How do I run a machine learning training in the background? - python-3.x

Question

I have a function in Support Vector Classifier which runs on a scheduler on Google Cloud Platform. That function, fetches the new data, adds it to the original data, trains the model on new data and saves it on google cloud storage. All of this takes 5 min to complete. I wish to not wait for the final output, instead I want to run it in the background and end the process without waiting.
Below is my function with comments:
def train_model():
users, tasks, tags, task_tags, task_user, boards = connect_postgres() ##loading the data from a postgres function
storage_client = storage.Client()
bucket = storage_client.get_bucket('my-bucket')
blob = bucket.blob('original_data.pkl')
pickle_in0 = blob.download_as_string()
data = pickle.loads(pickle_in0)
tasks = tasks.rename(columns={'id': 'task_id', 'name': 'task_name'})
# Joining tasks and task_user_assigns tables
tasks = tasks[tasks.task_name.isnull() == False]
task_user = task_user[['id', 'task_id', 'user_id']].rename(columns={'id': 'task_user_id'})
task_data = tasks.merge(task_user, on='task_id', how='left')
# Joining users with the task_data
users = users[['id', 'email']].rename(columns={'id': 'user_id'})
users_tasks = task_data.merge(users, on='user_id', how='left')
users_tasks = users_tasks[users_tasks.user_id.isnull() == False].reset_index(drop=True)
# Joining boards table to user_tasks
boards = boards[['id', 'name']].rename(columns={'id': 'board_id', 'name': 'board_name'})
users_board = users_tasks.merge(boards, on='board_id', how='left').reset_index(drop=True)
# Data Cleaning
translator = Translator() # This is to translate if the tasks are not in English
users_board["task_trans"] = users_board["task_name"].map(lambda x: translator.translate(x, dest="en").text)
users_board['task_trans'] = users_board['task_trans'].apply(lambda x: remove_emoji(x)) #This calls a function to remove Emoticons from text
users_board['task_trans'] = users_board['task_trans'].apply(lambda x: remove_punct(x)) #This calls a function to remove punctuations from text
users_board = users_board[['task_id', 'email', 'board_id', 'user_id', 'task_trans']]
data1 = pd.concat([data, users_board], axis=0)
df1 = data1.copy
X = df1.task_trans #all the observations
y = df1.user_id #all the lables
print(y.nunique())
#FROM HERE ON, THE TRAINING SCRIPT BEGINS
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(X)
tf_transformer = TfidfTransformer().fit(X_train_counts)
X_train_transformed = tf_transformer.transform(X_train_counts)
print('model 1 done')
labels = LabelEncoder()
y_train_labels_fit = labels.fit(y)
y_train_lables_trf = labels.transform(y)
linear_svc = LinearSVC()
clf = linear_svc.fit(X_train_transformed, y_train_lables_trf)
print('model 2 done')
calibrated_svc = CalibratedClassifierCV(base_estimator=linear_svc, cv="prefit")
calibrated_svc.fit(X_train_transformed, y_train_lables_trf)
print('model 3 done')
# SAVING THE MODELS ON GOOGLE CLOUD STORAGE
# storage_client = storage.Client()
fs = gcsfs.GCSFileSystem(project='my-project')
filename = '~path/svc.sav'
pickle.dump(calibrated_svc, fs.open(filename, 'wb'))
filename = '~path/count_vectorizer.sav'
pickle.dump(count_vect, fs.open(filename, 'wb'))
filename = '~path/tfidf_vectorizer.sav'
pickle.dump(tf_transformer, fs.open(filename, 'wb'))
blob = bucket.blob('original_data.pkl')
pickle_out = pickle.dumps(df1)
blob.upload_from_string(pickle_out)
return "success"
Now, I tried to do the following:
p = subprocess.Popen([sys.executable, '-c', train_model()], stdout=subprocess.PIPE, stderr=subprocess.STDOUT); print('finished')
This also took the same amount of time. Is there a way I can solve this?
Also, if I want to print the python logs for this process on client-side, is that possible?