[BUG] ResolutionImpossible promptflow-rag MacOS python3.11

Describe the bug
There is a ResolutionImpossible error when trying to install the promptflow-rag package on MacOS Sonoma 14.6.1 with python3.11.9
pip install promptflow-rag
Collecting promptflow-rag
  Using cached promptflow_rag-0.1.0-py3-none-any.whl.metadata (3.1 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.36-py3-none-any.whl.metadata (22 kB)
Requirement already satisfied: openai in ./.venv/lib/python3.11/site-packages (from promptflow-rag) (1.43.0)
Collecting azureml-dataprep>4.11 (from azureml-dataprep[parquet]>4.11->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_dataprep-5.2.0-py3-none-any.whl.metadata (2.1 kB)
Collecting azureml-fsspec (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_fsspec-1.3.1-py3-none-any.whl.metadata (3.4 kB)
Collecting fsspec~=2023.3 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached fsspec-2023.12.2-py3-none-any.whl.metadata (6.8 kB)
Requirement already satisfied: tiktoken<0.6 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (0.5.2)
Collecting cloudpickle (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached cloudpickle-3.0.0-py3-none-any.whl.metadata (7.0 kB)
Collecting mmh3 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached mmh3-4.1.0-cp311-cp311-macosx_11_0_arm64.whl.metadata (13 kB)
Requirement already satisfied: pyyaml<7.0.0,>=5.1.0 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (6.0.2)
Requirement already satisfied: requests in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (2.32.3)
Requirement already satisfied: azureml-core in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.57.0)
Requirement already satisfied: azure-ai-ml<=1.16.1,>=1.10.0 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.16.1)
Requirement already satisfied: azure-identity>=1.17.0 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.17.1)
Requirement already satisfied: azure-search-documents>=11.4.0 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (11.5.1)
Requirement already satisfied: pandas>=1 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (2.2.2)
Collecting nltk~=3.8.1 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached nltk-3.8.1-py3-none-any.whl.metadata (2.8 kB)
Collecting markdown (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached Markdown-3.7-py3-none-any.whl.metadata (7.0 kB)
Collecting beautifulsoup4~=4.11.2 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached beautifulsoup4-4.11.2-py3-none-any.whl.metadata (3.5 kB)
Collecting tika~=2.6.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached tika-2.6.0.tar.gz (27 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Collecting pypdf~=3.17.1 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached pypdf-3.17.4-py3-none-any.whl.metadata (7.5 kB)
Collecting unstructured (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached unstructured-0.15.9-py3-none-any.whl.metadata (29 kB)
Requirement already satisfied: GitPython>=3.1 in ./.venv/lib/python3.11/site-packages (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (3.1.43)
Collecting azure-ai-formrecognizer (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_ai_formrecognizer-3.3.3-py3-none-any.whl.metadata (64 kB)
Collecting langchain<0.3,>=0.0.236 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Downloading langchain-0.2.16-py3-none-any.whl.metadata (7.1 kB)
Collecting langchain-community<0.3 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Downloading langchain_community-0.2.16-py3-none-any.whl.metadata (2.7 kB)
Requirement already satisfied: anyio<5,>=3.5.0 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (4.4.0)
Requirement already satisfied: distro<2,>=1.7.0 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (1.9.0)
Requirement already satisfied: httpx<1,>=0.23.0 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (0.27.2)
Requirement already satisfied: jiter<1,>=0.4.0 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (0.5.0)
Requirement already satisfied: pydantic<3,>=1.9.0 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (2.8.2)
Requirement already satisfied: sniffio in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (1.3.1)
Requirement already satisfied: tqdm>4 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (4.66.5)
Requirement already satisfied: typing-extensions<5,>=4.11 in ./.venv/lib/python3.11/site-packages (from openai->promptflow-rag) (4.12.2)
Requirement already satisfied: idna>=2.8 in ./.venv/lib/python3.11/site-packages (from anyio<5,>=3.5.0->openai->promptflow-rag) (3.8)
Requirement already satisfied: msrest>=0.6.18 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (0.7.1)
Requirement already satisfied: azure-core>=1.23.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.30.2)
Requirement already satisfied: azure-mgmt-core>=1.3.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.4.0)
Requirement already satisfied: marshmallow>=3.5 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (3.22.0)
Requirement already satisfied: jsonschema>=4.0.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (4.23.0)
Requirement already satisfied: strictyaml in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.7.3)
Requirement already satisfied: colorama in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (0.4.6)
Requirement already satisfied: pyjwt in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (2.9.0)
Requirement already satisfied: azure-storage-blob>=12.10.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (12.22.0)
Requirement already satisfied: azure-storage-file-share in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (12.17.0)
Requirement already satisfied: azure-storage-file-datalake>=12.2.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (12.16.0)
Requirement already satisfied: pydash>=6.0.0 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (7.0.7)
Requirement already satisfied: isodate in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (0.6.1)
Requirement already satisfied: azure-common>=1.1 in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.1.28)
Requirement already satisfied: opencensus-ext-azure in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.1.13)
Requirement already satisfied: opencensus-ext-logging in ./.venv/lib/python3.11/site-packages (from azure-ai-ml<=1.16.1,>=1.10.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (0.1.1)
Requirement already satisfied: cryptography>=2.5 in ./.venv/lib/python3.11/site-packages (from azure-identity>=1.17.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (43.0.0)
Requirement already satisfied: msal>=1.24.0 in ./.venv/lib/python3.11/site-packages (from azure-identity>=1.17.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.30.0)
Requirement already satisfied: msal-extensions>=0.3.0 in ./.venv/lib/python3.11/site-packages (from azure-identity>=1.17.0->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.2.0)
INFO: pip is looking at multiple versions of azureml-dataprep to determine which version is compatible with other requirements. This could take a while.
Collecting azureml-dataprep>4.11 (from azureml-dataprep[parquet]>4.11->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_dataprep-5.1.6-py3-none-any.whl.metadata (2.2 kB)
  Using cached azureml_dataprep-5.1.5-py3-none-any.whl.metadata (2.2 kB)
  Using cached azureml_dataprep-5.1.4-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-5.1.3-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-5.1.2-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-5.1.1-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-5.1.0-py3-none-any.whl.metadata (2.1 kB)
INFO: pip is still looking at multiple versions of azureml-dataprep to determine which version is compatible with other requirements. This could take a while.
  Using cached azureml_dataprep-5.0.0-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.14.1-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.14.0-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.10-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.9-py3-none-any.whl.metadata (2.1 kB)
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.
  Using cached azureml_dataprep-4.12.8-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.7-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.6-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.5-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.4-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.2-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.1-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.12.0-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.7-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.6-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.5-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.4-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.3-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.2-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.11.1-py3-none-any.whl.metadata (2.1 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.35-py3-none-any.whl.metadata (22 kB)
  Using cached azureml_rag-0.2.34-py3-none-any.whl.metadata (22 kB)
Collecting langchain<0.2,>=0.0.236 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached langchain-0.1.20-py3-none-any.whl.metadata (13 kB)
Collecting langchain-community<=0.0.33 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached langchain_community-0.0.33-py3-none-any.whl.metadata (8.5 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.33-py3-none-any.whl.metadata (21 kB)
Collecting azure-ai-ml<=1.15.0,>=1.10.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_ai_ml-1.15.0-py3-none-any.whl.metadata (28 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.32-py3-none-any.whl.metadata (21 kB)
Collecting azure-ai-ml<1.15.0,>=1.10.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_ai_ml-1.14.0-py3-none-any.whl.metadata (27 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (21 kB)
  Using cached azureml_rag-0.2.31-py3-none-any.whl.metadata (20 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (20 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (20 kB)
  Using cached azureml_rag-0.2.30-py3-none-any.whl.metadata (20 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (19 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (19 kB)
  Using cached azureml_rag-0.2.29-py3-none-any.whl.metadata (19 kB)
  Using cached azureml_rag-0.2.28-py3-none-any.whl.metadata (19 kB)
  Using cached azureml_rag-0.2.27-py3-none-any.whl.metadata (19 kB)
Collecting azure-ai-ml<1.14.0,>=1.10.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_ai_ml-1.13.0-py3-none-any.whl.metadata (26 kB)
Collecting azure-search-documents==11.4.0b8 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_search_documents-11.4.0b8-py3-none-any.whl.metadata (22 kB)
Collecting langchain<=0.0.348,>=0.0.236 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached langchain-0.0.348-py3-none-any.whl.metadata (16 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.26-py3-none-any.whl.metadata (19 kB)
  Using cached azureml_rag-0.2.25-py3-none-any.whl.metadata (18 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (18 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (18 kB)
  Using cached azureml_rag-0.2.24-py3-none-any.whl.metadata (17 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (17 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (17 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (17 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (17 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (16 kB)
  Using cached azureml_rag-0.2.23-py3-none-any.whl.metadata (16 kB)
  Using cached azureml_rag-0.2.22-py3-none-any.whl.metadata (15 kB)
Collecting openai (from promptflow-rag)
  Using cached openai-0.28.1-py3-none-any.whl.metadata (11 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.21-py3-none-any.whl.metadata (15 kB)
Collecting pypdf~=3.7.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached pypdf-3.7.1-py3-none-any.whl.metadata (6.8 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.20-py3-none-any.whl.metadata (15 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (14 kB)
