[Bug]: Can't run on Windows 11 due to "np.Inf" error

Describe the bug
When trying to install Autogen Studio on my Windows machine, I am unable to run the program due to an error in the >ib\site-packages\numpy_init_.py file on line 411
Steps to reproduce
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>export OPENAI_API_KEY=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
'export' is not recognized as an internal or external command,
operable program or batch file.
(autogenstudio) C:\Users\ctavo>conda env config vars set OPENAI_API_KEY=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
To make your changes take effect please reactivate your environment
(autogenstudio) C:\Users\ctavo>conda deactivate
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>pip install autogenstudio
Collecting autogenstudio
Using cached autogenstudio-0.0.48-py3-none-any.whl.metadata (11 kB)
Collecting pydantic (from autogenstudio)
Using cached pydantic-2.6.3-py3-none-any.whl.metadata (84 kB)
Collecting fastapi (from autogenstudio)
Using cached fastapi-0.110.0-py3-none-any.whl.metadata (25 kB)
Collecting typer (from autogenstudio)
Using cached typer-0.9.0-py3-none-any.whl.metadata (14 kB)
Collecting uvicorn (from autogenstudio)
Using cached uvicorn-0.28.0-py3-none-any.whl.metadata (6.3 kB)
Collecting arxiv (from autogenstudio)
Using cached arxiv-2.1.0-py3-none-any.whl.metadata (6.1 kB)
Collecting pyautogen>=0.2.0 (from autogenstudio)
Using cached pyautogen-0.2.18-py3-none-any.whl.metadata (18 kB)
Collecting websockets (from autogenstudio)
Using cached websockets-12.0-cp311-cp311-win_amd64.whl.metadata (6.8 kB)
Collecting diskcache (from pyautogen>=0.2.0->autogenstudio)
Using cached diskcache-5.6.3-py3-none-any.whl.metadata (20 kB)
Collecting docker (from pyautogen>=0.2.0->autogenstudio)
Using cached docker-7.0.0-py3-none-any.whl.metadata (3.5 kB)
Collecting flaml (from pyautogen>=0.2.0->autogenstudio)
Using cached FLAML-2.1.1-py3-none-any.whl.metadata (15 kB)
Collecting openai>=1.3 (from pyautogen>=0.2.0->autogenstudio)
Using cached openai-1.13.3-py3-none-any.whl.metadata (18 kB)
Collecting python-dotenv (from pyautogen>=0.2.0->autogenstudio)
Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)
Collecting termcolor (from pyautogen>=0.2.0->autogenstudio)
Using cached termcolor-2.4.0-py3-none-any.whl.metadata (6.1 kB)
Collecting tiktoken (from pyautogen>=0.2.0->autogenstudio)
Using cached tiktoken-0.6.0-cp311-cp311-win_amd64.whl.metadata (6.8 kB)
Collecting annotated-types>=0.4.0 (from pydantic->autogenstudio)
Using cached annotated_types-0.6.0-py3-none-any.whl.metadata (12 kB)
Collecting pydantic-core==2.16.3 (from pydantic->autogenstudio)
Using cached pydantic_core-2.16.3-cp311-none-win_amd64.whl.metadata (6.6 kB)
Collecting typing-extensions>=4.6.1 (from pydantic->autogenstudio)
Using cached typing_extensions-4.10.0-py3-none-any.whl.metadata (3.0 kB)
Collecting feedparser==6.0.10 (from arxiv->autogenstudio)
Using cached feedparser-6.0.10-py3-none-any.whl.metadata (2.3 kB)
Collecting requests==2.31.0 (from arxiv->autogenstudio)
Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)
Collecting sgmllib3k (from feedparser==6.0.10->arxiv->autogenstudio)
Using cached sgmllib3k-1.0.0-py3-none-any.whl
Collecting charset-normalizer<4,>=2 (from requests==2.31.0->arxiv->autogenstudio)
Using cached charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl.metadata (34 kB)
Collecting idna<4,>=2.5 (from requests==2.31.0->arxiv->autogenstudio)
Using cached idna-3.6-py3-none-any.whl.metadata (9.9 kB)
Collecting urllib3<3,>=1.21.1 (from requests==2.31.0->arxiv->autogenstudio)
Using cached urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB)
Collecting certifi>=2017.4.17 (from requests==2.31.0->arxiv->autogenstudio)
Using cached certifi-2024.2.2-py3-none-any.whl.metadata (2.2 kB)
Collecting starlette<0.37.0,>=0.36.3 (from fastapi->autogenstudio)
Using cached starlette-0.36.3-py3-none-any.whl.metadata (5.9 kB)
Collecting click<9.0.0,>=7.1.1 (from typer->autogenstudio)
Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)
Collecting h11>=0.8 (from uvicorn->autogenstudio)
Using cached h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)
Collecting colorama (from click<9.0.0,>=7.1.1->typer->autogenstudio)
Using cached colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
Collecting anyio<5,>=3.5.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached anyio-4.3.0-py3-none-any.whl.metadata (4.6 kB)
Collecting distro<2,>=1.7.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)
Collecting httpx<1,>=0.23.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached httpx-0.27.0-py3-none-any.whl.metadata (7.2 kB)
Collecting sniffio (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)
Collecting tqdm>4 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)
Collecting packaging>=14.0 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached packaging-24.0-py3-none-any.whl.metadata (3.2 kB)
Collecting pywin32>=304 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached pywin32-306-cp311-cp311-win_amd64.whl.metadata (6.5 kB)
Collecting NumPy>=1.17.0rc1 (from flaml->pyautogen>=0.2.0->autogenstudio)
Using cached numpy-2.0.0b1-cp311-cp311-win_amd64.whl.metadata (60 kB)
Collecting regex>=2022.1.18 (from tiktoken->pyautogen>=0.2.0->autogenstudio)
Using cached regex-2023.12.25-cp311-cp311-win_amd64.whl.metadata (41 kB)
Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached httpcore-1.0.4-py3-none-any.whl.metadata (20 kB)
Using cached autogenstudio-0.0.48-py3-none-any.whl (2.7 MB)
Using cached pyautogen-0.2.18-py3-none-any.whl (214 kB)
Using cached pydantic-2.6.3-py3-none-any.whl (395 kB)
Using cached pydantic_core-2.16.3-cp311-none-win_amd64.whl (1.9 MB)
Using cached arxiv-2.1.0-py3-none-any.whl (11 kB)
Using cached feedparser-6.0.10-py3-none-any.whl (81 kB)
Using cached requests-2.31.0-py3-none-any.whl (62 kB)
Using cached fastapi-0.110.0-py3-none-any.whl (92 kB)
Using cached typer-0.9.0-py3-none-any.whl (45 kB)
Using cached uvicorn-0.28.0-py3-none-any.whl (60 kB)
Using cached websockets-12.0-cp311-cp311-win_amd64.whl (124 kB)
Using cached annotated_types-0.6.0-py3-none-any.whl (12 kB)
Using cached click-8.1.7-py3-none-any.whl (97 kB)
Using cached h11-0.14.0-py3-none-any.whl (58 kB)
Using cached openai-1.13.3-py3-none-any.whl (227 kB)
Using cached starlette-0.36.3-py3-none-any.whl (71 kB)
Using cached typing_extensions-4.10.0-py3-none-any.whl (33 kB)
Using cached diskcache-5.6.3-py3-none-any.whl (45 kB)
Using cached docker-7.0.0-py3-none-any.whl (147 kB)
Using cached FLAML-2.1.1-py3-none-any.whl (295 kB)
Using cached python_dotenv-1.0.1-py3-none-any.whl (19 kB)
Using cached termcolor-2.4.0-py3-none-any.whl (7.7 kB)
Using cached tiktoken-0.6.0-cp311-cp311-win_amd64.whl (798 kB)
Using cached anyio-4.3.0-py3-none-any.whl (85 kB)
Using cached certifi-2024.2.2-py3-none-any.whl (163 kB)
Using cached charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl (99 kB)
Using cached distro-1.9.0-py3-none-any.whl (20 kB)
Using cached httpx-0.27.0-py3-none-any.whl (75 kB)
Using cached httpcore-1.0.4-py3-none-any.whl (77 kB)
Using cached idna-3.6-py3-none-any.whl (61 kB)
Using cached numpy-2.0.0b1-cp311-cp311-win_amd64.whl (16.5 MB)
Using cached packaging-24.0-py3-none-any.whl (53 kB)
Using cached pywin32-306-cp311-cp311-win_amd64.whl (9.2 MB)
Using cached regex-2023.12.25-cp311-cp311-win_amd64.whl (269 kB)
Using cached sniffio-1.3.1-py3-none-any.whl (10 kB)
Using cached tqdm-4.66.2-py3-none-any.whl (78 kB)
Using cached urllib3-2.2.1-py3-none-any.whl (121 kB)
Using cached colorama-0.4.6-py2.py3-none-any.whl (25 kB)
Installing collected packages: sgmllib3k, pywin32, websockets, urllib3, typing-extensions, termcolor, sniffio, regex, python-dotenv, packaging, NumPy, idna, h11, feedparser, distro, diskcache, colorama, charset-normalizer, certifi, annotated-types, tqdm, requests, pydantic-core, httpcore, flaml, click, anyio, uvicorn, typer, tiktoken, starlette, pydantic, httpx, docker, arxiv, openai, fastapi, pyautogen, autogenstudio
Successfully installed NumPy-2.0.0b1 annotated-types-0.6.0 anyio-4.3.0 arxiv-2.1.0 autogenstudio-0.0.48 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 diskcache-5.6.3 distro-1.9.0 docker-7.0.0 fastapi-0.110.0 feedparser-6.0.10 flaml-2.1.1 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 idna-3.6 openai-1.13.3 packaging-24.0 pyautogen-0.2.18 pydantic-2.6.3 pydantic-core-2.16.3 python-dotenv-1.0.1 pywin32-306 regex-2023.12.25 requests-2.31.0 sgmllib3k-1.0.0 sniffio-1.3.1 starlette-0.36.3 termcolor-2.4.0 tiktoken-0.6.0 tqdm-4.66.2 typer-0.9.0 typing-extensions-4.10.0 urllib3-2.2.1 uvicorn-0.28.0 websockets-12.0
(autogenstudio) C:\Users\ctavo>autogenstudio ui
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>autogenstudio ui --port 8081
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>
Model Used
None
Expected Behavior
Install and use the app
Screenshots and logs
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>export OPENAI_API_KEY=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
'export' is not recognized as an internal or external command,
operable program or batch file.
(autogenstudio) C:\Users\ctavo>conda env config vars set OPENAI_API_KEY=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
To make your changes take effect please reactivate your environment
(autogenstudio) C:\Users\ctavo>conda deactivate
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>pip install autogenstudio
Collecting autogenstudio
Using cached autogenstudio-0.0.48-py3-none-any.whl.metadata (11 kB)
Collecting pydantic (from autogenstudio)
Using cached pydantic-2.6.3-py3-none-any.whl.metadata (84 kB)
Collecting fastapi (from autogenstudio)
Using cached fastapi-0.110.0-py3-none-any.whl.metadata (25 kB)
Collecting typer (from autogenstudio)
Using cached typer-0.9.0-py3-none-any.whl.metadata (14 kB)
Collecting uvicorn (from autogenstudio)
Using cached uvicorn-0.28.0-py3-none-any.whl.metadata (6.3 kB)
Collecting arxiv (from autogenstudio)
Using cached arxiv-2.1.0-py3-none-any.whl.metadata (6.1 kB)
Collecting pyautogen>=0.2.0 (from autogenstudio)
Using cached pyautogen-0.2.18-py3-none-any.whl.metadata (18 kB)
Collecting websockets (from autogenstudio)
Using cached websockets-12.0-cp311-cp311-win_amd64.whl.metadata (6.8 kB)
Collecting diskcache (from pyautogen>=0.2.0->autogenstudio)
Using cached diskcache-5.6.3-py3-none-any.whl.metadata (20 kB)
Collecting docker (from pyautogen>=0.2.0->autogenstudio)
Using cached docker-7.0.0-py3-none-any.whl.metadata (3.5 kB)
Collecting flaml (from pyautogen>=0.2.0->autogenstudio)
Using cached FLAML-2.1.1-py3-none-any.whl.metadata (15 kB)
Collecting openai>=1.3 (from pyautogen>=0.2.0->autogenstudio)
Using cached openai-1.13.3-py3-none-any.whl.metadata (18 kB)
Collecting python-dotenv (from pyautogen>=0.2.0->autogenstudio)
Using cached python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)
Collecting termcolor (from pyautogen>=0.2.0->autogenstudio)
Using cached termcolor-2.4.0-py3-none-any.whl.metadata (6.1 kB)
Collecting tiktoken (from pyautogen>=0.2.0->autogenstudio)
Using cached tiktoken-0.6.0-cp311-cp311-win_amd64.whl.metadata (6.8 kB)
Collecting annotated-types>=0.4.0 (from pydantic->autogenstudio)
Using cached annotated_types-0.6.0-py3-none-any.whl.metadata (12 kB)
Collecting pydantic-core==2.16.3 (from pydantic->autogenstudio)
Using cached pydantic_core-2.16.3-cp311-none-win_amd64.whl.metadata (6.6 kB)
Collecting typing-extensions>=4.6.1 (from pydantic->autogenstudio)
Using cached typing_extensions-4.10.0-py3-none-any.whl.metadata (3.0 kB)
Collecting feedparser==6.0.10 (from arxiv->autogenstudio)
Using cached feedparser-6.0.10-py3-none-any.whl.metadata (2.3 kB)
Collecting requests==2.31.0 (from arxiv->autogenstudio)
