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Trying to deploy image segmentation model locally WSL2+Ubuntu-22.04

Ildar Boltunov 5 Reputation points
2024-04-04T02:55:15.67+00:00

Trained the model. It looks like no errors.

But wheh I'm trying model generated conda.yaml file to recreate conda environment om WSL2+Ubuntu-22.04

it complains about recordclass==0.15.1. Tried everything possible without any luck.

Any help is really appreciated.

conda.yaml:

channels:

  • conda-forge
  • anaconda

dependencies:

  • python=3.9.7
  • pip:
    • adal==1.2.7
    • applicationinsights==0.11.10
    • arch==5.3.1
    • argcomplete==3.2.3
    • asttokens==2.4.1
    • attrs==23.2.0
    • azure-common==1.1.28
    • azure-core==1.30.1
    • azure-graphrbac==0.61.1
    • azure-identity==1.15.0
    • azure-mgmt-authorization==4.0.0
    • azure-mgmt-containerregistry==10.3.0
    • azure-mgmt-core==1.4.0
    • azure-mgmt-keyvault==10.3.0
    • azure-mgmt-network==25.2.0
    • azure-mgmt-resource==23.0.1
    • azure-mgmt-storage==21.1.0
    • azure-storage-blob==12.19.0
    • azure-storage-queue==12.9.0
    • azureml-automl-core==1.55.0.post2
    • azureml-automl-dnn-vision==1.55.0.post1
    • azureml-automl-runtime==1.55.0.post1
    • azureml-core==1.55.0.post2
    • azureml-dataprep==5.1.6
    • azureml-dataprep-native==41.0.0
    • azureml-dataprep-rslex==2.22.2
    • azureml-dataset-runtime==1.55.0
    • azureml-defaults==1.55.0
    • azureml-inference-server-http==1.0.0
    • azureml-interpret==1.55.0
    • azureml-mlflow==1.55.0
    • azureml-responsibleai==1.55.0
    • azureml-telemetry==1.55.0
    • azureml-train-automl-client==1.55.0
    • azureml-train-automl-runtime==1.55.0.post1
    • azureml-train-core==1.55.0
    • azureml-train-restclients-hyperdrive==1.55.0
    • azureml-training-tabular==1.55.0.post1
    • backports-tempfile==1.0
    • backports-weakref==1.0.post1
    • bcrypt==4.1.2
    • bokeh==2.4.3
    • cachetools==5.3.3
    • captum==0.5.0
    • cffi==1.16.0
    • colorcet==3.0.1
    • coloredlogs==15.0.1
    • contextlib2==21.6.0
    • cryptography==42.0.5
    • dask==2023.2.0
    • dataclasses==0.6
    • debugpy==1.8.1
    • decorator==5.1.1
    • dice-ml==0.11
    • dill==0.3.8
    • distributed==2023.2.0
    • distro==1.9.0
    • docker==7.0.0
    • dotnetcore2==3.1.23
    • econml==0.15.0
    • entrypoints==0.4
    • erroranalysis==0.5.3
    • exceptiongroup==1.2.0
    • executing==2.0.1
    • fairlearn==0.7.0
    • fire==0.6.0
    • flask==2.2.5
    • flask-cors==3.0.10
    • flatbuffers==24.3.7
    • fsspec==2024.2.0
    • fusepy==3.0.1
    • fvcore==0.1.5.post20221221
    • gensim==4.3.2
    • gitdb==4.0.11
    • gitpython==3.1.42
    • google-api-core==2.17.1
    • google-auth==2.28.2
    • googleapis-common-protos==1.63.0
    • gunicorn==20.1.0
    • humanfriendly==10.0
    • imageio==2.34.0
    • importlib-metadata==7.0.2
    • inference-schema==1.7.1
    • interpret-community==0.31.0
    • interpret-core==0.5.0
    • iopath==0.1.10
    • ipykernel==6.8.0
    • ipython==8.18.1
    • isodate==0.6.1
    • itsdangerous==2.1.2
    • jedi==0.19.1
    • jeepney==0.8.0
    • jinja2==3.1.3
    • jmespath==0.10.0
    • jsonpickle==3.0.3
    • jsonschema==4.21.1
    • jsonschema-specifications==2023.12.1
    • jupyter-client==7.4.9
    • jupyter-core==5.7.2
    • keras2onnx==1.6.0
    • knack==0.11.0
    • lightgbm==3.2.1
    • locket==1.0.0
    • markupsafe==2.1.2
    • matplotlib==3.6.3
    • matplotlib-inline==0.1.6
    • ml-wrappers==0.5.5
    • mlflow-skinny==2.11.1
    • mpmath==1.3.0
    • msal==1.27.0
    • msal-extensions==1.1.0
    • msgpack==1.0.8
    • msrest==0.7.1
    • msrestazure==0.6.4
    • munch==4.0.0