Collecting langchain==0.0.236 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached langchain-0.0.236-py3-none-any.whl.metadata (14 kB)
Requirement already satisfied: SQLAlchemy<3,>=1.4 in ./.venv/lib/python3.11/site-packages (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (2.0.32)
Collecting aiohttp<4.0.0,>=3.8.3 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached aiohttp-3.10.5-cp311-cp311-macosx_11_0_arm64.whl.metadata (7.5 kB)
Collecting dataclasses-json<0.6.0,>=0.5.7 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached dataclasses_json-0.5.14-py3-none-any.whl.metadata (22 kB)
Collecting langsmith<0.0.11,>=0.0.10 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached langsmith-0.0.10-py3-none-any.whl.metadata (8.7 kB)
Collecting numexpr<3.0.0,>=2.8.4 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached numexpr-2.10.1-cp311-cp311-macosx_11_0_arm64.whl.metadata (1.2 kB)
Requirement already satisfied: numpy<2,>=1 in ./.venv/lib/python3.11/site-packages (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (1.26.4)
Collecting openapi-schema-pydantic<2.0,>=1.2 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached openapi_schema_pydantic-1.2.4-py3-none-any.whl.metadata (8.5 kB)
Collecting pydantic<2,>=1 (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached pydantic-1.10.18-cp311-cp311-macosx_11_0_arm64.whl.metadata (152 kB)
Requirement already satisfied: tenacity<9.0.0,>=8.1.0 in ./.venv/lib/python3.11/site-packages (from langchain==0.0.236->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag) (8.5.0)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.18-py3-none-any.whl.metadata (14 kB)
  Using cached azureml_rag-0.2.17-py3-none-any.whl.metadata (13 kB)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (13 kB)
  Using cached azureml_rag-0.2.15-py3-none-any.whl.metadata (13 kB)
  Using cached azureml_rag-0.2.14-py3-none-any.whl.metadata (13 kB)
  Using cached azureml_rag-0.2.13-py3-none-any.whl.metadata (13 kB)
  Using cached azureml_rag-0.2.12-py3-none-any.whl.metadata (12 kB)
  Using cached azureml_rag-0.2.11-py3-none-any.whl.metadata (12 kB)
  Using cached azureml_rag-0.2.10-py3-none-any.whl.metadata (11 kB)
  Using cached azureml_rag-0.2.9-py3-none-any.whl.metadata (11 kB)
  Using cached azureml_rag-0.2.8-py3-none-any.whl.metadata (11 kB)
  Using cached azureml_rag-0.2.7-py3-none-any.whl.metadata (11 kB)
Collecting tiktoken~=0.3.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached tiktoken-0.3.3-cp311-cp311-macosx_11_0_arm64.whl.metadata (5.2 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.2.6-py3-none-any.whl.metadata (11 kB)
  Using cached azureml_rag-0.2.5-py3-none-any.whl.metadata (10 kB)
  Using cached azureml_rag-0.2.4-py3-none-any.whl.metadata (10 kB)
  Using cached azureml_rag-0.2.3-py3-none-any.whl.metadata (10 kB)
  Using cached azureml_rag-0.2.2-py3-none-any.whl.metadata (10.0 kB)
  Using cached azureml_rag-0.2.1-py3-none-any.whl.metadata (9.8 kB)
WARNING: azureml-rag 0.2.1 does not provide the extra 'azure'
WARNING: azureml-rag 0.2.1 does not provide the extra 'azure'
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (9.8 kB)
Collecting azureml-telemetry (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_telemetry-1.57.0-py3-none-any.whl.metadata (1.1 kB)
Collecting azureml-mlflow (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_mlflow-1.57.0.post1-py3-none-any.whl.metadata (2.5 kB)
Collecting azure-search-documents==11.4.0b6 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_search_documents-11.4.0b6-py3-none-any.whl.metadata (22 kB)
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (9.6 kB)
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.24-py3-none-any.whl.metadata (9.5 kB)
WARNING: azureml-rag 0.1.24 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.24 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.24 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.24 does not provide the extra 'langchain'
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (9.2 kB)
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
  Using cached azureml_rag-<IP_ADDRESS>-py3-none-any.whl.metadata (9.2 kB)
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'azure'
WARNING: azureml-rag <IP_ADDRESS> does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.23-py3-none-any.whl.metadata (9.1 kB)
WARNING: azureml-rag 0.1.23 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.23 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.23 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.23 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.22-py3-none-any.whl.metadata (9.0 kB)
WARNING: azureml-rag 0.1.22 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.22 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.22 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.22 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.21-py3-none-any.whl.metadata (8.9 kB)
WARNING: azureml-rag 0.1.21 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.21 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.21 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.21 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.20-py3-none-any.whl.metadata (8.3 kB)
WARNING: azureml-rag 0.1.20 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.20 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.20 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.20 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.19-py3-none-any.whl.metadata (8.3 kB)
WARNING: azureml-rag 0.1.19 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.19 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.19 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.19 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.18-py3-none-any.whl.metadata (8.3 kB)
WARNING: azureml-rag 0.1.18 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.18 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.18 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.18 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.17-py3-none-any.whl.metadata (7.4 kB)
WARNING: azureml-rag 0.1.17 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.17 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.17 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.17 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.16-py3-none-any.whl.metadata (7.3 kB)
WARNING: azureml-rag 0.1.16 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.16 does not provide the extra 'langchain'
Collecting azure-search-documents~=11.4.0b3 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azure_search_documents-11.4.0-py3-none-any.whl.metadata (22 kB)
WARNING: azureml-rag 0.1.16 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.16 does not provide the extra 'langchain'
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.1.15-py3-none-any.whl.metadata (7.2 kB)
WARNING: azureml-rag 0.1.15 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.15 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.15 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.15 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.14-py3-none-any.whl.metadata (7.1 kB)
WARNING: azureml-rag 0.1.14 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.14 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.14 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.14 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.13-py3-none-any.whl.metadata (7.0 kB)
WARNING: azureml-rag 0.1.13 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.13 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.13 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.13 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.12-py3-none-any.whl.metadata (6.9 kB)
WARNING: azureml-rag 0.1.12 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.12 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.12 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.12 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.11-py3-none-any.whl.metadata (6.8 kB)
WARNING: azureml-rag 0.1.11 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.11 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.11 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.11 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.10-py3-none-any.whl.metadata (6.6 kB)
WARNING: azureml-rag 0.1.10 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.10 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.10 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.10 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.9-py3-none-any.whl.metadata (6.5 kB)
WARNING: azureml-rag 0.1.9 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.9 does not provide the extra 'langchain'
Collecting openai (from promptflow-rag)
  Using cached openai-0.27.10-py3-none-any.whl.metadata (13 kB)
WARNING: azureml-rag 0.1.9 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.9 does not provide the extra 'langchain'
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.1.8-py3-none-any.whl.metadata (5.7 kB)
WARNING: azureml-rag 0.1.8 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.8 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.8 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.8 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.7-py3-none-any.whl.metadata (5.6 kB)
WARNING: azureml-rag 0.1.7 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.7 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.7 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.7 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.6-py3-none-any.whl.metadata (5.6 kB)
WARNING: azureml-rag 0.1.6 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.6 does not provide the extra 'langchain'
Collecting azureml-dataprep<4.11.0a,>=4.10.0a (from azureml-dataprep[parquet]<4.11.0a,>=4.10.0a->azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_dataprep-4.10.9-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.8-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.7-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.6-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.5-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.4-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.1-py3-none-any.whl.metadata (2.1 kB)
  Using cached azureml_dataprep-4.10.0-py3-none-any.whl.metadata (2.1 kB)
WARNING: azureml-rag 0.1.6 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.6 does not provide the extra 'langchain'
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.1.5-py3-none-any.whl.metadata (5.4 kB)
WARNING: azureml-rag 0.1.5 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.5 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.5 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.5 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.4-py3-none-any.whl.metadata (5.0 kB)
WARNING: azureml-rag 0.1.4 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.4 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.4 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.4 does not provide the extra 'langchain'
  Using cached azureml_rag-0.1.3-py3-none-any.whl.metadata (3.5 kB)
WARNING: azureml-rag 0.1.3 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.3 does not provide the extra 'langchain'
Collecting azureml-mlflow~=1.50.0 (from azureml-rag[azure,cognitive_search,document_parsing,langchain]->promptflow-rag)
  Using cached azureml_mlflow-1.50.0-py3-none-any.whl.metadata (2.5 kB)
WARNING: azureml-rag 0.1.3 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.3 does not provide the extra 'langchain'
Collecting azureml-rag[azure,cognitive_search,document_parsing,langchain] (from promptflow-rag)
  Using cached azureml_rag-0.1.2-py3-none-any.whl.metadata (3.2 kB)
WARNING: azureml-rag 0.1.2 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.2 does not provide the extra 'langchain'
WARNING: azureml-rag 0.1.2 does not provide the extra 'azure'
WARNING: azureml-rag 0.1.2 does not provide the extra 'langchain'
ERROR: Cannot install azureml-dataprep because these package versions have conflicting dependencies.

