From aba8aefc4b340f89707f0abcc82528d5b032c92d Mon Sep 17 00:00:00 2001 From: "SATO Naoki (Neo)" Date: Wed, 17 Feb 2021 06:20:23 +0900 Subject: [PATCH] development_setup.md update (#349) * development_setup.md update development_setup.md updated to use install_requirements.sh. See #158: > Use conda rather than pip packages when possible (as recommended in AML docs). > Dev environment is hence also constrained to conda (no more pip install -r requirements.txt). * Content of install_requirements.sh deleted * build_train_pipeline.py filename fixed * build_train_pipeline.py filename fixed --- docs/development_setup.md | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/docs/development_setup.md b/docs/development_setup.md index 68e6b6bf..1c8c2479 100644 --- a/docs/development_setup.md +++ b/docs/development_setup.md @@ -10,19 +10,12 @@ In order to configure the project locally, create a copy of `.env.example` in th [Install the Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli). The Azure CLI will be used to log you in interactively. -Create a virtual environment using [venv](https://docs.python.org/3/library/venv.html), [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) or [pyenv-virtualenv](https://github.com/pyenv/pyenv-virtualenv). +Install [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). -Here is an example for setting up and activating a `venv` environment with Python 3: +Install the required Python modules. [`install_requirements.sh`](https://github.com/microsoft/MLOpsPython/blob/master/environment_setup/install_requirements.sh) creates and activates a new conda environment with required Python modules. ``` -python3 -mvenv .venv -source .venv/bin/activate -``` - -Install the required Python modules in your virtual environment. - -``` -pip install -r environment_setup/requirements.txt +. environment_setup/install_requirements.sh ``` ### Running local code @@ -30,11 +23,11 @@ pip install -r environment_setup/requirements.txt To run your local ML pipeline code on Azure ML, run a command such as the following (in bash, all on one line): ``` -export BUILD_BUILDID=$(uuidgen); python ml_service/pipelines/build_train_pipeline.py && python ml_service/pipelines/run_train_pipeline.py +export BUILD_BUILDID=$(uuidgen); python ml_service/pipelines/diabetes_regression_build_train_pipeline.py && python ml_service/pipelines/run_train_pipeline.py ``` BUILD_BUILDID is a variable used to uniquely identify the ML pipeline between the -`build_train_pipeline.py` and `run_train_pipeline.py` scripts. In Azure DevOps it is +`diabetes_regression_build_train_pipeline.py` and `run_train_pipeline.py` scripts. In Azure DevOps it is set to the current build number. In a local environment, we can use a command such as `uuidgen` so set a different random identifier on each run, ensuring there are no collisions.