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-One possible way to use the repository is to run all the recommender utilities directly from a local copy of the source code (without building the package). This requires installing all the necessary dependencies from Anaconda and PyPI.
-
-To this end we provide a script, [generate_conda_file.py](tools/generate_conda_file.py), to generate a conda-environment yaml file which you can use to create the target environment using Python with all the correct dependencies.
-
-Assuming the repo is cloned as `Recommenders` in the local system, to install **a default (Python CPU) environment**:
-
- cd Recommenders
- python tools/generate_conda_file.py
- conda env create -f reco_base.yaml
-
-You can specify the environment name as well with the flag `-n`.
-
-Click on the following menus to see how to install Python GPU and PySpark environments:
-
-
-Python GPU environment
-
-Assuming that you have a GPU machine, to install the Python GPU environment:
-
- cd Recommenders
- python tools/generate_conda_file.py --gpu
- conda env create -f reco_gpu.yaml
-
-
-
-
-PySpark environment
-
-To install the PySpark environment:
-
- cd Recommenders
- python tools/generate_conda_file.py --pyspark
- conda env create -f reco_pyspark.yaml
-
-Additionally, if you want to test a particular version of spark, you may pass the `--pyspark-version` argument:
-
- python tools/generate_conda_file.py --pyspark-version 3.1.1
-
-
-
-
-Full (PySpark & Python GPU) environment
-
-With this environment, you can run both PySpark and Python GPU notebooks in this repository.
-To install the environment:
-
- cd Recommenders
- python tools/generate_conda_file.py --gpu --pyspark
- conda env create -f reco_full.yaml
-
-