The conflict is caused by:
    azureml-dataprep 4.10.9 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.8 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.7 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.6 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.5 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.4 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.1 depends on dotnetcore2<4.0.0 and >=3.0.0
    azureml-dataprep 4.10.0 depends on dotnetcore2<4.0.0 and >=3.0.0

To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip to attempt to solve the dependency conflict

ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts

How To Reproduce the bug
pip install promptflow-rag
Tried to install underlying packages that create the resolution error 'azureml-dataprep' seperately but that doesn't help.
When trying to install that package, the following error appears:
The conflict is caused by:
    azureml-dataprep 5.2.0 depends on azureml-dataprep-native<42.0.0 and >=41.0.0

Expected behavior
That the package would install
Screenshots
If applicable, add screenshots to help explain your problem.
Running Information(please complete the following information):

Promptflow Package Version using pf -v: not possible to install
Operating System: MacOS Sonoma 14.6.1
Python Version using python --version: 3.11.9

+1 I get the same issue installing it.

I also have the same error trying to install in the mcr.microsoft.com/devcontainers/python:3.12 and 3.11 containers (Debian 12.7)

Having the same issue. Did you manage to find a solution?

I have similar problem during create my env using.
**conda env create -f env.yaml**
I've got:
`INFO: pip is looking at multiple versions of azureml-dataprep to determine which version is compatible with other requirements. This could take a while.
Collecting azureml-dataprep<5.2.0a,>=5.1.0a (from azureml-dataset-runtime~=1.57.0->azureml-train-automl-client~=1.57.0->azureml-pipeline-steps~=1.57.0->azureml-pipeline->-r /Users/bha/Documents/project/leap/leap-llm-backend/condaenv.fx9c8yz7.requirements.txt (line 4))
Using cached azureml_dataprep-5.1.5-py3-none-any.whl.metadata (2.2 kB)
Using cached azureml_dataprep-5.1.4-py3-none-any.whl.metadata (2.1 kB)
Using cached azureml_dataprep-5.1.3-py3-none-any.whl.metadata (2.1 kB)
Using cached azureml_dataprep-5.1.2-py3-none-any.whl.metadata (2.1 kB)
Using cached azureml_dataprep-5.1.1-py3-none-any.whl.metadata (2.1 kB)
Using cached azureml_dataprep-5.1.0-py3-none-any.whl.metadata (2.1 kB)
The conflict is caused by:
azureml-dataprep 5.1.6 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.5 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.4 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.3 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.2 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.1 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
azureml-dataprep 5.1.0 depends on azureml-dataprep-native<42.0.0 and >=41.0.0
To fix this you could try to:

loosen the range of package versions you've specified
remove package versions to allow pip to attempt to solve the dependency conflict

Pip subprocess error:
ERROR: Cannot install azureml-dataset-runtime because these package versions have conflicting dependencies.
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
failed
CondaEnvException: Pip failed
`
while my friends who is working on Windows (I'm working on MAX OSX) can create env without error.

Here another Mac user. Please devs, make this work!


Clipboard stops working in the middle of the session

Describe the bug
After copying certain text on the server clipboard will stop working from server to client.
After the first time it happens:

I can copy text from client to server
When I try to copy text on the server I get an empty clipboard locally.

To Reproduce
Steps to reproduce the behavior:

Connect xfreerdp from linux to windows
Copy any text from remote Chrome several times, until the error message appears in console.
No more than 3-5 attemps should be required.

This is the error I get at trigger time:
[17:54:51:097] [28213:28225] [INFO][com.freerdp.client.x11] - skipping, empty data detected!!!
The only way to recover is to close xfreerdp and start a new instance.
Expected behavior
That clipboards didn't stop working randomly.
Application details

Version of FreeRDP
I tried 2 versions just in case:

Debian 10 default: 2.0.0-dev5 (2693389a+debian)
Just downloaded git master: 2.0.0-dev5 (ca34fe4c1)

Command line used
xfreerdp +clipboard /u:xxxxxxx /v:xx.xx.xx.xx

output of /buildconfig

Build configuration: BUILD_TESTING=OFF BUILTIN_CHANNELS=ON HAVE_AIO_H=1 HAVE_EXECINFO_H=1 HAVE_FCNTL_H=1 HAVE_INTTYPES_H=1 HAVE_JOURNALD_H=TRUE HAVE_MATH_C99_LONG_DOUBLE=1 HAVE_POLL_H=1 HAVE_PTHREAD_MUTEX_TIMEDLOCK=ON HAVE_PTHREAD_MUTEX_TIMEDLOCK_LIB=1 HAVE_PTHREAD_MUTEX_TIMEDLOCK_SYMBOL= HAVE_SYSLOG_H=1 HAVE_SYS_EVENTFD_H=1 HAVE_SYS_FILIO_H= HAVE_SYS_MODEM_H= HAVE_SYS_SELECT_H=1 HAVE_SYS_SOCKIO_H= HAVE_SYS_STRTIO_H= HAVE_SYS_TIMERFD_H=1 HAVE_TM_GMTOFF=1 HAVE_UNISTD_H=1 HAVE_XI_TOUCH_CLASS=1 WITH_ALSA=ON WITH_CCACHE=ON WITH_CHANNELS=ON WITH_CLIENT=ON WITH_CLIENT_AVAILABLE=1 WITH_CLIENT_CHANNELS=ON WITH_CLIENT_CHANNELS_AVAILABLE=1 WITH_CLIENT_COMMON=ON WITH_CLIENT_INTERFACE=OFF WITH_CUPS=ON WITH_DEBUG_ALL=OFF WITH_DEBUG_CAPABILITIES=OFF WITH_DEBUG_CERTIFICATE=OFF WITH_DEBUG_CHANNELS=OFF WITH_DEBUG_CLIPRDR=OFF WITH_DEBUG_DVC=OFF WITH_DEBUG_KBD=OFF WITH_DEBUG_LICENSE=OFF WITH_DEBUG_MUTEX=OFF WITH_DEBUG_NEGO=OFF WITH_DEBUG_NLA=OFF WITH_DEBUG_NTLM=OFF WITH_DEBUG_RAIL=OFF WITH_DEBUG_RDP=OFF WITH_DEBUG_RDPDR=OFF WITH_DEBUG_RDPEI=OFF WITH_DEBUG_REDIR=OFF WITH_DEBUG_RFX=OFF WITH_DEBUG_RINGBUFFER=OFF WITH_DEBUG_SCARD=OFF WITH_DEBUG_SND=OFF WITH_DEBUG_SVC=OFF WITH_DEBUG_SYMBOLS=OFF WITH_DEBUG_THREADS=OFF WITH_DEBUG_TIMEZONE=OFF WITH_DEBUG_TRANSPORT=OFF WITH_DEBUG_TSG=OFF WITH_DEBUG_TSMF=OFF WITH_DEBUG_WND=OFF WITH_DEBUG_X11=OFF WITH_DEBUG_X11_CLIPRDR=OFF WITH_DEBUG_X11_LOCAL_MOVESIZE=OFF WITH_DEBUG_XV=OFF WITH_DSP_EXPERIMENTAL=OFF WITH_DSP_FFMPEG=OFF WITH_EVENTFD_READ_WRITE=1 WITH_FAAC=OFF WITH_FAAD2=OFF WITH_FFMPEG=TRUE WITH_FFMPEG=TRUE WITH_GFX_H264=ON WITH_GPROF=OFF WITH_GSM=ON WITH_GSSAPI=OFF WITH_GSTREAMER_0_10=OFF WITH_GSTREAMER_1_0=ON WITH_ICU=ON WITH_IPP=OFF WITH_JPEG=ON WITH_LAME=OFF WITH_LIBRARY_VERSIONING=ON WITH_LIBSYSTEMD=ON WITH_MACAUDIO=OFF WITH_MACAUDIO=OFF WITH_MACAUDIO_AVAILABLE=0 WITH_MANPAGES=ON WITH_MBEDTLS=OFF WITH_OPENH264=OFF WITH_OPENSLES=OFF WITH_OPENSSL=ON WITH_OSS=ON WITH_PAM=ON WITH_PCSC=ON WITH_PROFILER=OFF WITH_PULSE=ON WITH_SAMPLE=OFF WITH_SANITIZE_ADDRESS=OFF WITH_SANITIZE_ADDRESS_AVAILABLE=1 WITH_SANITIZE_MEMORY=OFF WITH_SANITIZE_MEMORY_AVAILABLE=1 WITH_SANITIZE_THREAD=OFF WITH_SANITIZE_THREAD_AVAILABLE=1 WITH_SERVER=ON WITH_SERVER_CHANNELS=ON WITH_SERVER_INTERFACE=ON WITH_SMARTCARD_INSPECT=OFF WITH_SOXR=OFF WITH_SSE2=ON WITH_THIRD_PARTY=OFF WITH_VALGRIND_MEMCHECK=OFF WITH_VALGRIND_MEMCHECK_AVAILABLE=1 WITH_WAYLAND=ON WITH_X11=ON WITH_X264=OFF WITH_XCURSOR=ON WITH_XDAMAGE=ON WITH_XEXT=ON WITH_XFIXES=ON WITH_XI=ON WITH_XINERAMA=ON WITH_XKBFILE=ON WITH_XRANDR=ON WITH_XRENDER=ON WITH_XSHM=ON WITH_XTEST=ON WITH_XV=ON WITH_ZLIB=ON
Build type:          RELWITHDEBINFO
CFLAGS:              -g -O2 -fdebug-prefix-map=/build/freerdp2-MFZuNS/freerdp2-2.0.0~git20190204.1.2693389a+dfsg1=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -Wall -Wno-unused-result -Wno-unused-but-set-variable -Wno-deprecated-declarations -fvisibility=hidden -Wimplicit-function-declaration -Wredundant-decls -g -DWINPR_DLL
Compiler:            GNU, 8.3.0
Target architecture: x64

If available the log output from a run with /log-level:trace
I've selected the lines that contain cliprdr or the errore message.
You can see there is a difference, there are no ClientFormatDataRequest calls after the error.

