Make Modin on MPI through unidist component more explicit

Signed-off-by: Igoshev, Iaroslav <iaroslav.igoshev@intel.com>

.github/workflows/ci-notebooks.yml

@@ -8,6 +8,7 @@ on:
       - setup.cfg
       - setup.py
       - requirements/env_hdk.yml
+      - requirements/env_unidist.yml
 concurrency:
   # Cancel other jobs in the same branch. We don't care whether CI passes
   # on old commits.
@@ -28,12 +29,17 @@ jobs:
     steps:
       - uses: actions/checkout@v3
       - uses: ./.github/actions/python-only
-        if: matrix.execution != 'hdk_on_native'
+        if: matrix.execution != 'hdk_on_native' || matrix.execution != 'pandas_on_unidist'
       - uses: ./.github/actions/mamba-env
         with:
           environment-file: requirements/env_hdk.yml
           activate-environment: modin_on_hdk
         if: matrix.execution == 'hdk_on_native'
+      - uses: ./.github/actions/mamba-env
+        with:
+          environment-file: requirements/env_unidist.yml
+          activate-environment: modin_on_unidist
+        if: matrix.execution == 'pandas_on_unidist'
       - name: Cache datasets
         uses: actions/cache@v2
         with:
@@ -43,29 +49,29 @@ jobs:
       # replace modin with . in the tutorial requirements file for `pandas_on_ray` and
       # `pandas_on_dask` since we need Modin built from sources
       - run: sed -i 's/modin/./g' examples/tutorial/jupyter/execution/${{ matrix.execution }}/requirements.txt
-        if: matrix.execution != 'hdk_on_native'
+        if: matrix.execution != 'hdk_on_native' || matrix.execution != 'pandas_on_unidist'
       # install dependencies required for notebooks execution for `pandas_on_ray` and `pandas_on_dask`
       # Override modin-spreadsheet install for now
       - run: |
           pip install -r examples/tutorial/jupyter/execution/${{ matrix.execution }}/requirements.txt
           pip install git+https://github.com/modin-project/modin-spreadsheet.git@49ffd89f683f54c311867d602c55443fb11bf2a5
-        if: matrix.execution != 'hdk_on_native'
-      # Build Modin from sources for `hdk_on_native`
+        if: matrix.execution != 'hdk_on_native' || matrix.execution != 'pandas_on_unidist'
+      # Build Modin from sources for `hdk_on_native` and `pandas_on_unidist`
       - run: pip install -e .
-        if: matrix.execution == 'hdk_on_native'
+        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
       # install test dependencies
       # NOTE: If you are changing the set of packages installed here, make sure that
       # the dev requirements match them.
       - run: pip install pytest pytest-cov black flake8 flake8-print flake8-no-implicit-concat
-        if: matrix.execution != 'hdk_on_native'
+        if: matrix.execution != 'hdk_on_native' || matrix.execution != 'pandas_on_unidist'
       - run: pip install flake8-print jupyter nbformat nbconvert
-        if: matrix.execution == 'hdk_on_native'
+        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
       - run: pip list
-        if: matrix.execution != 'hdk_on_native'
+        if: matrix.execution != 'hdk_on_native' || matrix.execution != 'pandas_on_unidist'
       - run: |
           conda info
           conda list
-        if: matrix.execution == 'hdk_on_native'
+        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
       # setup kernel configuration for `pandas_on_unidist` execution with mpi backend
       - run: python examples/tutorial/jupyter/execution/${{ matrix.execution }}/setup_kernel.py
         if: matrix.execution == 'pandas_on_unidist'

.github/workflows/ci.yml

@@ -97,7 +97,6 @@ jobs:
         run: |
           MODIN_ENGINE=dask python -c "import modin.pandas as pd; print(pd.DataFrame([1,2,3]))"
           MODIN_ENGINE=ray python -c "import modin.pandas as pd; print(pd.DataFrame([1,2,3]))"
-          MODIN_ENGINE=unidist UNIDIST_BACKEND=mpi mpiexec -n 1 python -c "import modin.pandas as pd; print(pd.DataFrame([1,2,3]))"
 
   test-internals:
     needs: [lint-flake8, lint-black-isort]

