edit github workflow naming conventions (#364)
* edit github workflow naming conventions * edits to contributing * a few more minor edits while we're here * prevent bug
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@ -1,4 +1,4 @@
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name: notebook-tll
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name: notebook-train-lightgbm-local
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on:
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schedule:
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- cron: "0 0/2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- notebooks/train-lightgbm-local.ipynb
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- .github/workflows/notebook-tll.yml
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- .github/workflows/notebook-train-lightgbm-local.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-ai
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name: tutorial-an-introduction
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on:
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schedule:
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- cron: "0 0/1 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/an-introduction/**
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- .github/workflows/tutorial-ai.yml
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- .github/workflows/tutorial-an-introduction.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-awp
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name: tutorial-automl-with-pycaret
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on:
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schedule:
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- cron: "0 0/2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/automl-with-pycaret/**
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- .github/workflows/tutorial-awp.yml
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- .github/workflows/tutorial-automl-with-pycaret.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-de
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name: tutorial-deploy-edge
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on:
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schedule:
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- cron: "0 0/2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/deploy-edge/**
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- .github/workflows/tutorial-de.yml
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- .github/workflows/tutorial-deploy-edge.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-dt
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name: tutorial-deploy-triton
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on:
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schedule:
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- cron: "0 */2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/deploy-triton/**
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- .github/workflows/tutorial-dt.yml
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- .github/workflows/tutorial-deploy-triton.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-ud
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name: tutorial-using-dask
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on:
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schedule:
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- cron: "0 */2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/using-dask/**
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- .github/workflows/tutorial-ud.yml
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- .github/workflows/tutorial-using-dask.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-upl
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name: tutorial-using-pytorch-lightning
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on:
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schedule:
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- cron: "0 */2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/using-pytorch-lightning/**
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- .github/workflows/tutorial-upl.yml
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- .github/workflows/tutorial-using-pytorch-lightning.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-ur
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name: tutorial-using-rapids
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on:
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schedule:
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- cron: "0 */2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/using-rapids/**
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- .github/workflows/tutorial-ur.yml
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- .github/workflows/tutorial-using-rapids.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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@ -1,4 +1,4 @@
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name: tutorial-ux
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name: tutorial-using-xgboost
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on:
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schedule:
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- cron: "0 */2 * * *"
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@ -7,7 +7,7 @@ on:
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- main
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paths:
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- tutorials/using-xgboost/**
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- .github/workflows/tutorial-ux.yml
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- .github/workflows/tutorial-using-xgboost.yml
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jobs:
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build:
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runs-on: ubuntu-latest
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|
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@ -26,14 +26,15 @@ This repo is an opinionated set of examples using a subset of Azure Machine Lear
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## Issues
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All forms of feedback are welcome through issues - please follow the pre-defined templates where applicable.
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All forms of feedback are welcome through [issues](https://github.com/Azure/azureml-examples/issues) - please follow the pre-defined templates where applicable.
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*Note:* [Discussions](https://github.com/Azure/azureml-examples/discussions) are new to GitHub, feel free to start one!
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## Pull Requests
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Pull requests (PRs) to this repo require review and approval by the Azure Machine Learning team to merge. Please follow the pre-defined template and read all relevant sections below.
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> **Important:**
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> PRs from forks of this repository are likely to fail automated workflows due to access to secrets. PRs from forks will be considered but may experience additional delay for testing.
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**Important:** PRs from forks of this repository are likely to fail automated workflows due to access to secrets. PRs from forks will be considered but may experience additional delay for testing.