[18:06:21:912] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatDataResponse
[18:06:22:594] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:22:594] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:22:650] [28932:28934] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatDataRequest
[18:06:22:687] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatDataResponse
[18:06:22:794] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:22:794] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:22:850] [28932:28934] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatDataRequest
[18:06:22:932] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatDataResponse
[18:06:22:932] [28932:28941] [INFO][com.freerdp.client.x11] - skipping, empty data detected!!!
[18:06:22:982] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:22:982] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:23:139] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:23:139] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:23:048] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:23:048] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:24:283] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4
[18:06:24:283] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ClientFormatListResponse
[18:06:24:407] [28932:28941] [DEBUG][com.freerdp.channels.cliprdr.client] - ServerFormatList: numFormats: 4

Desktop (please complete the following information):

Local is Debian 10 with xfreerdp client.
Remote is Windows 10 Pro.

Additional context
Remote applications that can trigger the bug: Chrome, Notepad++, Windows Terminal
Remote applications that cannot trigger it: CMD, Notepad

Some updates:
You can really trigger by copying text in any remote application.
The trick is to hit Ctr+C as fast as you can (like 8 times in 2 seconds).
My PR works (for me) so far.
But please review it, I made it more on intuition rather on having deep knowledge on this system.

Same issue occurs here.
C&P stops working sometimes, also with local applications, as long as xfreerdp is running.
Debian: bullseye/sid
This is FreeRDP version 2.3.0 (2.3.0)
Build configuration: BUILD_TESTING=OFF BUILTIN_CHANNELS=ON HAVE_AIO_H=1 HAVE_EXECINFO_H=1 HAVE_FCNTL_H=1 HAVE_GETLOGIN_R=1 HAVE_INTTYPES_H=1 HAVE_JOURNALD_H=TRUE HAVE_MATH_C99_LONG_DOUBLE=1 HAVE_POLL_H=1 HAVE_PTHREAD_MUTEX_TIMEDLOCK=ON HAVE_PTHREAD_MUTEX_TIMEDLOCK_LIB=1 HAVE_PTHREAD_MUTEX_TIMEDLOCK_SYMBOL= HAVE_SYSLOG_H=1 HAVE_SYS_EVENTFD_H=1 HAVE_SYS_FILIO_H= HAVE_SYS_MODEM_H= HAVE_SYS_SELECT_H=1 HAVE_SYS_SOCKIO_H= HAVE_SYS_STRTIO_H= HAVE_SYS_TIMERFD_H=1 HAVE_TM_GMTOFF=1 HAVE_UNISTD_H=1 HAVE_XI_TOUCH_CLASS=1 WITH_ALSA=ON WITH_CAIRO=OFF WITH_CCACHE=ON WITH_CHANNELS=ON WITH_CLANG_FORMAT=ON WITH_CLIENT=ON WITH_CLIENT_AVAILABLE=1 WITH_CLIENT_CHANNELS=ON WITH_CLIENT_CHANNELS_AVAILABLE=1 WITH_CLIENT_COMMON=ON WITH_CLIENT_INTERFACE=OFF WITH_CUPS=ON WITH_DEBUG_ALL=ON WITH_DEBUG_CAPABILITIES=ON WITH_DEBUG_CERTIFICATE=ON WITH_DEBUG_CHANNELS=ON WITH_DEBUG_CLIPRDR=ON WITH_DEBUG_DVC=ON WITH_DEBUG_KBD=ON WITH_DEBUG_LICENSE=ON WITH_DEBUG_MUTEX=ON WITH_DEBUG_NEGO=ON WITH_DEBUG_NLA=ON WITH_DEBUG_NTLM=ON WITH_DEBUG_RAIL=ON WITH_DEBUG_RDP=ON WITH_DEBUG_RDPDR=ON WITH_DEBUG_RDPEI=ON WITH_DEBUG_RDPGFX=ON WITH_DEBUG_REDIR=ON WITH_DEBUG_RFX=ON WITH_DEBUG_RINGBUFFER=ON WITH_DEBUG_SCARD=ON WITH_DEBUG_SND=ON WITH_DEBUG_SVC=ON WITH_DEBUG_SYMBOLS=OFF WITH_DEBUG_THREADS=ON WITH_DEBUG_TIMEZONE=ON WITH_DEBUG_TRANSPORT=ON WITH_DEBUG_TSG=ON WITH_DEBUG_TSMF=OFF WITH_DEBUG_TSMF=OFF WITH_DEBUG_TSMF_AVAILABLE=0 WITH_DEBUG_URBDRC=OFF WITH_DEBUG_WND=ON WITH_DEBUG_X11=ON WITH_DEBUG_X11_CLIPRDR=ON WITH_DEBUG_X11_LOCAL_MOVESIZE=ON WITH_DEBUG_XV=ON WITH_DSP_EXPERIMENTAL=OFF WITH_DSP_FFMPEG=OFF WITH_EVENTFD_READ_WRITE=1 WITH_FAAC=OFF WITH_FAAD2=OFF WITH_FFMPEG=TRUE WITH_FFMPEG=TRUE WITH_GFX_H264=ON WITH_GPROF=OFF WITH_GSM=ON WITH_GSSAPI=OFF WITH_GSTREAMER_0_10=OFF WITH_GSTREAMER_1_0=ON WITH_ICU=ON WITH_IPP=OFF WITH_JPEG=ON WITH_LAME=OFF WITH_LIBRARY_VERSIONING=ON WITH_LIBSYSTEMD=ON WITH_MACAUDIO=OFF WITH_MACAUDIO=OFF WITH_MACAUDIO_AVAILABLE=0 WITH_MANPAGES=ON WITH_MBEDTLS=OFF WITH_OPENCL=OFF WITH_OPENH264=OFF WITH_OPENSLES=OFF WITH_OPENSSL=ON WITH_OSS=ON WITH_PAM=ON WITH_PCSC=ON WITH_PROFILER=OFF WITH_PROXY=OFF WITH_PULSE=ON WITH_SAMPLE=OFF WITH_SANITIZE_ADDRESS=OFF WITH_SANITIZE_ADDRESS_AVAILABLE=1 WITH_SANITIZE_MEMORY=OFF WITH_SANITIZE_MEMORY_AVAILABLE=1 WITH_SANITIZE_THREAD=OFF WITH_SANITIZE_THREAD_AVAILABLE=1 WITH_SERVER=ON WITH_SERVER_CHANNELS=ON WITH_SERVER_INTERFACE=ON WITH_SHADOW=ON WITH_SMARTCARD_INSPECT=OFF WITH_SOXR=OFF WITH_SSE2=ON WITH_SWSCALE=ON WITH_THIRD_PARTY=OFF WITH_VAAPI=OFF WITH_VALGRIND_MEMCHECK=OFF WITH_VALGRIND_MEMCHECK_AVAILABLE=1 WITH_WAYLAND=ON WITH_WINPR_TOOLS=ON WITH_X11=ON WITH_X264=OFF WITH_XCURSOR=ON WITH_XDAMAGE=ON WITH_XEXT=ON WITH_XFIXES=ON WITH_XI=ON WITH_XINERAMA=ON WITH_XKBFILE=ON WITH_XRANDR=ON WITH_XRENDER=ON WITH_XSHM=ON WITH_XTEST=ON WITH_XV=ON WITH_ZLIB=ON
Build type:          RelWithDebInfo
CFLAGS:              -g -O2 -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -Wall -Wno-unused-result -Wno-unused-but-set-variable -Wno-deprecated-declarations -fvisibility=hidden -Wimplicit-function-declaration -Wredundant-decls -g -fno-omit-frame-pointer -DWINPR_DLL
Compiler:            GNU, 10.2.1
Target architecture: x64

@rahra observe the release tag this belongs to. it is 2.3.2

Sorry for wrong release.
Still happens in 3.0.0-nightly.
As already mentioned above seems to be like #6999

Closing again, continue with #6999


Create EvensOrOdds

Hey , I was wondering for my next program if I could implement what we did till now into one program to show the workings of classes ?

Yeah! you can also show the working classes only.

Yeah! you can also show the working classes only.

could u verify what u meant by "show the working classes only"?


syn49637038 - BloodAge10xArtyomov

Project short name:
BloodAge10xArtyomov
Primary Wrangler:
Marina Terekhova / Arsenios
Secondary Wrangler:

Associated files

Google Drive: folder

Published study links

Paper: Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB-CD8+ memory T cells and accumulation of type 2 memory T cells

Accessioned data:
synapse: syn49637038
ingest uuid: 896f377c-8e88-463e-82b0-b2a5409d6fe4

Key Events

[ ] Convert published metadata to HCA spreadsheet
[ ] Manually curate dataset to meet HCA metadata standard
[ ] Collect any matrix and cell-type annotation files
[ ] Are the analysis files suitable for CellxGene? If something is missing get in touch with the authors to request it
[ ] Upload sheet to validate metadata
[ ] Transfer raw files to ingest to validate data files
[ ] Check linking using ingest graph validator
[ ] Ask the Secondary Wrangler for an end-to-end review of the project. Ask the Expertise Wrangler to review specific tabs if needed
[ ] Submit dataset to Production
[ ] Complete the Export SOP
[ ] Convert project data to SCEA format following the SCEA conversion SOP if appropriate

After this communications, contributor has submitted this spreadsheet.
hca_template_Artyomov_MT.xlsx

Notes

There are multiple specimens from the same donor in different time points and ages as well as BMIs. This cannot be recorded in our fields for now, but a workaround is to capture that in the description field at the specimen level.
The machine used for sequencing (Illumina NovaSeq S4) is not available in the current OLS
Processes in seq_files have yet to be grouped together

Notes

There are multiple specimens from the same donor in different time points and ages as well as BMIs. This cannot be recorded in our fields for now, but a workaround is to capture that in the description field at the specimen level.
The machine used for sequencing (Illumina NovaSeq S4) is not available in the current OLS
Processes in seq_files have yet to be grouped together

There is no machine named Illumina NovaSeq S4, but S4 can be the type of the cell flow used.
Based on the illumina website, there are two NovaSeq series available, Illumina NovaSeq 6000 and Illumina NovaSeq X. From those two, only the Illumina NovaSeq 6000 has an S4 cell flow type option. Therefore, this is the sequencer that is used.

