Running a Transform in a Virtual Environment

Here we address a simple use case of applying a single transform to a set of parquet files. We'll use the noop transform as an example, but in general, this process will work for any of the transforms contained in the repository. Additionally, what follows uses the python runtime (e.g., noop/python directory), but the examples below should also work for the ray (noop/ray directory) or spark (noop/spark directory) runtimes.

Creating the Virtual Environment

Each transform project contains a Makefile that will assist in building the virtual environment in a directory named venv. To create the virtual environment for the noop transform:

cd transforms/univeral/noop/python
make venv 

Note, if needed, you can override the default python command used in make venv above, with for example:

make PYTHON=python3.10 venv

Local Data

To process data in the /home/me/input directory and write it to the /home/me/output directory, activate the virtual environment and then call the transform referencing these directories. So for example, using the noop transform to read parquet files from /home/me/input:

cd transforms/univeral/noop/python
source venv/bin/activate
python src/noop_transform_python.py \
    --data_local_config "{ \
        'input_folder'  : '/home/me/input', \
        'output_folder' : '/home/me/output' \
        }"
deactivate

S3-located Data

When processing data located in S3 buckets, one can use the same approach and specify different --data_s3_* configuration as follows:

cd transforms/univerals/noop/python
source venv/bin/activate
python src/noop_transform_python.py \
    --data_s3_cred "{ \
        'access_key'  : '...', \
        'secret_key' : '...', \
        'url' : '...', \
        }"  \
    --data_s3_config "{ \
        'input_folder'  : '...', \
        'output_folder' : '...', \
        }"