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valohai.yaml
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---
- step:
name: preprocess-dataset
image: python:3.9
command:
- pip install numpy valohai-utils
- python ./preprocess_dataset.py
inputs:
- name: dataset
default: https://valohaidemo.blob.core.windows.net/mnist/mnist.npz
- step:
name: train-model
image: tensorflow/tensorflow:2.6.0
command:
- pip install valohai-utils
- python ./train_model.py {parameters}
parameters:
- name: epochs
default: 5
type: integer
- name: learning_rate
default: 0.001
type: float
inputs:
- name: dataset
default: https://valohaidemo.blob.core.windows.net/mnist/preprocessed_mnist.npz
- step:
name: batch-inference
image: tensorflow/tensorflow:2.6.0
command:
- pip install pillow valohai-utils
- python ./batch_inference.py
inputs:
- name: model
- name: images
default:
- https://valohaidemo.blob.core.windows.net/mnist/four-inverted.png
- https://valohaidemo.blob.core.windows.net/mnist/five-inverted.png
- https://valohaidemo.blob.core.windows.net/mnist/five-normal.jpg
- step:
name: compare-predictions
image: python:3.9
command:
- pip install numpy valohai-utils
- python ./compare_predictions.py
inputs:
- name: predictions
- name: models
optional: true
- pipeline:
name: Training Pipeline
nodes:
- name: preprocess
type: execution
step: preprocess-dataset
- name: train
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: evaluate
type: execution
step: batch-inference
edges:
- [preprocess.output.preprocessed_mnist.npz, train.input.dataset]
- [train.output.model*, evaluate.input.model]
- pipeline:
name: Three-Trainings Pipeline
nodes:
- name: preprocess
type: execution
step: preprocess-dataset
- name: train1
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: train2
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: train3
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: evaluate1
type: execution
step: batch-inference
- name: evaluate2
type: execution
step: batch-inference
- name: evaluate3
type: execution
step: batch-inference
- name: find-best-model
type: execution
step: compare-predictions
edges:
- [preprocess.output.preprocessed_mnist.npz, train1.input.dataset]
- [preprocess.output.preprocessed_mnist.npz, train2.input.dataset]
- [preprocess.output.preprocessed_mnist.npz, train3.input.dataset]
- [train1.output.model*, evaluate1.input.model]
- [train2.output.model*, evaluate2.input.model]
- [train3.output.model*, evaluate3.input.model]
- [evaluate1.output.*.json, find-best-model.input.predictions]
- [evaluate2.output.*.json, find-best-model.input.predictions]
- [evaluate3.output.*.json, find-best-model.input.predictions]
- [evaluate1.input.model*, find-best-model.input.models]
- [evaluate2.input.model*, find-best-model.input.models]
- [evaluate3.input.model*, find-best-model.input.models]
- pipeline:
name: Three-Trainings Pipeline with deployment
nodes:
- name: preprocess
type: execution
step: preprocess-dataset
- name: train1
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: train2
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: train3
type: execution
step: train-model
override:
inputs:
- name: dataset
- name: evaluate1
type: execution
step: batch-inference
- name: evaluate2
type: execution
step: batch-inference
- name: evaluate3
type: execution
step: batch-inference
- name: find-best-model
type: execution
step: compare-predictions
- name: deploy
type: deployment
deployment: deployment-test
endpoints:
- predict-digit
edges:
- [preprocess.output.preprocessed_mnist.npz, train1.input.dataset]
- [preprocess.output.preprocessed_mnist.npz, train2.input.dataset]
- [preprocess.output.preprocessed_mnist.npz, train3.input.dataset]
- [train1.output.model*, evaluate1.input.model]
- [train2.output.model*, evaluate2.input.model]
- [train3.output.model*, evaluate3.input.model]
- [evaluate1.output.*.json, find-best-model.input.predictions]
- [evaluate2.output.*.json, find-best-model.input.predictions]
- [evaluate3.output.*.json, find-best-model.input.predictions]
- [evaluate1.input.model*, find-best-model.input.models]
- [evaluate2.input.model*, find-best-model.input.models]
- [evaluate3.input.model*, find-best-model.input.models]
- [find-best-model.output.model*, deploy.file.predict-digit.model]
- endpoint:
name: greet
image: python:3.9
port: 8000
server-command: python -m wsgiref.simple_server
- endpoint:
name: predict-digit
description: predict digits from image inputs ("file" parameter)
image: tensorflow/tensorflow:2.6.0
wsgi: predict:predict
files:
- name: model
description: Model output file from TensorFlow
path: model.h5