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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2020 The TensorFlow Authors.
Step1:  Create a TFX pipeline using templates with Local orchestrator
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<td><a target="_blank" href="https
Step2:  NOTE
Step3:  Let's check the version of TFX.
bash
python -c "from tfx import version ; print('TFX version
Step4:  And, it's done. We are ready to create a pipeline.
Step 2. Copy 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.
bash
export PIPELINE_NAME="my_pipeline"
export PROJECT_DIR=~/tfx/${PIPELINE_NAME}
Step5:  TFX includes the taxi template with the TFX python package. If you are planning to solve a point-wise prediction problem, including classification and regresssion, this template could be used as a starting point.
The tfx template copy CLI command copies predefined template files into your project directory.
sh
tfx template copy \
   --pipeline_name="${PIPELINE_NAME}" \
   --destination_path="${PROJECT_DIR}" \
   --model=taxi
Step6:  Change the working directory context in this notebook to the project directory.
bash
cd ${PROJECT_DIR}
Step7:  Step 3. Browse your copied source files.
The TFX template provides basic scaffold files to build a pipeline, including Python source code, sample data, and Jupyter Notebooks to analyse the output of the pipeline. The taxi template uses the same Chicago Taxi dataset and ML model as the Airflow Tutorial.
In Google Colab, you can browse files by clicking a folder icon on the left. Files should be copied under the project directoy, whose name is my_pipeline in this case. You can click directory names to see the content of the directory, and double-click file names to open them.
Here is brief introduction to each of the Python files.
-   pipeline - This directory contains the definition of the pipeline
    -   configs.py — defines common constants for pipeline runners
    -   pipeline.py — defines TFX components and a pipeline
-   models - This directory contains ML model definitions.
    -   features.py, features_test.py — defines features for the model
    -   preprocessing.py, preprocessing_test.py — defines preprocessing
        jobs using tf
Step8:  Step 4. Run your first TFX pipeline
You can create a pipeline using pipeline create command.
bash
tfx pipeline create --engine=local --pipeline_path=local_runner.py
Step9:  Then, you can run the created pipeline using run create command.
sh
tfx run create --engine=local --pipeline_name="${PIPELINE_NAME}"
Step10:  If successful, you'll see Component CsvExampleGen is finished. When you copy the template, only one component, CsvExampleGen, is included in the pipeline.
Step 5. 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.
We will modify copied pipeline definition in pipeline/pipeline.py. If you are working on your local environment, use your favorite editor to edit the file. If you are working on Google Colab, 
Click folder icon on the left to open Files view.
Click my_pipeline to open the directory and click pipeline directory to open and double-click pipeline.py to open the file.
Find and uncomment the 3 lines which add StatisticsGen, SchemaGen, and ExampleValidator to the pipeline. (Tip
Step11:  You should be able to see the output log from the added components. Our pipeline creates output artifacts in tfx_pipeline_output/my_pipeline directory.
Step 6. Add components for training.
In this step, you will add components for training and model validation including Transform, Trainer, Resolver, Evaluator, and Pusher.
Open pipeline/pipeline.py. Find and uncomment 5 lines which add Transform, Trainer, Resolver, Evaluator and Pusher to the pipeline. (Tip
Step12:  When this execution run finishes successfully, you have now created and run your first TFX pipeline using Local orchestrator!
NOTE
Step13:  You should specify your GCP project name to access BigQuery resources using TFX. Set GOOGLE_CLOUD_PROJECT environment variable to your project name.
sh
export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_NAME_HERE
Step14:  Open pipeline/pipeline.py. Comment out CsvExampleGen and uncomment the line which create an instance of BigQueryExampleGen. You also need to uncomment query argument of the create_pipeline function.
We need to specify which GCP project to use for BigQuery again, and this is done by setting --project in beam_pipeline_args when creating a pipeline.
Open pipeline/configs.py. Uncomment the definition of BIG_QUERY__WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS and BIG_QUERY_QUERY. You should replace the project id and the region value in this file with the correct values for your GCP project.
Open local_runner.py. Uncomment two arguments, query and beam_pipeline_args, for create_pipeline() method.
Now the pipeline is ready to use BigQuery as an example source. Update the pipeline and create a run as we did in step 5 and 6.
