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valohai.yaml
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valohai.yaml
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---
- step:
name: Preprocess Dataset
image: python:3.6
command:
- pip install -r requirements.txt
- python preprocess_dataset.py
inputs:
- name: dataset
default: https://depprocureformstorage.blob.core.windows.net/semicond-yield/input/uci-secom.csv
- step:
name: train-model
image: valohai/notebook:sklearn-0.24.2
command:
- pip install -r requirements.txt
- python ./train.py {parameters}
inputs:
- name: dataset1
default: datum://01806ad7-33e7-2861-7b42-6c91dff8abb8
- name: dataset2
default: datum://01806ad7-2cdd-29c3-a149-82853dcf3105
- name: dataset3
default: datum://01806ad7-379d-1bec-23d4-af094908f350
- name: dataset4
default: datum://01806ad7-3059-b365-39bd-cf38416b8f81
parameters:
- name: n_estimators
type: string
default: 5
- name: max_depth
type: string
default: 4
- step:
name: Batch Inference
image: tensorflow/tensorflow:2.5.0
command:
- pip install pandas valohai-utils joblib sklearn
- python ./batch-inference.py
inputs:
- name: model1
default: datum://0180b70a-eed8-76eb-d5b1-6af1666a50ea
- name: model2
default: datum://0180b7ae-1318-eaf9-3823-2dc6afc9435b
- name: images
default: datum://0180b7fd-1512-0c79-fa36-a4557413ab49
- endpoint:
name: sc-predict
description: predict the values
image: tiangolo/uvicorn-gunicorn-fastapi:python3.7
server-command: uvicorn predict:app --host 0.0.0.0 --port 8000
files:
- name: model1
description: Model output file from train.py
path: model_rf.jbl
- name: model2
description: Model output file from pre-process.py
path: fatures_selected.jbl