@anu-shiva to sec review

Looks great- very tricky dataset!
Project contributors

Most of the authors are affiliated with another department- if you would like to include this information.

Donor

Could there be a way to specify the BMI range (like the age range is allowed) or at least a mention that the BMI value is for the first visit? I see that the precise BMIs are described in the specimen description, however, the donor BMIs can be a bit confusing with a single BMI value while a range of ages is recorded.

Specimen

Could this be added to the time course - Each age group followed over the course of 3 years with annual blood collection.

Collection protocol

500g to be replaced with 500 × g.
Enrichment is duplicated here and can be removed as it is included in the enrichment tab

Library prep

The library prep descriptions look swapped- FB has TCR, TCR has BCR, and BCR has FB.
cDNA amplification method can be added.
primer can be added
The UMI offset is 16 and the UMI length is 10 for 10x5v2 in the assay cheat sheet

Sequencing protocol

The current description applies to the gene expression libraries. The sequencing info for others (Enriched and Feature Barcode libraries were sequenced on a NovaSeq S4 (300 cycles) flow cell, targeting 5,000 read pairs per cell) can be added too in the same or different entry.

Analysis protocol

I think there is some description in the paper on the normalization steps- as the final matrices provided are normalized counts. (don't see RPM, TPM though). The raw matrix generation could possibly be replaced by normalized if we have enough necessary info?

Analysis files

four files are available as .tgz extension- unless you changed all files to .gz.

Thanks for the review Anu!
I incorporated all your comments, I have a few notes here.

Donor
Could there be a way to specify the BMI range (like the age range is allowed) or at least a mention that the BMI value is for the first visit? I see that the precise BMIs are described in the specimen description, however, the donor BMIs can be a bit confusing with a single BMI value while a range of ages is recorded.

About that, ideally I would like to have BMI in range too, and specify in the specimen description the exact BMI in the time of collection. However, BMI does not allow an hyphen in the regex of the schema therefore we cannot add range there. Since that's what the contributor filled I thought that this would be the best way to model it.

Specimen
Could this be added to the time course - Each age group followed over the course of 3 years with annual blood collection.

Thanks for the tip, I added the timecourse on time after first visit for 2nd and 3rd visit's specimens based on the Visit_2/3_Age - Visit_1_Age formula

Analysis files
four files are available as .tgz extension- unless you changed all files to .gz.

The contributor filled the spreadsheet for all the analysis files as .tar.gz instead of 4 files as .tar.tgz . I made sure all files were compressed with the same .tar.gz compression and named them likewise before uploading them.

[ ] update submission
[ ] graph valid
[ ] export
[ ] import form sent


additional environment vars

Hello,
I use ghost with Docker. This is my docker-compose file:
version: "3"

services:
  malte-blog:
    image: ghost:latest
    container_name: malte-blog
    restart: always
    environment:
      url: https://example.de
    networks:
      - web
      - internal
    expose:
      - "2368"
    volumes:
      - /srv/malte-blog:/var/lib/ghost/content
    labels:
      - traefik.backend=malteblog
      - traefik.docker.network=web
      - traefik.frontend.rule=Host:example.de
      - traefik.port=2368

networks:
  web:
    external: true
  internal:
    external: false

volumes:
  malte-blog:

Everything works perfectly. But, I want to set some additional environment vars like this one:
"privacy": {
    "useTinfoil": true
}

Or this one
"mail": {
    "from"<EMAIL_ADDRESS>}

How can I make this?

@beli3ver see https://docs.ghost.org/concepts/config/#running-ghost-with-config-env-variables - essentially you'd use:
services:
  malte-blog:
    environment:
      privacy__useTinfoil=true
<EMAIL_ADDRESS>

When I tries this, I get this:

services.malte-blog.environment.privacy__useTinfoil contains true, which is an invalid type, it should be a string, number, or a null

You need to put single quotes around 'true' for the yaml parser to work
properly.


Documentation incorrect for custom Shop

Found a bug? Please fill out the sections below.
Issue Summary
The documentation at https://github.com/django-oscar/django-oscar/blob/master/docs/source/howto/how_to_disable_an_app_or_feature.rst#how-to-disable-an-apps-urls for customising the shop (specifically - disabling the dashboard), does not seem to be correct, and seems to be missing information/
Steps to Reproduce

create a config.py at root of project

# myproject/config.py
from oscar.config import Shop
from oscar.core.application import OscarConfig

class MyShop(Shop):

    # Override the get_urls method to remove the URL configuration for the
    # dashboard app
    def get_urls(self):
        urls = super().get_urls()
        for urlpattern in urls[:]:
            if hasattr(urlpattern, 'app_name') and (urlpattern.app_name == 'dashboard'):
                urls.remove(urlpattern)
        return self.post_process_urls(urls)

Didn't do this part, as this seems outdated with how Django settings works nowadays:

# myproject/__init__.py
default_app_config = 'myproject.config.MyShop'

not documented - replaced oscar.config.Shop with myproject.config.Shop in INSTALLED_APPS

not documented - removed all Oscar dashboard apps (i.e.: oscar.apps.dashboard.*) from INSTALLED_APPS

Expected: server can run and dashboard is not bootstrapped
Actual: LookupError: No installed app with label 'dashboard'.
Technical details

Python version: 3.8.1
Django version: 3.0.1
Oscar version: c1cadba8f33b58fac2c87f374993c8b37a67bfa7

@solarissmoke @sasha0
I guess, it makes sense. Removing the URLs requires the apps to be registered. So, in that case - how would I remove the dashboard completely (deregistering URLs is not a good solution as the code is still loaded - I simply don't want to load parts of Oscar that I don't want, like, the entire dashboard)

Please post the full stack trace for the LookupError.

Please post the full stack trace for the LookupError.

Not really a need. as per my comment in https://github.com/django-oscar/django-oscar/issues/3371#issuecomment-631378751 it is correct that there would be a LookupError - this is not really documentation to disable an app but rather to disable an app's URLs which is fine, just not what I want (as I don't want to load code I am not using, and the disable URLs approach requires "installing" the app to "find" the URLs to disable).
I will just modify the shop object more comprehensively, now I have checked the source, so, closing this for now.


Fix python test cases

Continue to fix the remaining python test cases

yes this is looking ok to me.

ready for merge, thanks


Promote one node at a time

IMPORTANT: Please do not create a Pull Request without creating an issue first.
Problem:

promote two nodes a time will cause high IO, then vmi will be recreated
Solution:

promote one node a time
Related Issue:
#460
Test plan:

add unit test

link to PR #270


Update to latest Physicaloid with Android S support

Change-Id: If9915b58ff3bbfe03149b4bc212527d883072710

The previous version of the physicaloid library used by the Serial component does not follow Android S requirements. This version updates the PendingIntent references with the FLAG_IMMUTABLE required for newer Android versions.

Is the JAR being used here built from a fork of the original physicaloid library? If yes, can you please link it here? Asking because the latest commit on the physicaloid's GitHub repo is from April 2014.

From here: https://github.com/xxxajk/PhysicaloidLibrary


Add recipeName to EventReceived + some refactoring

Codecov Report

Merging #120 into master will increase coverage by 0.14%.
The diff coverage is 90%.

@@            Coverage Diff             @@
##           master     #120      +/-   ##
==========================================
+ Coverage   83.82%   83.97%   +0.14%
==========================================
  Files         119      119
  Lines        2591     2589       -2
  Branches      128      125       -3
==========================================
+ Hits         2172     2174       +2
+ Misses        419      415       -4

Impacted Files
Coverage Δ

...baker/runtime/actor/serialization/Encryption.scala
100% <ø> (ø)
:arrow_up:

...ala/com/ing/baker/runtime/core/BakerResponse.scala
69.56% <ø> (-0.65%)
:arrow_down:

.../baker/runtime/actor/LocalBakerActorProvider.scala
100% <ø> (ø)
:arrow_up:

...aker/runtime/actor/ClusterBakerActorProvider.scala
90.47% <ø> (ø)
:arrow_up:

.../main/scala/com/ing/baker/runtime/core/Baker.scala
72.95% <88.23%> (+1.4%)
:arrow_up:

...untime/actor/process_index/ProcessEventActor.scala
92.68% <88.88%> (+2.93%)
:arrow_up:

...ker/runtime/actor/process_index/ProcessIndex.scala
85.59% <92.85%> (+0.59%)
:arrow_up:

Continue to review full report at Codecov.