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### General rules
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@ -44,13 +45,14 @@ Pull requests (PRs) to this repo require review and approval by the Azure Machin
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### Miscellaneous
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- to modify `README.md`, you need to modify `readme.py` and accompanying markdown files
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- the tables in the `README.md` are auto-generated, including description, via other files
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- to modify `README.md`, you need to modify `readme.py` and accompanying markdown files other files (`prefix.md` and `suffix.md`)
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- you probably should not modify `README.md` or accompany markdown files
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- develop on a branch, not a fork, for workflows to run properly
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- use an existing environment where possible
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- use an existing dataset where possible
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- don't register environments
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- don't create compute targets
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- don't register datasets
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- don't modify `requirements.txt`
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- you probably shouldn't modify any files in the root of the repo
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- you can `!pip install --upgrade packages` as needed in notebooks
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@ -62,7 +64,7 @@ If modifying existing examples, before a PR:
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- run `python readme.py` from the root of the repo
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- this will generate the `README.md` file
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- this will generate github workflow files
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- this will generate github workflow files (for workflows and notebooks)
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- this will format Python code and notebooks
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### Enforced naming
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@ -73,7 +75,7 @@ Enforced naming includes:
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- directories under `tutorials` must be words separated by hyphens
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- directories under `workflows` must be one of [`train`, `deploy`, `score`, `dataprep`] - directories under are organized by ML tool
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- job definition file(s) under `workflows` must contain `job` in the name
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- tutorial workflows (and workflow files) use the naming convention `tutorial-*initials*`, where *initials* is the initials of the words
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- tutorial workflows (and workflow files) use the naming convention `tutorial-*name*`, where *name* is the directory name
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- `experiment_name` = "logical-words-example|tutorial" e.g. "hello-world-tutorial"
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- `compute_name` = "compute-defined-in-setup-workspace.py" e.g. "gpu-K80-2"
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@ -146,7 +148,6 @@ Checklist:
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- [ ] use an existing directory or add a new scenario and/or ML tool directory
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- [ ] add job definition file(s) under this directory with `job` in the name
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- [ ] add user code, preserving any licensing information, under a `src` dir specific to the workflow
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- [ ] add any required environment files under a `envs` dir, shared commonly across workflows
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- [ ] run `python readme.py`
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- [ ] test
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- [ ] submit PR, which will run the relevant workflow(s)
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@ -171,7 +172,7 @@ Checklist:
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- [ ] add the tutorial directory under `tutorials/`, following naming conventions
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- [ ] add tutorial files, which are usually notebooks and may be ordered
