Skip to content

Updated spelling in code_description.md #59

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

Merged
eedorenko merged 1 commit into from
Sep 3, 2019
Merged

Updated spelling in code_description.md #59

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
16 changes: 8 additions & 8 deletions docs/code_description.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,21 +2,21 @@

### Environment Setup

- `environment_setup/requirements.txt` : It consist of list of python packages which are needed by the train.py to run successfully on host agent (locally).
- `environment_setup/requirements.txt` : It consists of a list of python packages which are needed by the train.py to run successfully on host agent (locally).

- `environment_setup/install_requirements.sh` : This script prepare the python environment i.e. install the Azure ML SDK and the packages specified in requirements.txt
- `environment_setup/install_requirements.sh` : This script prepares the python environment i.e. install the Azure ML SDK and the packages specified in requirements.txt

- `environment_setup/iac-*.yml, arm-templates` : Infrastructure as Code piplines to create and delete required resources along with corresponding arm-templates.

- `environment_setup/Dockerfile` : Dockerfile of a building agent containing Python 3.6 and all required packages.
- `environment_setup/Dockerfile` : Dockerfile of a build agent containing Python 3.6 and all required packages.

- `environment_setup/docker-image-pipeline.yml` : An AzDo pipeline building and pushing [microsoft/mlopspython](https://hub.docker.com/_/microsoft-mlops-python) image.
- `environment_setup/docker-image-pipeline.yml` : An AzDo pipeline for building and pushing [microsoft/mlopspython](https://hub.docker.com/_/microsoft-mlops-python) image.

### Pipelines

- `.pipelines/azdo-base-pipeline.yml` : a pipeline template used by ci-build-train pipeline and pr-build-train pipelines. It contains steps performig linting, data and unit testing.
- `.pipelines/azdo-ci-build-train.yml` : a pipeline triggered when the code is merged into **master**. It profrorms linting, data integrity testing, unit testing, building and publishing an ML pipeline.
- `.pipelines/azdo-pr-build-train.yml` : a pipeline triggered when a **pull request** to the **master** branch is created. It profrorms linting, data integrity testing and unit testing only.
- `.pipelines/azdo-base-pipeline.yml` : a pipeline template used by ci-build-train pipeline and pr-build-train pipelines. It contains steps performing linting, data and unit testing.
- `.pipelines/azdo-ci-build-train.yml` : a pipeline triggered when the code is merged into **master**. It performs linting, data integrity testing, unit testing, building and publishing an ML pipeline.
- `.pipelines/azdo-pr-build-train.yml` : a pipeline triggered when a **pull request** to the **master** branch is created. It performs linting, data integrity testing and unit testing only.

### ML Services

Expand All @@ -28,7 +28,7 @@

- `code/training/train.py` : a training step of an ML training pipeline.
- `code/evaluate/evaluate_model.py` : an evaluating step of an ML training pipeline.
- `code/evaluate/register_model.py` : registers a new trained model if evaluation shows the new model is more performent than the previous one.
- `code/evaluate/register_model.py` : registers a new trained model if evaluation shows the new model is more performant than the previous one.

### Scoring
- code/scoring/score.py : a scoring script which is about to be packed into a Docker Image along with a model while being deployed to QA/Prod environment.
Expand Down