Using cached requests-2.31.0-py3-none-any.whl.metadata (4.6 kB)
Collecting sgmllib3k (from feedparser==6.0.10->arxiv->autogenstudio)
Using cached sgmllib3k-1.0.0-py3-none-any.whl
Collecting charset-normalizer<4,>=2 (from requests==2.31.0->arxiv->autogenstudio)
Using cached charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl.metadata (34 kB)
Collecting idna<4,>=2.5 (from requests==2.31.0->arxiv->autogenstudio)
Using cached idna-3.6-py3-none-any.whl.metadata (9.9 kB)
Collecting urllib3<3,>=1.21.1 (from requests==2.31.0->arxiv->autogenstudio)
Using cached urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB)
Collecting certifi>=2017.4.17 (from requests==2.31.0->arxiv->autogenstudio)
Using cached certifi-2024.2.2-py3-none-any.whl.metadata (2.2 kB)
Collecting starlette<0.37.0,>=0.36.3 (from fastapi->autogenstudio)
Using cached starlette-0.36.3-py3-none-any.whl.metadata (5.9 kB)
Collecting click<9.0.0,>=7.1.1 (from typer->autogenstudio)
Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)
Collecting h11>=0.8 (from uvicorn->autogenstudio)
Using cached h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)
Collecting colorama (from click<9.0.0,>=7.1.1->typer->autogenstudio)
Using cached colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
Collecting anyio<5,>=3.5.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached anyio-4.3.0-py3-none-any.whl.metadata (4.6 kB)
Collecting distro<2,>=1.7.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)
Collecting httpx<1,>=0.23.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached httpx-0.27.0-py3-none-any.whl.metadata (7.2 kB)
Collecting sniffio (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)
Collecting tqdm>4 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)
Collecting packaging>=14.0 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached packaging-24.0-py3-none-any.whl.metadata (3.2 kB)
Collecting pywin32>=304 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached pywin32-306-cp311-cp311-win_amd64.whl.metadata (6.5 kB)
Collecting NumPy>=1.17.0rc1 (from flaml->pyautogen>=0.2.0->autogenstudio)
Using cached numpy-2.0.0b1-cp311-cp311-win_amd64.whl.metadata (60 kB)
Collecting regex>=2022.1.18 (from tiktoken->pyautogen>=0.2.0->autogenstudio)
Using cached regex-2023.12.25-cp311-cp311-win_amd64.whl.metadata (41 kB)
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Using cached colorama-0.4.6-py2.py3-none-any.whl (25 kB)
Installing collected packages: sgmllib3k, pywin32, websockets, urllib3, typing-extensions, termcolor, sniffio, regex, python-dotenv, packaging, NumPy, idna, h11, feedparser, distro, diskcache, colorama, charset-normalizer, certifi, annotated-types, tqdm, requests, pydantic-core, httpcore, flaml, click, anyio, uvicorn, typer, tiktoken, starlette, pydantic, httpx, docker, arxiv, openai, fastapi, pyautogen, autogenstudio
Successfully installed NumPy-2.0.0b1 annotated-types-0.6.0 anyio-4.3.0 arxiv-2.1.0 autogenstudio-0.0.48 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 diskcache-5.6.3 distro-1.9.0 docker-7.0.0 fastapi-0.110.0 feedparser-6.0.10 flaml-2.1.1 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 idna-3.6 openai-1.13.3 packaging-24.0 pyautogen-0.2.18 pydantic-2.6.3 pydantic-core-2.16.3 python-dotenv-1.0.1 pywin32-306 regex-2023.12.25 requests-2.31.0 sgmllib3k-1.0.0 sniffio-1.3.1 starlette-0.36.3 termcolor-2.4.0 tiktoken-0.6.0 tqdm-4.66.2 typer-0.9.0 typing-extensions-4.10.0 urllib3-2.2.1 uvicorn-0.28.0 websockets-12.0
(autogenstudio) C:\Users\ctavo>autogenstudio ui
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>autogenstudio ui --port 8081
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>
Additional Information
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>export OPENAI_API_KEY=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
'export' is not recognized as an internal or external command,
operable program or batch file.
(autogenstudio) C:\Users\ctavo>conda env config vars set OPENAI_API_KEY=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
To make your changes take effect please reactivate your environment
(autogenstudio) C:\Users\ctavo>conda deactivate
(base) C:\Users\ctavo>conda activate autogenstudio
(autogenstudio) C:\Users\ctavo>pip install autogenstudio
Collecting autogenstudio
Using cached autogenstudio-0.0.48-py3-none-any.whl.metadata (11 kB)
Collecting pydantic (from autogenstudio)
Using cached pydantic-2.6.3-py3-none-any.whl.metadata (84 kB)
Collecting fastapi (from autogenstudio)
Using cached fastapi-0.110.0-py3-none-any.whl.metadata (25 kB)
Collecting typer (from autogenstudio)
Using cached typer-0.9.0-py3-none-any.whl.metadata (14 kB)
Collecting uvicorn (from autogenstudio)
Using cached uvicorn-0.28.0-py3-none-any.whl.metadata (6.3 kB)
Collecting arxiv (from autogenstudio)
Using cached arxiv-2.1.0-py3-none-any.whl.metadata (6.1 kB)
Collecting pyautogen>=0.2.0 (from autogenstudio)
Using cached pyautogen-0.2.18-py3-none-any.whl.metadata (18 kB)
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Using cached docker-7.0.0-py3-none-any.whl.metadata (3.5 kB)
Collecting flaml (from pyautogen>=0.2.0->autogenstudio)
Using cached FLAML-2.1.1-py3-none-any.whl.metadata (15 kB)
Collecting openai>=1.3 (from pyautogen>=0.2.0->autogenstudio)
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Collecting annotated-types>=0.4.0 (from pydantic->autogenstudio)
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Collecting pydantic-core==2.16.3 (from pydantic->autogenstudio)
Using cached pydantic_core-2.16.3-cp311-none-win_amd64.whl.metadata (6.6 kB)
Collecting typing-extensions>=4.6.1 (from pydantic->autogenstudio)
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Collecting feedparser==6.0.10 (from arxiv->autogenstudio)
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Collecting requests==2.31.0 (from arxiv->autogenstudio)
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Collecting sgmllib3k (from feedparser==6.0.10->arxiv->autogenstudio)
Using cached sgmllib3k-1.0.0-py3-none-any.whl
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Collecting idna<4,>=2.5 (from requests==2.31.0->arxiv->autogenstudio)
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Collecting urllib3<3,>=1.21.1 (from requests==2.31.0->arxiv->autogenstudio)
Using cached urllib3-2.2.1-py3-none-any.whl.metadata (6.4 kB)
Collecting certifi>=2017.4.17 (from requests==2.31.0->arxiv->autogenstudio)
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Collecting starlette<0.37.0,>=0.36.3 (from fastapi->autogenstudio)
Using cached starlette-0.36.3-py3-none-any.whl.metadata (5.9 kB)
Collecting click<9.0.0,>=7.1.1 (from typer->autogenstudio)
Using cached click-8.1.7-py3-none-any.whl.metadata (3.0 kB)
Collecting h11>=0.8 (from uvicorn->autogenstudio)
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Collecting colorama (from click<9.0.0,>=7.1.1->typer->autogenstudio)
Using cached colorama-0.4.6-py2.py3-none-any.whl.metadata (17 kB)
Collecting anyio<5,>=3.5.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached anyio-4.3.0-py3-none-any.whl.metadata (4.6 kB)
Collecting distro<2,>=1.7.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached distro-1.9.0-py3-none-any.whl.metadata (6.8 kB)
Collecting httpx<1,>=0.23.0 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached httpx-0.27.0-py3-none-any.whl.metadata (7.2 kB)
Collecting sniffio (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached sniffio-1.3.1-py3-none-any.whl.metadata (3.9 kB)
Collecting tqdm>4 (from openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached tqdm-4.66.2-py3-none-any.whl.metadata (57 kB)
Collecting packaging>=14.0 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached packaging-24.0-py3-none-any.whl.metadata (3.2 kB)
Collecting pywin32>=304 (from docker->pyautogen>=0.2.0->autogenstudio)
Using cached pywin32-306-cp311-cp311-win_amd64.whl.metadata (6.5 kB)
Collecting NumPy>=1.17.0rc1 (from flaml->pyautogen>=0.2.0->autogenstudio)
Using cached numpy-2.0.0b1-cp311-cp311-win_amd64.whl.metadata (60 kB)
Collecting regex>=2022.1.18 (from tiktoken->pyautogen>=0.2.0->autogenstudio)
Using cached regex-2023.12.25-cp311-cp311-win_amd64.whl.metadata (41 kB)
Collecting httpcore==1.* (from httpx<1,>=0.23.0->openai>=1.3->pyautogen>=0.2.0->autogenstudio)
Using cached httpcore-1.0.4-py3-none-any.whl.metadata (20 kB)
Using cached autogenstudio-0.0.48-py3-none-any.whl (2.7 MB)
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Using cached uvicorn-0.28.0-py3-none-any.whl (60 kB)
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Using cached colorama-0.4.6-py2.py3-none-any.whl (25 kB)
Installing collected packages: sgmllib3k, pywin32, websockets, urllib3, typing-extensions, termcolor, sniffio, regex, python-dotenv, packaging, NumPy, idna, h11, feedparser, distro, diskcache, colorama, charset-normalizer, certifi, annotated-types, tqdm, requests, pydantic-core, httpcore, flaml, click, anyio, uvicorn, typer, tiktoken, starlette, pydantic, httpx, docker, arxiv, openai, fastapi, pyautogen, autogenstudio
Successfully installed NumPy-2.0.0b1 annotated-types-0.6.0 anyio-4.3.0 arxiv-2.1.0 autogenstudio-0.0.48 certifi-2024.2.2 charset-normalizer-3.3.2 click-8.1.7 colorama-0.4.6 diskcache-5.6.3 distro-1.9.0 docker-7.0.0 fastapi-0.110.0 feedparser-6.0.10 flaml-2.1.1 h11-0.14.0 httpcore-1.0.4 httpx-0.27.0 idna-3.6 openai-1.13.3 packaging-24.0 pyautogen-0.2.18 pydantic-2.6.3 pydantic-core-2.16.3 python-dotenv-1.0.1 pywin32-306 regex-2023.12.25 requests-2.31.0 sgmllib3k-1.0.0 sniffio-1.3.1 starlette-0.36.3 termcolor-2.4.0 tiktoken-0.6.0 tqdm-4.66.2 typer-0.9.0 typing-extensions-4.10.0 urllib3-2.2.1 uvicorn-0.28.0 websockets-12.0
(autogenstudio) C:\Users\ctavo>autogenstudio ui
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>autogenstudio ui --port 8081
Traceback (most recent call last):
File "", line 198, in run_module_as_main
File "", line 88, in run_code
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Scripts\autogenstudio.exe_main.py", line 4, in
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio_init.py", line 1, in
from .chatmanager import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\chatmanager.py", line 11, in
from .utils import extract_successful_code_blocks, get_modified_files, summarize_chat_history
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils_init_.py", line 2, in
from .utils import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogenstudio\utils\utils.py", line 8, in
import autogen
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen_init_.py", line 3, in
from .oai import *
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai_init_.py", line 1, in
from autogen.oai.client import OpenAIWrapper, ModelClient
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\autogen\oai\client.py", line 8, in
from flaml.automl.logger import logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml_init_.py", line 2, in
from flaml.automl import AutoML, logger_formatter
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl_init_.py", line 1, in
from flaml.automl.automl import AutoML, size
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\automl.py", line 15, in
from flaml.automl.state import SearchState, AutoMLState
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\state.py", line 8, in
from flaml.automl.ml import compute_estimator, train_estimator
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py", line 326, in
best_val_loss=np.Inf,
^^^^^^
File "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\numpy_init_.py", line 411, in getattr
raise AttributeError(
AttributeError: np.Inf was removed in the NumPy 2.0 release. Use np.inf instead.. Did you mean: 'inf'?
(autogenstudio) C:\Users\ctavo>