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controller and builder services fail to start after node reboot

I've seen this issue multiple times, both against 0.13 and now against 0.14.1 (I haven't tried 0.15, but I don't see anything there which would fix it).  I've seen it in both AWS and Vagrant.
When I shut down the controller service's node (the registry service has been on the same node every time, fwiw), the controller service fails to come back up, as does the builder service which I believe is stuck in a loop waiting for the controller service.  This looks similar to a couple of other issues (#2235 #2132 #2146) except that my version is up-to-date with the recommendations, and my /deis/platform/domain key is set.
The controller service is in an inactive/dead state and does not seem to attempt to start when I run deisctl start controller.  I get no output, no journal, and the command hangs indefinitely.
This has been going on for around 20 hours, so I don't expect it to ever recover.
Some relevant logs and debug info:
$ deisctl list
UNIT				MACHINE			LOAD	ACTIVE		SUB
deis-builder.service		b5083ca9.../<IP_ADDRESS>	loaded	activating	start-post
deis-cache.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-controller.service		b5083ca9.../<IP_ADDRESS>	loaded	inactive	dead
deis-database.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-logger-data.service	b5083ca9.../<IP_ADDRESS>	loaded	active		exited
deis-logger.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-logspout.service		2fa78f14.../<IP_ADDRESS>	loaded	active		running
deis-logspout.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-logspout.service		b5420254.../<IP_ADDRESS>	loaded	active		running
deis-publisher.service		2fa78f14.../<IP_ADDRESS>	loaded	active		running
deis-publisher.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-publisher.service		b5420254.../<IP_ADDRESS>	loaded	active		running
deis-registry.service		b5083ca9.../<IP_ADDRESS>	loaded	active		running
<EMAIL_ADDRESS>b5420254.../<IP_ADDRESS>	loaded	active		running
<EMAIL_ADDRESS>2fa78f14.../<IP_ADDRESS>	loaded	active		running
<EMAIL_ADDRESS>b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-store-daemon.service	2fa78f14.../<IP_ADDRESS>	loaded	active		running
deis-store-daemon.service	b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-store-daemon.service	b5420254.../<IP_ADDRESS>	loaded	active		running
deis-store-gateway.service	b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-store-monitor.service	2fa78f14.../<IP_ADDRESS>	loaded	active		running
deis-store-monitor.service	b5083ca9.../<IP_ADDRESS>	loaded	active		running
deis-store-monitor.service	b5420254.../<IP_ADDRESS>	loaded	active		running

I fiddled with conf.toml to allow verbose output on the following:
$ deisctl journal builder
[repeats many times]
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /home/git/.ssh/authorized_keys in sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /home/git/builder out of sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: ERROR Config check failed: exit status 1
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /home/git/check-repos in sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /usr/local/bin/gitreceive in sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /usr/local/bin/push-images in sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: INFO Target config /home/git/receiver out of sync
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:33Z c031bba113db confd[2896]: ERROR Config check failed: exit status 1
Oct 28 16:04:33 ip-10-21-1-44.ec2.internal sh[6132]: builder: waiting for confd to write initial templates...
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /home/git/.ssh/authorized_keys in sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /home/git/builder out of sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: ERROR Config check failed: exit status 1
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /home/git/check-repos in sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /usr/local/bin/gitreceive in sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /usr/local/bin/push-images in sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: INFO Target config /home/git/receiver out of sync
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: 2014-10-28T16:04:38Z c031bba113db confd[2911]: ERROR Config check failed: exit status 1
Oct 28 16:04:38 ip-10-21-1-44.ec2.internal sh[6132]: builder: waiting for confd to write initial templates...
[repeats many times]

All builder dependencies, including controller seem to have keys:
$ etcdctl ls --recursive
/deis
/deis/platform
/deis/platform/domain
/deis/router
/deis/router/<IP_ADDRESS>
/deis/router/<IP_ADDRESS>/host
/deis/router/<IP_ADDRESS>/port
/deis/router/<IP_ADDRESS>
/deis/router/<IP_ADDRESS>/port
/deis/router/<IP_ADDRESS>/host
/deis/router/gzipHttpVersion
/deis/router/gzipMinLength
/deis/router/gzipTypes
/deis/router/<IP_ADDRESS>
/deis/router/<IP_ADDRESS>/port
/deis/router/<IP_ADDRESS>/host
/deis/router/gzip
/deis/router/gzipCompLevel
/deis/router/gzipDisable
/deis/router/gzipProxied
/deis/router/gzipVary
/deis/store
/deis/store/gatewayKeyring
/deis/store/hosts
/deis/store/hosts/<IP_ADDRESS>
/deis/store/hosts/<IP_ADDRESS>
/deis/store/hosts/<IP_ADDRESS>
/deis/store/pgNum
/deis/store/delayStart
/deis/store/fsid
/deis/store/gateway
/deis/store/gateway/accessKey
/deis/store/gateway/port
/deis/store/gateway/host
/deis/store/gateway/secretKey
/deis/store/minSize
/deis/store/monKeyring
/deis/store/monSetupComplete
/deis/store/monSetupLock
/deis/store/osds
/deis/store/osds/<IP_ADDRESS>
/deis/store/osds/<IP_ADDRESS>
/deis/store/osds/<IP_ADDRESS>
/deis/store/adminKeyring
/deis/store/size
/deis/builder
/deis/builder/users
/deis/builder/users/bilts
/deis/builder/users/bilts/73:5b:[redacted]
/deis/cache
/deis/cache/host
/deis/cache/port
/deis/controller
/deis/controller/registrationEnabled
/deis/controller/secretKey
/deis/controller/webEnabled
/deis/controller/builderKey
/deis/controller/protocol
/deis/database
/deis/database/adminUser
/deis/database/engine
/deis/database/host
/deis/database/name
/deis/database/adminPass
/deis/database/bucketName
/deis/database/password
/deis/database/port
/deis/database/user
/deis/domains
/deis/logs
/deis/logs/host
/deis/logs/port
/deis/registry
/deis/registry/secretKey
/deis/registry/bucketName
/deis/registry/host
/deis/registry/port
/deis/registry/protocol
/deis/services
/deis/services/example-ruby-sinatra
/deis/services/example-ruby-sinatra/example-ruby-sinatra_v6.web.1

controller journal.  Note I have tried to start it a couple of times and a couple of different ways since the last journaled message, and let it run for hours.  I never get any output or journal messages.
$ deisctl journal controller
-- Logs begin at Mon 2014-10-27 16:22:37 UTC. --
Oct 27 19:28:36 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "DELETE /v1/apps/example-ruby-sinatra HTTP/1.0" 204 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:28:41 ip-10-21-1-44.ec2.internal sh[2029]: INFO marble-original: config marble-original-8295c4a updated
Oct 27 19:28:41 ip-10-21-1-44.ec2.internal sh[2029]: INFO marble-original: release marble-original-v1 created
Oct 27 19:28:41 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/apps HTTP/1.0" 201 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:28:49 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "DELETE /v1/apps/marble-original HTTP/1.0" 204 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:28:56 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: config example-ruby-sinatra-725e194 updated
Oct 27 19:28:56 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v1 created
Oct 27 19:28:56 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/apps HTTP/1.0" 201 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:29:06 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/push HTTP/1.1" 201 - "curl/7.35.0"
Oct 27 19:29:06 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/config HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:30:26 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: build example-ruby-sinatra-045ead1 created
Oct 27 19:30:26 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v2 created
Oct 27 19:30:28 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: pquinn scaled containers web=1
Oct 27 19:31:02 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/build HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:34:41 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/push HTTP/1.1" 201 - "curl/7.35.0"
Oct 27 19:34:41 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/config HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:35:59 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: build example-ruby-sinatra-b045927 created
Oct 27 19:35:59 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v3 created
Oct 27 19:36:43 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/build HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:36:56 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "GET /v1/apps/example-ruby-sinatra/logs HTTP/1.0" 200 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:37:43 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/push HTTP/1.1" 201 - "curl/7.35.0"
Oct 27 19:37:43 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/config HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:38:59 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: build example-ruby-sinatra-5788149 created
Oct 27 19:38:59 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v4 created
Oct 27 19:39:40 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/build HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:42:02 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "GET /v1/apps/example-ruby-sinatra/logs HTTP/1.0" 200 - "python-requests/2.3.0 CPython/2.7.5 Darwin/12.5.0"
Oct 27 19:43:11 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/push HTTP/1.1" 201 - "curl/7.35.0"
Oct 27 19:43:11 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/config HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:44:28 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: build example-ruby-sinatra-f74f646 created
Oct 27 19:44:28 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v5 created
Oct 27 19:45:10 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/build HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:46:17 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/push HTTP/1.1" 201 - "curl/7.35.0"
Oct 27 19:46:17 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/config HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 19:47:35 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: build example-ruby-sinatra-ce6f1fa created
Oct 27 19:47:35 ip-10-21-1-44.ec2.internal sh[2029]: INFO example-ruby-sinatra: release example-ruby-sinatra-v6 created
Oct 27 19:48:08 ip-10-21-1-44.ec2.internal sh[2029]: <IP_ADDRESS> "POST /v1/hooks/build HTTP/1.1" 200 - "curl/7.35.0"
Oct 27 20:15:31 ip-10-21-1-44.ec2.internal sh[2029]: 2014/10/27 20:15:31 Post http:///var/run/docker.sock/v1.15/containers/6140f2546f83fc41a66fc925a6832b7ea8b0474d8a8d0910bb4683c2b45ef1ec/wait: read unix /var/run/docker.sock: connection reset by peer
Oct 27 20:15:32 ip-10-21-1-44.ec2.internal docker[31780]: write unix /var/run/docker.sock: broken pipe
Oct 27 20:15:32 ip-10-21-1-44.ec2.internal docker[31780]: 2014/10/27 20:15:32 Error: failed to remove one or more containers
Oct 27 20:15:32 ip-10-21-1-44.ec2.internal systemd[1]: Stopped deis-controller.

