# Copyright 2020 The Kubeflow Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import pathlib

from kfp import compiler
from kfp import components
from kfp import dsl

test_data_dir = pathlib.Path(__file__).parent.parent / 'v1_component_yaml'

ingestion_op = components.load_component_from_file(
    str(test_data_dir / 'ingestion_component.yaml'))

training_op = components.load_component_from_file(
    str(test_data_dir / 'fancy_trainer_component.yaml'))


@dsl.pipeline(
    name='two-step-pipeline-with-resource-spec',
    description='A linear two-step pipeline with resource specification.')
def my_pipeline(input_location: str = 'gs://test-bucket/pipeline_root',
                optimizer: str = 'sgd',
                n_epochs: int = 200):
    ingestor = ingestion_op(input_location=input_location)
    _ = (
        training_op(
            examples=ingestor.outputs['examples'],
            optimizer=optimizer,
            n_epochs=n_epochs).set_cpu_request('2').set_cpu_limit(
                '4').set_memory_request('4Gi').set_memory_limit('14Gi')
        .set_accelerator_type('tpu-v3').set_accelerator_limit(1))


if __name__ == '__main__':
    compiler.Compiler().compile(
        pipeline_func=my_pipeline,
        package_path=__file__.replace('.py', '.yaml'))
