import os

"""
Explanation: Guided Project 2
Learning Objective:

Learn how to adapt the tfx template to an existing model.

In this guided project, we will use the tfx template tool to create a TFX pipeline around the covertype dataset.
The goal is to adapt the template pipeline to make use of the model code for the covertype dataset we  already developed in
the first part of this course.
End of explanation
"""


ENDPOINT = '' # Enter your ENDPOINT here.

PATH=%env PATH
%env PATH={PATH}:/home/jupyter/.local/bin

shell_output=!gcloud config list --format 'value(core.project)' 2>/dev/null
GOOGLE_CLOUD_PROJECT=shell_output[0]

%env GOOGLE_CLOUD_PROJECT={GOOGLE_CLOUD_PROJECT}

# Docker image name for the pipeline image.
CUSTOM_TFX_IMAGE = 'gcr.io/' + GOOGLE_CLOUD_PROJECT + '/tfx-pipeline'
CUSTOM_TFX_IMAGE

"""
Explanation: Step 1. Environment setup
Envirnonment Variables
Setup the your Kubeflow pipelines endopoint below the same way you did in guided project 1.
End of explanation
"""


%%bash

LOCAL_BIN="/home/jupyter/.local/bin"
SKAFFOLD_URI="https://storage.googleapis.com/skaffold/releases/latest/skaffold-linux-amd64"

test -d $LOCAL_BIN || mkdir -p $LOCAL_BIN

which skaffold || (
    curl -Lo skaffold $SKAFFOLD_URI &&
    chmod +x skaffold               &&
    mv skaffold $LOCAL_BIN
)

"""
Explanation: skaffold tool setup
End of explanation
"""


!which skaffold

"""
Explanation: Modify the PATH environment variable so that skaffold is available:
At this point, you shoud see the skaffold tool with the command which:
End of explanation
"""


PIPELINE_NAME = "guided_project_2"
PROJECT_DIR = os.path.join(os.path.expanduser("."), PIPELINE_NAME)
PROJECT_DIR

"""
Explanation: Step 2. Copy the predefined template to your project directory.
In this step, we will create a working pipeline project directory and 
files by copying additional files from a predefined template.
You may give your pipeline a different name by changing the PIPELINE_NAME below. 
This will also become the name of the project directory where your files will be put.
End of explanation
"""


!tfx template copy \
  --pipeline-name={PIPELINE_NAME} \
  --destination-path={PROJECT_DIR} \
  --model=taxi

%cd {PROJECT_DIR}

"""
Explanation: TFX includes the taxi template with the TFX python package. 
If you are planning to solve a point-wise prediction problem,
including classification and regresssion, this template could be used as a starting point.
The tfx template copy CLI command copies predefined template files into your project directory.
End of explanation
"""


!python -m models.features_test
!python -m models.keras.model_test

"""
Explanation: Step 3. Browse your copied source files
The TFX template provides basic scaffold files to build a pipeline, including Python source code,
sample data, and Jupyter Notebooks to analyse the output of the pipeline. 
The taxi template uses the same Chicago Taxi dataset and ML model as 
the Airflow Tutorial.
Here is brief introduction to each of the Python files:
pipeline - This directory contains the definition of the pipeline
* configs.py — defines common constants for pipeline runners
* pipeline.py — defines TFX components and a pipeline
models - This directory contains ML model definitions.
* features.py, features_test.py — defines features for the model
* preprocessing.py, preprocessing_test.py — defines preprocessing jobs using tf::Transform
models/estimator - This directory contains an Estimator based model.
* constants.py — defines constants of the model
* model.py, model_test.py — defines DNN model using TF estimator
models/keras - This directory contains a Keras based model.
* constants.py — defines constants of the model
* model.py, model_test.py — defines DNN model using Keras
beam_dag_runner.py, kubeflow_dag_runner.py — define runners for each orchestration engine
Running the tests:
You might notice that there are some files with _test.py in their name. 
These are unit tests of the pipeline and it is recommended to add more unit 
tests as you implement your own pipelines. 
You can run unit tests by supplying the module name of test files with -m flag. 
You can usually get a module name by deleting .py extension and replacing / with ..
For example:
End of explanation
"""


GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + '-kubeflowpipelines-default'
GCS_BUCKET_NAME

!gsutil ls gs://{GCS_BUCKET_NAME} | grep {GCS_BUCKET_NAME} || gsutil mb gs://{GCS_BUCKET_NAME}