Please let me know if any other information would help in debugging this

Hey @carmstrong,
I was able to reproduce this very issue in the version 1.4.1
Unfortunately, even after trying all the restarts suggested here the status of the controller and logger stay as dead and logspout stays as auto-restart status.
As there are no logs, it is impossible to understand what is going on.
Dou you have any idea on how to fix it?
Thanks!

@fagiani This is during a platform start, right? That makes sense that controller and logger are dead, as logspout starts early in the start cycle. Can you open a new issue with the output of journalctl -fu deis-logspout on the host that's having issues?

@carmstrong thanks for the prompt response!
The issue happened with deis working fine and not on platform start. It was right after a git push of one of the apps.
As we are moving on to production environment, after several trial and error, we restarted the 3 deis hosts and somehow cloud formation has terminated them and recreated other hosts and after that, the whole thing stopped working.
We have then started from scratch therefore I am not able to open another issue unless it happens again. At this point, I guess I just want to make sure you guys are aware this very issue still happens somehow in the latest version.
I'll let you know if we face that again!

Thanks @fagiani. Definitely reach out to us in IRC the next time you run into an issue - nearly all are recoverable, and we don't want to see you have to redeploy Deis! I'll file an issue (#3428) to track logspout issues.


Feature request: Quad finder

We should add a feature that helps you find a quad by examining the signal strength difference between left and right patch antennas

Doesn't that mean leaving the vtx on? It'll fry / reduce power pretty quick.

@oldrootbeer Yes, if you leave it on all the time, but we can send a signal through the elrs communication channel to turn on the video link, for example, for 30 seconds to search, and then turn it off for 30 seconds to cool down, and so on through the cycle until we find the drone

It's not my idea, it's been done before - it's just one of those things that other systems have but HDZero does not, so if we have it, it removes a reason to not go HDZero

I plan to add a frequency analyzer instead of this.


Dev dependencies

Closes https://github.com/CartoDB/node-mapnik/issues/17
There is still a minor security report pending but there isn't a jshint released with the fix. All of these are development dependencies so no impact to performance.

Codecov Report

Merging #29 into v3.6.2-carto will not change coverage.
The diff coverage is n/a.

@@              Coverage Diff              @@
##           v3.6.2-carto      #29   +/-   ##
=============================================
  Coverage         96.21%   96.21%
=============================================
  Files                42       42
  Lines              8877     8877
=============================================
  Hits               8541     8541
  Misses              336      336

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
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Updated validations.py python script.

Updated validations.py python script.
Fixes #1

It's a good idea to open an issue first for discussion.

[ ] Tests pass
[ ] Appropriate changes to README are included in PR

Does it work now?


TortoiseHG version included in nixpkgs is incompatible with the Mercurial version included

I've installed both Mercurial and TortoiseHG from nixpkgs in nixos-16.03, but starting up TortoiseHG just aborts with this error message:
#!python
** Mercurial version (3.7.1).  TortoiseHg version (3.6)
** Command:
** CWD: /home/benme
** Encoding: UTF-8
** Extensions loaded:
** Python version: 2.7.11 (default, Dec  5 2015, 19:47:20) [GCC 5.3.0]
** System: Linux benme-nixos 4.4.6 #1-NixOS SMP Wed Mar 16 15:43:17 UTC 2016 x86_64
** Qt-4.8.7 PyQt-4.11.3 QScintilla-2.9

This version of TortoiseHg requires Mercurial version 3.5.n to 3.6.n, but found 3.7.1

They're both installed by being listed in the paths of a buildEnv in my config.nix (mercurialFull and tortoisehg). I haven't done any overriding of either.

System: NixOS: 16.03.503.3f96280 (Emu)
Nix version: 1.11.2
Nixpkgs version: 16.03.503.3f96280

Looks like #13523 would already fix this

#13523 is merged in a4ce0e233b086845324f6b4d38ce3a408612a8d7
Please check whether this works for you.

Yep, tortoisehg works with that build, thanks!


Minor typo

One of the notes states: "Granting permissions make take a few minutes to fully process."
The word 'make' should be 'may'.

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 4a4b8453-d2bc-9c8d-bb16-26fa7e4c6363
Version Independent ID: 160642fc-b5a9-1ebe-7daf-105509b6e2f4
Content: Use the Graph API in Azure Active Directory B2C
Content Source: articles/active-directory-b2c/active-directory-b2c-devquickstarts-graph-dotnet.md
Service: active-directory
GitHub Login: @davidmu1
Microsoft Alias: davidmu

@Ashera138 Thanks for the feedback! We are currently investigating and will update you shortly.

@Ashera138 Thanks a lot for reporting this. I have updated the doc and it will be fixed shortly.


general split_dataset functions

Description
Create a single split_dataset function to split datasets in sequential and random mode to fix #91
This deprecates the sequential_split function, which becomes a case of the more general split_dataset function.
Questions

By default, DeprecationWarnings are not shown. Is it better to print a UserWarning for the sequential_split function?

Status

[ ] Ready to go

Happy 100th PR guys :1st_place_medal: :tada:
This fixes #91


New version: CUDA_jll v10.2.89+3

Autogenerated JLL package registration

Registering JLL package CUDA_jll.jl
Repository: https://github.com/JuliaBinaryWrappers/CUDA_jll.jl
Version: v10.2.89+3

pause, I think there is something wrong in the build recipe


glscopeclient failed to compile with glslc not found on Ubuntu 20.04 Fossa

Followed the instruction line by line at the getting started manual at here: https://www.antikernel.net/temp/glscopeclient-manual.pdf
with master branch commit 3bc6b5f29650c555a83cbe3dfbb39e3d00467496, when I tried to compile it it failed with missing glslc error:
n310@n310-alienware:~/scopehal-apps/build$ make -j8
[  1%] Building CXX object lib/log/CMakeFiles/log.dir/log.cpp.o
[  1%] Copying rendering kernels...
[  1%] Building CXX object lib/xptools/CMakeFiles/xptools.dir/Socket.cpp.o
[  2%] Copying icons...
[  2%] Copying gradients...
[  2%] Copying shaders...
[  2%] Compile shader Convert16BitSamples
/bin/sh: 1: glslc: not found
make[2]: *** [lib/scopeprotocols/shaders/CMakeFiles/protocolshaders.dir/build.make:61: lib/scopeprotocols/shaders/Convert16BitSamples.spv] Error 127
make[1]: *** [CMakeFiles/Makefile2:511: lib/scopeprotocols/shaders/CMakeFiles/protocolshaders.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
[  2%] Built target renderkernels
[  2%] Building CXX object lib/xptools/CMakeFiles/xptools.dir/UART.cpp.o
[  2%] Built target gradients
[  2%] Built target shaders
[  2%] Built target icons
[  2%] Building CXX object lib/log/CMakeFiles/log.dir/ColoredSTDLogSink.cpp.o
[  2%] Building CXX object lib/graphwidget/CMakeFiles/graphwidget.dir/Graph.cpp.o
[  2%] Building CXX object lib/log/CMakeFiles/log.dir/STDLogSink.cpp.o
[  3%] Building CXX object lib/log/CMakeFiles/log.dir/FILELogSink.cpp.o
[  4%] Linking CXX static library libxptools.a
[  4%] Built target xptools
[  4%] Linking CXX static library liblog.a
[  4%] Built target log
[  4%] Linking CXX shared library libgraphwidget.so
[  4%] Built target graphwidget
make: *** [Makefile:130: all] Error 2

You need to install the Vulkan SDK from https://vulkan.lunarg.com/sdk/home#linux.
This dependency was added very recently and hasn't made it into the docs yet. If you have any problems you can roll back to 4a96248c588ed5c14818201c6135b14a6d385a04 and it should build fine without Vulkan.

Installed Vulkan SDK, compiled it and it crashes with this error:
n310@n310-alienware:~/scopehal-apps/build$ glscopeclient
MESA-INTEL: warning: Performance support disabled, consider sysctl dev.i915.perf_stream_paranoid=0

Warning: ReadDataFile: Could not open file "shaders/SubtractFilter.spv"
terminate called after throwing an instance of 'vk::UnknownError'
  what():  vkCreateShaderModule: ErrorUnknown
Aborted (core dumped)

If I try to revert to https://github.com/glscopeclient/scopehal-apps/commit/4a96248c588ed5c14818201c6135b14a6d385a04 CMake cannot find yaml config file:
n310@n310-alienware:~/scopehal-apps/build$ cmake ..
-- The C compiler identification is GNU 9.4.0
-- The CXX compiler identification is GNU 9.4.0
-- Check for working C compiler: /usr/bin/cc
-- Check for working C compiler: /usr/bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /usr/bin/c++
-- Check for working CXX compiler: /usr/bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Found PkgConfig: /usr/bin/pkg-config (found version "0.29.1")
-- Checking for module 'gtkmm-3.0'
--   Found gtkmm-3.0, version 3.24.2
-- Checking for module 'sigc++-2.0'
--   Found sigc++-2.0, version 2.10.2
-- Checking for module 'ffts'
--   Variable 'exec_prefix' not defined in '/usr/local/lib/pkgconfig/ffts.pc'
-- Found libffts: /usr/local/include/ffts, /usr/local/lib/libffts.so
-- Looking for CL_VERSION_2_2
-- Looking for CL_VERSION_2_2 - found
-- Found OpenCL: /usr/lib/x86_64-linux-gnu/libOpenCL.so (found version "2.2")
-- Found OpenMP_C: -fopenmp (found version "4.5")
-- Found OpenMP_CXX: -fopenmp (found version "4.5")
-- Found OpenMP: TRUE (found version "4.5")
-- Checking for one of the modules 'clFFT>=2.0'
CMake Error at lib/scopehal/CMakeLists.txt:17 (find_package):
  By not providing "FindYAML.cmake" in CMAKE_MODULE_PATH this project has
  asked CMake to find a package configuration file provided by "YAML", but
  CMake did not find one.