I am having the same issue, I am running on Debian Linux

solved by:
(optional: backup ml.py.bak)
C:\Users\%username%\anaconda3\envs\ag\Lib\site-packages\flaml\automl
copy ml.py ml.py.bak

open ml.pl in notepad:
notepad ml.py

Search for the line that contains best_val_loss=np.Inf. Modify it to best_val_loss=np.inf. Save the changes and close the file.
autogenstudio ui --port 8081

go to http://<IP_ADDRESS>:8081

solved by:
(optional: backup ml.py)
C:\Users\%username%\anaconda3\envs\ag\Lib\site-packages\flaml\automl
copy ml.py ml.py.bak

open ml.pl in notepad:
notepad ml.py

Search for the line that contains best_val_loss=np.Inf. Modify it to best_val_loss=np.inf. Save the changes and close the file.
autogenstudio ui --port 8081

go to http://<IP_ADDRESS>:8081
edit: I dont know if this is the right way to do things as I currently can't add any agent but that could be unrelated.

What if we are running autogen from a script and NOT from autogen studio how do we solve this ?

solved by:
(optional: backup ml.py)
C:\Users\%username%\anaconda3\envs\ag\Lib\site-packages\flaml\automl
copy ml.py ml.py.bak

open ml.pl in notepad:
notepad ml.py

Search for the line that contains best_val_loss=np.Inf. Modify it to best_val_loss=np.inf. Save the changes and close the file.
autogenstudio ui --port 8081

go to http://<IP_ADDRESS>:8081
edit: I dont know if this is the right way to do things as I currently can't add any agent but that could be unrelated.

What if we are running autogen from a script and NOT from autogen studio how do we solve this ?

you error code says your file should be here "C:\Users\ctavo\miniconda3\envs\autogenstudio\Lib\site-packages\flaml\automl\ml.py"

pyautogen is taking a dependency on a pre-release version of numpy(2.0.0b1). Forcing the use of numpy==1.26.4 solved the issue for me. The dev team can clarify if there is a real need for using the numpy pre-release featureset.

Yeah I finally fixed it by following the instructions by @BindingOx but I had to edit the file venv\ag\Lib\site-packages\flaml\automlin , as I created my project with python3 -m venv venv


Avoid having the default security group associated with a VPCEnpoint

What problem are you facing?
When creating an AWS VPC endpoint and it automatically attaches the default VPC security group, even if you attach your own security group using VPCEndpointSecurityGroupAssociation
I would like to create a VPCEnpoint that is only attached to a choosen security group and not the default one.
How could Official AWS Provider help solve your problem?
security_group_ids and securityGroupIdSelector fields should be proposed as parameter in VPCEnpoint CRD to match AWS CreateVpcEndpoint API. These fields could be optional to use the default security group by default.
The security_group_ids in status could represent an aggregation of the SG given as parameter + all the SG associated with VPCEndpointSecurityGroupAssociation resources.

FI, this issue is issued from the following Slack discussion : https://crossplane.slack.com/archives/C05E0UE46S2/p1689845947912269

+1


Update GermlineCNVCaller to gatk <IP_ADDRESS>

Updated gcnv to latest version for cohort and case mode
Address small risk of NaN errors by try-catch in task (bash) and
reproducibly scrambling sample order to get different random samples
Decrease wall-clock time of MakeBincovMatrix and call only once
Fix crashes and improve output of RawVcfQc
Update VM scaling to prevent crashes with larger cohorts

I'm about to get started on the single-sample branch. I can't believe I forgot to update that! Thanks also for pointing out the validation script which I was unaware of.
Quick question: should I remove my added line deleting of the second tabix file on RDTestChromosome.wdl::RDTest and BAFTestChromosome.wdl::BAFTest?

