https://github.com/gclouduniverse/workbench

GCP Vertex AI Prediction High Level SDK
https://github.com/gclouduniverse/workbench

Last synced: 5 days ago
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GCP Vertex AI Prediction High Level SDK

• Host: GitHub
• URL: https://github.com/gclouduniverse/workbench
• Owner: gclouduniverse
• License: mit
• Created: 2021-07-10T00:33:23.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-02-24T05:40:38.000Z (almost 3 years ago)
• Last Synced: 2024-05-28T15:16:34.470Z (7 months ago)
• Language: Jupyter Notebook
• Size: 91.8 KB
• Stars: 4
• Watchers: 5
• Forks: 1
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Vertex AI Workbench High Level SDK

Install it:

```bash

pip install ai-workbnech

```

Only python >= 3.8 is currently supported.

## Executing Notebooks

Submit local notebook to execution:

```bash

workbench execute-notebook --notebook ./notebooks/clean.ipynb --gcs-folder gs://gtc-conf-examples/ --location us-central1 --project ml-lab-152505

```

## Deploy To Prediction

This SDK/CLI makes it very-very simple to deploy your Python logic to Vertex AI Prediction. In fact you just need to do two things:

* write python prediction function

* name file that stores python prediction function ```prediction.py```

your prediction function must comply with the following interface:

```python

def predict(instance, **kwarg):

    pass

```

Workbench has to main actions:

* build (creates Docker image with your logic that is fully compatible with Vertex Prediction)

* deploy (cloud be done with as easily with ```gcloud```) - deploys container from the step #1 to the Vertex predciton

During the build stage, under the hood VIAP will do:

* it will create Docker container with Flask

* it will correctly configure Flask to recognize your funciton ```predict```

* it will copy all the files from the current folder to the container

* it will install all python requirenments from the ```requirenmnets.txt``` file

### Test Yourself

Do not belive my word. Install CLI:

```bash

pip install ai-workbnech

```

go to the ```demo``` directory and run the following command:

```bash

TAG=... # your tag that you can push somewhere, e.g."us.gcr.io/ml-lab-152505/model-poc"

workbnech build --tag "${TAG}" --path .

```

test it, start container locally:

```bash

TAG=... # your tag that you can push somewhere, e.g."us.gcr.io/ml-lab-152505/model-poc"

docker run -p 8080:8080 "${TAG}"

```

run the prediction:

```bash

curl -X POST -d '{"parameters": {}, "instances": ["1", "2"]}' -H "Content-Type: application/json" http://localhost:8080/predict

```

you should see:

```

{"predictions":["Hello Vertex","Hello Vertex"]}

```