  Could not find a package configuration file provided by "YAML" with any of
  the following names:

    YAMLConfig.cmake
    yaml-config.cmake

  Add the installation prefix of "YAML" to CMAKE_PREFIX_PATH or set
  "YAML_DIR" to a directory containing one of the above files.  If "YAML"
  provides a separate development package or SDK, be sure it has been
  installed.

-- Configuring incomplete, errors occurred!
See also "/home/n310/scopehal-apps/build/CMakeFiles/CMakeOutput.log".
n310@n310-alienware:~/scopehal-apps/build$

while I can compile at master

Warning: ReadDataFile: Could not open file "shaders/SubtractFilter.spv"
terminate called after throwing an instance of 'vk::UnknownError'
  what():  vkCreateShaderModule: ErrorUnknown
Aborted (core dumped)

This is a known issue. You can either copy the .spv files manually after the build or apply this patch: https://github.com/glscopeclient/scopehal/pull/671
The PR will probably be merged by @azonenberg when the current refactoring is done.

Should be fixed. Reopen if you still see the issue now that everything is merged.


Page-Up / Page-Down in git commit text box scrolls entire page

VSCode Version: 1.4.0
OS Version: Ubuntu 16.04 LTS

Steps to Reproduce:

In the Git view, enter multiple lines of text into the commit message text box, until the scroll bar appears.
While the text box is focused, press Page-Up / Page-Down.

At some point, the entire page is shifted up by approx. 15px. The tabs at the top are partly covered and there is some empty space below the status bar.

The (hidden) panel seems to overflow the whole workbench.

@isidorn I've attempted to unflow the panel when it gets hidden, which fixes the issue. Created a PR with that change for September, for you to review: #11436

I have merged the PR thus closing this issue


Various improvements to benchmarks

Signed-off-by: Andy Grove<EMAIL_ADDRESS>This PR makes it easier to submit multiple queries when running benchmarks, with results stored to disk so we can come back later to collect them, rather than monitoring one query at a time before starting the next one. It also records additional information beyond just the timings.
Specific changes include:

The logic for running cold and hot runs and recording timings is moved into a new BenchUtils object and it also records additional information, such as the Spark configuration, and writes one JSON output file per test.
The runBench method is now consistent between the two benchmarks, taking the query name as a parameter in both cases (previously the TPC-DS one took a lambda function). This makes it easier to run multiple queries from the spark-shell e.g. Seq("q5", "q6").foreach(q => TpcdsLikeBench.runQuery(spark, q)).
The main method in each benchmark now accepts multiple query names instead of just one, allowing us to call spark-submit to run multiple queries.

I will be filing a follow-up PR to improve the command-line interface when using spark-submit, so we can specify the number of cold and hot runs, and other options.

build

The main method in each benchmark now accepts multiple query names instead of just one, allowing us to call spark-submit to run multiple queries.

This would work well for a quick check IMHO, but then I am not sure what "cold" means. I am just saying that when we add this with cold vs hot runs, things start to be mostly hot, or at least medium heat ;)

The main method in each benchmark now accepts multiple query names instead of just one, allowing us to call spark-submit to run multiple queries.

This would work well for a quick check IMHO, but then I am not sure what "cold" means. I am just saying that when we add this with cold vs hot runs, things start to be mostly hot, or at least medium heat ;)

My main goal was to be able to kick off a run of all the queries without the need to manually monitor. Maybe a better approach is to just add a for loop to my bash script that runs spark-submit.

The main method in each benchmark now accepts multiple query names instead of just one, allowing us to call spark-submit to run multiple queries.

This would work well for a quick check IMHO, but then I am not sure what "cold" means. I am just saying that when we add this with cold vs hot runs, things start to be mostly hot, or at least medium heat ;)

My main goal was to be able to kick off a run of all the queries without the need to manually monitor. Maybe a better approach is to just add a for loop to my bash script that runs spark-submit.

Yes, agree, that is better. Doing it there allows us to improve testing also, potentially setting up/cleaning up between runs, if need be.

build

Actually I did have one thing, we should System.gc a couple of times after each run.

I personally would like to see this be optional.  Yes to get the best number possible we need to run System.gc a few times on the driver to make sure that any unused RDD has been collected to clear out the shuffle.  But in the real world I don't think anyone is doing this, so I would prefer to have it off by default.

Actually I did have one thing, we should System.gc a couple of times after each run.

I personally would like to see this be optional. Yes to get the best number possible we need to run System.gc a few times on the driver to make sure that any unused RDD has been collected to clear out the shuffle. But in the real world I don't think anyone is doing this, so I would prefer to have it off by default.

+1

build

build

build

build


how to handle dynamic classes in JS frameworks

I'm using Vue.js, and I have certain situations where I need to use semantic (in addition to) functional class names. For example when using vue-router, it will automatically put "vue-router-active" as one of the classes in the active menu item, and I need to be able to attach the necessary styles to that class.
Since this class name is dynamically added I cannot use the tachyon class names in the html. Instead, I need to define the "vue-router-active-class" in the style section of my component.
e.g.
.nav-item.vue-router-active {
color: #ffffff
background-color: #000000
}
Is there any way you could recommend for me to limit myself to the predefined tachyon styles

This is how I solved it:
<template>

CDI Admin

    <router-link class="nav-item" :class="getClassList('/contacts')" to="/contacts" tag="div">Contacts</router-link>
    <router-link class="nav-item" :class="getClassList('/matches')" to="/matches" tag="div">Matches</router-link>
    <router-link class="nav-item" :class="getClassList('/errors')" to="/errors" tag="div">Errors</router-link>
    <router-link class="nav-item" :class="getClassList('/jobs')" to="/jobs" tag="div">Jobs</router-link>
    <router-link class="nav-item" :class="getClassList('/logout')" to="/logout" tag="div">Logout</router-link>

</template>


Make iOS scanner view appear as Android does

With a scan window and a scan line. barcodes should only scan if they intersect with the scan line.

PR: https://github.com/apptreesoftware/flutter_barcode_reader/pull/34
@matthewtsmith ^

@dustin-graham I had to remove the scanLine change you added in #34 .
https://github.com/apptreesoftware/flutter_barcode_reader/pull/42
It was causing QR codes not to read.

@matthewtsmith I'll look back at it again to see if I can make it work. In my case I really need the scan line for precision in my app's scenarios.


[FLINK-2376] Raise the time limit in testFindConnectableAddress under Windows

Looks good, +1 to merge.


[BUG] ISM Allocation action does not match Opensearch server expectation

What is the bug?
I'm attempting to marshal a json file containing an ISM policy into the ism.Policy struct. The documentation for the allocation action specifies that the require value be of type string. This is also the type that the go struct's require value is.
However in the example in the docs, it shows that the value really should be an object.
    "allocation": {
      "require": { "temp": "warm" }
    }

and this is also what is expected by the Opensearch server.
If I encode the json as a string, i.e. "{ \"temp\": \"warm\" }", I can then marshal it into the go struct, but the server will then throw a parsing error saying that it expects an Object, not a String.
A similar thing happens with the wait_for field. The docs and go struct specify it needs to be a string, but the server expects a bool.
How can one reproduce the bug?
Attempt to json marshal the below json into an ism.Policy struct.
policyContent
{
    "policy": {
        "description": "hot warm workflow",
        "default_state": "hot",
        "schema_version": 1,
        "states": [
            {
                "name": "hot",
                "actions": [
                    {
                        "rollover": {
                            "min_index_age": "1d",
                            "min_size": "1gb"
                        }
                    }
                ],
                "transitions": [
                    {
                        "state_name": "warm",
                        "conditions": {
                            "min_rollover_age": "1m"
                        }
                    }
                ]
            },
            {
                "name": "warm",
                "actions": [
                    {
                        "allocation": {
                            "require": {
                                "temp": "warm"
                            },
                            "wait_for": false
                        }
                    }
                ]
            }
        ],
        "ism_template": [
            {
                "index_patterns": [
                    "myindex*"
                ],
                "priority": 100
            }
        ]
    }
}

What is the expected behavior?
The above json is what the opensearch server expects. If you directly send the above json to the server like so, it will succeed.
        // create ism client, open policy json file above and load policyContents

	req, err := opensearch.BuildRequest(http.MethodPut,
		"/_plugins/_ism/policies/myindex_policy",
		strings.NewReader(policyContents),
		nil, nil)
	if err != nil {
		return fmt.Errorf("failed to create ISM policy request for myindex: %w", err)
	}

	resp, err := ismClient.Client.Perform(req)
	if err != nil {
		return fmt.Errorf("failed to create ISM policy for myindex: %w", err)
	}
	if resp.StatusCode == http.StatusCreated {
		return nil
	}

I think the documentation needs to be updated to say that it accepts an object, which would allow the go struct value to be a type of map[string]string or json.RawMessage or whatever is deemed fit.
What is your host/environment?
Operating system, version.
Do you have any screenshots?
If applicable, add screenshots to help explain your problem.
Do you have any additional context?
Add any other context about the problem.

[Catch All Triage - 1, 2, 3]
There's an effort to generate some of this from spec @kellen-miller, see #284. Would be helpful if you want to ensure these APIs are present and properly documented in the spec itself?