Quick question: should I remove my added line deleting of the second tabix file on RDTestChromosome.wdl::RDTest and BAFTestChromosome.wdl::BAFTest?

Yes, I missed those


rate limiter code speculation and maybe allow custom listeners to be added to the rate limiter

last 2 commits are supposed to be together sorry
overriding the interfaces without an implementation or change in signature seemed odd to me but it might have had a reason.
last 2 commits are just to convey the kind of functionality that would be nice from a rate limiter listener.

I think actually being able to listen for arbitrary response codes would be much better.


Can you provide me with API information?

Hello, I want to control the P110 with ESP32. However, I'm blocked at the login step when accessing the HTTP. In which file can I find the related code?

You probably want to start with https://github.com/mihai-dinculescu/tapo/blob/main/tapo/src/api/protocol/klap_protocol.rs.

is there a possibility or an existing api that would interface with tapo via bluetooth as well to bypass tp link cloud entirely?

This library requires local network access either via WiFi or LAN.


Updating/work-with this template for GHA and Azd Devcontainer feature.

We are updating this template to support new features in Github Action and Azd Devcontainer.

For the Github Action feature: We will remove two lines of code on container image and add GHA code after checkout step in the .github/workflow/azure-dev.yml file. (Shown as below)

For the Azd Devcontainer feature: We will remove dockerfile in .devcontainer folder and add  the following code in devcontainer.json file,

"ghcr.io/azure/azure-dev/azd:latest": {}

and image also need to be add instead of build.

@rajeshkamal5050 , @digitarald , @jongio for notification.

Please use GA version of devcontainer, not test-azd

@jongio - Updated to GA version.


Fix SetImmutabilityPolicyOptions.format() NPE

The o.format() pattern is used as a convenient way to let people pass nil for options but there's an NPE in this case since .format() returns nil but SetImmutabilityPolicy then tries setting blobSetImmutabilityPolicyOptions.ImmutabilityPolicyExpiry. Returning an empty struct so this can be set without having an NPE.

@microsoft-github-policy-service agree

/azp run go - azblob

/azp run go - azblob

/azp run go - azblob


Bluetooth: Mesh: Add missing descriptions, fix fields rendering, fix 3rd-party links

This PR fixes the following issues:

Adds missing bt_mesh_dfd group to DFU doc page
Adds description for bt_mesh_comp_p1_elem struct
Adds missing description for bt_mesh_comp_p1_elem struct
Compiles bt_mesh_model fields with Doxygen to enable rendering these fields in the documentation
Fixes link to Assigned Numbers document as the old link doesn't exist anymore (though redirects to the new page).

LGTM 👍 I guess the build errors are not related?

Well, they are related :(


Add config and registry to processed source if the methods exist

Fixes issue with rubocop > 1.38.x
It seems that https://github.com/rubocop/rubocop/pull/10987 introduced registry and config to new versions of rubocop which were not there before.
Without those, rubocop still works, but it raises a lot of errors for some files.
I added the registry and source like in the rubocop code if the ProcessedSource responds to it, so I'm hoping it won't break compatibility with older rubocop versions.
This fixes #76

@renatolond can you specifically add 1.38.0 to this line so that we check this? You'll probably need to add to the excludes as well for Ruby 2.3 and 2.4.
I'm also wondering why I don't get an option to trigger the CI run on this PR thinking

Sure, done!
It's weird about the CI, I already saw the option of "allow run" for new contributors on some of my projects, but not showing at all is odd :thinking:

@renatolond can you do a rebase or something? I triggered a run manually, apparently scheduled actions got suspended on this repo, which were supposed to inform me about regressions -- another fun thing to research :see_no_evil:

@ashkulz Sure, done! :)

Thanks for the contribution, @renatolond! I'll push up a release by EOD.

Thanks for the contribution, @renatolond! I'll push up a release by EOD.

Thank you, @ashkulz :tada:

Released to RubyGems: https://rubygems.org/gems/pronto-rubocop/versions/0.11.3


[Docker Desktop] New Start tutorial in Docker Desktop

Tell us about your request
Replace the old 'welcome whale' in Docker Desktop with a better starting tutorial that is a bit more informative and makes use of our native UI
Which service(s) is this request for?
Desktop
Tell us about the problem you're trying to solve. What are you trying to do, and why is it hard?
The old welcome whale doesn't really teach you anything about Docker, we would like to give new users something a bit more constructive
Are you currently working around this issue?
N/A
Additional context
N/A
Attachments

Shipped! Let us know what you think and if you want more tutorials please add comments here: https://github.com/docker/roadmap/issues/62

I loved the new Tutorial, but if the Docker team is gearing towards the Docker Desktop, there should be Docker Desktop for Linux as well. It's really, really sad not to be able to follow your 101-tutorial in a Linux environment, specially considering Docker's inheritance. Please show some love to #39 and some respect to your starting platform.


Retro Achievements support

Nice to see the Neo Geo being separated away from Arcade MAME and using the .NEO game format. I understand the aim for the JGE emus is simplicity and stability. The only enhancement I feel that would be valid for this core is RetroAchievements support. It's early days for this port, hopefully it's something we'll see at some point.

I included functions in the core to access any raw memory block, so it is already possible, theoretically. I just don't know anything about RetroAchievements, so I don't know how to hook it all up.

I had a quick look at the RA website and their repo and found the documents page it contains a RAwiki + RAdocs. It contains all the information. Might be worth looking into when you have any spare time.

@Jamiras might give you some input.

Most of the information is here: https://docs.retroachievements.org/libretro-core-support/
We've recently added a clause about reset support as we assumed all cores already implemented it, but were wrong.
The "arcade" system is a unique beast as we only use the filename for identification, assuming the core will have it's own internal checksum logic for the individual files in the zip. Additionally, since it's not a single system, we rely on the core exposing the memory in a consistent manner between versions. Because of that limitation, many sets have been developed using memory layout as exposed by the FBNeo (previously FBAlpha) core. For compatibility with existing sets, you should look at their implementation.

@Jamiras why the decision to put this under “Arcade”? NeoGeo CD has its own entry on RA. Geolith takes .neo files, the contents of which could easily be hashed for identification. Why sticking with this burdensome filename system? There are nice standardized sets to be found that would cause little confusion for people. And besides, you’d actually know you’re working with the proper files regardless of what someone might have named them. Worst case hashes could be translated to “file names” so that both would work in a way. Though, again, should such a system of working with file names not be abandoned at some point anyway?
Would this not seem a great opportunity to for proper NeoGeo implementation on RA’s side? Forgive me if you’re not the right person to address this with but I found this page wanting to request RA support myself and thought I should share some ideas.


Webinar – Coronavirus: Can Artificial Intelligence Make A Difference? (3/26, 2pm EDT)

Here is the link to register for the webinar:
https://ehealthinitiative.zoom.us/webinar/register/WN_Py1oNxobRhqWLoUyP4q2FQ
With wide-scale responses underway by countries to counter the coronavirus’ spread, the use of technologies such as Artificial Intelligence (AI) may prove to be vitally important now and in the future.
The US health system at the brink of operating at above capacity and healthcare organizations need clarity on how to address COVID-19 risk within their population and their community more broadly. AI has the ability to deliver insights that can assist national response to COVID-19 by giving insights into which hospitals will have similar experiences and impacts, insights to help communities plan for when and how healthcare resources will be impacted, insights into highest risk patients, and provide clinicians with point-of-care clinical decision support.
Please join us while we discuss some important developments in AI with real-world examples AI insights and how providers and communities can apply them to drive optimal outcomes during times like these.
Speakers:

Lori Tremmel Freeman, CEO, NACCHO
John Frownfelter, MD, CMIO, Jvion
Priyanka Suiro, Director of Informatics, ASTHO
John Showalter, MD, Chief Product Officer, Jvion

@ngiangre @akilhylton @SomeMoosery @renegmed @BrianHHough Any interest?

Honestly I think we have a good idea of what our MVP is based on our zoom call yesterday so I wouldn't attend since I don't think we'll learn anything more. But I can be wrong. Someone should def attend though to be sure!

Unfortunately don't think I'd be able to join as it's during work hours, but I'll see if I can fit it into my schedule and at least listen in!

I'm in the same boat as @SomeMoosery but maybe there will be a recording of the webinar that we can listen to later?

I hope there is a recording ¯_(ツ)_/¯

Alternatively, there is a different webinar discussing Italy and Germany's responses:
http://pages.himss.org/dR0GVnTVMQ0Wg00LK3530Z0

Going to close this as there's no action items to be taken, should add to a Google Calendar


Navigation properties are not displayed in List (Index), Details, or Delete

For generated views, navigation properties are displayed in Create and Edit, but not List (Index), Details, or Delete.
Sample models:
    public class Director
    {
        public int DirectorID { get; set; }
        public string Name { get; set; }
    }
    public class Movie
    {
        public int ID { get; set; }
        public string Title { get; set; }
        public DateTime ReleaseDate { get; set; }
        public string Genre { get; set; }
        public decimal Price { get; set; }

        public int DirectorID { get; set; }
        public virtual Director Director { get; set; }
    }

Also, is there any way to attribute either model so that the scaffold knew which field(s) from Director to display? Right now Create and Edit default to:
ViewData["DirectorID"] = new SelectList(_context.Set<Director>(), "DirectorID", "DirectorID");

When I'd rather have it generate:
ViewData["DirectorID"] = new SelectList(_context.Set<Director>(), "DirectorID", "Name");

Also, is there any way to attribute either model so that the scaffold knew which field(s) from Director to display? Right now Create and Edit default to:
ViewData["DirectorID"] = new SelectList(_context.Set(), "DirectorID", "DirectorID");
When I'd rather have it generate:
ViewData["DirectorID"] = new SelectList(_context.Set(), "DirectorID", "Name");

Yes, scaffolding looks for a [Required] string property in the navigation. If none is found, then it uses the primary key.
This is because a non required string property may end up having empty entries in the select list.

