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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:
 Train locally
Import training data
For illustration purposes we will use the MNIST dataset.  The following code downloads the dataset and puts it in ./mnist_data.
The first 60000 images and targets are the original training set, while the last 10000 are the testing set.  The training set is ordered by the labels so we shuffle them since we will use a very small portion of the data to shorten training time.
Step1:  Instantiate the estimator and the SearchCV objects
For illustration purposes we will use the RandomForestClassifier with RandomizedSearchCV
Step2:  Fit the GridSearchCV object locally
After fitting we can examine the best score (accuracy) and the best parameters that achieve that score.
Step3:  Everything up to this point is what you would do when training locally.  With larger amount of data it would take much longer.
Train on Google Container Engine
Set up for training on Google Container Engine
Before we can start training on the Container Engine we need to
Step4:  A Google Cloud Storage bucket belonging to your project created through either
Step5:  Pick a cluster id for the cluster on Google Container Engine we will create.  Preferably not an existing cluster to avoid affecting its workload.
Step6:  Choose a name for the image that will be running on the container.
Step7:  Choose a zone to host the cluster.
List of zones
Step8:  Change this only if you have customized the source.
Step9:  Build a Docker image
This step builds a Docker image using the content in the source/ folder.  The image will be tagged with the provided image_name so the workers can pull it.  The main script source/worker.py would retrieve a pickled GridSearchCV object from Cloud Storage and fit it to data on GCS.
Note
Step10:  Create a cluster
This step creates a cluster on the Container Engine.
You can alternatively create the cluster with the gcloud command line tool or through the console, but
you must add the additional scope of write access to Google Clous Storage
Step11:  For GCE instance pricing
Step12:  Refresh access token to the cluster
To make it easy to gain access to the cluster through the Kubernetes client library, included in this sample is a script that retrieves credentials for the cluster with gcloud
and refreshes access token with kubectl.
Step13:  Deploy the fitting task
GKEParallel instances implement a similar (but different!) interface as GridSearchCV.
Calling fit(X, y) first uploads the training data to Cloud Storage as
Step14:  Inspect the GKEParallel object
In the background, the GKEParallel instance splits the param_grid into smaller param_grids
Each smaller param_grid is pickled and stored on GCS within each worker's workspace
Step15:  You could optionally specify a task_name when creating a GKEParallel instance.
If you did not specify a task_name, when you call fit(X, y) the task_name will be set to
Step16:  Similarly, each job is given a job_name.  The dictionary of job_names can be accessed as follows.  Each worker pod handles one job processing one of the smaller param_grids.  
To monitor the jobs
Step17:  Cancel the task
To cancel the task, run cancel().  This will delete all the deployed worker pods and jobs,
but will NOT delete the cluster, nor delete any data already persisted to Cloud Storage.
Step18:  Monitor the progress
GKEParallel instances implement a similar (but different!) interface as Future instances.
Calling done() checks whether each worker has completed the job and persisted its outcome
on GCS with uri
Step19:  When all the jobs are finished, the pods will stop running (but the cluster will remain), and we can retrieve the fitted model.
Calling result() will populate the gke_search.results attribute which is returned.
This attribute records all the fitted GridSearchCV from the jobs.  The fitted model is downloaded only if the download argument is set to True.
Calling result() also updates the pickled gke_search object on Cloud Storage
Step20:  You can also get the logs from the pods
Step21:  Once the jobs are finished, the cluster can be deleted.  All the fitted models are stored on GCS.
The cluster can also be deleted from the console
Step22:  The next cell continues to poll the jobs until they are all finished, downloads the results, and deletes the cluster.
Step23:  Restore the GKEParallel object
To restore the fitted gke_search object (for example from a different notebook), you can use the helper function included in this sample.
Step24:  Inspect the result
GKEParallel also implements part of the interface of GridSearchCV to allow easy access to best_score+, best_param_, and beat_estimator_.
Step25:  You can also call predict(), which deligates the call to the best_estimator_.
Below we calculate the accuracy on the 10000 test images.
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Python Code:
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle
mnist = fetch_mldata('MNIST original', data_home='./mnist_data')
X, y = shuffle(mnist.data[:60000], mnist.target[:60000])
X_small = X[:100]
y_small = y[:100]
# Note: using only 10% of the training data
X_large = X[:6000]
y_large = y[:6000]
Explanation: Train locally
Import training data
For illustration purposes we will use the MNIST dataset.  The following code downloads the dataset and puts it in ./mnist_data.
The first 60000 images and targets are the original training set, while the last 10000 are the testing set.  The training set is ordered by the labels so we shuffle them since we will use a very small portion of the data to shorten training time.