README.md

@@ -57,24 +57,30 @@ The charts below show the speedup you get by replacing pandas with Modin based o
 Modin can be installed with `pip` on Linux, Windows and MacOS:
 
 ```bash
-pip install "modin[all]" # (Recommended) Install Modin with all of Modin's currently supported engines.
+pip install "modin[all]" # (Recommended) Install Modin with Ray and Dask engines.
 ```
 
 If you want to install Modin with a specific engine, we recommend:
 
 ```bash
 pip install "modin[ray]" # Install Modin dependencies and Ray.
 pip install "modin[dask]" # Install Modin dependencies and Dask.
-pip install "modin[unidist]" # Install Modin dependencies and Unidist.
+pip install "modin[mpi]" # Install Modin dependencies and MPI through unidist.
 ```
 
+To get Modin on MPI through unidist (starting unidist 0.5.0) fully working
+it is required to have a working MPI implementation installed beforehand.
+Otherwise, installation of `modin[mpi]` may fail. Refer to
+[Installing with pip](https://unidist.readthedocs.io/en/latest/installation.html#installing-with-pip)
+section of the unidist documentation for more details about installation.
+
 Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
 
 #### From conda-forge
 
 Installing from [conda forge](https://github.com/conda-forge/modin-feedstock) using `modin-all`
 will install Modin and four engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask),
-[Unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
+[MPI through unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
 
 ```bash
 conda install -c conda-forge modin-all
@@ -85,10 +91,14 @@ Each engine can also be installed individually (and also as a combination of sev
 ```bash
 conda install -c conda-forge modin-ray  # Install Modin dependencies and Ray.
 conda install -c conda-forge modin-dask # Install Modin dependencies and Dask.
-conda install -c conda-forge modin-unidist # Install Modin dependencies and Unidist.
+conda install -c conda-forge modin-mpi # Install Modin dependencies and MPI through unidist.
 conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
 ```
 
+Refer to
+[Installing with conda](https://unidist.readthedocs.io/en/latest/installation.html#installing-with-conda)
+section of the unidist documentation for more details on how to install a specific MPI implementation to run on.
+
 To speed up conda installation we recommend using libmamba solver. To do this install it in a base environment:
 
 ```bash
@@ -119,7 +129,7 @@ export MODIN_ENGINE=unidist # Modin will use Unidist
 ```
 
 If you want to choose the Unidist engine, you should set the additional environment 
-variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
+variable ``UNIDIST_BACKEND``. Currently, Modin only supports MPI through unidist:
 
 ```bash
 export UNIDIST_BACKEND=mpi # Unidist will use MPI backend
@@ -144,7 +154,7 @@ _Note: You should not change the engine after your first operation with Modin as
 
 #### Which engine should I use?
 
-On Linux, MacOS, and Windows you can install and use either Ray, Dask or Unidist. There is no knowledge required
+On Linux, MacOS, and Windows you can install and use either Ray, Dask or MPI through unidist. There is no knowledge required
 to use either of these engines as Modin abstracts away all of the complexity, so feel
 free to pick either!
 