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Python Code:
#@title 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
#
# https://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.
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
import sys
!{sys.executable} -m pip install --upgrade "tfx<2"
Explanation: Create a TFX pipeline using templates with Local orchestrator
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<td><a target="_blank" href="https://www.tensorflow.org/tfx/tutorials/tfx/template_local">
<img src="https://www.tensorflow.org/images/tf_logo_32px.png"/>View on TensorFlow.org</a></td>
<td><a target="_blank" href="https://colab.research.google.com/github/tensorflow/tfx/blob/master/docs/tutorials/tfx/template_local.ipynb">
<img src="https://www.tensorflow.org/images/colab_logo_32px.png">Run in Google Colab</a></td>
<td><a target="_blank" href="https://github.com/tensorflow/tfx/tree/master/docs/tutorials/tfx/template_local.ipynb">
<img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png">View source on GitHub</a></td>
<td><a href="https://storage.googleapis.com/tensorflow_docs/tfx/docs/tutorials/tfx/template_local.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a></td>
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Introduction
This document will provide instructions to create a TensorFlow Extended (TFX) pipeline
using templates which are provided with TFX Python package.
Most of instructions are Linux shell commands, and corresponding
Jupyter Notebook code cells which invoke those commands using ! are provided.
You will build a pipeline using Taxi Trips dataset
released by the City of Chicago. We strongly encourage you to try to build
your own pipeline using your dataset by utilizing this pipeline as a baseline.
We will build a pipeline which runs on local environment. If you are interested in using Kubeflow orchestrator on Google Cloud, please see TFX on Cloud AI Platform Pipelines tutorial.
Prerequisites
Linux / MacOS
Python >= 3.5.3
You can get all prerequisites easily by running this notebook on Google Colab.
Step 1. Set up your environment.
Throughout this document, we will present commands twice. Once as a copy-and-paste-ready shell command, once as a jupyter notebook cell. If you are using Colab, just skip shell script block and execute notebook cells.
You should prepare a development environment to build a pipeline.
Install tfx python package. We recommend use of virtualenv in the local environment. You can use following shell script snippet to set up your environment.
```sh
Create a virtualenv for tfx.
virtualenv -p python3 venv
source venv/bin/activate
Install python packages.
python -m pip install --upgrade "tfx<2"
```
If you are using colab:
End of explanation
# Set `PATH` to include user python binary directory.
HOME=%env HOME
PATH=%env PATH
%env PATH={PATH}:{HOME}/.local/bin
Explanation: NOTE: There might be some errors during package installation. For example,
ERROR: some-package 0.some_version.1 has requirement other-package!=2.0.,&lt;3,&gt;=1.15, but you'll have other-package 2.0.0 which is incompatible.
Please ignore these errors at this moment.
End of explanation
!python3 -c "from tfx import version ; print('TFX version: {}'.format(version.__version__))"
Explanation: Let's check the version of TFX.
bash
python -c "from tfx import version ; print('TFX version: {}'.format(version.__version__))"
End of explanation
PIPELINE_NAME="my_pipeline"
import os
# Create a project directory under Colab content directory.
PROJECT_DIR=os.path.join(os.sep,"content",PIPELINE_NAME)
Explanation: And, it's done. We are ready to create a pipeline.
Step 2. Copy 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.
bash
export PIPELINE_NAME="my_pipeline"
export PROJECT_DIR=~/tfx/${PIPELINE_NAME}
End of explanation
!tfx template copy \
  --pipeline_name={PIPELINE_NAME} \
  --destination_path={PROJECT_DIR} \
  --model=taxi
Explanation: TFX includes the taxi template with the TFX python package. If you are planning to solve a point-wise prediction problem, including classification and regresssion, this template could be used as a starting point.
The tfx template copy CLI command copies predefined template files into your project directory.
sh
tfx template copy \
   --pipeline_name="${PIPELINE_NAME}" \
   --destination_path="${PROJECT_DIR}" \
   --model=taxi
End of explanation
%cd {PROJECT_DIR}
Explanation: Change the working directory context in this notebook to the project directory.
bash
cd ${PROJECT_DIR}
End of explanation
!{sys.executable} -m models.features_test
!{sys.executable} -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.
In Google Colab, you can browse files by clicking a folder icon on the left. Files should be copied under the project directoy, whose name is my_pipeline in this case. You can click directory names to see the content of the directory, and double-click file names to open them.
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
    -   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
    -   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
-   local_runner.py, kubeflow_runner.py — define runners for each orchestration engine
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:
bash
python -m models.features_test
End of explanation
!tfx pipeline create --engine=local --pipeline_path=local_runner.py
Explanation: Step 4. Run your first TFX pipeline
You can create a pipeline using pipeline create command.
bash
tfx pipeline create --engine=local --pipeline_path=local_runner.py
End of explanation
!tfx run create --engine=local --pipeline_name={PIPELINE_NAME}
Explanation: Then, you can run the created pipeline using run create command.
sh
tfx run create --engine=local --pipeline_name="${PIPELINE_NAME}"
End of explanation
# Update the pipeline
!tfx pipeline update --engine=local --pipeline_path=local_runner.py
# You can run the pipeline the same way.
!tfx run create --engine local --pipeline_name {PIPELINE_NAME}
Explanation: If successful, you'll see Component CsvExampleGen is finished. When you copy the template, only one component, CsvExampleGen, is included in the pipeline.
