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

Question

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

Answer 1

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

Answer 2

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.

Answer 3

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)