End of explanation
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform
rfc = RandomForestClassifier(n_jobs=-1)
param_distributions = {
    'max_features': uniform(0.6, 0.4),
    'n_estimators': range(20, 401, 20),
    'max_depth': range(5, 41, 5) + [None],
    'min_samples_split': [0.01, 0.05, 0.1]
}
n_iter = 100
search = RandomizedSearchCV(estimator=rfc, param_distributions=param_distributions, n_iter=n_iter, n_jobs=-1, verbose=3)
Explanation: Instantiate the estimator and the SearchCV objects
For illustration purposes we will use the RandomForestClassifier with RandomizedSearchCV:
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
End of explanation
%time search.fit(X_small, y_small)
print(search.best_score_, search.best_params_)
Explanation: Fit the GridSearchCV object locally
After fitting we can examine the best score (accuracy) and the best parameters that achieve that score.
End of explanation
project_id = 'YOUR-PROJECT-ID'
Explanation: Everything up to this point is what you would do when training locally.  With larger amount of data it would take much longer.
Train on Google Container Engine
Set up for training on Google Container Engine
Before we can start training on the Container Engine we need to:
Build the Docker image which will be handling the workloads.
Create a cluster.
For these we will first set up some configuration variables.
Your Google Cloud Platform project id.
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bucket_name = 'YOUR-BUCKET-NAME'
Explanation: A Google Cloud Storage bucket belonging to your project created through either:
- gsutil mb gs://YOUR-BUCKET-NAME; or
- https://console.cloud.google.com/storage
This bucket will be used for storing temporary data during Docker image building, for storing training data, and for storing trained models.
This can be an existing bucket, but we recommend you create a new one.
End of explanation
cluster_id = 'YOUR-CLUSTER-ID'
Explanation: Pick a cluster id for the cluster on Google Container Engine we will create.  Preferably not an existing cluster to avoid affecting its workload.
End of explanation
image_name = 'YOUR-IMAGE-NAME'
Explanation: Choose a name for the image that will be running on the container.
End of explanation
zone = 'us-central1-b'
Explanation: Choose a zone to host the cluster.
List of zones: https://cloud.google.com/compute/docs/regions-zones/
End of explanation
source_dir = 'source'
Explanation: Change this only if you have customized the source.
End of explanation
from helpers.cloudbuild_helper import build
build(project_id, source_dir, bucket_name, image_name)
Explanation: Build a Docker image
This step builds a Docker image using the content in the source/ folder.  The image will be tagged with the provided image_name so the workers can pull it.  The main script source/worker.py would retrieve a pickled GridSearchCV object from Cloud Storage and fit it to data on GCS.
Note: This step only needs to be run once the first time you follow these steps,
and each time you modify the codes in source/.  If you have not modified source/ then
you can just re-use the same image.
Note: This could take a couple minutes.
To monitor the build process: https://console.cloud.google.com/gcr/builds
End of explanation
from helpers.gke_helper import create_cluster
create_cluster(project_id, zone, cluster_id, n_nodes=1, machine_type='n1-standard-64')
Explanation: Create a cluster
This step creates a cluster on the Container Engine.
You can alternatively create the cluster with the gcloud command line tool or through the console, but
you must add the additional scope of write access to Google Clous Storage: 'https://www.googleapis.com/auth/devstorage.read_write'
Note: This could take several minutes.
To monitor the cluster creation process: https://console.cloud.google.com/kubernetes/list
End of explanation
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform
rfc = RandomForestClassifier(n_jobs=-1)
param_distributions = {
    'max_features': uniform(0.6, 0.4),
    'n_estimators': range(20, 401, 20),
    'max_depth': range(5, 41, 5) + [None],
    'min_samples_split': [0.01, 0.05, 0.1]
}
n_iter = 100
search = RandomizedSearchCV(estimator=rfc, param_distributions=param_distributions, n_iter=n_iter, n_jobs=-1, verbose=3)
from gke_parallel import GKEParallel
gke_search = GKEParallel(search, project_id, zone, cluster_id, bucket_name, image_name)
Explanation: For GCE instance pricing: https://cloud.google.com/compute/pricing
Instantiate the GKEParallel object
The GKEParallel class is a helper wrapper around a GridSearchCV object that manages deploying fitting jobs to the Container Engine cluster created above.
We pass in the GridSearchCV object, which will be pickled and stored on Cloud Storage with
uri of the form: 
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/search.pkl
End of explanation
! bash get_cluster_credentials.sh $cluster_id $zone
Explanation: Refresh access token to the cluster
To make it easy to gain access to the cluster through the Kubernetes client library, included in this sample is a script that retrieves credentials for the cluster with gcloud
and refreshes access token with kubectl.