Step 5. 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.
We will modify copied pipeline definition in pipeline/pipeline.py. If you are working on your local environment, use your favorite editor to edit the file. If you are working on Google Colab, 
Click folder icon on the left to open Files view.
Click my_pipeline to open the directory and click pipeline directory to open and double-click pipeline.py to open the file.
Find and uncomment the 3 lines which add StatisticsGen, SchemaGen, and ExampleValidator to the pipeline. (Tip: find comments containing TODO(step 5):).
Your change will be saved automatically in a few seconds. Make sure that the * mark in front of the pipeline.py disappeared in the tab title. There is no save button or shortcut for the file editor in Colab. Python files in file editor can be saved to the runtime environment even in playground mode.
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.
```sh
Update the pipeline
tfx pipeline update --engine=local --pipeline_path=local_runner.py
You can run the pipeline the same way.
tfx run create --engine local --pipeline_name "${PIPELINE_NAME}"
```
End of explanation
!tfx pipeline update --engine=local --pipeline_path=local_runner.py
!tfx run create --engine local --pipeline_name {PIPELINE_NAME}
Explanation: You should be able to see the output log from the added components. Our pipeline creates output artifacts in tfx_pipeline_output/my_pipeline directory.
Step 6. Add components for training.
In this step, you will add components for training and model validation including Transform, Trainer, Resolver, Evaluator, and Pusher.
Open pipeline/pipeline.py. Find and uncomment 5 lines which add Transform, Trainer, Resolver, Evaluator and Pusher to the pipeline. (Tip: find TODO(step 6):)
As you did before, you now need to update the existing pipeline with the modified pipeline definition. The instructions are the same as Step 5. Update the pipeline using tfx pipeline update, and create an execution run using tfx run create.
sh
tfx pipeline update --engine=local --pipeline_path=local_runner.py
tfx run create --engine local --pipeline_name "${PIPELINE_NAME}"
End of explanation
if 'google.colab' in sys.modules:
  from google.colab import auth
  auth.authenticate_user()
  print('Authenticated')
Explanation: When this execution run finishes successfully, you have now created and run your first TFX pipeline using Local orchestrator!
NOTE: You might have noticed that every time we create a pipeline run, every component runs again and again even though the input and the parameters were not changed.
It is waste of time and resources, and you can skip those executions with pipeline caching. You can enable caching by specifying enable_cache=True for the Pipeline object in pipeline.py.
Step 7. (Optional) Try BigQueryExampleGen.
[BigQuery] is a serverless, highly scalable, and cost-effective cloud data warehouse. BigQuery can be used as a source for training examples in TFX. In this step, we will add BigQueryExampleGen to the pipeline.
You need a Google Cloud Platform account to use BigQuery. Please prepare a GCP project.
Login to your project using colab auth library or gcloud utility.
```sh
You need gcloud tool to login in local shell environment.
gcloud auth login
```
End of explanation
# Set your project name below.
# WARNING! ENTER your project name before running this cell.
%env GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_NAME_HERE
Explanation: You should specify your GCP project name to access BigQuery resources using TFX. Set GOOGLE_CLOUD_PROJECT environment variable to your project name.
sh
export GOOGLE_CLOUD_PROJECT=YOUR_PROJECT_NAME_HERE
End of explanation
!tfx pipeline update --engine=local --pipeline_path=local_runner.py
!tfx run create --engine local --pipeline_name {PIPELINE_NAME}
Explanation: Open pipeline/pipeline.py. Comment out CsvExampleGen and uncomment the line which create an instance of BigQueryExampleGen. You also need to uncomment query argument of the create_pipeline function.
We need to specify which GCP project to use for BigQuery again, and this is done by setting --project in beam_pipeline_args when creating a pipeline.
Open pipeline/configs.py. Uncomment the definition of BIG_QUERY__WITH_DIRECT_RUNNER_BEAM_PIPELINE_ARGS and BIG_QUERY_QUERY. You should replace the project id and the region value in this file with the correct values for your GCP project.
Open local_runner.py. Uncomment two arguments, query and beam_pipeline_args, for create_pipeline() method.
Now the pipeline is ready to use BigQuery as an example source. Update the pipeline and create a run as we did in step 5 and 6.
End of explanation
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