docs/development/architecture.rst

@@ -216,8 +216,8 @@ documentation page on :doc:`contributing </development/contributing>`.
     - Uses the `Dask Futures`_ execution framework.
     - The storage format is `pandas` and the in-memory partition type is a pandas DataFrame.
     - For more information on the execution path, see the :doc:`pandas on Dask </flow/modin/core/execution/dask/implementations/pandas_on_dask/index>` page.
-- :doc:`pandas on Unidist </development/using_pandas_on_unidist>`
-    - Uses the Unidist_ execution framework.
+- :doc:`pandas on MPI </development/using_pandas_on_mpi>`
+    - Uses MPI_ through the Unidist_ execution framework.
     - The storage format is `pandas` and the in-memory partition type is a pandas DataFrame.
     - For more information on the execution path, see the :doc:`pandas on Unidist </flow/modin/core/execution/unidist/implementations/pandas_on_unidist/index>` page.
 - :doc:`pandas on Python </development/using_pandas_on_python>`
@@ -228,8 +228,8 @@ documentation page on :doc:`contributing </development/contributing>`.
     - Uses the Ray_ execution framework.
     - The storage format is `pandas` and the in-memory partition type is a pandas DataFrame.
     - For more information on the execution path, see the :doc:`experimental pandas on Ray </flow/modin/experimental/core/execution/ray/implementations/pandas_on_ray/index>` page.
-- pandas on Unidist (experimental)
-    - Uses the Unidist_ execution framework.
+- pandas on MPI (experimental)
+    - Uses MPI_ through the Unidist_ execution framework.
     - The storage format is `pandas` and the in-memory partition type is a pandas DataFrame.
     - For more information on the execution path, see the :doc:`experimental pandas on Unidist </flow/modin/experimental/core/execution/unidist/implementations/pandas_on_unidist/index>` page.
 - pandas on Dask (experimental)
@@ -375,6 +375,7 @@ details. The documentation covers most modules, with more docs being added every
 .. _Arrow tables: https://arrow.apache.org/docs/python/generated/pyarrow.Table.html
 .. _Ray: https://github.com/ray-project/ray
 .. _Unidist: https://github.com/modin-project/unidist
+.. _MPI: https://www.mpi-forum.org/
 .. _code: https://github.com/modin-project/modin/blob/master/modin/core/dataframe
 .. _Dask: https://github.com/dask/dask
 .. _Dask Futures: https://docs.dask.org/en/latest/futures.html

docs/development/index.rst

@@ -10,7 +10,7 @@ Development
     using_pandas_on_ray
     using_pandas_on_dask
     using_pandas_on_python
-    using_pandas_on_unidist
+    using_pandas_on_mpi
     using_hdk
     using_pyarrow_on_ray
 

docs/development/partition_api.rst

@@ -36,7 +36,7 @@ in the worker process that processes a function (please, refer to `Dask document
 
 Unidist engine
 --------------
-Currently, Modin only supports unidist on MPI backend. There is no mentioned above issue for
+Currently, Modin only supports MPI through unidist. There is no mentioned above issue for
 Modin on ``Unidist`` engine using ``MPI`` backend with ``pandas`` in-memory format
 because ``Unidist`` saves any objects in the MPI worker process that processes a function
 (please, refer to `Unidist documentation`_ for more information).

docs/development/using_pandas_on_unidist.rst → docs/development/using_pandas_on_mpi.rst

@@ -1,14 +1,14 @@
-pandas on Unidist
-=================
+pandas on MPI through unidist
+=============================
 
-This section describes usage related documents for the pandas on Unidist component of Modin.
+This section describes usage related documents for the pandas on MPI through unidist component of Modin.
 
 Modin uses pandas as a primary memory format of the underlying partitions and optimizes queries
 ingested from the API layer in a specific way to this format. Thus, there is no need to care of choosing it
 but you can explicitly specify it anyway as shown below.
 
-One of the execution engines that Modin uses is Unidist. Currently, Modin only supports Unidist on MPI backend.
-To enable the pandas on Unidist execution using MPI backend you should set the following environment variables:
+One of the execution engines that Modin uses is MPI through unidist.
+To enable the pandas on MPI through unidist execution you should set the following environment variables:
 
 .. code-block:: bash
 

docs/flow/modin/core/execution/unidist/implementations/pandas_on_unidist/index.rst

@@ -6,7 +6,8 @@ PandasOnUnidist Execution
 Queries that perform data transformation, data ingress or data egress using the `pandas on Unidist` execution
 pass through the Modin components detailed below.
 
-To enable `pandas on Unidist` execution, please refer to the usage section in :doc:`pandas on Unidist </development/using_pandas_on_unidist>`.
+To enable `pandas on MPI through unidist` execution,
+please refer to the usage section in :doc:`pandas on MPI through unidist </development/using_pandas_on_mpi>`.
 