"""
Explanation: Step 4. Create the artifact store bucket
Note: You probably already have completed this step in guided project 1, so you may
may skip it if this is the case.
Components in the TFX pipeline will generate outputs for each run as
ML Metadata Artifacts, and they need to be stored somewhere.
You can use any storage which the KFP cluster can access, and for this example we
will use Google Cloud Storage (GCS).
Let us create this bucket if you haven't created it in guided project 1.
Its name will be &lt;YOUR_PROJECT&gt;-kubeflowpipelines-default.
End of explanation
"""


FEATURE_PY  = '../../tfx_pipelines/02-pipeline/solutions/pipeline/features.py'
PREPROC_PY = '../../tfx_pipelines/02-pipeline/solutions/pipeline/preprocessing.py'

!cp {FEATURE_PY} ./models/features.py
!cp {PREPROC_PY} ./models/preprocessing.py

"""
Explanation: Step 5. Change the dataset
Now we need to have the TFX pipeline dataset point to the GCS bucket
where our covertype data is located. For that


open kubeflow_dag_runner.py


Set the variable DATA_PATH to gs://workshop-datasets/covertype/small which contains the covertype dataset


Step 6. Change the pre-processing module
At this step we want to reuse the features.py and preprocessing.py we already have for the covertype dataset. To do that, we will just copy these files over the template ones:
End of explanation
"""


!python -m models.features_test

!python -m models.preprocessing_test

"""
Explanation: Now when you run the tests in the two cells below they should fail 
because they were written for the template taxi dataset. 
Exercise: Modify the tests features_test.py and preprocessing_test.py as well as possibly the original modules until the tests pass.
End of explanation
"""


MODEL_PY  = '../../tfx_pipelines/02-pipeline/solutions/pipeline/model.py'

!cp {MODEL_PY} ./models/keras/model.py

"""
Explanation: Step 6. Change the model
Similarly as for the pre-processing we want to reuse the model we develop for the covertype dataset,
so we will simply copy it over the template model:
End of explanation
"""


!tfx pipeline create  \
--pipeline-path=kubeflow_dag_runner.py \
--endpoint={ENDPOINT} \
--build-target-image={CUSTOM_TFX_IMAGE}

"""
Explanation: Exercise: Move the constants defined in model.py into constants.py and import them from model.py to respect the template structure.
Step 7. Run the covertype TFX pipeline
Let's create a TFX pipeline using the tfx pipeline create command.
Note: When creating a pipeline for KFP, we need a container image which will 
be used to run our pipeline. And skaffold will build the image for us. Because skaffold
pulls base images from the docker hub, it will take 5~10 minutes when we build
the image for the first time, but it will take much less time from the second build.
End of explanation
"""


!tfx run create --pipeline-name={PIPELINE_NAME} --endpoint={ENDPOINT}

"""
Explanation: While creating a pipeline, Dockerfile and build.yaml will be generated to build a Docker image.
Don't forget to add these files to the source control system (for example, git) along with other source files.
A pipeline definition file for argo will be generated, too. 
The name of this file is ${PIPELINE_NAME}.tar.gz. 
For example, it will be guided_project_2.tar.gz if the name of your pipeline is guided_project_1. 
It is recommended NOT to include this pipeline definition file into source control, because it will be generated from other Python files and will be updated whenever you update the pipeline. For your convenience, this file is already listed in .gitignore which is generated automatically.
Now start an execution run with the newly created pipeline using the tfx run create command.
Note: You may see the following error Error importing tfx_bsl_extension.coders. Please ignore it.
End of explanation
"""