Thanks, that worked.

TFS Bug#235742


Resource monitor creation fails, yet objects are created.

Provider Version
0.62.0
Terraform Version
1.4.5
Describe the bug
When attempting to create new resource monitor in snowflake the "terraform init" runs successfully and as expected however the apply fails, yet the objects are still created in snowflake.
When attempting to terraform apply again it now complains that the objects already exists and are not found in the state.

│ Error: Plugin did not respond
│
│   with module.snowflake_monitor["MRCH"].snowflake_resource_monitor.monitor,
│   on resource-monitor/main.tf line 17, in resource "snowflake_resource_monitor" "monitor":
│   17: resource "snowflake_resource_monitor" "monitor" {
│
│ The plugin encountered an error, and failed to respond to the plugin.(*GRPCProvider).ReadResource call. The plugin logs may contain more
│ details.
╵

Stack trace from the terraform-provider-snowflake_v0.62.0 plugin:

panic: runtime error: index out of range [0] with length 0

goroutine 37 [running]:
github.com/Snowflake-Labs/terraform-provider-snowflake/pkg/resources.ReadResourceMonitor(0x140008b6380, {0x102069960?, 0x140008b8b60})
        github.com/Snowflake-Labs/terraform-provider-snowflake/pkg/resources/resource_monitor.go:322 +0x9cc
github.com/hashicorp/terraform-plugin-sdk/v2/helper/schema.(*Resource).read(0x102094140?, {0x102094140?, 0x1400084f560?}, 0xd?, {0x102069960?, 0x140008b8b60?})
        github.com/hashicorp/terraform-plugin-sdk/v2@v2.26.1/helper/schema/resource.go:712 +0x134
github.com/hashicorp/terraform-plugin-sdk/v2/helper/schema.(*Resource).RefreshWithoutUpgrade(0x140005bb880, {0x102094140, 0x1400084f560}, 0x140009e6410, {0x102069960, 0x140008b8b60})
        github.com/hashicorp/terraform-plugin-sdk/v2@v2.26.1/helper/schema/resource.go:1015 +0x468
github.com/hashicorp/terraform-plugin-sdk/v2/helper/schema.(*GRPCProviderServer).ReadResource(0x1400078eb40, {0x102094140?, 0x1400084f440?}, 0x140004aa340)
        github.com/hashicorp/terraform-plugin-sdk/v2@v2.26.1/helper/schema/grpc_provider.go:613 +0x400
github.com/hashicorp/terraform-plugin-go/tfprotov5/tf5server.(*server).ReadResource(0x140001b10e0, {0x102094140?, 0x1400084ec90?}, 0x1400067e4e0)
        github.com/hashicorp/terraform-plugin-go@v0.14.3/tfprotov5/tf5server/server.go:748 +0x3e8
github.com/hashicorp/terraform-plugin-go/tfprotov5/internal/tfplugin5._Provider_ReadResource_Handler({0x102030900?, 0x140001b10e0}, {0x102094140, 0x1400084ec90}, 0x14000162000, 0x0)
        github.com/hashicorp/terraform-plugin-go@v0.14.3/tfprotov5/internal/tfplugin5/tfplugin5_grpc.pb.go:349 +0x170
google.golang.org/grpc.(*Server).processUnaryRPC(0x140007b2000, {0x10209a580, 0x14000502340}, 0x14000c98fc0, 0x140007ba990, 0x1029158d0, 0x0)
        google.golang.org/grpc@v1.53.0/server.go:1336 +0xb7c
google.golang.org/grpc.(*Server).handleStream(0x140007b2000, {0x10209a580, 0x14000502340}, 0x14000c98fc0, 0x0)
        google.golang.org/grpc@v1.53.0/server.go:1704 +0x82c
google.golang.org/grpc.(*Server).serveStreams.func1.2()
        google.golang.org/grpc@v1.53.0/server.go:965 +0x84
created by google.golang.org/grpc.(*Server).serveStreams.func1
        google.golang.org/grpc@v1.53.0/server.go:963 +0x290

Error: The terraform-provider-snowflake_v0.62.0 plugin crashed!

This is always indicative of a bug within the plugin. It would be immensely
helpful if you could report the crash with the plugin's maintainers so that it
can be fixed. The output above should help diagnose the issue.

Expected behavior
terraform apply should not fails as it does actually create the objects, in version 0.61.0 this is not an issue.
Code samples and commands
resource "time_offset" "next_month" {
  offset_months = 1
}

resource "snowflake_resource_monitor" "monitor" {
  name                    = "MRCH_MONITOR"
  credit_quota            = 100
  frequency               = "MONTHLY"
  start_timestamp         = "${formatdate("YYYY-MM", time_offset.next_month.rfc3339)}-01 00:00:00"
  suspend_trigger         = 110
  warehouses              = values(merge(local.warehouses...))
}

Additional context
Add any other context about the problem here.

I have yet another issue with resource monitor.

It always detects a change to datetime format and tries to apply it and when you apply it, it fails saying :
"Must specify frequency and start time together"

Timestamp changes ->
start_timestamp :
"2023-05-03T00:00:00Z"
change to
"2023-05-03 00:00 PST"

Yeah, I've this a lot in other issues. I handled it and the timestamps shouldn't cause those issues anymore in the upcoming version. I'll also test it with this particular case, Thank You.

Hey 👋
The new and refactored resource monitor was released yesterday in version 0.96.0 of the provider. Please migrate with migration guide and let me know if the issue has been resolved so we could close the ticket. Thanks in advance 🙏.

Closing due to long inactivity. Please, create another issue if you think the problem is still not resolved.


DDF-1158: improved documentation profile to include .zip format.

@shaundmorris

Reviewed files:

pom.xml @ r1

Comments from the review on Reviewable.io


WorldsApi.SearchWorlds return null if can't find "favorites" property.

I'm just searching with sort "name", count "50" and offset "300". Result of that request is null. So I was get http response and see, that element with index 6 not have "favorites" property. I think this is problem, why result is null.
Normal world:
{
      "id": "wrld_62478d20-875e-47a2-97eb-951026061051",
      "name": "［hs］_meteor Avatar World",
      "authorId": "usr_62b4988f-0d47-46d8-a82a-c4705d70b188",
      "authorName": "sparkuality",
      "capacity": 16,
      "imageUrl": "https://api.vrchat.cloud/api/1/file/file_38809458-e5cd-4511-8f14-4c3fd1531b82/1/file",
      "thumbnailImageUrl": "https://api.vrchat.cloud/api/1/image/file_38809458-e5cd-4511-8f14-4c3fd1531b82/1/256",
      "releaseStatus": "public",
      "organization": "vrchat",
      "tags": [
        "content_sex",
        "content_violence",
        "content_gore",
        "content_other",
        "author_tag_homestuck",
        "author_tag_chill",
        "author_tag_vibes",
        "author_tag_hangout",
        "author_tag_avatars",
        "system_approved"
      ],
      "favorites": 330,
      "created_at": "2022-01-11T18:35:51.768Z",
      "updated_at": "2022-02-13T19:26:26.251Z",
      "publicationDate": "2022-02-13T15:53:45.034Z",
      "labsPublicationDate": "2022-02-12T21:53:43.453Z",
      "unityPackages": [
        {
          "platform": "android",
          "unityVersion": "2019.4.31f1"
        },
        {
          "platform": "standalonewindows",
          "unityVersion": "2019.4.31f1"
        }
      ],
      "popularity": 4,
      "heat": 2,
      "occupants": 0
}

Problem world:
{
      "id": "wrld_ac449e72-ca01-4e87-8758-5bbeb9a9b8a1",
      "name": "［HotPocket］'s Hideout",
      "authorId": "usr_2e7586f3-c87e-473f-a3b6-84678369ba59",
      "authorName": "［HotPocket］",
      "capacity": 16,
      "imageUrl": "https://api.vrchat.cloud/api/1/file/file_aec2fd98-6629-409f-8172-f92416ec190d/4/file",
      "thumbnailImageUrl": "https://api.vrchat.cloud/api/1/image/file_aec2fd98-6629-409f-8172-f92416ec190d/4/256",
      "releaseStatus": "public",
      "organization": "vrchat",
      "tags": [
        "system_labs",
        "system_updated_recently"
      ],
      "created_at": "2022-05-22T00:20:42.158Z",
      "updated_at": "2022-05-24T08:57:21.847Z",
      "publicationDate": "none",
      "labsPublicationDate": "2022-05-22T00:20:42.274Z",
      "unityPackages": [
        {
          "platform": "standalonewindows",
          "unityVersion": "2019.4.31f1"
        }
      ],
      "popularity": 0,
      "heat": 0,
      "occupants": 0
}

Thank you for this discovery


fix extend assigment not working

https://app.screencast.com/GZVtObxA6X2OU?conversation=UGaH5XNw3Ihv4JgFALzx4M

@arsenijesavic can't test this because of the blocking issue related to the section "select date".
https://user-images.githubusercontent.com/2647958/214646364-211d7443-41f5-4721-96d2-77ac63262a29.mp4


Add geometry keeps in edit mode after saving it

Steps to Reproduce

New Map --> New Empty Map
Go to the empty layer and draw a polygon.
Press save

Current Result
Geometry is saved, but if you click in the map, you keep adding points.
Expected result
Geometry is saved, and if you click in the map, you begin creating a new geometry.

cc/ @noguerol

That was a decision we took (good or bad) when we implement it. If we think we should change it, let's go for it! :).


