightning/issues/20218 | [
"bug",
"needs triage"
] | hongsixin | 0 |
ploomber/ploomber | jupyter | 791 | make >>> unselectable in the documentation | Our docstring examples need to start with `>>>` for pytest to detect them as tests. [Example](https://docs.ploomber.io/en/latest/api/_modules/tasks/ploomber.tasks.PythonCallable.html#ploomber.tasks.PythonCallable):
```python
>>> from pathlib import Path
>>> from ploomber import DAG
>>> from ploomber.tasks import PythonCallable
>>> from ploomber.products import File
>>> from ploomber.executors import Serial
>>> dag = DAG(executor=Serial(build_in_subprocess=False))
>>> def my_function(product):
... # create data.csv
... Path(product).touch()
>>> PythonCallable(my_function, File('data.csv'), dag=dag)
PythonCallable: my_function -> File('data.csv')
>>> summary = dag.build()
```
However, if users want to select them, they'll also copy the `>>>` characters, and it will break if they put it in the Python interpreter (although IPython is smart enough to ignore them).
I noticed that the [pandas](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby) documentation prevents the user from selecting them, along with the `...` characters:
we should to the same
| closed | 2022-05-18T19:40:56Z | 2022-05-19T07:10:12Z | https://github.com/ploomber/ploomber/issues/791 | [
"documentation",
"good first issue"
] | edublancas | 2 |
apache/airflow | machine-learning | 47,992 | Task instance state metrics (ti.start/finish) not appearing in Prometheus via OpenTelemetry | ### Apache Airflow version
Other Airflow 2 version (please specify below)
### If "Other Airflow 2 version" selected, which one?
2.9.0
### What happened?
I've configured Airflow 2.9.0 with OpenTelemetry metrics integration to send metrics to Prometheus via an OpenTelemetry collector. While task instance creation metrics (airflow_task_instance_created_*) are correctly appearing in Prometheus, task instance state metrics like ti.start and ti.finish are not appearing, despite being emitted in the code.
In taskinstance.py the code emits these metrics in the _run_raw_task function:
```
Stats.incr(f"ti.start.{ti.task.dag_id}.{ti.task.task_id}", tags=ti.stats_tags)
# Same metric with tagging
Stats.incr("ti.start", tags=ti.stats_tags)
```
And later:
```
Stats.incr(f"ti.finish.{ti.dag_id}.{ti.task_id}.{ti.state}", tags=ti.stats_tags)
# Same metric with tagging
Stats.incr("ti.finish", tags={**ti.stats_tags, "state": str(ti.state)})
```
However, neither of these metrics appear in Prometheus, while other metrics like dag processing metrics and task instance creation metrics are visible.
### What you think should happen instead?
The task instance state metrics (ti.start, ti.finish) should be visible in Prometheus, as they are critical metrics for monitoring task execution and completion.
I suspect one of these issues might be occurring:
1. The metrics might be named or formatted differently in the OpenTelemetry implementation compared to how the filter is set up
2. There could be an issue in the OpenTelemetry metrics system where these specific counters aren't being properly sent to the collector
3. The metrics might be filtered at some level that isn't immediately obvious in the configuration
### How to reproduce
1. Configure Airflow 2.9.0 with OpenTelemetry using these settings:
```
AIRFLOW__METRICS__OTEL_ON=True
AIRFLOW__METRICS__STATSD_ON=False
AIRFLOW__METRICS__OTEL_HOST=otel-collector
AIRFLOW__METRICS__OTEL_PORT=4318
AIRFLOW__METRICS__OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4318
AIRFLOW__METRICS__OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
AIRFLOW__METRICS__OTEL_RESOURCE_ATTRIBUTES=service.name=airflow
AIRFLOW__METRICS__OTEL_METRICS_EXPORTER=otlp
AIRFLOW__METRICS__METRICS_USE_PATTERN_MATCH=True
AIRFLOW__METRICS__METRICS_OTEL_SSL_ACTIVE=False
AIRFLOW__METRICS__OTEL_DEBUGGING_ON=True
AIRFLOW__METRICS__METRICS_ALLOW_LIST=ti|task
```
2. Configure an OpenTelemetry collector with a filter to allow task metrics:
```
processors:
filter:
metrics:
include:
match_type: regexp
metric_names:
- "task_instance.*"
- "airflow.*task.*state.*"
- "airflow.*task.*status.*"
```
3. Run tasks in Airflow
4. Check Prometheus metrics - you'll see airflow_task_instance_created_* metrics but no ti.start or ti.finish metrics
### Operating System
mac-OS
### Versions of Apache Airflow Providers
apache-airflow[postgres,google_auth,crypto,celery,amazon,cncf.kubernetes,statsd,pandas] +otel
### Deployment
Docker-Compose
### Deployment details
Using Docker Compose with Airflow 2.9.0, OpenTelemetry Collector, Prometheus, and Grafana. The OpenTelemetry collector is configured to receive metrics via OTLP HTTP and export them to Prometheus.
### Anything else?
I've tried various filter configurations and have confirmed that other metrics from Airflow are being properly sent to Prometheus, just not the task instance state metrics. I've also enabled debugging on the OpenTelemetry collector and don't see these specific metrics in the debug output.
I added custom debug logging to the _run_raw_task function to confirm that the code is being executed and the Stats.incr calls are being made, but these metrics still don't appear in the collector or Prometheus.
### Are you willing to submit PR?
- [x] Yes I am willing to submit a PR!
### Code of Conduct
- [x] I agree to follow this project's [Code of Conduct](https://github.com/apache/airflow/blob/main/CODE_OF_CONDUCT.md)
| open | 2025-03-20T10:04:56Z | 2025-03-20T10:07:17Z | https://github.com/apache/airflow/issues/47992 | [
"kind:bug",
"area:metrics",
"area:core",
"needs-triage"
] | ahronrosenboimvim | 1 |
xorbitsai/xorbits | numpy | 98 | BUG: DataFrame.at is not properly handled | ### Describe the bug
DataFrame.at returns a mars object.
### To Reproduce
```python
>>> import xorbits.pandas as pd
>>> df = pd.DataFrame((1, 2, 3))
>>> df.at[0, 0]
Tensor <op=DataFrameLocGetItem, shape=(), key=f5caa0e174b3142b0aa648f305532703_0>
```
### Expected behavior
```python
>>> import xorbits.pandas as pd
>>> df = pd.DataFrame((1, 2, 3))
>>> df.at[0, 0]
1
```
| closed | 2022-12-16T04:38:46Z | 2022-12-30T02:54:15Z | https://github.com/xorbitsai/xorbits/issues/98 | [
"bug"
] | UranusSeven | 0 |
Lightning-AI/pytorch-lightning | machine-learning | 19,761 | Apply the ignore of the `save_hyperparameters` function to args | ### Description & Motivation
Currently, the ignore parameter of the `save_hyperparameters` function only filters its own arguments.
Can we apply `hp = {k: v for k, v in hp.items() if k not in ignore}` before `obj._set_hparams(hp)`?
### Pitch
_No response_
### Alternatives
_No response_
### Additional context
_No response_
cc @borda | open | 2024-04-11T13:33:48Z | 2025-01-08T15:49:08Z | https://github.com/Lightning-AI/pytorch-lightning/issues/19761 | [
"feature",
"help wanted",
"good first issue"
] | doveppp | 4 |
JaidedAI/EasyOCR | pytorch | 1,378 | RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! | Filtering the images containing characters which are not in opt.character
Filtering the images whose label is longer than opt.batch_max_length
--------------------------------------------------------------------------------
dataset_root: all_data
opt.select_data: ['all_data']
opt.batch_ratio: ['1']
--------------------------------------------------------------------------------
dataset_root: all_data dataset: all_data
all_data/en_sample
sub-directory: /en_sample num samples: 882
all_data/rec\test
sub-directory: /rec\test num samples: 0
all_data/rec\train
sub-directory: /rec\train num samples: 0
all_data/rec\val
sub-directory: /rec\val num samples: 0
num total samples of all_data: 882 x 1.0 (total_data_usage_ratio) = 882
num samples of all_data per batch: 10 x 1.0 (batch_ratio) = 10
--------------------------------------------------------------------------------
Total_batch_size: 10 = 10
--------------------------------------------------------------------------------
dataset_root: all_data/en_sample dataset: /
all_data/en_sample/
sub-directory: /. num samples: 882
--------------------------------------------------------------------------------
...
---------------------------------------
continue to train, start_iter: 300000
training time: 11.559250354766846
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?21978a74-9e14-4883-bf7b-f33cc19f5f0d) or open in a [text editor](command:workbench.action.openLargeOutput?21978a74-9e14-4883-bf7b-f33cc19f5f0d). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In[6], [line 2](vscode-notebook-cell:?execution_count=6&line=2)
[1](vscode-notebook-cell:?execution_count=6&line=1) opt = get_config("config_files/en_fine_tunning_config.yaml")
----> [2](vscode-notebook-cell:?execution_count=6&line=2) train(opt, amp=False)
File c:\Users\mengfoong\Desktop\Train_Docling_2\EasyOCR-Trainer\train.py:233, in train(opt, show_number, amp)
[230](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:230) model.eval()
[231](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:231) with torch.no_grad():
[232](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:232) valid_loss, current_accuracy, current_norm_ED, preds, confidence_score, labels,\
--> [233](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:233) infer_time, length_of_data = validation(model, criterion, valid_loader, converter, opt, device)
[234](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:234) model.train()
[235](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/train.py:235) print(infer_time, length_of_data)
File c:\Users\mengfoong\Desktop\Train_Docling_2\EasyOCR-Trainer\test_1.py:45, in validation(model, criterion, evaluation_loader, converter, opt, device)
[43](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:43) preds_size = torch.IntTensor([preds.size(1)] * batch_size)
[44](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:44) # permute 'preds' to use CTCloss format
---> [45](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:45) cost = criterion(preds.log_softmax(2).permute(1, 0, 2), text_for_loss, preds_size, length_for_loss)
[47](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:47) if opt.decode == 'greedy':
[48](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:48) # Select max probabilty (greedy decoding) then decode index to character
[49](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/EasyOCR-Trainer/test_1.py:49) _, preds_index = preds.max(2)
File c:\Users\mengfoong\Desktop\Train_Docling_2\venv\Lib\site-packages\torch\nn\modules\module.py:1739, in Module._wrapped_call_impl(self, *args, **kwargs)
[1737](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/venv/Lib/site-packages/torch/nn/modules/module.py:1737) return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
[1738](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/venv/Lib/site-packages/torch/nn/modules/module.py:1738) else:
...
[3085](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/venv/Lib/site-packages/torch/nn/functional.py:3085) _Reduction.get_enum(reduction),
[3086](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/venv/Lib/site-packages/torch/nn/functional.py:3086) zero_infinity,
[3087](file:///C:/Users/mengfoong/Desktop/Train_Docling_2/venv/Lib/site-packages/torch/nn/functional.py:3087) )
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and CPU!
I'm facing this error when I run the cell, anyone can share how you resolved this ?
| open | 2025-02-19T10:06:04Z | 2025-02-19T10:06:04Z | https://github.com/JaidedAI/EasyOCR/issues/1378 | [] | MengFoong | 0 |
jupyter-book/jupyter-book | jupyter | 2,310 | Citations rendering correctly | Hi Folks,
If you've had problems with citations not rendering to author, date in your md text adding this line of code to the config.yml file should help (and way easier than the suggested software downgrades):
```
sphinx:
config:
bibtex_reference_style: author_year
```
Alter the bibtx_reference_style line to how you want the citations to occur.