End of explanation
gke_search.fit(X_large, y_large)
Explanation: Deploy the fitting task
GKEParallel instances implement a similar (but different!) interface as GridSearchCV.
Calling fit(X, y) first uploads the training data to Cloud Storage as:
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/X.pkl
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/y.pkl
This allows reusing the same uploaded datasets for future training tasks.
For instance, if you already have pickled data on Cloud Storage:
gs://DATA-BUCKET/X.pkl
gs://DATA-BUCKET/y.pkl
then you can deploy the fitting task with:
gke_search.fit(X='gs://DATA-BUCKET/X.pkl', y='gs://DATA-BUCKET/y.pkl')
Calling fit(X, y) also pickles the wrapped search and gke_search, stores them on Cloud Storage as:
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/search.pkl
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl
End of explanation
gke_search.param_distributions
Explanation: Inspect the GKEParallel object
In the background, the GKEParallel instance splits the param_grid into smaller param_grids
Each smaller param_grid is pickled and stored on GCS within each worker's workspace:
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/WORKER-ID/param_grid.pkl
The param_distributions can be accessed as follows.
End of explanation
gke_search.task_name
Explanation: You could optionally specify a task_name when creating a GKEParallel instance.
If you did not specify a task_name, when you call fit(X, y) the task_name will be set to:
YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME
End of explanation
gke_search.job_names
Explanation: Similarly, each job is given a job_name.  The dictionary of job_names can be accessed as follows.  Each worker pod handles one job processing one of the smaller param_grids.  
To monitor the jobs: https://console.cloud.google.com/kubernetes/workload
End of explanation
#gke_search.cancel()
Explanation: Cancel the task
To cancel the task, run cancel().  This will delete all the deployed worker pods and jobs,
but will NOT delete the cluster, nor delete any data already persisted to Cloud Storage.
End of explanation
gke_search.done(), gke_search.dones
Explanation: Monitor the progress
GKEParallel instances implement a similar (but different!) interface as Future instances.
Calling done() checks whether each worker has completed the job and persisted its outcome
on GCS with uri:
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/WORKER-ID/fitted_search.pkl
To monitor the jobs: https://console.cloud.google.com/kubernetes/workload
To access the persisted data directly: https://console.cloud.google.com/storage/browser/YOUR-BUCKET-NAME/
End of explanation
result = gke_search.result(download=False)
Explanation: When all the jobs are finished, the pods will stop running (but the cluster will remain), and we can retrieve the fitted model.
Calling result() will populate the gke_search.results attribute which is returned.
This attribute records all the fitted GridSearchCV from the jobs.  The fitted model is downloaded only if the download argument is set to True.
Calling result() also updates the pickled gke_search object on Cloud Storage:
gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl
End of explanation
from helpers.kubernetes_helper import get_pod_logs
for pod_name, log in get_pod_logs().items():
    print('=' * 20)
    print('\t{}\n'.format(pod_name))
    print(log)
Explanation: You can also get the logs from the pods:
End of explanation
from helpers.gke_helper import delete_cluster
#delete_cluster(project_id, zone, cluster_id)
Explanation: Once the jobs are finished, the cluster can be deleted.  All the fitted models are stored on GCS.
The cluster can also be deleted from the console: https://console.cloud.google.com/kubernetes/list
End of explanation
import time
from helpers.gke_helper import delete_cluster
while not gke_search.done():
    n_done = len([d for d in gke_search.dones.values() if d])
    print('{}/{} finished'.format(n_done, len(gke_search.job_names)))
    time.sleep(60)
delete_cluster(project_id, zone, cluster_id)
result = gke_search.result(download=True)
Explanation: The next cell continues to poll the jobs until they are all finished, downloads the results, and deletes the cluster.
End of explanation
from helpers.gcs_helper import download_uri_and_unpickle
gcs_uri = 'gs://YOUR-BUCKET-NAME/YOUR-CLUSTER-ID.YOUR-IMAGE-NAME.UNIX-TIME/gke_search.pkl'
gke_search_restored = download_uri_and_unpickle(gcs_uri)
Explanation: Restore the GKEParallel object
To restore the fitted gke_search object (for example from a different notebook), you can use the helper function included in this sample.
End of explanation
gke_search.best_score_, gke_search.best_params_, gke_search.best_estimator_
Explanation: Inspect the result
GKEParallel also implements part of the interface of GridSearchCV to allow easy access to best_score+, best_param_, and beat_estimator_.
End of explanation
predicted = gke_search.predict(mnist.data[60000:])
print(len([p for i, p in enumerate(predicted) if p == mnist.target[60000:][i]]))
Explanation: You can also call predict(), which deligates the call to the best_estimator_.
Below we calculate the accuracy on the 10000 test images.
End of explanation
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