 Data Transformation
 '''''''''''''''''''

docs/getting_started/faq.rst

@@ -119,7 +119,7 @@ and Modin will do computation with that engine:
     pip install "modin[dask]" # Install Modin dependencies and Dask to run on Dask
     export MODIN_ENGINE=dask  # Modin will use Dask
 
-    pip install "modin[unidist]" # Install Modin dependencies and Unidist to run on Unidist.
+    pip install "modin[mpi]" # Install Modin dependencies and MPI to run on MPI through unidist.
     export MODIN_ENGINE=unidist  # Modin will use Unidist
     export UNIDIST_BACKEND=mpi   # Unidist will use MPI backend.
 

docs/getting_started/installation.rst

@@ -30,8 +30,13 @@ Modin can be used with :doc:`Ray</development/using_pandas_on_ray>`, :doc:`Dask<
 
   pip install "modin[ray]" # Install Modin dependencies and Ray to run on Ray
   pip install "modin[dask]" # Install Modin dependencies and Dask to run on Dask
-  pip install "modin[unidist]" # Install Modin dependencies and Unidist to run on Unidist
-  pip install "modin[all]" # Install all of the above
+  pip install "modin[mpi]" # Install Modin dependencies and MPI to run on MPI through unidist
+  pip install "modin[all]" # Install Ray and Dask
+
+To get Modin on MPI through unidist (starting unidist 0.5.0) fully working
+it is required to have a working MPI implementation installed beforehand.
+Otherwise, installation of ``modin[mpi]`` may fail. Refer to
+`Installing with pip`_ section of the unidist documentation for more details about installation.
 
 Modin will automatically detect which engine you have installed and use that for
 scheduling computation! See below for HDK engine installation.
@@ -65,7 +70,7 @@ storage formats or for different functionalities of Modin. Here is a list of dep
 
 .. code-block:: bash
 
-  pip install "modin[unidist]" # If you want to use the Unidist execution engine
+  pip install "modin[mpi]" # If you want to use MPI through unidist execution engine
 
 Installing on Google Colab
 """""""""""""""""""""""""""
@@ -106,18 +111,18 @@ it is possible to install modin with chosen engine(s) alongside. Current options
 +---------------------------------+---------------------------+-----------------------------+
 | modin-ray                       | Ray_                      |       Linux, Windows        |
 +---------------------------------+---------------------------+-----------------------------+
-| modin-unidist                   | Unidist_                  |   Linux, Windows, MacOS     |
+| modin-mpi                       | MPI_ through unidist_     |   Linux, Windows, MacOS     |
 +---------------------------------+---------------------------+-----------------------------+
 | modin-hdk                       | HDK_                      |          Linux              |
 +---------------------------------+---------------------------+-----------------------------+
 | modin-all                       | Dask, Ray, Unidist, HDK   |          Linux              |
 +---------------------------------+---------------------------+-----------------------------+
 
-For installing Dask, Ray and Unidist engines into conda environment following command should be used:
+For installing Dask, Ray and MPI through unidist engines into conda environment following command should be used:
 
 .. code-block:: bash
 
-  conda install -c conda-forge modin-ray modin-dask modin-unidist
+  conda install -c conda-forge modin-ray modin-dask modin-mpi
 
 All set of engines could be available in conda environment by specifying:
 
@@ -129,7 +134,10 @@ or explicitly:
 
 .. code-block:: bash
 
-  conda install -c conda-forge modin-ray modin-dask modin-unidist modin-hdk
+  conda install -c conda-forge modin-ray modin-dask modin-mpi modin-hdk
+
+Refer to `Installing with conda`_ section of the unidist documentation
+for more details on how to install a specific MPI implementation to run on.
 
 ``conda`` may be slow installing ``modin-all`` or combitations of execution engines so we currently recommend using libmamba solver for the installation process.
 To do this install it in a base environment:
@@ -171,7 +179,7 @@ also use ``pip``.
 This will install directly from the repo without you having to manually clone it! Please be aware
 that these changes have not made it into a release and may not be completely stable.
 