If you start with the Jupyterbook template the citations will render as a abbreviation of the authors. It would be nice for the software developers to change or reference this. Thanks. | open | 2025-01-29T11:27:18Z | 2025-01-29T11:27:18Z | https://github.com/jupyter-book/jupyter-book/issues/2310 | [] | VRConservation | 0 |
Lightning-AI/LitServe | fastapi | 181 | Add health check endpoint | ## 🚀 Feature
<!-- A clear and concise description of the feature proposal -->
### Motivation
<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
### Pitch
<!-- A clear and concise description of what you want to happen. -->
### Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
### Additional context
<!-- Add any other context or screenshots about the feature request here. -->
| closed | 2024-07-24T17:00:14Z | 2024-07-29T07:18:32Z | https://github.com/Lightning-AI/LitServe/issues/181 | [
"enhancement",
"help wanted"
] | aniketmaurya | 4 |
ml-tooling/best-of-python-dev | pytest | 8 | Add project: snoop | **Project details:**
- Project Name: snoop
- Github URL: https://github.com/alexmojaki/snoop
- Category: Debugging Tools
- License: MIT
- Package Managers: pypi:snoop
**Additional context:**
snoop is an alternative to pysnooper. While I may be biased, I do think it's significantly better, and I've explained why in detail here: https://github.com/alexmojaki/snoop/wiki/Comparison-to-PySnooper
While pysnooper has many more stars, AFAICT this comes down to luck and the GitHub trending page in its early days, or at least better marketing. | closed | 2021-01-19T18:57:34Z | 2021-01-19T19:44:07Z | https://github.com/ml-tooling/best-of-python-dev/issues/8 | [
"add-project"
] | alexmojaki | 1 |
collerek/ormar | sqlalchemy | 858 | Skip data validation when fetching data from the DB | ## Problem Desctription
I have noticed that it takes a long time to return the result of simple query. At first glance, I thought it is because of some unnecessary joins. But it turns out that the queries are too fast (less than 10ms), while creating and validating pydantic models take a lot of time (~600ms).
## Proposed solution
When I check pydantic [docs](https://pydantic-docs.helpmanual.io/usage/models/#creating-models-without-validation) I find this:
> pydantic also provides the construct() method which allows models to be created without validation this can be useful when data has already been validated or comes from a trusted source and you want to create a model as efficiently as possible (construct() is generally around 30x faster than creating a model with full validation).
I changed the method `ModelRow.from_row` to use `construct` method:
```diff
- instance = cast("Model", cls(**item))
+ instance = cast("Model", cls.construct(**item))
```
this significantly reduces response time. I think it is safe to skip data validation since it has been validated at creation time.
If that sounds reasonable I can create a PR to fix this.
| open | 2022-09-29T16:30:24Z | 2024-05-28T01:29:19Z | https://github.com/collerek/ormar/issues/858 | [
"enhancement"
] | Abdeldjalil-H | 6 |
benbusby/whoogle-search | flask | 154 | [FEATURE] Make Social Media Alt-Sites to be configurable via enviroment variables | **Describe the feature you'd like to see added**
The alternative social media site are hardcoded in [app/utils/filter_utils.py#L19](https://github.com/benbusby/whoogle-search/tree/develop/app/utils/filter_utils.py#L19)
```python
SITE_ALTS = {
'twitter.com': 'nitter.net',
'youtube.com': 'invidious.snopyta.org',
'instagram.com': 'bibliogram.art/u'
}
```
I prefer other instances and always have to correct the file after an upstream update.
It would be great if this site could be configured/overridden via enviroment variables.
Like:
```bash
WHOOGLE_ALT_TWITTER=nitter.nixnet.services
```
Thanks for the amazing proxy!
| closed | 2020-12-05T11:38:41Z | 2020-12-05T22:01:22Z | https://github.com/benbusby/whoogle-search/issues/154 | [
"enhancement"
] | pred2k | 0 |
xinntao/Real-ESRGAN | pytorch | 327 | setup error on ubuntu 22.04 | While the install process the compilation of numpy 1.20.3 fails on ubuntu 22.04. newer versions are working. would it be possible to bump the version to 1.21.x or 1.22.x? according to the error messages the problem might be the missing python 3.10 support. | open | 2022-05-11T19:28:40Z | 2022-08-20T14:07:35Z | https://github.com/xinntao/Real-ESRGAN/issues/327 | [] | TailsxKyuubi | 2 |
deepset-ai/haystack | nlp | 8,301 | Update recipes in Haystack docs | A community member pointed me to outdated code in one of our recipes: https://github.com/deepset-ai/haystack/discussions/8299#discussion-7103259 That particular recipe is fixed now but we should check and update all others too. For example, the "Getting Started Try out a simple pre-build Pipeline!" seems outdated too as there is no `build_rag_pipeline` in Haystack anymore.
We need to update the code and adjust the highlighted lines of code if necessary.
https://docs.haystack.deepset.ai/v2.5-unstable/recipes
```[tasklist]
### Tasks
- [x] 2.0 RAG Pipeline
- [x] Custom Components in a Pipeline
- [x] Getting Started
- [x] TopPSampler in a Pipeline
```
| closed | 2024-08-28T07:18:08Z | 2024-09-05T12:24:25Z | https://github.com/deepset-ai/haystack/issues/8301 | [
"type:documentation",
"P1"
] | julian-risch | 0 |
seleniumbase/SeleniumBase | web-scraping | 3,527 | get_page_source() does not return correct page source | On this cloudflare protected page, i dont get the correct page source when i call sb.get_page_source()
site: https://solelist.app/share/wtb/647889860010573876
```
with SB(uc=True, headless=False) as sb:
sb.uc_open_with_reconnect("https://solelist.app/share/wtb/647889860010573876")
print(sb.get_page_source())
```
returns
```
<html><head>
<script src="thunk.js"></script>
</head>
<body>
</body></html>
```
but actual page source contains a lot more | closed | 2025-02-16T19:39:13Z | 2025-02-18T16:35:20Z | https://github.com/seleniumbase/SeleniumBase/issues/3527 | [
"invalid usage",
"UC Mode / CDP Mode"
] | seanwachs | 3 |
deepfakes/faceswap | deep-learning | 1,115 | Crash Resport | *Note: For general usage questions and help, please use either our [FaceSwap Forum](https://faceswap.dev/forum)
or [FaceSwap Discord server](https://discord.gg/FC54sYg). General usage questions are liable to be closed without
response.*
**Crash reports MUST be included when reporting bugs.**
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Steps to reproduce the behavior:
1. Go to Train
2. Click on Train button
3. Scroll down to logs section
4. See error
File "C:\Users\Omar\faceswap\lib\align\aligned_face.py", line 380, in _convert_centering
out[slices["out"][0], slices["out"][1], :] = image[slices["in"][0], slices["in"][1], :]
ValueError: could not broadcast input array from shape (210,171,3) into shape (210,0,3)
**Expected behavior**
No exception
**Screenshots**
**Desktop (please complete the following information):**
- OS: Windows 10
- Python Version 3.8.5
- Conda Version 4.9.2
-
**Additional context**
Add any other context about the problem here.
**Crash Report**
[crash_report.2021.01.08.111420686448.log](https://github.com/deepfakes/faceswap/files/5784523/crash_report.2021.01.08.111420686448.log)
| closed | 2021-01-08T00:20:44Z | 2021-01-25T11:03:18Z | https://github.com/deepfakes/faceswap/issues/1115 | [] | OmarMashaal | 6 |
sinaptik-ai/pandas-ai | data-science | 1,023 | Inconsistent generation response | ### System Info
PandasAI version = "1.5.15"
Python version = 3.9.0
langchain version = 0.1.9
### 🐛 Describe the bug
When using the PandasAI_Agent method from the pandasai library in combination with LangChain models the results generated are inconsistent. This seems to be a problem with all models tested (everything from Mixtral 8x7b to Llama 2 70b, Gemma 7b, StarcoderPlus, and similar LLMs) except GPT-4.
```
import pandas as pd
from pandasai import Agent as PandasAI_Agent
from langchain_nvidia_ai_endpoints import ChatNVIDIA
os.environ["NVIDIA_API_KEY"] =#api key neccessary is not already set as env var
df2 = pd.DataFrame({
"Quarter" : [ "Q2", "Q3", "Q4", "Q1", "Q2", "Q3", "Q4", "Q1", "Q2", "Q3" ]
,"Date" : ["31-Jul-21", "31-Oct-21", "31-Jan-22", "30-Apr-22", "31-Jul-22", "31-Oct-22", "31-Jan-23", "30-Apr-23", "31-Jul-23", "31-Oct-23" ]
,"Revenue GAAP": [6543.0, 7873.0, 7603.0, 8218.0, 6704.0, 5931.0, 601.0, 10192.0, 18507.0, 20120.0]
})
#add prompt
template = "You are a data analyst and must answer the questions by using the given dataframe and extracting data from it. {query}"
data_retrieval_prompt = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template(template)
],
input_variables=["query"],
)
#create llm agent
llm= ChatNVIDIA(model="playground_mixtral_8x7b", temperature=0)
#add config to dictionary
agent_config= {
'llm': llm,
"enable_cache": False
}
#crete agent
agent_data_retrieval = PandasAI_Agent([df2], config=agent_config, memory_size=0)
```
```
sentence = "Show quarterly revenue over the past 5 quarters."
_input = data_retrieval_prompt.format_prompt(query = sentence, dataframe=df2).to_string()
pandasai.clear_cache()
resp = agent_data_retrieval.chat(_input)
print(resp)
```
Output: Quarterly revenue over the past 5 quarters: 50801.0
```
sentence = "Show quarterly revenue over the past 5 quarters."
_input = data_retrieval_prompt.format_prompt(query = sentence, dataframe=df2).to_string()
pandasai.clear_cache()
resp = agent_data_retrieval.chat(_input)
print(resp)
```
Output: 51365.0 | closed | 2024-03-12T05:11:21Z | 2024-06-20T16:04:08Z | https://github.com/sinaptik-ai/pandas-ai/issues/1023 | [] | Marcus-M1999 | 2 |
sktime/pytorch-forecasting | pandas | 1,689 | [MNT] move linting to `ruff`, resolve strange linting behaviour | We should move the linting to `ruff` - good first issue for a maintenance affine contributor.
There are also some strange behaviours in the precommits which I cannot pinpoint, e.g., the linter insisting on long single lines instead of line breaks where they would be appropriate. Though I would hope this is resolved as a side effect of moving to `ruff`. | closed | 2024-09-25T08:40:38Z | 2024-10-18T05:12:40Z | https://github.com/sktime/pytorch-forecasting/issues/1689 | [
"enhancement",
"good first issue",
"maintenance"
] | fkiraly | 10 |
PaddlePaddle/PaddleHub | nlp | 1,682 | The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object | 1）PaddleHub2.1.1和PaddlePaddle2.1.3
2）AMD显卡，windows环境，conda配置CPU环境
3）想直接调用官方的模型来用，导入模块Module都报同个错误
The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object
W1103 02:49:23.760025 13752 analysis_predictor.cc:1183] Deprecated. Please use CreatePredictor instead.
(NotFound) Cannot open file C:\Users\娜娜\.paddlehub\modules\deeplabv3p_xception65_humanseg\deeplabv3p_xception65_humanseg_model/__model__, please confirm whether the file is normal.
[Hint: Expected static_cast<bool>(fin.is_open()) == true, but received static_cast<bool>(fin.is_open()):0 != true:1.] (at C:\home\workspace\Paddle_release\paddle\fluid\inference\api\analysis_predictor.cc:953)
| open | 2021-11-02T18:53:21Z | 2021-11-18T09:49:18Z | https://github.com/PaddlePaddle/PaddleHub/issues/1682 | [
"installation"
] | 1372646599 | 1 |
CorentinJ/Real-Time-Voice-Cloning | pytorch | 640 | Use venvs for program due to outdated dependancies | I noticed this program has outdated dependancies. A few suggestions:
- Use a venv to isolate the packages from the main Python instance
Ooooor...
- Update your dependancies
Thank you for your time, this looks awesome.
(Might be related to #638, #639) | closed | 2021-01-24T01:56:51Z | 2021-02-17T20:39:20Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/640 | [] | tomodachi94 | 3 |
seleniumbase/SeleniumBase | pytest | 3,151 | How disable notifications ? (popups, cookies ...) | Hello !
I want to disable notifications,
I tried this command
```
from seleniumbase import Driver
self.driver = Driver(
browser = 'chrome',
uc=True,
headless=True,
chromium_arg="--disable-extensions, --disable-gpu, --disable-dev-shm-usage, --disable-notifications, --hide-scrollbars, --disabled-new-style-notification, --allow-silent-push"
)
```
But it's not working ...
I tried on this URL : https://www.nvidia.com/fr-fr/
| closed | 2024-09-20T12:11:48Z | 2024-10-01T10:21:04Z | https://github.com/seleniumbase/SeleniumBase/issues/3151 | [
"question",
"UC Mode / CDP Mode"
] | JonasZaoui2 | 8 |
nltk/nltk | nlp | 2,848 | Invalid levenstein distance for duplicated letters | When a letter is duplicated,
When transpositions is allowed,
When a duplicated letter is present on the right argument,
Then the duplicated letter does not contribute to the distance which is wrong - should be 1 (deletion).
