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Azure Machine Learning Examples

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Examples

Simple Example

Train

python run_on_azureml.py \
  --experiment test-exp \
  --script simple_example/run.py \
  --message Howdy!

Deploy

python deploy_on_azureml.py \
  --run_id test-exp_1618105880_601b57cd \
  --experiment_name test-exp \
  --model_artifact_path logs/message.txt \
  --score_file simple_example/score.py \
  --model_name message-model \
  --service_name message-service

Predict on Endpoint

python simple_example/score.py --endpoint <YOUR ENDPOINT>

Keras

Train

python run_on_azureml.py \
  --experiment test-exp \
  --script keras_mnist_example/train.py \
  --max_epochs 10 \
  --batch_size 64

Deploy

In the Azure ML Portal, navigate to the run you're happy with, and copy its Run ID. Note this is not just an int like " Run 5", but a longer unique identifier. Pass this to the --run_id flag of deploy_on_azure.py. Your Run ID should look similar to the one below, but will not be the same.

python deploy_on_azureml.py \
  --run_id test-exp_1618103328_5e55046d \
  --experiment_name test-exp \
  --model_artifact_path logs/saved_model \
  --score_file keras_mnist_example/score.py \
  --model_name test-keras-model \
  --service_name test-keras-service

Predict from Endpoint

python keras_mnist_example/score.py --endpoint <YOUR ENDPOINT>

PyTorch Lightning Example

Train

python run_on_azureml.py \
  --experiment test-exp \
  --script lightning_mnist_example/train.py \
  --max_epochs 10 \
  --batch_size 64 \
  --default_root_dir logs/

Deploy

 python deploy_on_azureml.py \
  --run_id test-exp_1618104960_e34305b3 \
  --experiment_name test-exp \
  --model_artifact_path logs/lightning_logs/version_0/checkpoints/epoch=9-step=8599.ckpt \
  --score_file lightning_mnist_example/score.py \
  --model_name test-lit-model \
  --service_name test-lit-service

Predict from Endpoint

python lightning_mnist_example/score.py --endpoint <YOUR ENDPOINT>
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