# Update the pipeline
!tfx pipeline update \
--pipeline-path=kubeflow_dag_runner.py \
--endpoint={ENDPOINT}

# You can run the pipeline the same way.
!tfx run create --pipeline-name {PIPELINE_NAME} --endpoint={ENDPOINT}

"""
Explanation: Or, you can also run the pipeline in the KFP Dashboard. The new execution run will be listed 
under Experiments in the KFP Dashboard. 
Clicking into the experiment will allow you to monitor progress and visualize 
the artifacts created during the execution run.
However, we recommend visiting the KFP Dashboard. You can access the KFP Dashboard from 
the Cloud AI Platform Pipelines menu in Google Cloud Console. Once you visit the dashboard, 
you will be able to find the pipeline, and access a wealth of information about the pipeline. 
For example, you can find your runs under the Experiments menu, and when you open your
execution run under Experiments you can find all your artifacts from the pipeline under Artifacts menu.
Note: If your pipeline run fails, you can see detailed logs for each TFX component in the Experiments tab in the KFP Dashboard.
One of the major sources of failure is permission related problems. 
Please make sure your KFP cluster has permissions to access Google Cloud APIs.
This can be configured when you create a KFP cluster in GCP,
or see Troubleshooting document in GCP.
Step 8. Add components for data validation.
In this step, you will add components for data validation including StatisticsGen, SchemaGen, and ExampleValidator.
If you are interested in data validation, please see 
Get started with Tensorflow Data Validation.
Double-click to change directory to pipeline and double-click again to open pipeline.py. 
Find and uncomment the 3 lines which add StatisticsGen, SchemaGen, and ExampleValidator to the pipeline.
(Tip: search for comments containing TODO(step 5):). Make sure to save pipeline.py after you edit it.
You now need to update the existing pipeline with modified pipeline definition. Use the tfx pipeline update command to update your pipeline, followed by the tfx run create command to create a new execution run of your updated pipeline.
End of explanation
"""


print('https://' + ENDPOINT)

"""
Explanation: Check pipeline outputs
Visit the KFP dashboard to find pipeline outputs in the page for your pipeline run. Click the Experiments tab on the left, and All runs in the Experiments page. You should be able to find the latest run under the name of your pipeline.
See link below to access the dashboard:
End of explanation
"""


!tfx pipeline update \
--pipeline-path=kubeflow_dag_runner.py \
--endpoint={ENDPOINT}

print("https://" + ENDPOINT)

!tfx run create --pipeline-name {PIPELINE_NAME} --endpoint={ENDPOINT}

"""
Explanation: Step 9. Add components for training
In this step, you will add components for training and model validation including Transform, Trainer, ResolverNode, Evaluator, and Pusher.
Double-click to open pipeline.py. Find and uncomment the 5 lines which add Transform, Trainer, ResolverNode, Evaluator and Pusher to the pipeline. (Tip: search for TODO(step 6):)
Hints: 


In pipeline.py make sure you turn the cache of by setting enable_cache=False for debugging purposes (otherwise components that have been previously run won't be).


In pipeline.py, you'll need to set infer_feature_shape=False, otherwise you'll run into sparse/dense tensor mismatch.


In pipeline.py, you'll need to set the label big_tipper from the default template to the right label for our dataset in 


python
tfma.EvalConfig(
      model_specs=[tfma.ModelSpec(label_key='big_tipper')],

In configs.py, adapt the values of the variables TRAIN_NUM_STEPS and EVAL_NUM_STEPS to match what we had in the original covertype pipeline.

As you did before, you now need to update the existing pipeline with the modified pipeline definition. The instructions are the same as Step 5. Update the pipeline using tfx pipeline update, and create an execution run using tfx run create.
Verify that the pipeline DAG has changed accordingly in the Kubeflow UI:
End of explanation
"""


!tfx pipeline update \
--pipeline-path=kubeflow_dag_runner.py \
--endpoint={ENDPOINT}

!tfx run create --pipeline-name {PIPELINE_NAME} --endpoint={ENDPOINT}

"""
Explanation: When this execution run finishes successfully, you have now created and run your first TFX pipeline in AI Platform Pipelines!
Step 10. Try Cloud AI Platform Training and Prediction with KFP
TFX interoperates with several managed GCP services, such as Cloud AI Platform for Training and Prediction. You can set your Trainer component to use Cloud AI Platform Training, a managed service for training ML models. Moreover, when your model is built and ready to be served, you can push your model to Cloud AI Platform Prediction for serving. In this step, we will set our Trainer and Pusher component to use Cloud AI Platform services.
Before editing files, you might first have to enable AI Platform Training & Prediction API.
Double-click pipeline to change directory, and double-click to open configs.py. Uncomment the definition of GOOGLE_CLOUD_REGION, GCP_AI_PLATFORM_TRAINING_ARGS and GCP_AI_PLATFORM_SERVING_ARGS. We will use our custom built container image to train a model in Cloud AI Platform Training, so we should set masterConfig.imageUri in GCP_AI_PLATFORM_TRAINING_ARGS to the same value as CUSTOM_TFX_IMAGE above.
Change directory one level up, and double-click to open kubeflow_dag_runner.py. Uncomment ai_platform_training_args and ai_platform_serving_args.
Update the pipeline and create an execution run as we did in step 5 and 6.
End of explanation
"""