    • ndg-httpsclient==0.5.1
    • nest-asyncio==1.6.0
    • networkx==2.5
    • nose==1.3.7
    • nvidia-cublas-cu11==11.10.3.66
    • nvidia-cuda-nvrtc-cu11==11.7.99
    • nvidia-cuda-runtime-cu11==11.7.99
    • nvidia-cudnn-cu11==8.5.0.96
    • oauthlib==3.2.2
    • onnx==1.14.1
    • onnxconverter-common==1.13.0
    • onnxmltools==1.11.2
    • onnxruntime==1.14.1
    • opencensus==0.11.4
    • opencensus-context==0.1.3
    • opencensus-ext-azure==1.1.13
    • opencv-python-headless==4.4.0.46
    • packaging==23.2
    • param==2.0.2
    • paramiko==3.4.0
    • parso==0.8.3
    • partd==1.4.1
    • pathspec==0.12.1
    • patsy==0.5.6
    • pexpect==4.9.0
    • pkginfo==1.10.0
    • pmdarima==1.8.5
    • portalocker==2.8.2
    • pretrainedmodels==0.7.4
    • prompt-toolkit==3.0.43
    • property-cached==1.6.4
    • protobuf==3.20.3
    • ptyprocess==0.7.0
    • pure-eval==0.2.2
    • pyarrow==14.0.2
    • pyasn1==0.5.1
    • pyasn1-modules==0.3.0
    • pycparser==2.21
    • pyct==0.5.0
    • pydantic==1.10.14
    • pygments==2.17.2
    • pyjwt==2.8.0
    • pynacl==1.5.0
    • pynvml==8.0.4
    • pyopenssl==24.1.0
    • pytorch-ignite==0.4.13
    • pywavelets==1.5.0
    • pyyaml==6.0.1
    • pyzmq==25.1.2
    • raiutils==0.4.1
    • referencing==0.33.0
    • requests-oauthlib==1.4.0
    • resnest==0.0.6b20210504
    • responsibleai==0.33.0
    • rpds-py==0.18.0
    • rsa==4.9
    • s3transfer==0.5.2
    • saliency==0.1.3
    • scikit-image==0.19.3
    • secretstorage==3.3.3
    • semver==2.13.0
    • simplification==0.5.10
    • skl2onnx==1.14.1
    • sklearn-pandas==1.7.0
    • smart-open==6.4.0
    • smmap==5.0.1
    • sortedcontainers==2.4.0
    • sparse==0.15.1
    • sqlparse==0.4.4
    • stack-data==0.6.3
    • statsmodels==0.13.5
    • sympy==1.12
    • tabulate==0.9.0
    • tblib==3.0.0
    • termcolor==2.4.0
    • tifffile==2024.2.12
    • timm==0.4.12
    • toolz==0.12.1
    • torch==1.13.1
    • torchvision==0.14.1
    • tornado==6.4
    • traitlets==5.14.2
    • typing-extensions==4.10.0
    • urllib3==1.26.18
    • wcwidth==0.2.13
    • werkzeug==3.0.1
    • wrapt==1.16.0
    • yacs==0.1.8
    • zict==3.0.0
  • boto3=1.20.19
  • botocore=1.23.19
  • brotli=1.1.0
  • brotli-bin=1.1.0
  • brotli-python=1.1.0
  • ca-certificates=2024.2.2
  • certifi=2024.2.2
  • charset-normalizer=3.3.2
  • cloudpickle=2.2.1
  • colorama=0.4.6
  • contourpy=1.2.0
  • cycler=0.12.1
  • cython=3.0.9
  • fonttools=4.49.0
  • freetype=2.12.1
  • idna=3.6
  • importlib-resources=6.3.0
  • importlib_resources=6.3.0
  • joblib=1.2.0
  • kiwisolver=1.4.5
  • lcms2=2.16
  • lerc=4.0.0
  • libblas=3.9.0
  • libbrotlicommon=1.1.0
  • libbrotlidec=1.1.0
  • libbrotlienc=1.1.0
  • libcblas=3.9.0
  • libdeflate=1.19
  • libffi=3.3
  • libgfortran-ng=13.2.0
  • libgfortran5=13.2.0
  • libjpeg-turbo=3.0.0
  • liblapack=3.9.0
  • libllvm11=11.1.0
  • libopenblas=0.3.25
  • libpng=1.6.43
  • libsqlite=3.45.2
  • libtiff=4.6.0
  • libwebp-base=1.3.2
  • libxcb=1.15
  • libzlib=1.2.13
  • llvmlite=0.39.1
  • munkres=1.1.4
  • numba=0.56.4
  • numpy=1.23.5
  • openjpeg=2.5.2
  • pandas=1.3.5
  • pillow=10.2.0
  • pip=21.3.1
  • platformdirs=4.2.0
  • pooch=1.8.1
  • psutil=5.8.0
  • pthread-stubs=0.4
  • pycocotools=2.0.6
  • pyparsing=3.1.2
  • pysocks=1.7.1
  • python-dateutil=2.9.0
  • python-tzdata=2024.1
  • python_abi=3.9
  • pytz=2024.1
  • recordclass=0.15.1
  • requests=2.31.0
  • scikit-learn=1.1.3
  • scipy=1.10.1
  • setuptools=65.5.1
  • six=1.16.0
  • slicer=0.0.7
  • tbb=2021.1.1
  • threadpoolctl=3.3.0
  • tqdm=4.66.2
  • tzdata=2024a
  • unicodedata2=15.1.0
  • wheel=0.38.1
  • xorg-libxau=1.0.11
  • xorg-libxdmcp=1.1.3
  • zipp=3.17.0
  • zstd=1.5.5

