# Copyright 2018 Google LLC
#
# 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 kfp.dsl as dsl
import kfp.gcp as gcp


@dsl.pipeline(
  name='Github issue summarization',
  description='Demonstrate Tensor2Tensor-based training and TF-Serving'
)
def gh_summ(  #pylint: disable=unused-argument
  train_steps=2019300,
  project='YOUR_PROJECT_HERE',
  github_token='YOUR_GITHUB_TOKEN_HERE',
  working_dir='YOUR_GCS_DIR_HERE',
  checkpoint_dir='gs://aju-dev-demos-codelabs/kubecon/model_output_tbase.bak2019000',
  deploy_webapp='true',
  data_dir='gs://aju-dev-demos-codelabs/kubecon/t2t_data_gh_all/'
  ):


  train = dsl.ContainerOp(
      name='train',
      image='gcr.io/google-samples/ml-pipeline-t2ttrain:v1ap',
      arguments=["--data-dir", data_dir,
          "--checkpoint-dir", checkpoint_dir,
          "--working-dir", working_dir,
          "--model-dir", '%s/%s/model_output' % (working_dir, '{{workflow.name}}'),
          "--train-steps", train_steps, "--deploy-webapp", deploy_webapp],
      file_outputs={'output': '/tmp/output'}

      ).apply(gcp.use_gcp_secret('user-gcp-sa'))

  serve = dsl.ContainerOp(
      name='serve',
      image='gcr.io/google-samples/ml-pipeline-kubeflow-tfserve',
      arguments=["--model_name", 'ghsumm-%s' % ('{{workflow.name}}',),
          "--model_path", '%s/%s/model_output/export' % (working_dir, '{{workflow.name}}')
          ]
      )
  serve.after(train)
  train.set_gpu_limit(4)
  train.set_memory_limit('48G')

  with dsl.Condition(train.output == 'true'):
    webapp = dsl.ContainerOp(
        name='webapp',
        image='gcr.io/google-samples/ml-pipeline-webapp-launcher',
        arguments=["--model_name", 'ghsumm-%s' % ('{{workflow.name}}',),
            "--github_token", github_token]

        )
    webapp.after(serve)


if __name__ == '__main__':
  import kfp.compiler as compiler
  compiler.Compiler().compile(gh_summ, __file__ + '.tar.gz')