```python3
from nltk.distance import edit_distance
edit_distance("duuplicated", "duplicated", transpositions=False)
edit_distance("duplicated", "duuplicated", transpositions=False)
edit_distance("duuplicated", "duplicated", transpositions=True)
# all return 1 - correct
edit_distance("duplicated", "duuplicated", transpositions=True)
# returns 0 - incorrect
```
(tested on version 3.6.4)
I believe this is a bug introduced by https://github.com/nltk/nltk/pull/2736/files.
| closed | 2021-10-07T11:32:41Z | 2021-10-27T05:07:52Z | https://github.com/nltk/nltk/issues/2848 | [
"bug",
"metrics"
] | p9f | 0 |
piskvorky/gensim | machine-learning | 2,928 | The type information about job_queue in word2vec is wrong | <!--
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- Use the [Gensim mailing list](https://groups.google.com/forum/#!forum/gensim) to ask general or usage questions. Github issues are only for bug reports.
- Check [Recipes&FAQ](https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ) first for common answers.
Github bug reports that do not include relevant information and context will be closed without an answer. Thanks!
-->
#### Problem description
`_worker_loop` and `_job_producer` say that the `job_queue` element is a `(list of object, dict of (str, int))` type, when in fact it appears to be a `(list of object, float)` type.
Is this a statement for a future change, or is it a mistake?
#### Steps/code/corpus to reproduce
```python
def _worker_loop(self, job_queue, progress_queue):
"""Train the model, lifting batches of data from the queue.
This function will be called in parallel by multiple workers (threads or processes) to make
optimal use of multicore machines.
Parameters
----------
job_queue : Queue of (list of objects, (str, int))
A queue of jobs still to be processed. The worker will take up jobs from this queue.
Each job is represented by a tuple where the first element is the corpus chunk to be processed and
the second is the dictionary of parameters.
```
but
```python
def _job_producer(self, data_iterator, job_queue, cur_epoch=0, total_examples=None, total_words=None):
...
next_job_params = self._get_job_params(cur_epoch)
job_no = 0
for data_idx, data in enumerate(data_iterator):
data_length = self._raw_word_count([data])
# can we fit this sentence into the existing job batch?
if batch_size + data_length <= self.batch_words:
# yes => add it to the current job
job_batch.append(data)
batch_size += data_length
else:
job_no += 1
job_queue.put((job_batch, next_job_params))
```
In `_get_job_params`
```python
def _get_job_params(self, cur_epoch):
"""Get the learning rate used in the current epoch.
Parameters
----------
cur_epoch : int
Current iteration through the corpus
Returns
-------
float
The learning rate for this epoch (it is linearly reduced with epochs from `self.alpha` to `self.min_alpha`).
"""
alpha = self.alpha - ((self.alpha - self.min_alpha) * float(cur_epoch) / self.epochs)
return alpha
```
#### Versions
```python
Darwin-19.6.0-x86_64-i386-64bit
Python 3.7.4 (default, Jan 24 2020, 20:34:38)
[Clang 11.0.0 (clang-1100.0.33.16)]
Bits 64
NumPy 1.19.1
SciPy 1.5.2
gensim 4.0.0.dev0
```
| closed | 2020-08-31T10:24:05Z | 2020-09-08T07:22:47Z | https://github.com/piskvorky/gensim/issues/2928 | [] | lunastera | 3 |
Avaiga/taipy | automation | 2,302 | [🐛 BUG] Issue when rebuilding bar charts | ### What went wrong? 🤔
I'm using Taipy 4.0.1
I created a bar chart, where I want a variable to determine the y axis, with always the same x. For example, I want to display the sales for a list of products, but sometimes I want the number of items sold, and sometimes I want the total amount in money units. And I can change the metric to use in y with a toggle button.
While I don't get log errors, the chart has a weird behavior, like if it resets the index or something like that.
### Expected Behavior
The x axis shouldn't change, and the y axis should show the values of the selected column
### Steps to Reproduce Issue
```python
import pandas as pd
import taipy as tp
import taipy.gui.builder as tgb
from taipy.gui import Gui
df = pd.DataFrame(
{
"class": ["a", "b", "c", "d", "e"],
"number_sales": [100, 200, 150, 180, 220],
"amount_sales": [1503, 2560, 2009, 2302, 2708],
}
)
sales_display = "number_sales"
with tgb.Page() as bug_chart:
tgb.toggle("{sales_display}", lov=["number_sales", "amount_sales"])
tgb.chart(
data="{df}",
type="bar",
x="class",
y="{sales_display}",
rebuild=True,
)
gui = Gui(page=bug_chart)
gui.run(rebuild=True)
```
### Runtime Environment
Windows 10, I used Python 12 within Anaconda, Taipy 4.0.1
### Browsers
Chrome
### OS
Windows
### Version of Taipy
4.0.1
### Acceptance Criteria
- [ ] A unit test reproducing the bug is added.
- [ ] Any new code is covered by a unit tested.
- [ ] Check code coverage is at least 90%.
- [ ] The bug reporter validated the fix.
- [ ] Related issue(s) in taipy-doc are created for documentation and Release Notes are updated.
### 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 | 2024-12-03T18:33:50Z | 2024-12-18T13:39:12Z | https://github.com/Avaiga/taipy/issues/2302 | [
"🟥 Priority: Critical",
"🖰 GUI",
"💥Malfunction"
] | enarroied | 1 |
sammchardy/python-binance | api | 1,346 | Binance API Exception: APIError(code=0): Invalid JSON error message from Binance | When i run my code it's fine, but when about 1-2 hour i got the error. Can someone help me please?
| open | 2023-08-07T01:26:39Z | 2023-12-21T16:41:07Z | https://github.com/sammchardy/python-binance/issues/1346 | [] | ArieArepunk | 3 |
marshmallow-code/apispec | rest-api | 603 | Multiple schemas resolved to the name `xxx` | _Note: I searched for a while for the solution, this issue may help other people_
The resources exposed by my API endpoints are all under the key `data`, for example:
```
GET /endpoint
{
"data": {
"id": xxx,
"name": xxx
}
}
```
To avoid code duplication, I use a function taking a marshmallow `Schema` as argument and returning it wrapped by a "DataSchema", such as:
```python
def wrap(Class):
class DataSchema(Schema):
wrapped = fields.Nested(Class)
return DataSchema
```
apispec generates the following warning: ```UserWarning: Multiple schemas resolved to the name Data. The name has been modified. Either manually add each of the schemas with a different name or provide a custom schema_name_resolver.```
Fully working example:
```python
from apispec import APISpec
from apispec.ext.marshmallow import MarshmallowPlugin
from apispec_webframeworks.flask import FlaskPlugin
from flask import Flask
from marshmallow import Schema, fields, class_registry
# Create an APISpec
spec = APISpec(
title="Swagger Petstore",
version="1.0.0",
openapi_version="3.0.2",
plugins=[FlaskPlugin(), MarshmallowPlugin()],
)
def wrap(Class):
class DataSchema(Schema):
wrapped = fields.Nested(Class)
return DataSchema
class CategorySchema(Schema):
id = fields.Int()
name = fields.Str(required=True)
class PetSchema(Schema):
category = fields.List(fields.Nested(CategorySchema))
name = fields.Str()
WrappedPetSchema = wrap(PetSchema())
class_registry.register('WrappedPetSchema', WrappedPetSchema)
WrappedCategorySchema = wrap(CategorySchema())
class_registry.register('WrappedCategorySchema', WrappedCategorySchema)
# Optional Flask support
app = Flask(__name__)
@app.route("/random")
def views():
"""Example of a view using the wrapped schema for documentation.
---
get:
description: Get a random pet
security:
- ApiKeyAuth: []
responses:
200:
content:
application/json:
schema: WrappedPetSchema
post:
description: Create
security:
- ApiKeyAuth: []
responses:
200:
content:
application/json:
schema: WrappedCategorySchema
"""
pass
# Register the path and the entities within it
with app.test_request_context():
spec.path(view=views)
``` | closed | 2020-10-14T10:13:32Z | 2020-10-30T11:52:51Z | https://github.com/marshmallow-code/apispec/issues/603 | [] | brmzkw | 5 |
zappa/Zappa | flask | 393 | [Migrated] KeyError when there are no remote aws lambda env vars | Originally from: https://github.com/Miserlou/Zappa/issues/987 by [yuric](https://github.com/yuric)
## Context
Current implementation raises a `KeyError` when there are no remote lambda env variables etc with the (["Environment"]) key is missing from when `lambda_client.get_function_configuration(FunctionName=function_name)` returns. We can check the key ["Environment"] exists to fix this.
## Expected Behavior
It should handle the merging of aws_environement_variables even if the are no env vars or the Environement key is missing in the remote server. If there is nothing to replace I would expect it to just add the new ones.
## Actual Behavior
It raises a `KeyError` when there are no remote lambda env variables with the Environment key missing. Stops the zappa update.
## Possible Fix
Check for that key (["Environment"]) exists.
## Steps to Reproduce
1. Try `zappa update {env}` where there are no ["Environment"] settings key.
| closed | 2021-02-20T08:27:51Z | 2022-07-16T07:44:28Z | https://github.com/zappa/Zappa/issues/393 | [] | jneves | 1 |
sunscrapers/djoser | rest-api | 221 | [RFC] Rename the repository | Hi, in this kinda RFC issue I'd like to propose a respository name change. There are quite few reasons to do so:
1. People have a hard time searching and finding our project, because the name is kinda out of this world (https://en.wikipedia.org/wiki/Djoser)
2. The name is short, however it might not be the easiest one to remember.
3. SEO is almost non-existent and it would take a great effort to improve it.
4. For some time it was not being actively maintained and it might have brought a bad reputation.
At the moment I do not have a proposition for a new name. First thing I'd like to do is to find out what everyone is thinking about the idea. | closed | 2017-08-02T10:06:06Z | 2017-10-30T12:08:40Z | https://github.com/sunscrapers/djoser/issues/221 | [] | pszpetkowski | 12 |
paulbrodersen/netgraph | matplotlib | 12 | interactive graph not working when changing shape | I created a graph with networkx 2.1. Then, I used netgraph 3.1.2 to display my graph in Jupiter Notebook (using Python 3.5) as an Interactive graph, when I want to use a different shape for different nodes, the nodes aren't moving unless they are circles.
I tried both changing all nodes to different shapes, and having some circles and some different shapes. The selection (multiple nodes) functionality is no longer functional when I change shapes too. (I'm guessing it has something to do with the node creation that is being somehow altered) | closed | 2018-06-16T22:19:30Z | 2018-06-18T14:59:12Z | https://github.com/paulbrodersen/netgraph/issues/12 | [] | godinezmaciask | 1 |
matterport/Mask_RCNN | tensorflow | 2,083 | Overfitting altough using Augmentation | Hey Guys,
im training the Mask R-CNN Model on my own Dataset consisting of 440 Train images and 60 Validation images. The Goal is to detect structurally Damaged Areas of Bridges, Railings and so on.
