Remove hdk

Signed-off-by: Igoshev, Iaroslav <[email protected]>

.github/workflows/ci-notebooks.yml

@@ -7,7 +7,6 @@ on:
       - .github/workflows/ci-notebooks.yml
       - setup.cfg
       - setup.py
-      - requirements/env_hdk.yml
       - requirements/env_unidist_linux.yml
 concurrency:
   # Cancel other jobs in the same branch. We don't care whether CI passes
@@ -25,16 +24,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        execution: [pandas_on_ray, pandas_on_dask, pandas_on_unidist, hdk_on_native]
+        execution: [pandas_on_ray, pandas_on_dask, pandas_on_unidist]
     steps:
       - uses: actions/checkout@v4
       - uses: ./.github/actions/python-only
-        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'
+        if: matrix.execution != 'pandas_on_unidist'
       - uses: ./.github/actions/mamba-env
         with:
           environment-file: requirements/env_unidist_linux.yml
@@ -49,29 +43,29 @@ 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/${{ matrix.execution }}/requirements.txt
-        if: matrix.execution != 'hdk_on_native' && matrix.execution != 'pandas_on_unidist'
+        if: matrix.execution != 'pandas_on_unidist'
       # install dependencies required for notebooks execution for `pandas_on_ray` and `pandas_on_dask`
       # Override modin-spreadsheet install for now
       - run: |
           pip install -r examples/tutorial/jupyter/execution/${{ matrix.execution }}/requirements.txt
           pip install git+https://github.com/modin-project/modin-spreadsheet.git@49ffd89f683f54c311867d602c55443fb11bf2a5
-        if: matrix.execution != 'hdk_on_native' && matrix.execution != 'pandas_on_unidist'
-      # Build Modin from sources for `hdk_on_native` and `pandas_on_unidist`
+        if: matrix.execution != 'pandas_on_unidist'
+      # Build Modin from sources for `pandas_on_unidist`
       - run: pip install -e .
-        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
+        if: 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' && matrix.execution != 'pandas_on_unidist'
+        if: matrix.execution != 'pandas_on_unidist'
       - run: pip install flake8-print jupyter nbformat nbconvert
-        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
+        if: matrix.execution == 'pandas_on_unidist'
       - run: pip list
-        if: matrix.execution != 'hdk_on_native' && matrix.execution != 'pandas_on_unidist'
+        if: matrix.execution != 'pandas_on_unidist'
       - run: |
           conda info
           conda list
-        if: matrix.execution == 'hdk_on_native' || matrix.execution == 'pandas_on_unidist'
+        if: 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-required.yml

@@ -90,19 +90,6 @@ jobs:
             modin/experimental/pandas/__init__.py
       - run: python scripts/doc_checker.py modin/core/storage_formats/base
       - run: python scripts/doc_checker.py modin/core/storage_formats/pandas
-      - run: |
-          python scripts/doc_checker.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/io \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/partitioning \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/calcite_algebra.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/calcite_builder.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/calcite_serializer.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/df_algebra.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/expr.py \
-            modin/experimental/core/execution/native/implementations/hdk_on_native/hdk_worker.py \
-      - run: python scripts/doc_checker.py modin/experimental/core/storage_formats/hdk
-      - run: python scripts/doc_checker.py modin/experimental/core/execution/native/implementations/hdk_on_native/interchange/dataframe_protocol
       - run: python scripts/doc_checker.py modin/experimental/batch/pipeline.py
       - run: python scripts/doc_checker.py modin/logging
 

.github/workflows/ci.yml

@@ -150,62 +150,6 @@ jobs:
           runner: python -m pytest --execution=${{ matrix.execution }}
       - uses: ./.github/actions/upload-coverage
 