rewriteRun treats all xml as pom if pomXml is not last

rewriteRun tries to parse both of the following xml files as poms if pomXml is not last. I would expect order not to matter since i've defined one as xml and the other as pomXml.
    void addsSuppressionIfNotExists() {
        rewriteRun(
          pomXml(
            //language=xml
            """
              <project>
                  <groupId>org.openrewrite</groupId>
                  <artifactId>test</artifactId>
                  <version>1.0.0</version>
                  <dependencies>
                      <dependency>
                          <groupId>com.fasterxml.jackson</groupId>
                          <artifactId>jackson-base</artifactId>
                          <version>2.14.2</version>
                      </dependency>
                  </dependencies>
              </project>"""
          ),
          xml(
            """
              <?xml version="1.0" encoding="UTF-8" ?>
              <suppressions xmlns="<https://jeremylong.github.io/DependencyCheck/dependency-suppression.1.3.xsd>">
              </suppressions>""",
            """
              <?xml version="1.0" encoding="UTF-8" ?>
              <suppressions xmlns="<https://jeremylong.github.io/DependencyCheck/dependency-suppression.1.3.xsd>">
                  <suppress>
                      <notes><![CDATA[
                          file name: com/fasterxml/jackson/core/JsonFactory.java
                          ]]></notes>
                      <gav regex="true">^com\\\\.fasterxml\\\\.jackson\\\\.core:.*$</gav>
                      <cve>CVE-2016-3720</cve>
                  </suppress>
              </suppressions>""",
            spec -> spec.path("suppressions.xml")
          )
        );
    }```

[Slack Message](https://moderneinc.slack.com/archives/C01VADFPJQZ/p1676422968250399)

If pomXml is first, both are Pom. If pomXml is last, both are Xml$Document.

i think the problem is this method. the first step checks if there is a parser for this source spec type yet. because pomXml and xml are both Xml$Document, whichever one gets here first will choose the parser for all Xml$Document.
that being said, the solution space is not clear to me yet. we need a way to prevent creating a new parser per source spec, but we definitely want to check for each file to see if we're using the right parser.


Functional Approach: Consumer is never called when producing tombstone records

The issue is pretty much the same as https://github.com/spring-cloud/spring-cloud-stream-binder-kafka/issues/455 but for the Functional approach
    @Bean
    public Consumer<Message<Foo>> fooConsumer() {
        return message -> {
            String key = // From message header - KafkaHeaders.RECEIVED_MESSAGE_KEY
            Foo payLoad = message.getPayload();

            if (payLoad == null) {
                deleteSomething(key);
            } else {
                addSomething(key, payLoad);
            }
        };
    }

When a proper 'Foo' (json) is produced the fooConsumer is invoked. But when a 'null' payload/tombstone is produced the consumer function is never called.
Workaround I tried with 'CustomConvertor' which is working:
@Component
public class KafkaNullConverter extends MappingJackson2MessageConverter {

    @Override
    protected Object convertFromInternal(Message<?> message, @NonNull Class<?> targetClass, Object conversionHint) {
        if (message.getPayload() instanceof KafkaNull) {
            return Foo.builder().isDeleted(true).build();
        }

        return super.convertFromInternal(message, targetClass, conversionHint);
    }
}

But this is verbose, pollutes pojo/model classes and have to repeat for every consumers.
I understand that spring-integration cannot working with null. But are there any better/standard approach to handle the 'tombstone' use-case with Functional approach?
Version: 3.0.4.RELEASE
<dependency>
   <groupId>org.springframework.cloud</groupId>
   <artifactId>spring-cloud-stream-binder-kafka</artifactId>
   <version>3.0.4.RELEASE</version>
</dependency>

Just saw the recommendation to post only in stack-overflow:
https://stackoverflow.com/questions/62389775/functional-approach-consumer-is-never-called-when-producing-tombstone-records


gpr_mu_lock assertion when trying to make RPCs from iOS

Something between commits fb956aa878bddc7a55f2baea760d3c90a7822be5 (worked) and 64b357ce8ffac125c21981dfdb795502562a263a (broken) broke iOS.
Stack trace (I can get line numbers and local variables values if needed):

Commit 705a49a81270794ea04a93a2d6e0afb53f27d0f7 was good too.

I saw this too at 9ed4be48955e3b65b0540f70060b7191d8c1eef1.
On Thu, Sep 17, 2015 at 1:49 PM, Jorge Canizales<EMAIL_ADDRESS>wrote:

Commit 705a49a
https://github.com/grpc/grpc/commit/705a49a81270794ea04a93a2d6e0afb53f27d0f7
was good too.
—
Reply to this email directly or view it on GitHub
https://github.com/grpc/grpc/issues/3364#issuecomment-141223902.

stack trace
#0  0x00007f019fcd3cc9 in __GI_raise (sig=sig@entry=6)
    at ../nptl/sysdeps/unix/sysv/linux/raise.c:56
#1  0x00007f019fcd70d8 in __GI_abort () at abort.c:89
#2  0x00007f01a09af998 in gpr_mu_lock (mu=0x1eb0760)
    at src/core/support/sync_posix.c:48
#3  0x00007f01a0c29436 in grpc_pollset_add_fd (pollset=0x1eb0758, fd=0x1ead350)
    at src/core/iomgr/pollset_posix.c:143
#4  0x00007f01a0c2a4e3 in grpc_pollset_set_add_fd (pollset_set=0x1eb55e8,
    fd=0x1ead350) at src/core/iomgr/pollset_set_posix.c:111
#5  0x00007f01a0c2c1b9 in grpc_tcp_client_connect (cb=0x7f01a0c389d1 <connected>,
    arg=0x7f0198000aa0, interested_parties=0x1eb55e8, addr=0x7f0198001a80,
    addr_len=28, deadline=...) at src/core/iomgr/tcp_client_posix.c:264
#6  0x00007f01a0c38c34 in connector_connect (con=0x7f0198000aa0,
    args=0x7f019eee8930, result=0x7f0198001cb8, notify=0x7f0198001cd0)
    at src/core/surface/channel_create.c:103
#7  0x00007f01a0c1c4f4 in grpc_connector_connect (connector=0x7f0198000aa0,
    in_args=0x7f019eee8930, out_args=0x7f0198001cb8, notify=0x7f0198001cd0)
    at src/core/client_config/connector.c:48
#8  0x00007f01a0c208ca in continue_connect (c=0x7f0198001c70)
    at src/core/client_config/subchannel.c:318
#9  0x00007f01a0c216f2 in on_alarm (arg=0x7f0198001c70, iomgr_success=1)
    at src/core/client_config/subchannel.c:634
#10 0x00007f01a0c25dce in run_some_expired_alarms (drop_mu=0x7f01a1044e80 <g_mu>,
    now=..., next=0x7f019eee8e60, success=1) at src/core/iomgr/alarm.c:351
#11 0x00007f01a0c25eae in grpc_alarm_check (drop_mu=0x7f01a1044e80 <g_mu>,
    now=..., next=0x7f019eee8e60) at src/core/iomgr/alarm.c:363
#12 0x00007f01a0c27756 in background_callback_executor (ignored=0x0)
    at src/core/iomgr/iomgr.c:72
#13 0x00007f01a09afda5 in thread_body (v=0x1eb29d0)
    at src/core/support/thd_posix.c:57
#14 0x00007f019f604182 in start_thread (arg=0x7f019eee9700)
    at pthread_create.c:312
#15 0x00007f019fd9747d in clone ()
    at ../sysdeps/unix/sysv/linux/x86_64/clone.S:111

Mine is only happening for SSL: cleartext works fine.

The crash doesn't happen if GRPC_OP_SEND_INITIAL_METADATA isn't passed to grpc_call_start_batch.

@jboeuf, might this be related to the recent auth plugin work? This client doesn't use it, but "only after send_metadata is called over SSL" sounds pretty close to that area of the code.

The auth plugin work has not been merged yet to master (#3176). @ctiller has been adding locks to fix a memory leak lately in the security code (see https://github.com/grpc/grpc/commit/131f6edafa1a281604fffc24019dcb99134eec9f). Could that be related?

I now think our issues may not be the same one. The one I had were about
channel connectivity checker timed out but did not remove the pollset
registered with the channel fast enough. Then when a channel state change
comes, it will try to hold the pollset which may be gone with the
completion queue. The issue could have been in existence all the time. Any
way, I might create a new issue.
On Thu, Sep 17, 2015 at 9:35 PM, jboeuf<EMAIL_ADDRESS>wrote:

The auth plugin work has not been merged yet to master (#3176
https://github.com/grpc/grpc/pull/3176). @ctiller
https://github.com/ctiller has been adding locks to fix a memory leak
lately in the security code (see 131f6ed
https://github.com/grpc/grpc/commit/131f6edafa1a281604fffc24019dcb99134eec9f).
Could that be related?
—
Reply to this email directly or view it on GitHub
https://github.com/grpc/grpc/issues/3364#issuecomment-141341348.

I've confirmed that this bug was introduced by PR #3338.

Specifically, by commit 131f6edafa1a281604fffc24019dcb99134eec9f.


Azure character limits on resource groups

What kind of request is this (question/bug/enhancement/feature request):
bug
Steps to reproduce (least amount of steps as possible):
Create a resource Group in azure with a very long  >60 resource name
Attempt to use that resource group to provision a cluster
Result:
Azure cluster bring up returns a error about the name of one of the parameters exceeding a limit specified in https://docs.microsoft.com/en-us/azure/architecture/best-practices/naming-conventions#general
Other details that may be helpful:
Rancher should never send data to azure that we know will exceed a known naming limit
Error seen:
Operationalinsights.WorkspacesClient#CreateOrUpdate: Invalid input: autorest/validation: validation failed: parameter=workspaceName constraint=MaxLength value="rancher-kube-cluster-dev2-c24b7341-10ad-4e4c-b3f7-f07c483184a9-WEU" details: value length must be less than or equal to 63; waiting on cluster-provisioner-controller (thanks @dbsanfte )
Environment information

Rancher version (rancher/rancher/rancher/server image tag or shown bottom left in the UI):
2.2.4

Duplicate of https://github.com/rancher/rancher/issues/20783 I believe.
We may want to close #20783 in favor of this issue.