For That im using 4 classes. Eventough im using this code of Augmentation:
`max_augs = 3
augmentation = imgaug.augmenters.SomeOf((0, max_augs), [
imgaug.augmenters.Fliplr(0.5),
imgaug.augmenters.Flipud(0.5),
imgaug.augmenters.OneOf([imgaug.augmenters.Affine(rotate=30 * i) for i in range(0, 12)]),
imgaug.augmenters.Affine(scale={"x": (0.8, 1.2), "y": (0.8, 1.2)}),
imgaug.augmenters.Add((-40, 40)),
imgaug.augmenters.Multiply((0.8, 1.5)),
imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0))
])`
it doesnt train really well... Im training on a ResNet101 which is pretrained on the ImageNet Dataset
When i plot the loss and the val Loss i get these results:
This is the code in my main class:
`class BridgesConfig(Config):
NAME="bridges"
#Because of weaker GPU:
IMAGES_PER_GPU = 2
# Number of classes (including background):
NUM_CLASSES = 1 + 4 # Background + TK + B_fehlend + B_korrodiert + B_Blasen
# Number of training steps per epoch
STEPS_PER_EPOCH = 520
VALIDATION_STEPS = 62
BACKBONE = "resnet101"
RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512)
USE_MINI_MASK = True
# Skip detections with <70% confidence
DETECTION_MIN_CONFIDENCE = 0
GPU_COUNT = 1` | open | 2020-04-03T08:08:39Z | 2024-01-31T21:46:06Z | https://github.com/matterport/Mask_RCNN/issues/2083 | [] | WhyWouldYouTilt | 3 |
reloadware/reloadium | flask | 67 | Mac 13 M1 and reloadium 9.0 installs x64 version | `file ~/.reloadium/package/3.10/reloadium/corium.cpython-310-darwin.so`
~/.reloadium/package/3.10/reloadium/corium.cpython-310-darwin.so: Mach-O 64-bit dynamically linked shared library x86_64
`file ~/Downloads/reloadium4/lib/reloadium-0.9.0/META-INF/wheels/reloadium-0.9.5-cp310-cp310-macosx_12_0_arm64/reloadium/corium.cpython-310-darwin.so`
~/Downloads/reloadium4/lib/reloadium-0.9.0/META-INF/wheels/reloadium-0.9.5-cp310-cp310-macosx_12_0_arm64/reloadium/corium.cpython-310-darwin.so: Mach-O 64-bit dynamically linked shared library arm64
I tried to manually install with
`pip install ~/Downloads/reloadium/lib/reloadium-0.9.0/META-INF/wheels/reloadium-0.9.5-cp310-cp310-macosx_12_0_arm64.whl` | closed | 2022-11-21T21:52:41Z | 2022-11-29T23:10:24Z | https://github.com/reloadware/reloadium/issues/67 | [] | siilats | 2 |
biolab/orange3 | data-visualization | 6,121 | AttributeError: module 'xlrd' has no attribute 'Book' | <!--
Thanks for taking the time to report a bug!
If you're raising an issue about an add-on (i.e., installed via Options > Add-ons), raise an issue in the relevant add-on's issue tracker instead. See: https://github.com/biolab?q=orange3
To fix the bug, we need to be able to reproduce it. Please answer the following questions to the best of your ability.
-->
When import Orange model arise " AttributeError: module 'xlrd' has no attribute 'Book' "
**What's wrong?**
<!-- Be specific, clear, and concise. Include screenshots if relevant. -->
<!-- If you're getting an error message, copy it, and enclose it with three backticks (```). -->
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-2-b46b66c87f2c> in <module>
----> 1 import Orange
2 import matplotlib.pyplot as plt
3 names = ["first", "third", "second", "fourth" ]
4 avranks = [1.9, 3.2, 2.8, 3.3 ]
5 cd = Orange.evaluation.compute_CD(avranks, 30) #tested on 30 datasets
D:\Users\admin\anaconda3\lib\site-packages\Orange\__init__.py in <module>
2 # pylint: disable=wrong-import-position,wrong-import-order
3
----> 4 from Orange import data
5
6 from .misc.lazy_module import _LazyModule
D:\Users\admin\anaconda3\lib\site-packages\Orange\data\__init__.py in <module>
8 from .table import *
9 from .io_util import *
---> 10 from .io import *
11 from .filter import *
12 from .pandas_compat import *
D:\Users\admin\anaconda3\lib\site-packages\Orange\data\io.py in <module>
350
351
--> 352 class XlsReader(_BaseExcelReader):
353 """Reader for .xls files"""
354 EXTENSIONS = ('.xls',)
D:\Users\admin\anaconda3\lib\site-packages\Orange\data\io.py in XlsReader()
360
361 @property
--> 362 def workbook(self) -> xlrd.Book:
363 if not self._workbook:
364 self._workbook = xlrd.open_workbook(self.filename, on_demand=True)
AttributeError: module 'xlrd' has no attribute 'Book'
**How can we reproduce the problem?**
<!-- Upload a zip with the .ows file and data. -->
<!-- Describe the steps (open this widget, click there, then add this...) -->
**What's your environment?**
<!-- To find your Orange version, see "Help → About → Version" or `Orange.version.full_version` in code -->
- Operating system: window 10, anaconda3
- Orange version: Version: 3.32.0
- How you installed Orange: pip install Orange3
| closed | 2022-09-04T02:33:21Z | 2022-09-30T14:06:50Z | https://github.com/biolab/orange3/issues/6121 | [
"bug report"
] | vengozhang | 2 |
sktime/sktime | scikit-learn | 8,016 | [BUG] AutoEnsembleForecaster turns global forecasters into local forecasters | **Describe the bug**
When using `AutoEnsembleForecaster` with global forecasters via `make_reduction(pooling='global')` on multi index data, the `make_reduction` forecaster is fit locally per instance instead of on the whole dataset. I will attempt to display this using an sklearn`RandomForestRegressor` as the estimator, and then printing the `._n_samples` of the fit model
**To Reproduce**
```python
import numpy as np
import pandas as pd
from sktime.forecasting.exp_smoothing import ExponentialSmoothing
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.compose import make_reduction
from sklearn.ensemble import RandomForestRegressor
from sktime.forecasting.compose import AutoEnsembleForecaster
# --- Create a hierarchical multi-index dataset ---
# We want the multi-index order to be: region, store, date (with date as the last level)
regions = ["North", "South"]
stores = ["Store1", "Store2"]
date_rng = pd.date_range(start="2020-01-01", periods=100, freq="D")
# Create the multi-index
multi_index = pd.MultiIndex.from_product(
[regions, stores, date_rng],
names=["region", "store", "date"]
)
# Generate random data and convert to a DataFrame.
y_multi = pd.DataFrame({"value": np.random.randn(len(multi_index))}, index=multi_index)
# --- Group the data using all index levels and sum the values ---
y_train = y_multi.groupby(level=y_multi.index.names).sum()
# --- Build the global forecaster using make_reduction with RandomForest ---
global_regressor = RandomForestRegressor(n_estimators=100, random_state=42)
global_forecaster = make_reduction(
estimator=global_regressor,
strategy="recursive",
window_length=3,
pooling='global'
)
local_forecaster = ExponentialSmoothing()
# --- Combine forecasters using AutoEnsembleForecaster ---
ensemble = AutoEnsembleForecaster(
forecasters=[("global", global_forecaster), ("local", local_forecaster)]
)
# --- Fit the ensemble forecaster on the training data ---
ensemble.fit(y_train)
print('auto ensemble forecaster sample size:',ensemble.forecasters_.loc[('North', 'Store1')]['forecasters'].forecasters_[0].estimator_._n_samples)
global_forecaster.fit(y_train)
print('global forecaster sample size:',global_forecaster.estimator_._n_samples)
```
**Expected behavior**
auto ensemble forecaster sample size: 97
global forecaster sample size: 388
**Versions**
<details>
System:
python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:41:22) [Clang 13.0.1 ]
machine: macOS-15.3.2-arm64-arm-64bit
Python dependencies:
pip: 22.2.1
sktime: 0.36.0
sklearn: 1.6.1
skbase: 0.12.0
numpy: 1.26.4
scipy: 1.15.2
pandas: 2.2.3
matplotlib: 3.10.1
joblib: 1.4.2
numba: 0.61.0
statsmodels: 0.14.4
pmdarima: 2.0.4
statsforecast: 2.0.1
tsfresh: None
tslearn: None
torch: None
tensorflow: None
</details>
| open | 2025-03-19T19:58:32Z | 2025-03-19T20:15:04Z | https://github.com/sktime/sktime/issues/8016 | [
"bug",
"module:forecasting"
] | bruscaj | 0 |
psf/requests | python | 6,433 | GBK Encoding Error | <!-- Summary. -->
I encounter an encoding error when use requests to send request (method:post&get).
## Expected Result
<!-- What you expected. -->
## Actual Result
<!-- What happened instead. -->
The return text is wrong.
Exactly, the ret.content is wrong, too.
I think the error happens when reading content.
## Reproduction Steps
```python
import requests
print(requests.post(url, params={'method':'pageInfo'}, data={'queryString':queryString}).text)
# Note: My URL is a local area network website, and it send data with gbk encoding.
```
## System Information
$ python -m requests.help
```json
{
"chardet": {
"version": null
},
"charset_normalizer": {
"version": "2.1.1"
},
"cryptography": {
"version": ""
},
"idna": {
"version": "3.4"
},
"implementation": {
"name": "CPython",
"version": "3.11.1"
},
"platform": {
"release": "10",
"system": "Windows"
},
"pyOpenSSL": {
"openssl_version": "",
"version": null
},
"requests": {
"version": "2.29.0"
},
"system_ssl": {
"version": "1010111f"
},
"urllib3": {
"version": "1.26.13"
},
"using_charset_normalizer": true,
"using_pyopenssl": false
}
```
<!-- This command is only available on Requests v2.16.4 and greater. Otherwise,
please provide some basic information about your system (Python version,
operating system, &c). -->
| closed | 2023-04-27T16:02:48Z | 2024-04-27T00:04:05Z | https://github.com/psf/requests/issues/6433 | [] | liyanes | 1 |
BeanieODM/beanie | pydantic | 859 | Add Progress Indicator for Schema Migrations | Hello Beanie Team,
While migrating large datasets using Beanie we face challenges in monitoring the migration progress. A real-time progress indicator would be extremely helpful for better time and workflow management.
Feature Request:
- **Real-Time Progress Updates:** A progress indicator or percentage display showing the current status of the migration.
- **Data Metrics:** Total number of records to be migrated, migrated so far, and remaining.
- **Error and Success Logging:** For efficient troubleshooting and confirmation of successful migrations.
_Originally posted by @aakrem in https://github.com/roman-right/beanie/discussions/858_ | open | 2024-02-13T10:39:53Z | 2024-12-08T21:53:13Z | https://github.com/BeanieODM/beanie/issues/859 | [
"feature request"
] | aakrem | 1 |
xinntao/Real-ESRGAN | pytorch | 587 | Does anyone know an AI Image Upscaler that work great for Text ? | open | 2023-03-14T14:41:06Z | 2023-03-14T20:58:46Z | https://github.com/xinntao/Real-ESRGAN/issues/587 | [] | brokeDude2901 | 2 | |
postmanlabs/httpbin | api | 226 | Server Sent Events | An API for opening an HTTP connection for receiving push notifications from a server in the form of DOM events.
Specification: http://www.w3.org/TR/eventsource/
This is a feature request. I wrote up some code but could not get it to work.
``` python
@app.route('/sse')
def sse():
"""Sends a stream of Server Sent Events (SSE)."""