-If you would like to install Modin with a specific engine, you can use ``modin[ray]`` or ``modin[dask]`` or ``modin[unidist]`` instead of ``modin[all]`` in the command above.
+If you would like to install Modin with a specific engine, you can use ``modin[ray]`` or ``modin[dask]`` or ``modin[mpi]`` instead of ``modin[all]`` in the command above.
 
 Windows
 -------
@@ -214,7 +222,10 @@ Once cloned, ``cd`` into the ``modin`` directory and use ``pip`` to install:
 .. _WSL: https://docs.microsoft.com/en-us/windows/wsl/install-win10
 .. _Ray: http://ray.readthedocs.io
 .. _Dask: https://github.com/dask/dask
+.. _MPI: https://www.mpi-forum.org/
 .. _Unidist: https://github.com/modin-project/unidist
+.. _`Installing with pip`: https://unidist.readthedocs.io/en/latest/installation.html#installing-with-pip
+.. _`Installing with conda`: https://unidist.readthedocs.io/en/latest/installation.html#installing-with-conda
 .. _HDK: https://github.com/intel-ai/hdk
 .. _`Intel Distribution of Modin`: https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html#gs.86stqv
 .. _`Intel Distribution of Modin Getting Started`: https://www.intel.com/content/www/us/en/developer/articles/technical/intel-distribution-of-modin-getting-started-guide.html

docs/index.rst

@@ -61,7 +61,7 @@ of the targets:
 
    pip install "modin[ray]" # Install Modin dependencies and Ray to run on Ray
    pip install "modin[dask]" # Install Modin dependencies and Dask to run on Dask
-   pip install "modin[unidist]" # Install Modin dependencies and Unidist to run on Unidist
+   pip install "modin[mpi]" # Install Modin dependencies and MPI to run on MPI through unidist
    pip install "modin[all]" # Install all of the above
 
 Modin will automatically detect which engine you have installed and use that for
@@ -77,7 +77,7 @@ variable ``MODIN_ENGINE`` and Modin will do computation with that engine:
    export MODIN_ENGINE=unidist # Modin will use Unidist
 
 If you want to choose the Unidist engine, you should set the additional environment 
-variable ``UNIDIST_BACKEND``, because currently Modin only supports Unidist on MPI:
+variable ``UNIDIST_BACKEND``, because currently Modin only supports MPI through unidist:
 
 .. code-block:: bash
 

examples/tutorial/jupyter/README.md

@@ -4,7 +4,7 @@ Currently we provide tutorial notebooks for the following execution backends:
 
 - [PandasOnRay](https://modin.readthedocs.io/en/latest/development/using_pandas_on_ray.html)
 - [PandasOnDask](https://modin.readthedocs.io/en/latest/development/using_pandas_on_dask.html)
-- [PandasOnUnidist](https://modin.readthedocs.io/en/latest/development/using_pandas_on_unidist.html)
+- [PandasOnMPI through unidist](https://modin.readthedocs.io/en/latest/development/using_pandas_on_mpi.html)
 - [HdkOnNative](https://modin.readthedocs.io/en/latest/development/using_hdk.html)
 
 ## Creating a development environment

examples/tutorial/jupyter/execution/pandas_on_unidist/Dockerfile

@@ -1,5 +1,4 @@
 FROM continuumio/miniconda3
 
+RUN conda env create -f jupyter_unidist_env.yml
 RUN conda install -c conda-forge psutil setproctitle
-RUN pip install -r requirements-dev.txt
-

examples/tutorial/jupyter/execution/pandas_on_unidist/jupyter_unidist_env.yml

@@ -0,0 +1,11 @@
+name: jupyter_modin_on_unidist
+channels:
+  - conda-forge
+dependencies:
+  - pip
+  - fsspec>=2022.05.0
+  - jupyterlab
+  - ipywidgets
+  - modin-mpi
+  - pip:
+      - modin[spreadsheet]