fix

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

README.md

@@ -65,7 +65,7 @@ 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.
 ```
 
 Modin automatically detects which engine(s) you have installed and uses that for scheduling computation.
@@ -85,7 +85,7 @@ 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.
 ```
 

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/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 +6,7 @@ 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 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

@@ -30,7 +30,7 @@ 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[mpi]" # Install Modin dependencies and Unidist to run on Unidist
   pip install "modin[all]" # Install all of the above
 
 Modin will automatically detect which engine you have installed and use that for
@@ -65,7 +65,7 @@ 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 the Unidist execution engine
 
 Installing on Google Colab
 """""""""""""""""""""""""""
@@ -106,7 +106,7 @@ 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              |
 +---------------------------------+---------------------------+-----------------------------+
@@ -117,7 +117,7 @@ For installing Dask, Ray and Unidist engines into conda environment following co
 
 .. 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 +129,7 @@ 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
 
 ``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 +171,7 @@ 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
 -------
@@ -214,6 +214,7 @@ 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
 .. _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

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

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/requirements.txt

@@ -1,5 +1,5 @@
 fsspec>=2022.05.0
 jupyterlab
 ipywidgets
-modin[unidist]
+modin[mpi]
 modin[spreadsheet]