args = CaseInsensitiveDict(request.args.items())
pause = int(args.get('pause', 2000))
name = args.get('name')
def generate_events():
while True:
timestamp = time.strftime('%Y-%m-%d %H:%M:%S')
event = []
if name:
event.append(u"event: " + name)
event.append(u"data: " + timestamp)
yield (u"\n".join(event) + u"\n").encode('utf-8')
time.sleep(pause)
response = Response(generate_events(), headers={
"Content-Type": "text/event-stream",
"Cache-Control": "no-cache"
})
return response
```
| closed | 2015-04-27T17:44:55Z | 2018-04-26T17:51:06Z | https://github.com/postmanlabs/httpbin/issues/226 | [
"feature-request"
] | vanillajonathan | 1 |
youfou/wxpy | api | 415 | 各位都怎么搞能登网页版的微信啊 | 微信网页版被封了，登不上去，想入门都没法入。。各位老哥都是怎么搞到能登录网页版的微信的呢？ | open | 2019-10-18T01:17:03Z | 2020-04-30T16:54:00Z | https://github.com/youfou/wxpy/issues/415 | [] | Xutongcc | 3 |
eriklindernoren/ML-From-Scratch | data-science | 58 | l1 regularization | Shouldn't `class l1_regularization()` be defined as `self.alpha * np.linalg.norm(w, 1)` instead of `self.alpha * np.linalg.norm(w)`? | open | 2019-09-20T21:43:19Z | 2022-10-26T05:08:10Z | https://github.com/eriklindernoren/ML-From-Scratch/issues/58 | [] | forcoogle | 2 |
gradio-app/gradio | data-visualization | 10,723 | Dataframe examples problem | ### Describe the bug
The code below that uses Gradio dataframe with pandas dataframe examples used to work but I tried it on Hugging Face & it doesn't work anymore. It doesn't work with an older Gradio version 3.50.2 either. When I tried it with version 3.50.2, Gradio treated the first row of the pandas data frame as string / header but it shouldn't since Xhpd=pd.read_csv("data_NB.csv",header=None)
Any idea how can I fix this problem?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from copy import deepcopy
import time
import gradio as gr
plt.rcParams['figure.figsize'] = [10, 10]
plt.rcParams['figure.dpi'] = 100
Xhpd=pd.read_csv("data_NB.csv",header=None)
yhpd=pd.read_csv("labels_NB.csv",header=None)
def visualise_gr(models,names,lsData,inTitle="Data Set",kNum=200,th=0.001):
fig=plt.figure()
plt.scatter(lsData[0][:,0],lsData[0][:,1], color='red')
plt.scatter(lsData[1][:,0],lsData[1][:,1], color='blue')
plt.xticks(range(0,11))
plt.yticks(range(0,11))
plt.xlabel("X")
plt.ylabel("Y")
plt.grid()
plt.title(inTitle)
#
plt.axis('scaled')
print("visualize gr")
return fig
def user_greeting2(inLogregC,inGNBC,inM,dfData,dfLabs):
X=dfData.to_numpy(dtype=np.float64)
y=dfLabs.to_numpy(dtype=np.float64)
y=y.reshape((y.shape[1],))
#
print("X dims",X.shape)
print("y dims",y.shape)
#
#
labnos=np.unique(y)
labnos=np.int32(labnos)
kK=labnos.shape[0]
lsData=[]
lslabs=[]
for k in range(kK):
yk=y[y==labnos[k]]
datak=X[y==labnos[k],:]
lsData.append(datak)
lslabs.append(yk)
#
strTitle='Title'
fig2=visualise_gr([],[],lsData,inTitle=strTitle,th=0.001,kNum=90)
#
strTitle2='Title2'
fig3=visualise_gr([],[],lsData,inTitle=strTitle2,kNum=90)
#
fig1=visualise_gr([],[],lsData)
return fig1,fig2,fig3
#
def user_greeting1(inLogregC,inGNBC,inM,dfData,dfLabs):
#
print('dbg 1 in')
print(dfData)
print(dfLabs)
#
X=dfData.to_numpy(dtype=np.float64)
y=dfLabs.to_numpy(dtype=np.float64)
y=y.reshape((y.shape[1],))
#
print('dbg 1 after to_numpy')
print("X dims",X.shape)
print("y dims",y.shape)
#
labnos=np.unique(y)
labnos=np.int32(labnos)
kK=labnos.shape[0]
lsData=[]
lslabs=[]
for k in range(kK):
yk=y[y==labnos[k]]
datak=X[y==labnos[k],:]
lsData.append(datak)
lslabs.append(yk)
#
strTitle="Title1"
fig2=visualise_gr([],[],lsData,inTitle=strTitle,th=0.001,kNum=200)
#
strTitle2="Title2"
fig3=visualise_gr([],[],lsData,inTitle=strTitle2,kNum=200)
fig1=visualise_gr([],[],lsData)
return fig1,fig2,fig3
#
inputs1 = [ gr.Number(label='xxx C'),gr.Number(label='yyy C'),gr.Number(label='mmm'),\
gr.Dataframe(row_count = (3, "dynamic"), col_count=(4,"dynamic"), label="Labeled Data", interactive=1), \
gr.Dataframe(row_count = (1, "fixed"), col_count=(4,"dynamic"), label="Labels", interactive=1), \
]
outputs1=[gr.Plot(),gr.Plot(),gr.Plot()]
app1 = gr.Interface(fn = user_greeting1, inputs=inputs1, outputs=outputs1, examples =[['1.0','10.0','0.001', 'data_NB.csv', 'labels_NB.csv']])
inputs2 = [ gr.Number(label='xxx C'),gr.Number(label='yyy C'),gr.Number(label='mmm'),\
gr.Dataframe(row_count = (3, "dynamic"), col_count=(4,"dynamic"), label="Labeled Data", interactive=1), \
gr.Dataframe(row_count = (1, "fixed"), col_count=(4,"dynamic"), label="Labels", interactive=1), \
]
outputs2=[gr.Plot(),gr.Plot(),gr.Plot()]
app2 = gr.Interface(fn = user_greeting2, inputs=inputs2, outputs=outputs2,examples =[[1.0,1.0,0.001, Xhpd,yhpd]])
demo = gr.TabbedInterface([app1, app2], ["App1", "App2"])
#demo.launch(share=True)
demo.launch(share=False)
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from copy import deepcopy
import time
import gradio as gr
plt.rcParams['figure.figsize'] = [10, 10]
plt.rcParams['figure.dpi'] = 100
Xhpd=pd.read_csv("data_NB.csv",header=None)
yhpd=pd.read_csv("labels_NB.csv",header=None)
def visualise_gr(models,names,lsData,inTitle="Data Set",kNum=200,th=0.001):
fig=plt.figure()
plt.scatter(lsData[0][:,0],lsData[0][:,1], color='red')
plt.scatter(lsData[1][:,0],lsData[1][:,1], color='blue')
plt.xticks(range(0,11))
plt.yticks(range(0,11))
plt.xlabel("X")
plt.ylabel("Y")
plt.grid()
plt.title(inTitle)
#
plt.axis('scaled')
print("visualize gr")
return fig
def user_greeting2(inLogregC,inGNBC,inM,dfData,dfLabs):
X=dfData.to_numpy(dtype=np.float64)
y=dfLabs.to_numpy(dtype=np.float64)
y=y.reshape((y.shape[1],))
#
print("X dims",X.shape)
print("y dims",y.shape)
#
#
labnos=np.unique(y)
labnos=np.int32(labnos)
kK=labnos.shape[0]
lsData=[]
lslabs=[]
for k in range(kK):
yk=y[y==labnos[k]]
datak=X[y==labnos[k],:]
lsData.append(datak)
lslabs.append(yk)
#
strTitle='Title'
fig2=visualise_gr([],[],lsData,inTitle=strTitle,th=0.001,kNum=90)
#
strTitle2='Title2'
fig3=visualise_gr([],[],lsData,inTitle=strTitle2,kNum=90)
#
fig1=visualise_gr([],[],lsData)
return fig1,fig2,fig3
#
def user_greeting1(inLogregC,inGNBC,inM,dfData,dfLabs):
#
print('dbg 1 in')
print(dfData)
print(dfLabs)
#
X=dfData.to_numpy(dtype=np.float64)
y=dfLabs.to_numpy(dtype=np.float64)
y=y.reshape((y.shape[1],))
#
print('dbg 1 after to_numpy')
print("X dims",X.shape)
print("y dims",y.shape)
#
labnos=np.unique(y)
labnos=np.int32(labnos)
kK=labnos.shape[0]
lsData=[]
lslabs=[]
for k in range(kK):
yk=y[y==labnos[k]]
datak=X[y==labnos[k],:]
lsData.append(datak)
lslabs.append(yk)
#
strTitle="Title1"
fig2=visualise_gr([],[],lsData,inTitle=strTitle,th=0.001,kNum=200)
#
strTitle2="Title2"
fig3=visualise_gr([],[],lsData,inTitle=strTitle2,kNum=200)
fig1=visualise_gr([],[],lsData)
return fig1,fig2,fig3
#
inputs1 = [ gr.Number(label='xxx C'),gr.Number(label='yyy C'),gr.Number(label='mmm'),\
gr.Dataframe(row_count = (3, "dynamic"), col_count=(4,"dynamic"), label="Labeled Data", interactive=1), \
gr.Dataframe(row_count = (1, "fixed"), col_count=(4,"dynamic"), label="Labels", interactive=1), \
]
outputs1=[gr.Plot(),gr.Plot(),gr.Plot()]
app1 = gr.Interface(fn = user_greeting1, inputs=inputs1, outputs=outputs1, examples =[['1.0','10.0','0.001', 'data_NB.csv', 'labels_NB.csv']])
inputs2 = [ gr.Number(label='xxx C'),gr.Number(label='yyy C'),gr.Number(label='mmm'),\
gr.Dataframe(row_count = (3, "dynamic"), col_count=(4,"dynamic"), label="Labeled Data", interactive=1), \
gr.Dataframe(row_count = (1, "fixed"), col_count=(4,"dynamic"), label="Labels", interactive=1), \
]
outputs2=[gr.Plot(),gr.Plot(),gr.Plot()]
app2 = gr.Interface(fn = user_greeting2, inputs=inputs2, outputs=outputs2,examples =[[1.0,1.0,0.001, Xhpd,yhpd]])
demo = gr.TabbedInterface([app1, app2], ["App1", "App2"])
#demo.launch(share=True)
demo.launch(share=False)
[data_NB.csv](https://github.com/user-attachments/files/19066778/data_NB.csv)
[labels_NB.csv](https://github.com/user-attachments/files/19066780/labels_NB.csv)
### Screenshot
_No response_
### Logs
```shell
Traceback (most recent call last):
File "/home/user/app/app.py", line 114, in <module>
app2 = gr.Interface(fn = user_greeting2, inputs=inputs2, outputs=outputs2,examples =[[1.0,1.0,0.001, Xhpd,yhpd]])
File "/usr/local/lib/python3.10/site-packages/gradio/interface.py", line 546, in __init__
self.render_examples()
File "/usr/local/lib/python3.10/site-packages/gradio/interface.py", line 904, in render_examples
self.examples_handler = Examples(
File "/usr/local/lib/python3.10/site-packages/gradio/helpers.py", line 57, in create_examples
examples_obj = Examples(
File "/usr/local/lib/python3.10/site-packages/gradio/helpers.py", line 265, in __init__
self.dataset = components.Dataset(
File "/usr/local/lib/python3.10/site-packages/gradio/component_meta.py", line 181, in wrapper
return fn(self, **kwargs)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataset.py", line 118, in __init__
ex = component.as_example(ex)
File "/usr/local/lib/python3.10/site-packages/gradio/components/base.py", line 309, in as_example
return self.process_example(value)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataframe.py", line 507, in process_example
value_df_data = self.postprocess(value)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataframe.py", line 431, in postprocess
return DataframeData(
File "/usr/local/lib/python3.10/site-packages/pydantic/main.py", line 214, in __init__
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 2 validation errors for DataframeData
headers.0
Input should be a valid string [type=string_type, input_value=0, input_type=int]
For further information visit https://errors.pydantic.dev/2.10/v/string_type
headers.1
Input should be a valid string [type=string_type, input_value=1, input_type=int]
For further information visit https://errors.pydantic.dev/2.10/v/string_type
Traceback (most recent call last):
File "/home/user/app/app.py", line 114, in <module>
app2 = gr.Interface(fn = user_greeting2, inputs=inputs2, outputs=outputs2,examples =[[1.0,1.0,0.001, Xhpd,yhpd]])
File "/usr/local/lib/python3.10/site-packages/gradio/interface.py", line 546, in __init__
self.render_examples()
File "/usr/local/lib/python3.10/site-packages/gradio/interface.py", line 904, in render_examples
self.examples_handler = Examples(
File "/usr/local/lib/python3.10/site-packages/gradio/helpers.py", line 57, in create_examples
examples_obj = Examples(
File "/usr/local/lib/python3.10/site-packages/gradio/helpers.py", line 265, in __init__
self.dataset = components.Dataset(
File "/usr/local/lib/python3.10/site-packages/gradio/component_meta.py", line 181, in wrapper
return fn(self, **kwargs)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataset.py", line 118, in __init__
ex = component.as_example(ex)
File "/usr/local/lib/python3.10/site-packages/gradio/components/base.py", line 309, in as_example
return self.process_example(value)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataframe.py", line 507, in process_example
value_df_data = self.postprocess(value)
File "/usr/local/lib/python3.10/site-packages/gradio/components/dataframe.py", line 431, in postprocess
return DataframeData(
File "/usr/local/lib/python3.10/site-packages/pydantic/main.py", line 214, in __init__
validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self)
pydantic_core._pydantic_core.ValidationError: 2 validation errors for DataframeData