name: project_environment

Azure Machine Learning

3 answers

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  1. Halvor Yttredal 15 Reputation points
    2024-04-07T22:59:16.4+00:00

    Solved it by editing the conda.yaml in the workspaceartifact store to say recordclass==0.21.1 for the model in question. Before it was deployed to the batch endpoint.

    I assume the same should work for your deployment

    2 people found this answer helpful.
  2. Ildar Boltunov 5 Reputation points
    2024-04-23T19:24:28.9333333+00:00

    I was able to resolve it. Kudos to Abdul Gafar Manuel Meque from MS tech support.

    Details:

    Symptom: While trying to create conda environment with “conda env create ...”, the following error is thrown: “PackagesNotFoundError: The following packages are not available from current channels:   - recordclass=0.15.1*”  

    Troubleshooting Summary Re-run the conda environment creation command and analyze the entire output.  

    Cause: pip was trying to install some packages from source, which requires gcc to be installed.  

    Resolution: 1. Removed recordclass from list of packages in conda.yml. 2. refresh local package index with apt update 3. installed gcc 4. re-run the conda environment creation. (since recordclass is automatically install)

    1 person found this answer helpful.
    0 comments
  3. Halvor Yttredal 15 Reputation points
    2024-04-07T21:10:43.5766667+00:00

    Having the same issue when deploying batch endpoint in ML studio. When creating a job for the endpoint, the prepare-image task fails, Saying that recordclass==0.15.1* is not available in the given channels.

    1 person found this answer helpful.

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