Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

development_setup.md update #349

Merged
merged 4 commits into from
Feb 16, 2021
Merged

development_setup.md update #349

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
dfbab6a
development_setup.md update
satonaoki Feb 10, 2021
1433e39
Content of install_requirements.sh deleted
satonaoki Feb 15, 2021
b632451
build_train_pipeline.py filename fixed
satonaoki Feb 16, 2021
bdca743
build_train_pipeline.py filename fixed
satonaoki Feb 16, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 5 additions & 12 deletions docs/development_setup.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -10,31 +10,24 @@ 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

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.