readme

modules/ml-pipeline/.gitignore

@@ -4,3 +4,8 @@ __pycache__/
 .DS_Store
 .vscode/
 data/
+/.DS_Store/
+*/.ipynb_checkpoints/
+*/.idea/
+*/.pytest_cache/
+*/.tox/

modules/ml-pipeline/README.MD

@@ -18,13 +18,13 @@ Within `src` folder, the structure is as follows:
 # How to develop using this pipeline:
 
 Run `make init`, which will:
-- Download pyenv (Python version management)
+- Download miniconda (Conda version management)
 - Download Python 3.X.X as defined in the `make` file - current 3.10.12
 - Create a virtual environment with this version of python
 - Install packages in the training and version control directories in the pipeline folder (dev version if applicable)
 - Install pre-commit to enable pre-commit hooks
 
-To use the environment, run `source .dev_env_pipeline/bin/activate`.
+To use the environment, run `conda activate dev_env_pipeline`
 
 To enable the virtual envrionemnt created in vscode:
 - Open settings
@@ -41,11 +41,11 @@ To use the docker environment for coding in VSCODE:
 - Add the Git extension (for dvc)
 
 For running experiment, everything will be cached but the workflow will be:
-- `dvc repro` to regenerate the current experiement 
+- `dvc repro` to regenerate the current experiement
 - Change parameters if needed
 - Use `dvc exp run`
 - Cachec the results by using `dvc push -r REMOTE_NAME`
 - Repeat as needed
 - When happy with results, use `dvc exp apply EXPERIMENT_NAME`
-- Use `dvc pull` 
-- Commit code
+- Use `dvc pull`
+- Commit code