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- [ ] add `README.md` in the tutorial directory with a description (see other tutorials for format)
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- [ ] add `run-tutorial-initials`, where *initials* are the initials of the description directory (see other tutorial workflows)
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- [ ] add `tutorial-*name*`, where *name* is the name of the directory (see other tutorial workflows)
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- [ ] run `python readme.py`
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- [ ] test
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- [ ] submit PR, which will run your tutorial if setup properly
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|
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22
README.md
22
README.md
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@ -48,7 +48,7 @@ A lightweight template repository for automating the ML lifecycle can be found [
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|`.cloud`|cloud templates|
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|`.github`|GitHub specific files like Actions workflow yaml definitions and issue templates|
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|`notebooks`|interactive jupyter notebooks for iterative ML development|
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|`tutorials`|self-contained directories of end-to-end tutorials|
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|`tutorials`|self-contained directories of tutorials|
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|`workflows`|self-contained directories of job to be run, organized by scenario then tool then project|
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## Examples
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@ -57,20 +57,20 @@ A lightweight template repository for automating the ML lifecycle can be found [
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|
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path|status|notebooks|description
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-|-|-|-
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||||
[an-introduction](tutorials/an-introduction)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-ai)|[1.hello-world.ipynb](tutorials/an-introduction/1.hello-world.ipynb)<br>[2.pytorch-model.ipynb](tutorials/an-introduction/2.pytorch-model.ipynb)<br>[3.pytorch-model-cloud-data.ipynb](tutorials/an-introduction/3.pytorch-model-cloud-data.ipynb)|learn the basics of Azure Machine Learning
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[automl-with-pycaret](tutorials/automl-with-pycaret)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-awp)|[1.classification.ipynb](tutorials/automl-with-pycaret/1.classification.ipynb)|learn how to automate ML with [PyCaret](https://github.com/pycaret/pycaret)
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[deploy-edge](tutorials/deploy-edge)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-de)|[ase-gpu.ipynb](tutorials/deploy-edge/ase-gpu.ipynb)|learn how to use Edge device for model deployment and scoring
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[deploy-triton](tutorials/deploy-triton)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-dt)|[1.densenet-local.ipynb](tutorials/deploy-triton/1.densenet-local.ipynb)<br>[2.bidaf-aks-v100.ipynb](tutorials/deploy-triton/2.bidaf-aks-v100.ipynb)|learn how to efficiently deploy to GPUs using [triton inference server](https://github.com/triton-inference-server/server)
|
||||
[using-dask](tutorials/using-dask)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-ud)|[1.intro-to-dask.ipynb](tutorials/using-dask/1.intro-to-dask.ipynb)<br>[2.dask-cloudprovider.ipynb](tutorials/using-dask/2.dask-cloudprovider.ipynb)|learn how to read from cloud data and scale PyData tools (Numpy, Pandas, Scikit-Learn, etc.) with [Dask](https://dask.org)
|
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[using-pytorch-lightning](tutorials/using-pytorch-lightning)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-upl)|[1.train-single-node.ipynb](tutorials/using-pytorch-lightning/1.train-single-node.ipynb)<br>[2.log-with-tensorboard.ipynb](tutorials/using-pytorch-lightning/2.log-with-tensorboard.ipynb)<br>[3.log-with-mlflow.ipynb](tutorials/using-pytorch-lightning/3.log-with-mlflow.ipynb)<br>[4.train-multi-node-ddp.ipynb](tutorials/using-pytorch-lightning/4.train-multi-node-ddp.ipynb)|learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)
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[using-rapids](tutorials/using-rapids)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-ur)|[1.train-and-hpo.ipynb](tutorials/using-rapids/1.train-and-hpo.ipynb)<br>[2.train-multi-gpu.ipynb](tutorials/using-rapids/2.train-multi-gpu.ipynb)|learn how to accelerate PyData tools (numpy, pandas, scikit-learn, etc) on NVIDIA GPUs with [rapids](https://github.com/rapidsai)
