train.sh

# <hello_world>
az ml job create -f jobs/basics/hello-world.yml --web
# </hello_world>

# <hello_world_set>
az ml job create -f jobs/basics/hello-world.yml \
  --set environment.image="python:3.8" \
  --web
# </hello_world_set>

# <hello_world_name>
run_id=$(az ml job create -f jobs/basics/hello-world.yml --query name -o tsv)
# </hello_world_name>

# <hello_world_show>
az ml job show -n $run_id --web
# </hello_world_show>

# <hello_world_org>
az ml job create -f jobs/basics/hello-world-org.yml --web
# </hello_world_org>

run_id=$(az ml job create -f jobs/basics/hello-world-org.yml --query name -o tsv)
# <hello_world_org_set>
az ml job update -n $run_id --set \
  display_name="updated display name" \
  experiment_name="updated experiment name" \
  description="updated description"  \
  tags.hello="updated tag"
# </hello_world_org_set>

# <hello_world_env_var>
az ml job create -f jobs/basics/hello-world-env-var.yml --web
# </hello_world_env_var>

# <hello_mlflow>
az ml job create -f jobs/basics/hello-mlflow.yml --web
# </hello_mlflow>

# <mlflow_uri>
az ml workspace show --query mlflow_tracking_uri -o tsv
# </mlflow_uri>

# <hello_world_input>
az ml job create -f jobs/basics/hello-world-input.yml --web
# </hello_world_input>

# <hello_world_input_set>
az ml job create -f jobs/basics/hello-world-input.yml --set \
  inputs.hello_string="hello there" \
  inputs.hello_number=24 \
  --web
# </hello_world_input_set>

# <hello_sweep>
az ml job create -f jobs/basics/hello-sweep.yml --web
# </hello_sweep>

# <hello_world_output>
az ml job create -f jobs/basics/hello-world-output.yml --web
# </hello_world_output>

run_id=$(az ml job create -f jobs/basics/hello-world-output.yml --query name -o tsv)
if [[ -z "$run_id" ]]
then
  echo "Job creation failed"
  exit 3
fi
status=$(az ml job show -n $run_id --query status -o tsv)
if [[ -z "$status" ]]
then
  echo "Status query failed"
  exit 4
fi
running=("Queued" "Starting" "Preparing" "Running" "Finalizing")
while [[ ${running[*]} =~ $status ]]
do
  sleep 8 
  status=$(az ml job show -n $run_id --query status -o tsv)
  echo $status
done

# <hello_world_output_download>
az ml job download -n $run_id
# </hello_world_output_download>
rm -r $run_id

# <iris_file>
az ml job create -f jobs/basics/hello-iris-file.yml --web
# </iris_file>

# <iris_folder>
az ml job create -f jobs/basics/hello-iris-folder.yml --web
# </iris_folder>

# <iris_datastore_file>
az ml job create -f jobs/basics/hello-iris-datastore-file.yml --web
# </iris_datastore_file>

# <iris_datastore_folder>
az ml job create -f jobs/basics/hello-iris-datastore-folder.yml --web
# </iris_datastore_folder>

# <hello_world_output_data>
az ml job create -f jobs/basics/hello-world-output-data.yml --web
# </hello_world_output_data>

# <hello_pipeline>
az ml job create -f jobs/basics/hello-pipeline.yml --web
# </hello_pipeline>

# <hello_pipeline_io>
az ml job create -f jobs/basics/hello-pipeline-io.yml --web
# </hello_pipeline_io>

# <hello_pipeline_settings>
az ml job create -f jobs/basics/hello-pipeline-settings.yml --web
# </hello_pipeline_settings>

# <hello_pipeline_abc>
az ml job create -f jobs/basics/hello-pipeline-abc.yml --web
# </hello_pipeline_abc>

# <sklearn_iris>
az ml job create -f jobs/single-step/scikit-learn/iris/job.yml --web
# </sklearn_iris>

run_id=$(az ml job create -f jobs/single-step/scikit-learn/iris/job.yml --query name -o tsv)
if [[ -z "$run_id" ]]
then
  echo "Job creation failed"
  exit 3
fi
status=$(az ml job show -n $run_id --query status -o tsv)
if [[ -z "$status" ]]
then
  echo "Status query failed"
  exit 4
fi
running=("Queued" "Starting" "Preparing" "Running" "Finalizing")
while [[ ${running[*]} =~ $status ]]
do
  sleep 8 
  status=$(az ml job show -n $run_id --query status -o tsv)
  echo $status
done

# <sklearn_download_register_model>
az ml model create -n sklearn-iris-example -v 1 -p runs:/$run_id/model --type mlflow_model
# </sklearn_download_register_model>
rm -r $run_id

# <sklearn_sweep>
az ml job create -f jobs/single-step/scikit-learn/iris/job-sweep.yml --web
# </sklearn_sweep>

# <pytorch_cifar>
az ml job create -f jobs/single-step/pytorch/cifar-distributed/job.yml --web
# </pytorch_cifar>

# # <pipeline_cifar>
# az ml job create -f jobs/pipelines/cifar-10/pipeline.yml --web
# # </pipeline_cifar>