Version: master-head (6/24/19 9am mst)
I was asked to reproduce the issue.
I see an error like:
2019/06/24 18:34:42 [ERROR] NodeController c-lnzmj/m-wh9dz [node-controller] failed with : Error creating machine: Error in driver during machine creation: #: : StatusCode=%!!(MISSING)d(<nil>)
When I attempt to create a cluster with a node template that defines a resource group with a string of longer than 63 chars.
So I am not seeing the exact same error, but perhaps it has something to do with the string used david-sadgjasdlkfjalsdkjglksadjflkasdjlgkajsdklfjasdlkjgalksdjflkasjdlkgjasldkjfklasdgasdgasdjfkjskdjigjdkfjd

Current status:
the issue is caused by this line https://github.com/rancher/rancher/blob/7e115d273fbe56d069fe99aa399aa7e745700569/vendor/github.com/rancher/kontainer-engine/drivers/aks/aks_driver.go#L978
This concatenates 3 values, the resource Group, the subscription id and the workspace region code.
This only occurs when monitoring has been enabled (https://docs.microsoft.com/en-us/azure/aks/view-master-logs).
My current thoughts are that this field should be visible in the UI when monitoring is enabled and contain a unique hint field (likely a hash) if the user opts to use the default value.
Furthermore, we should clarify the max character limits for user configurable fields. A good rule of thumb should be to keep fields under 63 characters.

The "log analytics workspace" already lives in a particular subscription, so it doesn't seem to me like appending the 36-char ID of it to the name is really accomplishing anything.  All the workspaces in that subscription are going to have the same ID appended.
The region code is possibly helpful since you can presumably have a cluster named "dev" in each AKS region within one subscription.
So I would:

Set maxLength on Resource Group name to 63 so that the UI limits it correctly if not already the case.

Take the SubscriptionID (and a hyphen) out of the generated workspaceName entirely (but leave the short region code)

Check the length of that result.  If it's still >63 chars, switch to a different strategy like workspaceName=${ substr(workspaceName,0,45) }-${ substr(sha256(workspaceName),0,16) } so that it is unique but still mostly recognizable to a human.*

*: You could also just truncate it, but that might not be unique and would result in 2 clusters writing to the same workspace since the next block does CreateOrUpdate(workspaceName)

pulled back to final review bc we need it backported

Actual PR for 2.2 branch: https://github.com/rancher/rancher/pull/21235

v2.2-head
This is also having the same issue as in master -- there appears to be another limit we are now hitting. When supply Resoruce Group string of 70 chars we get an error like:
Containerservice.ManagedClustersClient#CreateOrUpdate: Failure sending request: StatusCode=0 -- Original Error: autorest/azure: Service returned an error. Status=400 Code="InvalidParameter" Message="The length of the node resource group name is too long. The maximum length is 80 and the length of the value provided is 88. Please see https://aka.ms/aks-naming-rules for more details." Target="name"

Reproduction:
Please refer to https://github.com/rancher/rancher/issues/21234#issuecomment-507412994 where this was already reproduced in v2.2.4.
Validation:
v2.2-head

Create AKS cluster with RG string greater than 63 chars, 64 chars in my case. You must have metrics enabled (but this is by default)
david-abcMHCqGl3L08ME8rk13ISYfc2wD57nORtegG7fks4pX43f3u7FBdmufh1

We now successfully create the Log Analytics resource in Azure console, we did not before.

Note: We do get another separate error but this is a new, and separate issue. Azure seems to be appending our RG string with extra characters, this is not something Rancher is doing. Refer to https://github.com/rancher/rancher/issues/21264 for this separate Azure issue.


Upgrade express to the latest stable version

Express 4.3.2, which we use, has a vulnerability. The stable version
should not have this vulnerability (the version we are using is very
old, from 2014).

Deployed to: http://staging.quran.com:32781

Deployed to: http://staging.quran.com:32782


[FEAT]: Text color change is required

Is your feature request related to a problem? Please describe.
The text color should be changed for better readability purpose
Describe the solution you'd like
I would to change the font-color to black
Describe alternatives you've considered
N/A
Additional context

I would like to work on this issue
I am a GSSoC 2024 contributor
I agree to follow the contributing guidelines of the project
I agree to follow the Code of Conduct of the project

Duplicate of #189


Support aiida-2.0

Some minor and backwards-compatible changes to support AiiDA 2.0. With this PR we can easily support both AiiDA 1.0 and 2.0 at the same time.
Due to unrelated issues in AiiDAlab environment, I had problems with Pymatgen. I looked around and we could get rid of the Pymatgen dependency (and in turn many other transitive dependencies from aiida[atomic_tools]) and use ASE instead for parsing. ASE seems to have much less dependencies,
$ pip show ase
Name: ase
Version: 3.19.3
Requires: matplotlib, numpy, scipy

$ pip show pymatgen
Name: pymatgen
Version: 2022.2.1
Summary: Python Materials Genomics is a robust materials analysis code that defines core object representations for structures and molecules with support for many electronic structure codes. It is currently the core analysis code powering the Materials Project (https://www.materialsproject.org).
Requires: Cython, matplotlib, monty, networkx, numpy, palettable, pandas, plotly, pybtex, requests, ruamel.yaml, scipy, spglib, sympy, tabulate, tqdm, uncertainties

I tried to test orca_asa as well, but unfortunately the orca_asa module is broken in ORCA >5.0 (segfaults) so I couldn't test it. The bug was reported a year ago and still not fixed detail at https://orcaforum.kofo.mpg.de/viewtopic.php?f=11&t=7716&p=40653#p40653. The recommendation is to switch to the new orca_esd module.
TODO:

[x] Test orca.asa calculation
[x] Use ase.io.write to write XYZ coordinates for ORCA
[x] Turn on AiiDA deprecation warnings, export AIIDA_WARN_v3=1
[x] Test on fresh environments for AiiDA 1.0
[x] Test on fresh environments for AiiDA 2.0

Closes #35

Turning on AiiDA2 deprecation warnings with export AIIDA_WARN_v3=1 results only on these warnings:
  /home/jovyan/aiida-orca/aiida_orca/parsers/__init__.py:38: AiidaDeprecationWarning: `FolderData.open` is deprecated, use `FolderData.base.repository.open` instead. (this will be removed in v3)
    with self.retrieved.open(fname_out) as handler:

  /home/jovyan/aiida-orca/aiida_orca/parsers/__init__.py:73: AiidaDeprecationWarning: `FolderData.open` is deprecated, use `FolderData.base.repository.open` instead. (this will be removed in v3)
    with out_folder.open(fname_relaxed) as handler:

I am ignoring these in this PR so that we can support both AiiDA v1.x and v2.x.

You should delete the .github/workflows/build.yml file in this branch. It was deleted in the previous PR.

Hi @danielhollas and @sphuber
Thanks very much for the PRs. Sorry been hectic couple of months.
I do not have much experience with AiiDA v2.0 to comment on the code. The only note on my side is that let's have a release for the AiiDA v1.0 compatible version and have it in a separate branch rather than develop before merging this PR into develop.

Hi @pzarabadip. I don't think separate branch is needed, since we can easily support 1.0 and 2.0 versions.
I would therefore prefer to merge these PRs first, and then release a new version if that is okay with you.
(Not that we have some functioning CI tests, it is now easier to make sure we support both versions, just need to merge in latest changes into this branch)

Hi @pzarabadip. I don't think a separate branch is needed, since we can easily support 1.0 and 2.0 versions, as is done in this PR and already on the develop branch. I would therefore prefer to merge these PRs first, and then release a new version if that is okay with you.
(Not that we have some functioning CI tests, it is now easier to make sure we support both versions, just need to merge in latest changes into this branch)

Hi @danielhollas
I'd like to have a latest aiida-1 compatibale version pushed in a branch such as aiida-1. That makes using and developing it much less painful (if needed). There is no harm in having it.
I see no reason why not having a release for latest aiida-1 compatible version.
Then, this aiida-2 one will sit in develop and with stable one in main.

The only suggestion from my side is splitting the CI into aiida-1 and aiida-2. Besides that rest all good to me. Thanks very much.

Just for my understanding, do you mean to have two separate yml files with each an individual workflow? What is the advantage in your mind of having to separate CI workflows?

The only suggestion from my side is splitting the CI into aiida-1 and aiida-2. Besides that rest all good to me. Thanks very much.

Just for my understanding, do you mean to have two separate yml files with each an individual workflow? What is the advantage in your mind of having to separate CI workflows?

Hi @sphuber. I thought of this as if we separate a separate branch for aiida-1-only version of the plugin. Hence, if the development version does not require checking against aiida-1, we can drop them in CI here.

Hi @sphuber. I thought of this as if we separate a separate branch for aiida-1-only version of the plugin. Hence, if the development version does not require checking against aiida-1, we can drop them in CI here.

I see, but I though @danielhollas idea was to keep a single branch/version that is compatible with 1.6.x and v2.x at the same time, which is currently the idea. The changes between 1.x and 2.x were kept minimal on purpose and almost all changes were backwards-compatible, with just deprecation warnings being emitted. So just to say that at least technically, you don't need two separate branches to support both versions

First of all, my apologies that this PR is a bit of a mess. While it originally started as a PR to support AiiDA-2 (in a backwards compatible manner), it accrued a bunch of other unrelated changes. I've decided to split it up into separate PRs to aid review and make the Git history cleaner. CI related changes are in #55, the switch from Pymatgen to ASE in #54, and the rest will follow.
Additionally, in the meantime due to the kind help of @sphuber, the changes that were actually needed to support 2.0 were already merged to the develop branch as part of his PRs that introduced basic tests in CI.  The only thing left in this PR for 2.0 support is relaxing the aiida-core requirement in pyproject.toml (and some fixes in examples/*py). All other changes here are unrelated and apply equally to AiiDA 1.x and 2.x.
Hence, as @sphuber mentioned, I really don't see a need why we should split the development now. It would only make things more complicated, since every change would need to be backported to the 1.x branch as well. Note that all the currently opened PRs from me are compatible with 1.x, and I don't see any immediate need to make changes that would be incompatible with 1.x in the near future.