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[using-xgboost](tutorials/using-xgboost)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-ux)|[1.local-eda.ipynb](tutorials/using-xgboost/1.local-eda.ipynb)<br>[2.distributed-cpu.ipynb](tutorials/using-xgboost/2.distributed-cpu.ipynb)|learn how to use [XGBoost](https://github.com/dmlc/xgboost) on Azure
|
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[an-introduction](tutorials/an-introduction)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-an-introduction)|[1.hello-world.ipynb](tutorials/an-introduction/1.hello-world.ipynb)<br>[2.pytorch-model.ipynb](tutorials/an-introduction/2.pytorch-model.ipynb)<br>[3.pytorch-model-cloud-data.ipynb](tutorials/an-introduction/3.pytorch-model-cloud-data.ipynb)|learn the basics of Azure Machine Learning
|
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[automl-with-pycaret](tutorials/automl-with-pycaret)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-automl-with-pycaret)|[1.classification.ipynb](tutorials/automl-with-pycaret/1.classification.ipynb)|learn how to automate ML with [PyCaret](https://github.com/pycaret/pycaret)
|
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[deploy-edge](tutorials/deploy-edge)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-deploy-edge)|[ase-gpu.ipynb](tutorials/deploy-edge/ase-gpu.ipynb)|learn how to use Edge device for model deployment and scoring
|
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[deploy-triton](tutorials/deploy-triton)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-deploy-triton)|[1.densenet-local.ipynb](tutorials/deploy-triton/1.densenet-local.ipynb)<br>[2.bidaf-aks-v100.ipynb](tutorials/deploy-triton/2.bidaf-aks-v100.ipynb)|learn how to efficiently deploy to GPUs using [triton inference server](https://github.com/triton-inference-server/server)
|
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[using-dask](tutorials/using-dask)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-dask)|[1.intro-to-dask.ipynb](tutorials/using-dask/1.intro-to-dask.ipynb)<br>[2.dask-cloudprovider.ipynb](tutorials/using-dask/2.dask-cloudprovider.ipynb)|learn how to read from cloud data and scale PyData tools (Numpy, Pandas, Scikit-Learn, etc.) with [Dask](https://dask.org)
|
||||
[using-pytorch-lightning](tutorials/using-pytorch-lightning)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-pytorch-lightning)|[1.train-single-node.ipynb](tutorials/using-pytorch-lightning/1.train-single-node.ipynb)<br>[2.log-with-tensorboard.ipynb](tutorials/using-pytorch-lightning/2.log-with-tensorboard.ipynb)<br>[3.log-with-mlflow.ipynb](tutorials/using-pytorch-lightning/3.log-with-mlflow.ipynb)<br>[4.train-multi-node-ddp.ipynb](tutorials/using-pytorch-lightning/4.train-multi-node-ddp.ipynb)|learn how to train and log metrics with [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)
|
||||
[using-rapids](tutorials/using-rapids)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-rapids)|[1.train-and-hpo.ipynb](tutorials/using-rapids/1.train-and-hpo.ipynb)<br>[2.train-multi-gpu.ipynb](tutorials/using-rapids/2.train-multi-gpu.ipynb)|learn how to accelerate PyData tools (numpy, pandas, scikit-learn, etc) on NVIDIA GPUs with [rapids](https://github.com/rapidsai)
|
||||
[using-xgboost](tutorials/using-xgboost)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-using-xgboost)|[1.local-eda.ipynb](tutorials/using-xgboost/1.local-eda.ipynb)<br>[2.distributed-cpu.ipynb](tutorials/using-xgboost/2.distributed-cpu.ipynb)|learn how to use [XGBoost](https://github.com/dmlc/xgboost) on Azure
|
||||
|
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**Notebooks** ([notebooks](notebooks))
|
||||
|
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path|status|description
|
||||
-|-|-
|
||||
[notebooks/train-lightgbm-local.ipynb](notebooks/train-lightgbm-local.ipynb)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Anotebook-tll)|use mlflow for tracking local notebook experimentation in the cloud
|
||||
[notebooks/train-lightgbm-local.ipynb](notebooks/train-lightgbm-local.ipynb)|[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Anotebook-train-lightgbm-local)|use mlflow for tracking local notebook experimentation in the cloud
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|
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**Train** ([workflows/train](workflows/train))
|
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|
||||
|
|
@ -111,5 +111,5 @@ This project has adopted the [Microsoft Open Source Code of Conduct](https://ope
|
|||
## Reference
|
||||
|
||||
- [GitHub Template](https://github.com/Azure/azureml-template)
|
||||
- [Cheat Sheet, VSCode Snippets, and Templates](https://azure.github.io/azureml-web)
|
||||
- [Open Source Azure ML Cheat Sheet](https://azure.github.io/azureml-web)
|
||||
