How to find which libraries and packages and versions are installed on Azure ML Studio?

Question

I am trying to upload a machine learning project onto Azure ML Studio. I need to figure out the following: What python packages/libraries and their versions are currently available? How do I add new packages/libraries to an environment in my workspace? 

Basically, I want to see if the following packages/libraries listed below are available and in the corresponding versions. Is there a list somewhere that I can search and compare to? If they are not available, then I need to install these packages in the correct versions which I do not know how to do.

non-python dependencies: bzip2=1.0.8=h620ffc9_4 ca-certificates=2023.08.22=hca03da5_0 libffi=3.4.4=hca03da5_0 ncurses=6.4=h313beb8_0 openssl=3.0.10=h1a28f6b_2 pip=23.2.1=py311hca03da5_0 python=3.11.5=hb885b13_0 readline=8.2=h1a28f6b_0 setuptools=68.0.0=py311hca03da5_0 sqlite=3.41.2=h80987f9_0 tk=8.6.12=hb8d0fd4_0 wheel=0.38.4=py311hca03da5_0 xz=5.4.2=h80987f9_0 zlib=1.2.13=h5a0b063_0

python dependencies: alembic==1.12.0 certifi==2023.7.22 charset-normalizer==3.2.0 cloudpickle==3.0.0 cmaes==0.10.0 colorlog==6.7.0 contourpy==1.1.0 cycler==0.11.0 docopt==0.6.2 et-xmlfile==1.1.0 fonttools==4.42.1 idna==3.4 imageio==2.33.1 joblib==1.3.2 kiwisolver==1.4.5 lazy-loader==0.3 lime==0.2.0.1 llvmlite==0.41.1 mako==1.2.4 markupsafe==2.1.3 matplotlib==3.7.3 mlxtend==0.22.0 networkx==3.2.1 numba==0.58.1 numpy==1.25.2 openpyxl==3.1.2 optuna==3.3.0 packaging==23.1 pandas==2.1.0 patsy==0.5.3 pillow==10.0.0 pipreqs==0.4.13 pyparsing==3.1.1 python-dateutil==2.8.2 pytz==2023.3.post1 pyyaml==6.0.1 requests==2.31.0 scikit-image==0.22.0 scikit-learn==1.3.0 scipy==1.11.2 seaborn==0.12.2 six==1.16.0 slicer==0.0.7 sqlalchemy==2.0.21 statsmodels==0.14.0 threadpoolctl==3.2.0 tifffile==2023.12.9 tqdm==4.66.1 typing-extensions==4.8.0 tzdata==2023.3 urllib3==2.0.4 xgboost==2.0.2 yarg==0.1.9

Answer 1

@Nami Mottaghi Thanks for reaching out to us, there was page for the installed package, but it is not available now. However, you can also find the installed packages and their versions directly in the Azure ML Studio by following these steps:

1. In ML Studio, go to the "Notebooks" tab.
2. Create a new notebook or open an existing one.
3. In a new cell, type !pip freeze and run the cell. This command will list all installed packages and their versions.

To install new packages, you can use the pip command. Here is an example:

!pip install your-package-name==version-number  

 
Replace "your-package-name" with the name of the package you want to install, and "version-number" with the version you want.

Remember, any packages you install this way will be available only in the current notebook session. If you need a package to be available across all notebooks and experiments, you need to create a Python script and upload it to the "Scripts" section of ML Studio. This script should contain the pip install command for the required package.

For more advanced package management, you might want to consider using Azure ML environments. Environments allow you to define a collection of resources that Azure ML will use to run your training scripts. Resources include Python packages, environment variables, software settings, and others. You can find more information about Azure ML environments in the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/concept-environments Please let us know how it works and we can discuss further.

Regards,

Yutong

-Please kindly accept the answer if you feel helpful to support the community, thanks a lot.

Answer 2

What python packages/libraries and their versions are currently available?

If you want to check out the Python packages included in the curated environment, you can take a look at the Dockerfile of the relevant image in the following GitHub repository. https://github.com/Azure/AzureML-Containers

How do I add new packages/libraries to an environment in my workspace?

You may create a new Azure Machine Learning environment with your required Python dependencies. I recommend the following documentation to get started with building environments in AML. https://learn.microsoft.com/en-us/azure/machine-learning/concept-environments?view=azureml-api-2