-  test-hdk:
-    needs: [lint-flake8]
-    runs-on: ubuntu-latest
-    defaults:
-      run:
-        shell: bash -l {0}
-    env:
-      MODIN_EXPERIMENTAL: "True"
-      MODIN_ENGINE: "native"
-      MODIN_STORAGE_FORMAT: "hdk"
-    name: Test HDK storage format, Python 3.9
-    services:
-      moto:
-        image: motoserver/moto
-        ports:
-          - 5000:5000
-        env:
-          AWS_ACCESS_KEY_ID: foobar_key
-          AWS_SECRET_ACCESS_KEY: foobar_secret
-    steps:
-      - uses: actions/checkout@v4
-      - uses: ./.github/actions/mamba-env
-        with:
-          environment-file: requirements/env_hdk.yml
-          activate-environment: modin_on_hdk
-      - name: Install HDF5
-        run: sudo apt update && sudo apt install -y libhdf5-dev
-      - run: python -m pytest modin/tests/core/storage_formats/hdk/test_internals.py
-      - run: python -m pytest modin/tests/experimental/hdk_on_native/test_init.py
-      - run: python -m pytest modin/tests/experimental/hdk_on_native/test_dataframe.py
-      - run: python -m pytest modin/tests/experimental/hdk_on_native/test_utils.py
-      - run: python -m pytest modin/tests/pandas/test_io.py --verbose
-      - run: python -m pytest modin/tests/interchange/dataframe_protocol/test_general.py
-      - run: python -m pytest modin/tests/interchange/dataframe_protocol/hdk
-      - run: python -m pytest modin/tests/experimental/test_sql.py
-      - run: python -m pytest modin/tests/pandas/test_concat.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_binary.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_reduce.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_join_sort.py
-      - run: python -m pytest modin/tests/pandas/test_general.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_indexing.py
-      - run: python -m pytest modin/tests/pandas/test_series.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_map_metadata.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_window.py
-      - run: python -m pytest modin/tests/pandas/dataframe/test_default.py
-      - run: python examples/docker/modin-hdk/census-hdk.py examples/data/census_1k.csv -no-ml
-      - run: python examples/docker/modin-hdk/nyc-taxi-hdk.py examples/data/nyc-taxi_1k.csv
-      - run: |
-          python examples/docker/modin-hdk/plasticc-hdk.py \
-          examples/data/plasticc_training_set_1k.csv \
-          examples/data/plasticc_test_set_1k.csv \
-          examples/data/plasticc_training_set_metadata_1k.csv \
-          examples/data/plasticc_test_set_metadata_1k.csv \
-          -no-ml
-      - uses: ./.github/actions/upload-coverage
-
   test-asv-benchmarks:
     if: github.event_name == 'pull_request'
     needs: [lint-flake8]
@@ -249,18 +193,6 @@ jobs:
               # check pure pandas
               MODIN_ASV_USE_IMPL=pandas asv run --quick --dry-run --python=same --strict --show-stderr --launch-method=spawn \
                 -b ^benchmarks -b ^io | tee benchmarks.log
-
-              # TODO: Remove manual environment creation after fix https://github.com/airspeed-velocity/asv/issues/1310
-              conda deactivate
-              mamba env create -f ../requirements/env_hdk.yml
-              conda activate modin_on_hdk
-              pip install asv==0.5.1
-              pip install ..
-
-              # check Modin on HDK
-              MODIN_ENGINE=native MODIN_STORAGE_FORMAT=hdk MODIN_EXPERIMENTAL=true asv run --quick --dry-run --python=same --strict --show-stderr \
-                --launch-method=forkserver --python=same --config asv.conf.hdk.json \
-                -b ^hdk | tee benchmarks.log
           else
               echo "Benchmarks did not run, no changes detected"
           fi
@@ -703,7 +635,7 @@ jobs:
       - run: python -m pytest modin/tests/experimental/spreadsheet/test_general.py
 
   merge-coverage-artifacts:
-    needs: [test-internals, test-api-and-no-engine, test-defaults, test-hdk, test-all-unidist, test-all, test-experimental, test-sanity]
+    needs: [test-internals, test-api-and-no-engine, test-defaults, test-all-unidist, test-all, test-experimental, test-sanity]
     if: always()  # we need to run it regardless of some job being skipped, like in PR
     runs-on: ubuntu-latest
     defaults:

.github/workflows/codeql/codeql-config.yml

@@ -2,5 +2,3 @@ name: "Modin CodeQL config"
 
 paths:
   - modin/** 
-paths-ignore:
-  - modin/tests/experimental/hdk_on_native/** # TODO: fix unhashable list error, see #5227