- [Azure Machine Learning Documentation](https://docs.microsoft.com/azure/machine-learning)
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@ A lightweight template repository for automating the ML lifecycle can be found [
|
|||
|`.cloud`|cloud templates|
|
||||
|`.github`|GitHub specific files like Actions workflow yaml definitions and issue templates|
|
||||
|`notebooks`|interactive jupyter notebooks for iterative ML development|
|
||||
|`tutorials`|self-contained directories of end-to-end tutorials|
|
||||
|`tutorials`|self-contained directories of tutorials|
|
||||
|`workflows`|self-contained directories of job to be run, organized by scenario then tool then project|
|
||||
|
||||
## Examples
|
||||
|
|
|
|||
23
readme.py
23
readme.py
|
|
@ -67,12 +67,11 @@ def write_readme(tutorials, notebooks, workflows):
|
|||
nbs = [f"[{nb}]({tutorial}/{nb})" for nb in nbs]
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||||
nbs = "<br>".join(nbs)
|
||||
|
||||
# get tutorial name and initials
|
||||
# get tutorial name
|
||||
name = tutorial.split("/")[-1]
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||||
initials = "".join(word[0][0] for word in name.split("-"))
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||||
|
||||
# build entries for tutorial table
|
||||
status = f"[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-{initials})"
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||||
status = f"[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Atutorial-{name})"
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description = "*no description*"
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||||
try:
|
||||
with open(f"{tutorial}/README.md", "r") as f:
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||||
|
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@ -89,16 +88,15 @@ def write_readme(tutorials, notebooks, workflows):
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|||
|
||||
# process notebooks
|
||||
for notebook in notebooks:
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||||
# get notebook name and initials
|
||||
# get notebook name
|
||||
name = notebook.split("/")[-1].replace(".ipynb", "")
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initials = "".join(word[0][0] for word in name.split("-"))
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|
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# read in notebook
|
||||
with open(notebook, "r") as f:
|
||||
data = json.load(f)
|
||||
|
||||
# build entries for notebook table
|
||||
status = f"[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Anotebook-{initials})"
|
||||
status = f"[](https://github.com/Azure/azureml-examples/actions?query=workflow%3Anotebook-{name})"
|
||||
description = "*no description*"
|
||||
try:
|
||||
if "description: " in str(data["cells"][0]["source"]):
|
||||
|
|
@ -161,12 +159,11 @@ def write_readme(tutorials, notebooks, workflows):
|
|||
def write_workflows(notebooks, workflows):
|
||||
# process notebooks
|
||||
for notebook in notebooks:
|
||||
# get notebook name and initials
|
||||
# get notebook name
|
||||
name = notebook.split("/")[-1].replace(".ipynb", "")
|
||||
initials = "".join(word[0][0] for word in name.split("-"))
|
||||
|
||||
# write workflow file
|
||||
write_notebook_workflow(notebook, name, initials)
|
||||
write_notebook_workflow(notebook, name)
|
||||
|
||||
# process workflows
|
||||
for workflow in workflows:
|
||||
|
|
@ -215,9 +212,9 @@ def format_code():
|
|||
os.system("black-nb --clear-output .")
|
||||
|
||||
|
||||
def write_notebook_workflow(notebook, name, initials):
|
||||
def write_notebook_workflow(notebook, name):
|
||||
creds = "${{secrets.AZ_AE_CREDS}}"
|
||||
workflow_yaml = f"""name: notebook-{initials}
|
||||
workflow_yaml = f"""name: notebook-{name}
|
||||
on:
|
||||
schedule:
|
||||
- cron: "0 0/2 * * *"
|
||||
|
|
@ -226,7 +223,7 @@ on:
|
|||
- main
|
||||
paths:
|
||||
- {notebook}
|
||||
- .github/workflows/notebook-{initials}.yml
|
||||
- .github/workflows/notebook-{name}.yml
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
|
|
@ -251,7 +248,7 @@ jobs:
|
|||
run: papermill {notebook} out.ipynb -k python\n"""
|
||||
|
||||
# write workflow
|
||||
with open(f".github/workflows/notebook-{initials}.yml", "w") as f:
|
||||
with open(f".github/workflows/notebook-{name}.yml", "w") as f:
|
||||
f.write(workflow_yaml)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -10,5 +10,5 @@ This project has adopted the [Microsoft Open Source Code of Conduct](https://ope
|
|||
## Reference
|
||||
|
||||
- [GitHub Template](https://github.com/Azure/azureml-template)
|
||||
- [Cheat Sheet, VSCode Snippets, and Templates](https://azure.github.io/azureml-web)
|
||||
- [Open Source Azure ML Cheat Sheet](https://azure.github.io/azureml-web)
|
||||
- [Azure Machine Learning Documentation](https://docs.microsoft.com/azure/machine-learning)
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче