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Anandhu eng patch 1 #1369

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merged 4 commits into from
Nov 29, 2024
Merged

Anandhu eng patch 1 #1369

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56748cd
corrected hyperlinks
anandhu-eng Oct 29, 2024
aec10d1
Update getting-started.md
anandhu-eng Oct 29, 2024
c9adefe
Fix in cm through virtual env
anandhu-eng Oct 29, 2024
fe68f26
Merge branch 'master' into anandhu-eng-patch-1
gfursin Nov 29, 2024
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10 changes: 5 additions & 5 deletions docs/getting-started.md
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Expand Up @@ -192,7 +192,7 @@ Such architecture makes it possible to easily chain existing user scripts and to
instead of substituting or rewriting them.

It is possible to chain CM scripts using simple
[`deps` list](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/app-image-classification-onnx-py/_cm.yaml#L21)
[`deps` list](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/app-image-classification-onnx-py/_cm.yaml#L23)
in a meta description of a given script:

<sup>
Expand Down Expand Up @@ -262,7 +262,7 @@ cmr "python app image-classification onnx _cpu" --input=computer_mouse.jpg -j
variables at the host. However, CM allows you to do that
by explicitly specifying which environment variables and state keys
will be updated at the host using `new_env_keys` and `new_state_keys`
in the meta of a given script as shown [here](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/app-image-classification-onnx-py/_cm.yaml#L83).
in the meta of a given script as shown [here](https://github.com/mlcommons/ck/blob/master/cm-mlops/script/app-image-classification-onnx-py/_cm.yaml#L88).
This helped us make behavior of complex CM workflows more deterministic
and reproducible.*

Expand Down Expand Up @@ -294,7 +294,7 @@ detect or install Python and PIP packages, download and preprocess data sets and

## How to add new CM scripts?

One the main requirement for CM was to provide a very light-weight connectors
One of the main requirement for CM was to provide a very light-weight connectors
between existing automation scripts and tools rather than substituting them.

You can add your own scripts and tools to CM using the following command
Expand All @@ -314,7 +314,7 @@ You can also run it from python as follows:
import cmind
output=cmind.access({'action':'run',
'automation':'script',
'tags':'my,script})
'tags':'my,script'})
if output['return']==0: print (output)
```

Expand Down Expand Up @@ -412,7 +412,7 @@ export CM_SCRIPT_EXTRA_CMD="--adr.python.name=mlperf"

If you now run our image classification automation recipe,
it will reuse model and dataset from the cache, but will
use
use the newly created virtual environment `mlperf` for running the script.


## How to debug CM scripts?
Expand Down