@danielhollas you want me to review #54 and #55 ?

@sphuber oh, thanks for the offer. Would of course love to have your eyes on the code, but feel free to skip if you're busy.


fix #2435

Codecov Report
Base: 79.56% // Head: 79.54% // Decreases project coverage by -0.02% :warning:

Coverage data is based on head (8b69d63) compared to base (005668a).
Patch coverage: 100.00% of modified lines in pull request are covered.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
Additional details and impacted files
@@            Coverage Diff             @@
##           master    #2437      +/-   ##
==========================================
- Coverage   79.56%   79.54%   -0.02%
==========================================
  Files         598      598
  Lines       49739    49739
==========================================
- Hits        39574    39566       -8
- Misses       8165     8174       +9
+ Partials     2000     1999       -1

Flag
Coverage Δ

go-1.15-386
79.59% <100.00%> (?)

go-1.15-amd64
79.59% <100.00%> (-0.02%)
:arrow_down:

go-1.16-386
79.59% <100.00%> (ø)

go-1.16-amd64
79.59% <100.00%> (+<0.01%)
:arrow_up:

go-1.17-386
79.59% <100.00%> (?)

go-1.17-amd64
?

go-1.18-386
79.51% <100.00%> (?)

go-1.18-amd64
79.52% <100.00%> (ø)

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Impacted Files
Coverage Δ

database/gdb/gdb_core_config.go
71.42% <100.00%> (ø)

os/gfsnotify/gfsnotify_watcher_loop.go
78.15% <0.00%> (-5.05%)
:arrow_down:

contrib/registry/zookeeper/zookeeper_watcher.go
48.88% <0.00%> (-2.23%)
:arrow_down:

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enable testing using Dune

As a proof-of-concept of the idea discussed in #183.

Decided to go down a native testing path. If we run into issues with it then we can come back to this in the future if need be.


[REQUEST] write bots manually

btw actually just do better ob then modded one bcs its offical one so why not ?

just follow modded one for what he do this then make a more good base

idk this offical ob have same bugs from modded one bcs every modded ob changelog have some bug fixes

Just open an issue commit if there is any bug or any requests.  Now he is spam posting trying to make this version just like that one. Can we silence this man please lol

? i do this bcs its not bad suggestions lol

btw modded one is not dead its still alive lol

I'll be very brief as I'm trying not to lose my patience:

90% of the fixes you see in the "changelog" of Forlax's version are just my commits that he copied over to his version, you can compare them here https://github.com/openbullet/openbullet/commits/master so stop saying I should follow his example
If you go to the Runner Manager and click on "bots" you can see that exact window to set the bots manually
"just follow modded one for what he do this then make a more good base" please stop comparing this version to modded versions. This is a warning, next time you will be banned from posting on this repository.

kk


[GSSoC'23] Fix: Blogs descriptions and images are not visible well (blurred) in mobile phones screens

The issue can be due to excessive shadow and text color choosen

Hey @akshitagupta15june @davidkilamlya
I'm a GSSOC'23 contributor and I would like to work on this issue.
I suppose this issue could be resolved by removing on hover css when in mobile view

contributors are working on it


New Prototypes and Updates to Tw2AnimationController

Added support for pending commands that have no arguments _DoRebuildCachedData @prototype
Added GetAnimation @prototype which returns a loaded Tw2Animation by it's name, used this prototype to simplify some of the others
Added ResetAnimation @prototype which resets a loaded Tw2Animation by it's name
Added PlayAnimationFrom @prototype which plays a loaded Tw2Animation from a specific time. (Didn't update the existing PlayAnimation as it could break people's existing code)
Updated StopAnimation @prototype so that it can also take a list of Tw2Animation names aswell as just a single name. It can now also be called before any animations have been loaded, and will stop them when they have
Updated StopAllAnimations @prototype so that it can now be called before any animations have been loaded, and will stop them when they have
Added StopAllAnimationsExcept @prototype that stops all Tw2Animations except those that are supplied by name in an array

Changes made as per @filipppavlov 's suggestions.

defined Tw2AnimationController @property pendingCommands to simplify it's usage elsewhere
removed pendingCommands definition in all @prototypes, they are no longer necessary
set the default pendingCommands value to be an Array and changed the method in which pending commands were executed and reset


Ignore retry option when --update-refs is passed

It doesn't make much sense to retry when you want to update refs

@Hypnosphi: it does in the case of flaky infrastructure.

Hello guys. Option retry is not ignored because the browser may not start or any other error may occur during the test. So in these cases a screenshot comparison inside test may not be performed. To my mind, option retry should be managed by the user himself.
Do you have any counterarguments?
btw, sorry that it took so long to respond.


Auth works properly now

 Here is an overview of what got changed by this pull request:

Issues
======
+ Solved 1
- Added 10

Complexity increasing per file
==============================
- src/components/containers/PrivateRoute.js  2
- src/api/apiUtils.js  1
- src/routes.js  3
- src/state/auth/authActionCreator.js  1

Clones added
============
- src/components/containers/PrivateRoute.js  2
- src/routes.js  2

Clones removed
==============
+ src/components/auth/Signup.js  -1
+ src/components/auth/Login.js  -1

See the complete overview on Codacy


Save Module/Page/Post for multi languages isn't working.

When I create a module or page, and go to languages and select both defined languages and save, it is only created for a specific language. Please see attached file.
Thanks
multi-language-content.zip

Fixed https://github.com/mixcore/mix.core/pull/461


不定期打印Time has been called back错误

请问如下的错误是何原因？有什么影响？谢谢
github.com/antlabs/timer:Time has been called back?from(165726941031)(165726941036)
github.com/antlabs/timer:Time has been called back?from(165726941031)(165726941036)
github.com/antlabs/timer:Time has been called back?from(165726941032)(165726941036)
github.com/antlabs/timer:Time has been called back?from(165726941032)(165726941036)
github.com/antlabs/timer:Time has been called back?from(165726941032)(165726941036)

机器的时间准吗?

可否发下用法的代码?

可否发下用法的代码?

就是简单的使用，类似
if retryNext != time.Duration(0) {
TM.AfterFunc(retryNext,  callback)
}

最新版本暂未发现问题，先关闭了


is this challenge still open ?

Yes @Srishtihere, this remains our official Front-end hiring challenge


[NEMO-231] Add onWatermark() method to Transform

JIRA: NEMO-231: Add onWatermark() method to Transform
Major changes:

Add onWatermark(watermark) method to Transform
Add emitWatermark(watermark) method to OutputCollector

Minor changes to note:
Tests for the changes:
Other comments:
Closes #134

@taegeonum Can you add a simple unit test in TaskExecutor.java to test watermark emission?

@johnyangk Thanks! I've fixed testUnboundedSourceVertexDataFetching() to check whether watermarks are emitted or not to OutputCollector


Move dynamic library location related logic into wasmer_locator.dart

This cleans up the dynamic library discovery logic.
In many cases (e.g. where sophisticated build systems are not available) it is much more practical to have a single globally built dynamic library, and to reuse that dynamic library everywhere where it is needed. This doesn't make that possible yet (due to the runtime singleton), but it moves this package in the right direction UX-wise by decoupling the default locations from the actual implementation.

macos 2.12 passed now


Increase the deadline for spanner rpcs

Cloud Spanner RPCs such as Commit and Streaming calls can take a long time to run in certain cases. Increased the deadline for those to be 1 hour. Ideally we would not want any deadlines for these calls.
Also marked most of the rpcs as idempotent.

Coverage remained the same at 99.988% when pulling 779a76a0b19395abe6cbb4c68f71eafa69f9dc84 on vkedia-patch-1 into ac8967c92035f469c745995af61f261e9c37e427 on master.

This is a generated file-- does https://github.com/googleapis/api-client-staging/blob/master/generated/nodejs/spanner/src/v1/spanner_client_config.json need to be updated first?

Also, PHP's file: https://github.com/googleapis/api-client-staging/blob/master/generated/php/google-cloud-spanner-v1/src/Spanner/V1/resources/spanner_client_config.json, and Python's: https://github.com/googleapis/api-client-staging/blob/master/generated/python/gapic-google-cloud-spanner-v1/google/cloud/gapic/spanner/v1/spanner_client_config.json

I see. Ok I will update that. Once I do that, will it automatically get copied here?

I don't believe it's automatic, but @lukesneeringer or @bjwatson should know how to get it copied over here.

It is not automatic, but we can do it. @stephenplusplus, if you know the new values, feel free to just change it to the new values. That would be faster.


Interface for common calculation of analysis attributes

So far extrema for density plots and bins for histograms are computed globally (they are the same for all variables in all subplots).
We currently do not have an interface to decide whether they should be computed globally, per subplot (to allow different subplots to have different bins, while still having consistent bins per subplots to stack or dodge) or per trace.
A possible approach could be to completely outsource this to the user, and pass as bins (or any other attribute) a function that takes as input a Dict whose keys encode the "primary variables" that specify which trace it is and whose values are positional and named arguments for the analysis. This function would return another Dict with the bins information.

This sounds involved, why not density(extrema=:global) where :global could be replaced with :subplot or :trace?

A possible approach could be to completely outsource this to the user

such an approach might also solve https://github.com/JuliaPlots/AlgebraOfGraphics.jl/issues/266 -- namely "Interface for common access to analysis attributes"