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Mkdocs facility added
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anandhu-eng committed May 24, 2024
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33 changes: 33 additions & 0 deletions .github/workflows/publish.yaml
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# This is a basic workflow to help you get started with Actions

name: Publish site


on:
release:
types: [published]
push:
branches:
- main
- docs

jobs:

publish:
name: Publish the site
runs-on: ubuntu-latest

steps:
- name: Checkout repository normally
uses: actions/checkout@v3

- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.11"

- name: Install Mkdocs
run: pip install -r docs/requirements.txt

- name: Run Mkdocs deploy
run: mkdocs gh-deploy --force
126 changes: 126 additions & 0 deletions docs/AI-ML-datasets/get-croissant.md
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Automatically generated README for this automation recipe: **get-croissant**

Category: **AI/ML datasets**

License: **Apache 2.0**

Maintainers: [Public MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md)

---
*[ [Online info and GUI to run this CM script](https://access.cknowledge.org/playground/?action=scripts&name=get-croissant,8fd653eac8da4c14) ]*

---
#### Summary

* CM GitHub repository: *[mlcommons@cm4mlops](https://github.com/mlcommons/cm4mlops/tree/dev)*
* GitHub directory for this script: *[GitHub](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant)*
* CM meta description for this script: *[_cm.yaml](_cm.yaml)*
* All CM tags to find and reuse this script (see in above meta description): *get,mlcommons,croissant*
* Output cached? *True*
* See [pipeline of dependencies](#dependencies-on-other-cm-scripts) on other CM scripts


---
### Reuse this script in your project

#### Install MLCommons CM automation meta-framework

* [Install CM](https://access.cknowledge.org/playground/?action=install)
* [CM Getting Started Guide](https://github.com/mlcommons/ck/blob/master/docs/getting-started.md)

#### Pull CM repository with this automation recipe (CM script)

```cm pull repo mlcommons@cm4mlops```

#### Print CM help from the command line

````cmr "get mlcommons croissant" --help````

#### Customize and run this script from the command line with different variations and flags

`cm run script --tags=get,mlcommons,croissant`

`cm run script --tags=get,mlcommons,croissant `

*or*

`cmr "get mlcommons croissant"`

`cmr "get mlcommons croissant " `


#### Run this script from Python

<details>
<summary>Click here to expand this section.</summary>

```python

import cmind

r = cmind.access({'action':'run'
'automation':'script',
'tags':'get,mlcommons,croissant'
'out':'con',
...
(other input keys for this script)
...
})

if r['return']>0:
print (r['error'])

```

</details>


#### Run this script via GUI

```cmr "cm gui" --script="get,mlcommons,croissant"```

#### Run this script via Docker (beta)

`cm docker script "get mlcommons croissant" `

___
### Customization

#### Default environment

<details>
<summary>Click here to expand this section.</summary>

These keys can be updated via `--env.KEY=VALUE` or `env` dictionary in `@input.json` or using script flags.


</details>

___
### Dependencies on other CM scripts


1. ***Read "deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/_cm.yaml)***
* detect,os
- CM script: [detect-os](https://github.com/mlcommons/cm4mlops/tree/master/script/detect-os)
* get,python3
* CM names: `--adr.['python3', 'python']...`
- CM script: [get-python3](https://github.com/mlcommons/cm4mlops/tree/master/script/get-python3)
* git,repo,_repo.https://github.com/mlcommons/croissant
* CM names: `--adr.['git-mlcommons-croissant']...`
- CM script: [get-git-repo](https://github.com/mlcommons/cm4mlops/tree/master/script/get-git-repo)
1. ***Run "preprocess" function from [customize.py](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/customize.py)***
1. Read "prehook_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/_cm.yaml)
1. ***Run native script if exists***
* [run.bat](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/run.bat)
* [run.sh](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/run.sh)
1. Read "posthook_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/_cm.yaml)
1. ***Run "postrocess" function from [customize.py](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/customize.py)***
1. Read "post_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-croissant/_cm.yaml)

___
### Script output
`cmr "get mlcommons croissant " -j`
#### New environment keys (filter)

#### New environment keys auto-detected from customize
164 changes: 164 additions & 0 deletions docs/AI-ML-datasets/get-dataset-cifar10.md
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Automatically generated README for this automation recipe: **get-dataset-cifar10**

Category: **AI/ML datasets**

License: **Apache 2.0**

Maintainers: [Public MLCommons Task Force on Automation and Reproducibility](https://github.com/mlcommons/ck/blob/master/docs/taskforce.md)

---
*[ [Online info and GUI to run this CM script](https://access.cknowledge.org/playground/?action=scripts&name=get-dataset-cifar10,2f0c0bb3663b4ed7) ]*

---
#### Summary

* CM GitHub repository: *[mlcommons@cm4mlops](https://github.com/mlcommons/cm4mlops/tree/dev)*
* GitHub directory for this script: *[GitHub](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10)*
* CM meta description for this script: *[_cm.json](_cm.json)*
* All CM tags to find and reuse this script (see in above meta description): *get,dataset,cifar10,image-classification,validation,training*
* Output cached? *True*
* See [pipeline of dependencies](#dependencies-on-other-cm-scripts) on other CM scripts


---
### Reuse this script in your project

#### Install MLCommons CM automation meta-framework

* [Install CM](https://access.cknowledge.org/playground/?action=install)
* [CM Getting Started Guide](https://github.com/mlcommons/ck/blob/master/docs/getting-started.md)

#### Pull CM repository with this automation recipe (CM script)

```cm pull repo mlcommons@cm4mlops```

#### Print CM help from the command line

````cmr "get dataset cifar10 image-classification validation training" --help````

#### Customize and run this script from the command line with different variations and flags

`cm run script --tags=get,dataset,cifar10,image-classification,validation,training`

`cm run script --tags=get,dataset,cifar10,image-classification,validation,training[,variations] `

*or*

`cmr "get dataset cifar10 image-classification validation training"`

`cmr "get dataset cifar10 image-classification validation training [variations]" `


* *See the list of `variations` [here](#variations) and check the [Gettings Started Guide](https://github.com/mlcommons/ck/blob/dev/docs/getting-started.md) for more details.*

#### Run this script from Python

<details>
<summary>Click here to expand this section.</summary>

```python

import cmind

r = cmind.access({'action':'run'
'automation':'script',
'tags':'get,dataset,cifar10,image-classification,validation,training'
'out':'con',
...
(other input keys for this script)
...
})

if r['return']>0:
print (r['error'])

```

</details>


#### Run this script via GUI

```cmr "cm gui" --script="get,dataset,cifar10,image-classification,validation,training"```

#### Run this script via Docker (beta)

`cm docker script "get dataset cifar10 image-classification validation training[variations]" `

___
### Customization


#### Variations

* *No group (any variation can be selected)*
<details>
<summary>Click here to expand this section.</summary>

* `_tiny`
- Environment variables:
- *CM_DATASET_CONVERT_TO_TINYMLPERF*: `yes`
- Workflow:
1. ***Read "deps" on other CM scripts***
* get,python3
* CM names: `--adr.['python', 'python3']...`
- CM script: [get-python3](https://github.com/mlcommons/cm4mlops/tree/master/script/get-python3)
* get,tinymlperf,src
- CM script: [get-mlperf-tiny-src](https://github.com/mlcommons/cm4mlops/tree/master/script/get-mlperf-tiny-src)
* get,src,eembc,energy-runner
- CM script: [get-mlperf-tiny-eembc-energy-runner-src](https://github.com/mlcommons/cm4mlops/tree/master/script/get-mlperf-tiny-eembc-energy-runner-src)

</details>


* Group "**data_format**"
<details>
<summary>Click here to expand this section.</summary>

* **`_python`** (default)
- Environment variables:
- *CM_DATASET*: `CIFAR10`
- *CM_DATASET_FILENAME*: `cifar-10-python.tar.gz`
- *CM_DATASET_FILENAME1*: `cifar-10-python.tar`
- *CM_DATASET_CIFAR10*: `https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz`
- Workflow:

</details>


#### Default variations

`_python`
#### Default environment

<details>
<summary>Click here to expand this section.</summary>

These keys can be updated via `--env.KEY=VALUE` or `env` dictionary in `@input.json` or using script flags.


</details>

___
### Dependencies on other CM scripts


1. ***Read "deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/_cm.json)***
* detect,os
- CM script: [detect-os](https://github.com/mlcommons/cm4mlops/tree/master/script/detect-os)
1. ***Run "preprocess" function from [customize.py](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/customize.py)***
1. Read "prehook_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/_cm.json)
1. ***Run native script if exists***
* [run.bat](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/run.bat)
* [run.sh](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/run.sh)
1. Read "posthook_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/_cm.json)
1. ***Run "postrocess" function from [customize.py](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/customize.py)***
1. Read "post_deps" on other CM scripts from [meta](https://github.com/mlcommons/cm4mlops/tree/dev/script/get-dataset-cifar10/_cm.json)

___
### Script output
`cmr "get dataset cifar10 image-classification validation training [,variations]" -j`
#### New environment keys (filter)

* `CM_DATASET_*`
#### New environment keys auto-detected from customize
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