headers.0
Input should be a valid string [type=string_type, input_value=0, input_type=int]
For further information visit https://errors.pydantic.dev/2.10/v/string_type
headers.1
Input should be a valid string [type=string_type, input_value=1, input_type=int]
For further information visit https://errors.pydantic.dev/2.10/v/string_type
```
### System Info
```shell
Hugging Face
```
### Severity
Blocking usage of gradio | closed | 2025-03-04T08:37:49Z | 2025-03-09T09:18:15Z | https://github.com/gradio-app/gradio/issues/10723 | [
"bug",
"needs repro",
"💾 Dataframe"
] | caglarari | 5 |
amisadmin/fastapi-amis-admin | fastapi | 68 | sqlalchemy.exc.ArgumentError: Mapper mapped class UserRoleLink->auth_user_roles could not assemble any primary key columns for mapped table 'auth_user_roles' | 您好，我在使用的过程中有一点点问题，希望能得到您的思路指导。
报错信息如下：
```
INFO: Started reloader process [1] using StatReload
Process SpawnProcess-1:
Traceback (most recent call last):
File "/opt/conda/lib/python3.9/multiprocessing/process.py", line 315, in _bootstrap
self.run()
File "/opt/conda/lib/python3.9/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.9/site-packages/uvicorn/_subprocess.py", line 76, in subprocess_started
target(sockets=sockets)
File "/opt/conda/lib/python3.9/site-packages/uvicorn/server.py", line 60, in run
return asyncio.run(self.serve(sockets=sockets))
File "/opt/conda/lib/python3.9/asyncio/runners.py", line 44, in run
return loop.run_until_complete(main)
File "/opt/conda/lib/python3.9/asyncio/base_events.py", line 647, in run_until_complete
return future.result()
File "/opt/conda/lib/python3.9/site-packages/uvicorn/server.py", line 67, in serve
config.load()
File "/opt/conda/lib/python3.9/site-packages/uvicorn/config.py", line 477, in load
self.loaded_app = import_from_string(self.app)
File "/opt/conda/lib/python3.9/site-packages/uvicorn/importer.py", line 21, in import_from_string
module = importlib.import_module(module_str)
File "/opt/conda/lib/python3.9/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1030, in _gcd_import
File "<frozen importlib._bootstrap>", line 1007, in _find_and_load
File "<frozen importlib._bootstrap>", line 986, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 680, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 850, in exec_module
File "<frozen importlib._bootstrap>", line 228, in _call_with_frames_removed
File "/home/jovyan/./ui_server/main.py", line 7, in <module>
from fastapi_user_auth.auth import AuthBackend
File "/opt/conda/lib/python3.9/site-packages/fastapi_user_auth/auth/__init__.py", line 1, in <module>
from .auth import Auth as Auth
File "/opt/conda/lib/python3.9/site-packages/fastapi_user_auth/auth/auth.py", line 37, in <module>
from .backends.base import BaseTokenStore
File "/opt/conda/lib/python3.9/site-packages/fastapi_user_auth/auth/backends/base.py", line 3, in <module>
from fastapi_user_auth.auth.schemas import BaseTokenData
File "/opt/conda/lib/python3.9/site-packages/fastapi_user_auth/auth/schemas.py", line 5, in <module>
from .models import BaseUser, EmailMixin, PasswordMixin, UsernameMixin
File "/opt/conda/lib/python3.9/site-packages/fastapi_user_auth/auth/models.py", line 56, in <module>
class UserRoleLink(SQLModel, table=True):
File "/opt/conda/lib/python3.9/site-packages/sqlmodelx/main.py", line 182, in __init__
DeclarativeMeta.__init__(cls, classname, bases, dict_used, **kw)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_api.py", line 72, in __init__
_as_declarative(reg, cls, dict_)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_base.py", line 126, in _as_declarative
return _MapperConfig.setup_mapping(registry, cls, dict_, None, {})
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_base.py", line 177, in setup_mapping
return cfg_cls(registry, cls_, dict_, table, mapper_kw)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_base.py", line 326, in __init__
self._early_mapping(mapper_kw)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_base.py", line 209, in _early_mapping
self.map(mapper_kw)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/decl_base.py", line 1004, in map
mapper_cls(self.cls, self.local_table, **self.mapper_args),
File "<string>", line 2, in __init__
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/util/deprecations.py", line 298, in warned
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/mapper.py", line 685, in __init__
self._configure_pks()
File "/opt/conda/lib/python3.9/site-packages/sqlalchemy/orm/mapper.py", line 1370, in _configure_pks
raise sa_exc.ArgumentError(
sqlalchemy.exc.ArgumentError: Mapper mapped class UserRoleLink->auth_user_roles could not assemble any primary key columns for mapped table 'auth_user_roles'
```
我的环境：
`
FROM jupyterhub/k8s-singleuser-sample:1.2.0
`
我的pip包版本：
`
aiosqlite 0.17.0
sqlalchemy_database 0.1.0
sqlalchemy2-stubs 0.0.2a29
sqlmodel 0.0.8
sqlmodelx 0.0.4
sqlparse 0.4.3
` | closed | 2022-11-22T08:52:08Z | 2022-11-29T14:48:49Z | https://github.com/amisadmin/fastapi-amis-admin/issues/68 | [] | Mrzhangjwei | 1 |
sktime/sktime | scikit-learn | 7,054 | [ENH] Spatio-Temporal Clustering algorithms from the Cakmak et al benchmark | Hey there,
I am working with collective behavioral data. My goal is to cluster agents in space (ST) and time, so as input I have the coordinates of the agents at each time point and as output I want to have the label for each agent and each timestamp of the assignment to the cluster. See the figure for a graphical representation [adopted from 1]:
<img width="527" alt="image" src="https://github.com/user-attachments/assets/d779cfcf-7cf5-4331-a5f5-439057d80401">
[Here is the repository](https://github.com/eren-ck/spatio-temporal-clustering-benchmark) for the paper that implements most of the existing algorithms [2]. [Here is the dataset](https://zenodo.org/records/5495226) for the paper.
**Is your feature request related to a problem? Please describe.**
Implementation of the ST-clustering models
**Describe the solution you'd like**
Sktime interface for ST-clustering
**Additional context**
[This](https://github.com/GISerWang/Spatio-temporal-Clustering) github page may also be relevant.
**References**
1. Ansari, M. Y., Ahmad, A., Khan, S. S., Bhushan, G., & Mainuddin. (2020). Spatiotemporal clustering: A review. Artificial Intelligence Review, 53(4), 2381–2423. https://doi.org/10.1007/s10462-019-09736-1
2. Cakmak, E., Plank, M., Calovi, D. S., Jordan, A., & Keim, D. (2021). Spatio-temporal clustering benchmark for collective animal behavior. Proceedings of the 1st ACM SIGSPATIAL International Workshop on Animal Movement Ecology and Human Mobility, 5–8. https://doi.org/10.1145/3486637.3489487
| open | 2024-08-30T10:45:36Z | 2024-11-07T13:17:21Z | https://github.com/sktime/sktime/issues/7054 | [
"feature request",
"good first issue",
"implementing algorithms",
"module:clustering",
"enhancement"
] | vagechirkov | 8 |
biolab/orange3 | pandas | 6,343 | MONGO DB not running in my computer- windows 10 | Hi, I am trying to run mongodb for the first time on my computer but it is not working. Help, please!
C:\Users\Stephanny>mongo
'mongo' is not recognized as an internal or external command,
operable program or batch file.
C:\Users\Stephanny>mongod
{"t":{"$date":"2023-02-21T19:39:04.244-05:00"},"s":"I", "c":"NETWORK", "id":4915701, "ctx":"thread1","msg":"Initialized wire specification","attr":{"spec":{"incomingExternalClient":{"minWireVersion":0,"maxWireVersion":17},"incomingInternalClient":{"minWireVersion":0,"maxWireVersion":17},"outgoing":{"minWireVersion":6,"maxWireVersion":17},"isInternalClient":true}}}
{"t":{"$date":"2023-02-21T19:39:04.246-05:00"},"s":"I", "c":"CONTROL", "id":23285, "ctx":"thread1","msg":"Automatically disabling TLS 1.0, to force-enable TLS 1.0 specify --sslDisabledProtocols 'none'"}
{"t":{"$date":"2023-02-21T19:39:05.624-05:00"},"s":"I", "c":"NETWORK", "id":4648602, "ctx":"thread1","msg":"Implicit TCP FastOpen in use."}
{"t":{"$date":"2023-02-21T19:39:05.626-05:00"},"s":"I", "c":"REPL", "id":5123008, "ctx":"thread1","msg":"Successfully registered PrimaryOnlyService","attr":{"service":"TenantMigrationDonorService","namespace":"config.tenantMigrationDonors"}}
{"t":{"$date":"2023-02-21T19:39:05.626-05:00"},"s":"I", "c":"REPL", "id":5123008, "ctx":"thread1","msg":"Successfully registered PrimaryOnlyService","attr":{"service":"TenantMigrationRecipientService","namespace":"config.tenantMigrationRecipients"}}
{"t":{"$date":"2023-02-21T19:39:05.626-05:00"},"s":"I", "c":"REPL", "id":5123008, "ctx":"thread1","msg":"Successfully registered PrimaryOnlyService","attr":{"service":"ShardSplitDonorService","namespace":"config.tenantSplitDonors"}}
{"t":{"$date":"2023-02-21T19:39:05.627-05:00"},"s":"I", "c":"CONTROL", "id":5945603, "ctx":"thread1","msg":"Multi threading initialized"}
{"t":{"$date":"2023-02-21T19:39:05.628-05:00"},"s":"I", "c":"CONTROL", "id":4615611, "ctx":"initandlisten","msg":"MongoDB starting","attr":{"pid":9320,"port":27017,"dbPath":"C:/data/db/","architecture":"64-bit","host":"Stephanny"}}
{"t":{"$date":"2023-02-21T19:39:05.628-05:00"},"s":"I", "c":"CONTROL", "id":23398, "ctx":"initandlisten","msg":"Target operating system minimum version","attr":{"targetMinOS":"Windows 7/Windows Server 2008 R2"}}
{"t":{"$date":"2023-02-21T19:39:05.628-05:00"},"s":"I", "c":"CONTROL", "id":23403, "ctx":"initandlisten","msg":"Build Info","attr":{"buildInfo":{"version":"6.0.4","gitVersion":"44ff59461c1353638a71e710f385a566bcd2f547","modules":[],"allocator":"tcmalloc","environment":{"distmod":"windows","distarch":"x86_64","target_arch":"x86_64"}}}}
{"t":{"$date":"2023-02-21T19:39:05.629-05:00"},"s":"I", "c":"CONTROL", "id":51765, "ctx":"initandlisten","msg":"Operating System","attr":{"os":{"name":"Microsoft Windows 10","version":"10.0 (build 22621)"}}}
{"t":{"$date":"2023-02-21T19:39:05.629-05:00"},"s":"I", "c":"CONTROL", "id":21951, "ctx":"initandlisten","msg":"Options set by command line","attr":{"options":{}}}
{"t":{"$date":"2023-02-21T19:39:05.630-05:00"},"s":"I", "c":"STORAGE", "id":22270, "ctx":"initandlisten","msg":"Storage engine to use detected by data files","attr":{"dbpath":"C:/data/db/","storageEngine":"wiredTiger"}}
{"t":{"$date":"2023-02-21T19:39:05.631-05:00"},"s":"I", "c":"STORAGE", "id":22315, "ctx":"initandlisten","msg":"Opening WiredTiger","attr":{"config":"create,cache_size=7563M,session_max=33000,eviction=(threads_min=4,threads_max=4),config_base=false,statistics=(fast),log=(enabled=true,remove=true,path=journal,compressor=snappy),builtin_extension_config=(zstd=(compression_level=6)),file_manager=(close_idle_time=600,close_scan_interval=10,close_handle_minimum=2000),statistics_log=(wait=0),json_output=(error,message),verbose=[recovery_progress:1,checkpoint_progress:1,compact_progress:1,backup:0,checkpoint:0,compact:0,evict:0,history_store:0,recovery:0,rts:0,salvage:0,tiered:0,timestamp:0,transaction:0,verify:0,log:0],"}}
{"t":{"$date":"2023-02-21T19:39:06.042-05:00"},"s":"I", "c":"STORAGE", "id":4795906, "ctx":"initandlisten","msg":"WiredTiger opened","attr":{"durationMillis":411}}
{"t":{"$date":"2023-02-21T19:39:06.042-05:00"},"s":"I", "c":"RECOVERY", "id":23987, "ctx":"initandlisten","msg":"WiredTiger recoveryTimestamp","attr":{"recoveryTimestamp":{"$timestamp":{"t":0,"i":0}}}}
{"t":{"$date":"2023-02-21T19:39:06.048-05:00"},"s":"W", "c":"CONTROL", "id":22120, "ctx":"initandlisten","msg":"Access control is not enabled for the database. Read and write access to data and configuration is unrestricted","tags":["startupWarnings"]}
{"t":{"$date":"2023-02-21T19:39:06.049-05:00"},"s":"W", "c":"CONTROL", "id":22140, "ctx":"initandlisten","msg":"This server is bound to localhost. Remote systems will be unable to connect to this server. Start the server with --bind_ip <address> to specify which IP addresses it should serve responses from, or with --bind_ip_all to bind to all interfaces. If this behavior is desired, start the server with --bind_ip 127.0.0.1 to disable this warning","tags":["startupWarnings"]}
{"t":{"$date":"2023-02-21T19:39:06.051-05:00"},"s":"I", "c":"NETWORK", "id":4915702, "ctx":"initandlisten","msg":"Updated wire specification","attr":{"oldSpec":{"incomingExternalClient":{"minWireVersion":0,"maxWireVersion":17},"incomingInternalClient":{"minWireVersion":0,"maxWireVersion":17},"outgoing":{"minWireVersion":6,"maxWireVersion":17},"isInternalClient":true},"newSpec":{"incomingExternalClient":{"minWireVersion":0,"maxWireVersion":17},"incomingInternalClient":{"minWireVersion":17,"maxWireVersion":17},"outgoing":{"minWireVersion":17,"maxWireVersion":17},"isInternalClient":true}}}
{"t":{"$date":"2023-02-21T19:39:06.052-05:00"},"s":"I", "c":"REPL", "id":5853300, "ctx":"initandlisten","msg":"current featureCompatibilityVersion value","attr":{"featureCompatibilityVersion":"6.0","context":"startup"}}
{"t":{"$date":"2023-02-21T19:39:06.052-05:00"},"s":"I", "c":"STORAGE", "id":5071100, "ctx":"initandlisten","msg":"Clearing temp directory"}
{"t":{"$date":"2023-02-21T19:39:06.053-05:00"},"s":"I", "c":"CONTROL", "id":20536, "ctx":"initandlisten","msg":"Flow Control is enabled on this deployment"}
{"t":{"$date":"2023-02-21T19:39:06.184-05:00"},"s":"I", "c":"FTDC", "id":20625, "ctx":"initandlisten","msg":"Initializing full-time diagnostic data capture","attr":{"dataDirectory":"C:/data/db/diagnostic.data"}}
{"t":{"$date":"2023-02-21T19:39:06.187-05:00"},"s":"I", "c":"REPL", "id":6015317, "ctx":"initandlisten","msg":"Setting new configuration state","attr":{"newState":"ConfigReplicationDisabled","oldState":"ConfigPreStart"}}
{"t":{"$date":"2023-02-21T19:39:06.187-05:00"},"s":"I", "c":"STORAGE", "id":22262, "ctx":"initandlisten","msg":"Timestamp monitor starting"}
{"t":{"$date":"2023-02-21T19:39:06.189-05:00"},"s":"I", "c":"NETWORK", "id":23015, "ctx":"listener","msg":"Listening on","attr":{"address":"127.0.0.1"}}
{"t":{"$date":"2023-02-21T19:39:06.189-05:00"},"s":"I", "c":"NETWORK", "id":23016, "ctx":"listener","msg":"Waiting for connections","attr":{"port":27017,"ssl":"off"}}
| closed | 2023-02-22T00:44:38Z | 2023-02-22T22:46:09Z | https://github.com/biolab/orange3/issues/6343 | [] | StephyCamacho | 1 |
noirbizarre/flask-restplus | flask | 62 | @api.doc can't handle docstrings from decorated functions | I think I have spot a bug which operates as follows. If I pass the @api.doc decorator a function that has already been decorated several times, it fails to set its docstring in the swagger summary bar. Exemple :
```
@ns_groups.route(
'',
endpoint='group',
)
class Group(Resource):
@api.doc(parser=get_parser)
@auth.login_required
def get(self):
"""Retrieves a group"""
args = get_parser.parse_args()
... some more code
```
Will give me the correct Swagger documentation, whereas:
```
@api.doc(parser=delete_parser)
@auth.login_required
@admin_required
def delete(self):
"""Deletes a group
"""
args = delete_parser.parse_args()
... some more code
```
Will not set anything, regardless where in the decorator chain I put @api.doc.
Altough it works if I only use two decorators.
| closed | 2015-08-21T06:35:37Z | 2023-08-03T21:09:03Z | https://github.com/noirbizarre/flask-restplus/issues/62 | [] | thomas-maurice | 1 |
seleniumbase/SeleniumBase | web-scraping | 3,139 | It is unreasonable that the 'chromium_arg' parameter must be provided as a comma-separated list. | i can't use
--window-size=100,100
--window-position=100,100
and
--app=data:,
If using the following function will cause the window to flash.:
set_window_position(100,100)
set_window_size(100,100)
How to solve this problem? | closed | 2024-09-17T23:53:46Z | 2024-09-23T05:49:45Z | https://github.com/seleniumbase/SeleniumBase/issues/3139 | [
"duplicate",
"question"
] | WinXsp | 4 |
joerick/pyinstrument | django | 279 | replace deprecated appdirs with platformdirs | from https://github.com/ActiveState/appdirs
`Note: This project has been officially deprecated. You may want to check out https://pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years.`
relevant discussion: https://github.com/ActiveState/appdirs/issues/79
maintained package:
https://github.com/platformdirs/platformdirs
| open | 2023-11-02T20:00:18Z | 2023-11-02T22:30:02Z | https://github.com/joerick/pyinstrument/issues/279 | [] | woutervh | 1 |
mage-ai/mage-ai | data-science | 5,288 | [BUG] Can't define usage of reserved words on streaming data export | ### Mage version
0.9.72
### Describe the bug
I have a streaming pipeline that receives kafka messages, transforms it and then it goes to a postgres data exporter block (which is configured only with the yaml file).
Similar to other issues reported here I'm using some columns that it tries to change, for example `timestamp` throws this error:
```psycopg2.errors.UndefinedColumn: column "_timestamp" of relation "transactions" does not exist```
I tried to include `allow_reserved_words: true` in the yaml but I get: ```TypeError: PostgresConfig.__init__() got an unexpected keyword argument 'allow_reserved_words'```
Is there a way for me to still use this words in this setup?
### To reproduce
_No response_
### Expected behavior
_No response_
### Screenshots
_No response_
### Operating system
_No response_
### Additional context
_No response_ | closed | 2024-07-24T15:25:26Z | 2024-09-13T06:48:27Z | https://github.com/mage-ai/mage-ai/issues/5288 | [
"bug",
"good first issue"
] | MatheusAnciloto | 2 |
Miserlou/Zappa | django | 1,432 | Support AWS SSM Parameter store for Environment Variables | <!--- Provide a general summary of the issue in the Title above -->
SSM Allows the creation of SecureString's which are variables protected by a KMS string which allows you to store sensitive information in a secure manner.[1]
This is the preferred method of storing environment or other data in plaintext as it stays encrypted in-flight. | open | 2018-03-05T21:12:29Z | 2019-01-05T15:19:21Z | https://github.com/Miserlou/Zappa/issues/1432 | [] | OverlordQ | 7 |
AUTOMATIC1111/stable-diffusion-webui | deep-learning | 16,907 | [Feature Request]: Newer version of Python | ### Is there an existing issue for this?
- [x] I have searched the existing issues and checked the recent builds/commits
### What would your feature do ?
I was getting a little bit of help from Arch Linux community and when they saw I am installing Python 310 they frowned. I argued: "Big apps can't really catch up that fast to newer versions of libraries they are using. It break compatibility in nasty ways and there is tradeoff between fixing that and other stuff"
They said: "That argument falls kinda flat being python 310 is dead and eol for nearly 2 years already and even things like blender are on python 3.13"
### Proposed workflow
I is a software dependency related issue.
### Additional information
_No response_ | open | 2025-03-20T06:11:52Z | 2025-03-21T10:41:00Z | https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/16907 | [
"enhancement"
] | siakc | 2 |
Lightning-AI/pytorch-lightning | data-science | 19,983 | Another profiling tool is already active | ### Bug description
When I try to use profiler='advanced' when creating a trainer, this error will be raised inside trainer.fit():
```cmd
ValueError: Another profiling tool is already active
```
It will be ok if use profiler='simple'
### What version are you seeing the problem on?
master
### How to reproduce the bug
```python
trainer = L.Trainer(
default_root_dir=config.train.save_dir,
callbacks=[
ModelCheckpoint(
dirpath=config.train.save_dir,
every_n_train_steps=config.train.save_step,
save_top_k=config.train.save_ckpt_keep_num,
mode='max',
monitor='global_step'
),
ModelSummary(max_depth=9)
],
logger=WandbLogger(log_model="all"),
**config.train.trainer
)
if config.train.resume_from_ckpt:
trainer.fit(
model=model,
train_dataloaders=train_loader, # TODO: dose dataloader needed?
val_dataloaders=val_loader,
ckpt_path=config.train.resume_from_ckpt
)
else:
trainer.fit(
model=model,
train_dataloaders=train_loader,
val_dataloaders=val_loader
)
```
### Error messages and logs
```
# Error messages and logs here please
```
### Environment
<details>
<summary>Current environment</summary>
```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0):
#- Lightning App Version (e.g., 0.5.2):
#- PyTorch Version (e.g., 2.0):
#- Python version (e.g., 3.9):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
#- Running environment of LightningApp (e.g. local, cloud):
```
</details>
### More info
_No response_
cc @carmocca | open | 2024-06-17T10:21:22Z | 2024-07-13T00:56:44Z | https://github.com/Lightning-AI/pytorch-lightning/issues/19983 | [
"bug",
"help wanted",
"profiler",
"ver: 2.2.x"
] | zhaohm14 | 3 |
tflearn/tflearn | data-science | 282 | How to cite TFLearn? | Hi TFlearn Team,
Could you tell me how to cite TFlern library with bibtex or just mention using it in the literature?
Best,
Rui
| closed | 2016-08-15T16:58:20Z | 2016-08-16T09:25:29Z | https://github.com/tflearn/tflearn/issues/282 | [] | ruizhaogit | 2 |
IvanIsCoding/ResuLLMe | streamlit | 22 | does not creating star explanation in experience | open | 2024-04-09T16:35:46Z | 2024-04-09T16:35:46Z | https://github.com/IvanIsCoding/ResuLLMe/issues/22 | [] | unaisshemim | 0 | |
browser-use/browser-use | python | 786 | Cant open website issue in Web-UI | ### Bug Description
Hi, I managed install web-UI and the Deep Research part works. However when I try to run an agent. I get:
```
INFO [agent] 🚀 Starting task: got www.google.com and search tyres and show first result
INFO [src.agent.custom_agent]
📍 Step 1
WARNING [browser] Page load failed, continuing...
ERROR [agent] ❌ Result failed 1/3 times:
Browser.new_context: Target page, context or browser has been closed
INFO [src.agent.custom_agent]
📍 Step 1
WARNING [browser] Page load failed, continuing...
ERROR [agent] ❌ Result failed 2/3 times:
Browser.new_context: Target page, context or browser has been closed
INFO [src.agent.custom_agent]
📍 Step 1
WARNING [browser] Page load failed, continuing...
ERROR [agent] ❌ Result failed 3/3 times:
Browser.new_context: Target page, context or browser has been closed
ERROR [agent] ❌ Stopping due to 3 consecutive failures
WARNING [src.agent.custom_agent] No history to create GIF from
```
The model is qwen. Browser settings: Headless on. Disable security on. Video recording on. Others off.
Thanks & Best Regards
AMJS
### Reproduction Steps
Used this installation steps.: I used this guide: https://github.com/browser-use/web-ui.
Used qwen model
agent run gives the error.
But Deep Research works fine
### Code Sample
```python
I used this guide: https://github.com/browser-use/web-ui
Used curl to install uv : curl -LsSf https://astral.sh/uv/install.sh | sh
```
### Version
Name: browser-use Version: 0.1.37
### LLM Model
Other (specify in description)
### Operating System
Linux Ubuntu jammy chroot on FreeBSD 14.2
### Relevant Log Output
```shell
``` | open | 2025-02-20T15:51:45Z | 2025-02-21T07:17:10Z | https://github.com/browser-use/browser-use/issues/786 | [
"bug"
] | MichaelSchroter | 1 |
CorentinJ/Real-Time-Voice-Cloning | deep-learning | 1,059 | Add Arabic language for a fee | Hello my friend,
I want to add the Arabic language to the program for a fee. If you can help, I will contact you. | open | 2022-05-01T17:58:12Z | 2023-08-12T05:52:36Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1059 | [] | mahdi-salah | 1 |
quasarstream/python-ffmpeg-video-streaming | dash | 108 | how to make multiple representations for HLS stream while capturing webcam | **Describe the bug**
I cannot make multiple representations for HLS stream while capturing webcam. When i try this code, it crashes.
**To Reproduce**
This is my code:
import ffmpeg_streaming
from ffmpeg_streaming import Formats,Bitrate, Representation, Size
video = ffmpeg_streaming.input('Fantech Luminous C30', capture = True)
_360p = Representation(Size(640, 360), Bitrate(276 * 1024, 128 * 1024))
_480p = Representation(Size(854, 480), Bitrate(750 * 1024, 192 * 1024))
_720p = Representation(Size(1280, 720), Bitrate(2048 * 1024, 320 * 1024))
hls = video.hls(Formats.h264(),hls_list_size=10, hls_time=5)
hls.flags('delete_segments')
hls.representations( _360p,_480p,_720p)
hls.output('C:\\Users\\ltadic\\Desktop\\camera_stream\\hls1.m3u8')
**Expected behavior**
hls.output() creates HLs stream with _360p, _480p and _720p quality
**Local machine (please complete the following information):**
- OS: Windows or Linux
- FFMpeg
| closed | 2023-01-18T09:06:12Z | 2023-04-09T10:15:43Z | https://github.com/quasarstream/python-ffmpeg-video-streaming/issues/108 | [
"bug"
] | 7luka7tadic7 | 1 |
modin-project/modin | pandas | 6,782 | Unexpected warning: `PerformanceWarning: Adding/subtracting object-dtype array to DatetimeArray not vectorized.` | Modin: 76d741bec279305b041ba5689947438884893dad
```python
import modin.pandas as pd
pd.Series([datetime.datetime(2000, 1, 1)]) - datetime.datetime(1970, 1, 1, 0, 0) # <- pandas doesn't raise the warning here
```
Traceback:
```bash
..\modin\modin\pandas\series.py:464: in __sub__
return self.sub(right)
..\modin\modin\logging\logger_decorator.py:129: in run_and_log
return obj(*args, **kwargs)
..\modin\modin\pandas\series.py:1820: in sub
return super(Series, new_self).sub(
..\modin\modin\logging\logger_decorator.py:129: in run_and_log
return obj(*args, **kwargs)
..\modin\modin\pandas\base.py:3061: in sub
return self._binary_op(
..\modin\modin\logging\logger_decorator.py:129: in run_and_log
return obj(*args, **kwargs)
..\modin\modin\pandas\base.py:471: in _binary_op
new_query_compiler = getattr(self._query_compiler, op)(other, **kwargs)
..\modin\modin\logging\logger_decorator.py:129: in run_and_log
return obj(*args, **kwargs)
..\modin\modin\core\dataframe\algebra\binary.py:345: in caller
dtypes = try_compute_new_dtypes(
..\modin\modin\core\dataframe\algebra\binary.py:252: in try_compute_new_dtypes
dtypes = maybe_compute_dtypes_common_cast(
..\modin\modin\core\dataframe\algebra\binary.py:129: in maybe_compute_dtypes_common_cast
dtypes = func(df1, df2).dtypes.set_axis(common_columns)
```
The problem is where the types are evaluated. As an option, we can ignore all warnings in that place using `warnings.catch_warnings` context manager. | closed | 2023-11-29T22:07:00Z | 2023-12-08T16:21:29Z | https://github.com/modin-project/modin/issues/6782 | [
"pandas concordance 🐼",
"P2"
] | anmyachev | 0 |
modin-project/modin | pandas | 6,723 | Use `_shape_hint = "column"` in `DataFrame.squeeze` | To avoid index materialization. | closed | 2023-11-07T22:46:17Z | 2023-11-09T13:07:43Z | https://github.com/modin-project/modin/issues/6723 | [
"Performance 🚀"
] | anmyachev | 0 |
huggingface/transformers | pytorch | 35,978 | HPD-Transformer: A Hybrid Parsing-Density Transformer for Efficient Structured & Probabilistic Reasoning | ### Model description
**Overview**
HPD‑Transformer is a hybrid AI model combining structured parsing (syntax/semantic analysis) and probabilistic density estimation (uncertainty-aware reasoning) within a single, energy-efficient framework. Developed under the brand name **OpenSeek**, HPD‑Transformer outperforms several general-purpose LLMs (e.g., ChatGPT‑4, Qwen 2.5 Max, DeepSeek) on specialized tasks while reducing computational costs by up to 60–70%.
### Key Features
- **Hybrid Architecture**: Integrates parsing and density modules.
- **Sparse Mixture of Experts (MoE)**: Domain‑specific experts reduce compute cost.
- **Energy Efficiency**: Uses quantization, pruning, and Performer attention for ~60% lower FLOPs.
- **Multi‑Modal & Multilingual**: Handles text, tables, and 50+ languages.
- **Real‑Time UI**: Interactive visualization for parsing, uncertainty estimates, and more.
### Methodology Highlights
1. **Hybrid Parsing-Density**:
- Parsing Module: Lightweight transformer blocks (Performer) for syntactic/semantic analysis.
- Density Module: Monte Carlo dropout & Sparse Gaussian Processes for uncertainty modeling.
2. **Sparse MoE**:
- 32 experts (small feed-forward networks), each specialized in a domain (medical, legal, finance, etc.).
- Top-2 routing activates only the most relevant experts per token.
3. **Training**:
- **Knowledge Distillation** from teacher models (ChatGPT‑4, Qwen 2.5 Max, etc.).
- **RLHF**: Reinforcement Learning from Human Feedback for correctness and clarity.
- **Curriculum Learning**: General pretraining → domain-specific → task-specific.
- **Online Meta-Learning**: Real-time adaptation without full retraining.
4. **Efficiency**:
- 8-bit Quantization, structured pruning, and mixed-precision training.
- Performer (FAVOR+) attention for O(n) complexity.
5. **Evaluation & Benchmarks**:
- Targets >80% accuracy on MMLU, surpassing ChatGPT‑4 (~78%).
- Achieves lower inference cost ($0.001/query) vs. ChatGPT‑4’s ($0.005/query).
6. **Use Cases**:
- High-stakes fields (healthcare, legal, finance) needing interpretable outputs.
- Edge deployments where compute/energy are limited.
7. **Limitations**:
- Context window limited to ~8k tokens (less than some mega-LLMs).
- May require additional domain experts for niche tasks.
**Reference Implementation**
We provide a reference PyTorch implementation (see code snippets below) that includes:
- Shared Embedding Layer
- Parsing Module (Performer-based)
- Density Module (Bayesian Neural Network + MC dropout)
- Sparse Mixture of Experts (Top-2 gating)
- Simple training loop for demonstration
**UI/Deployment**
- FastAPI backend with Docker support for cloud or on-prem deployment.
- Optional Streamlit/React UI to visualize dependency parsing and uncertainty in real-time.
- Supports edge deployments via ONNX or TensorFlow Lite.
**License**
- Core modules are open-sourced under Apache 2.0.
- Extended enterprise features available for commercial use.
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
[HPD.docx](https://github.com/user-attachments/files/18615085/HPD.docx) | open | 2025-01-31T08:27:11Z | 2025-01-31T08:27:11Z | https://github.com/huggingface/transformers/issues/35978 | [
"New model"
] | infodevlovable | 0 |
onnx/onnx | machine-learning | 5,884 | `pip install onnx` fails on aarch64 mac | # Bug Report
### Is the issue related to model conversion?
No.
### Describe the bug
<!-- Please describe the bug clearly and concisely -->
```
pip install onnx
```
fails with
```
[ 86%] Linking CXX executable protoc
ld: warning: ignoring duplicate libraries: 'third_party/abseil-cpp/absl/base/libabsl_base.a', 'third_party/abseil-cpp/absl/base/libabsl_log_severity.a', 'third_party/abseil-cpp/absl/hash/libabsl_hash.a', 'third_party/abseil-cpp/absl/status/libabsl_status.a', 'third_party/abseil-cpp/absl/strings/libabsl_cord.a', 'third_party/abseil-cpp/absl/strings/libabsl_strings.a', 'third_party/abseil-cpp/absl/synchronization/libabsl_synchronization.a', 'third_party/abseil-cpp/absl/time/libabsl_time.a'
ld: multiple errors: archive member '/' not a mach-o file in '/private/var/folders/4h/9st3qln16jz5g87f6fv69gcw0000gn/T/pip-install-1sftzdwb/onnx_e7f8e028aa4042eaaf83f71e87c3a6ad/.setuptools-cmake-build/_deps/protobuf-build/third_party/abseil-cpp/absl/flags/libabsl_flags.a'; archive member '/' not a mach-o file in '/private/var/folders/4h/9st3qln16jz5g87f6fv69gcw0000gn/T/pip-install-1sftzdwb/onnx_e7f8e028aa4042eaaf83f71e87c3a6ad/.setuptools-cmake-build/_deps/protobuf-build/third_party/abseil-cpp/absl/log/libabsl_log_entry.a'
clang++: error: linker command failed with exit code 1 (use -v to see invocation)
```
This is with homebrew aarch64 python and llvm and no `protobuf`, `abseil` or `protobuf-c` installed. With those installed the pip command fails with a different error:
```
In file included from /opt/homebrew/include/absl/container/internal/compressed_tuple.h:40:
/opt/homebrew/include/absl/utility/utility.h:164:12: error: no member named 'in_place_t' in namespace 'std'
164 | using std::in_place_t;
| ~~~~~^
```
(which is due to a missing `-std=c++17` flag from what I understand)
### System information
- OS Platform and Distribution: Mac OS Sonoma 14.3
- ONNX version: 1.15
- Python version: 3.12.1
- GCC/Compiler version (if compiling from source): Homebrew clang version 17.0.6
- CMake version: 3.28.2
- Protobuf version: Either none or 25.2
- Visual Studio version (if applicable):
### Reproduction instructions
On an Apple Silicon Mac in a virtualenv using /opt/homebrew/bin/python3.12
```
pip install onnx
```
### Expected behavior
```
Successfully installed onnx-1.15.0
```
### Notes
<!-- Any additional information -->
| open | 2024-01-31T12:38:27Z | 2024-02-08T22:23:56Z | https://github.com/onnx/onnx/issues/5884 | [] | recmo | 1 |
cookiecutter/cookiecutter-django | django | 4,903 | Incorrect Python version in Docker. | ## What happened?
Aliases for Docker image builds aren't functioning as intended. Currently, Python version 3.11 is specified, but the image is built from 3.12 because aliases aren't used in stages 2 and 3, and the reference to Python is made without specifying the version. As a result, besides having the incorrect Python version, each image also occupies about ~1.5 gigabytes instead of ~800 megabytes.
## What should've happened instead?
The image should be built based on the base image with the specified version. | closed | 2024-03-05T13:48:22Z | 2024-03-06T13:50:03Z | https://github.com/cookiecutter/cookiecutter-django/issues/4903 | [
"bug"
] | NikolayCherniy | 1 |
ivy-llc/ivy | tensorflow | 28,526 | Fix Frontend Failing Test: tensorflow - math.paddle.diff | To-do List: https://github.com/unifyai/ivy/issues/27499 | closed | 2024-03-09T20:57:12Z | 2024-04-02T09:25:05Z | https://github.com/ivy-llc/ivy/issues/28526 | [
"Sub Task"
] | ZJay07 | 0 |
CorentinJ/Real-Time-Voice-Cloning | tensorflow | 858 | How many hours of speech do you need to get the quality as in paper? | How many hours of speech do you need to get the quality as in paper? | closed | 2021-10-04T16:45:49Z | 2021-10-06T18:05:40Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/858 | [] | fancat-programer | 3 |
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