CODEOWNERS

@@ -1,8 +1,3 @@
 # These owners will be the default owners for everything in
 # the repo unless a later match takes precedence,
 *    @modin-project/modin-core @devin-petersohn @mvashishtha @RehanSD @YarShev @vnlitvinov @anmyachev @dchigarev
-
-# These owners will review everything in the HDK engine component
-# of Modin.
-/modin/experimental/core/storage_formats/hdk/**    @modin-project/modin-hdk @aregm @gshimansky @ienkovich @Garra1980 @YarShev @vnlitvinov @anmyachev @dchigarev @AndreyPavlenko
-/modin/experimental/core/execution/native/implementations/hdk_on_native/**    @modin-project/modin-hdk @aregm @gshimansky @ienkovich @Garra1980 @YarShev @vnlitvinov @anmyachev @dchigarev @AndreyPavlenko

README.md

@@ -85,8 +85,8 @@ Modin automatically detects which engine(s) you have installed and uses that for
 #### 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),
-[MPI through unidist](https://github.com/modin-project/unidist) and [HDK](https://github.com/intel-ai/hdk).
+will install Modin and three engines: [Ray](https://github.com/ray-project/ray), [Dask](https://github.com/dask/dask) and
+[MPI through unidist](https://github.com/modin-project/unidist).
 
 ```bash
 conda install -c conda-forge modin-all
@@ -98,7 +98,6 @@ Each engine can also be installed individually (and also as a combination of sev
 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-mpi # Install Modin dependencies and MPI through unidist.
-conda install -c conda-forge modin-hdk # Install Modin dependencies and HDK.
 ```
 
 **Note:** Since Modin 0.30.0 we use a reduced set of Ray dependencies: `ray-core` instead of `ray-default`.
@@ -118,13 +117,13 @@ conda install -n base conda-libmamba-solver
 and then use it during istallation either like:
 
 ```bash
-conda install -c conda-forge modin-ray modin-hdk --experimental-solver=libmamba
+conda install -c conda-forge modin-ray --experimental-solver=libmamba
 ```
 
 or starting from conda 22.11 and libmamba solver 22.12 versions:
 
 ```bash
-conda install -c conda-forge modin-ray modin-hdk --solver=libmamba
+conda install -c conda-forge modin-ray --solver=libmamba
 ```
 
 #### Choosing a Compute Engine
@@ -158,8 +157,6 @@ modin_cfg.Engine.put('unidist') # Modin will use Unidist
 unidist_cfg.Backend.put('mpi') # Unidist will use MPI backend
 ```
 
-Check [this Modin docs section](https://modin.readthedocs.io/en/latest/development/using_hdk.html) for HDK engine setup.
-
 _Note: You should not change the engine after your first operation with Modin as it will result in undefined behavior._
 
 #### Which engine should I use?
@@ -168,11 +165,6 @@ On Linux, MacOS, and Windows you can install and use either Ray, Dask or MPI thr
 to use either of these engines as Modin abstracts away all of the complexity, so feel
 free to pick either!
 
-On Linux you also can choose [HDK](https://modin.readthedocs.io/en/latest/development/using_hdk.html), which is an experimental
-engine based on [HDK](https://github.com/intel-ai/hdk) and included in the
-[Intel® Distribution of Modin](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/distribution-of-modin.html),
-which is a part of [Intel® oneAPI AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html).
-
 ### Pandas API Coverage
 
 <p align="center">

asv_bench/benchmarks/benchmarks.py

@@ -36,7 +36,6 @@
     random_columns,
     random_string,
     translator_groupby_ngroups,
-    trigger_import,
 )
 
 
@@ -675,7 +674,6 @@ class TimeIndexing:
 
     def setup(self, shape, indexer_type):
         self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
-        trigger_import(self.df)
 
         self.indexer = self.indexer_getters[indexer_type](self.df)
         if isinstance(self.indexer, (IMPL.Series, IMPL.DataFrame)):
@@ -701,7 +699,6 @@ class TimeIndexingColumns:
 
     def setup(self, shape):
         self.df = generate_dataframe("int", *shape, RAND_LOW, RAND_HIGH)
-        trigger_import(self.df)
         self.numeric_indexer = [0, 1]
         self.labels_indexer = self.df.columns[self.numeric_indexer].tolist()