titanic

Introduction

This sample code illustrates how to start a Google Cloud Engine (GCE) instance and train a model using scikit-learn on the instance. We will be using the Titanic dataset from Kaggle. The goal is to train a classifier to predict whether a given passenger on Titanic survived or not.

Prerequisites

Before we start, we need to:

• login to Kaggle and download the Titanic dataset, mainly train.csv.

• have a GCP account, and download, install, and configure the Google Cloud SDK on our local computer.

• create a project on GCP and set the project, region, and zone properties as instructed here.

• set these environment variables on the local computer:

  • $MY_INSTANCE: the name of the new instance (e.g. export MY_INSTANCE=titanic_trainer).
  • $MY_BUCKET: the bucket name to be created on Google Cloud Storage (GCS) (e.g. export MY_BUCKET=titanic_bucket).
  • $MY_FOLDER: the folder name to be used in the bucket (e.g. export MY_FOLDER=titanic_folder).

Steps:

We will create a VM instance, configure it, and use it to train a model. While some of the steps can be done using the web portal here, we will try to accomplish this using the SDK and mainly the gcloud command. Unless otherwise specified as a comment, all the commands are to be run locally.

1. Create a Google Compute Engine Instance

First, we should create a GCE instance that we can use to train our model on. In this case, we can rely on many of the default arguments:

gcloud compute instances create $MY_INSTANCE

which creates an instance of type n1-standard-1 (1 CPU, 3.75GB of RAM), with a Debian disk image and no scopes. This is fine as long as we access the dataset locally and not through GCS. Otherwise, we will have to provide the right scope:

gcloud compute instances create $MY_INSTANCE --scopes storage-rw

The scopes argument grants the instance the right permission to access GCS.

We can also specify a different machine type or image. For example, the following command creates an Ubuntu instance of type n1-standard-2:

gcloud compute instances create $MY_INSTANCE --machine-type=n1-standard-2 --image-family=ubuntu-1604-lts --image-project ubuntu-os-cloud --scopes storage-rw

You may want to learn more about the machine types and images, or see a list of available machine types and images with the following two commands:

gcloud compute machine-types list
gcloud compute images list

A more detailed instruction about gcloud compute instance create can be found here.

2. Accessing and Configuring the Instance

Accessing the newly created instance is quite simple:

gcloud compute ssh $MY_INSTANCE

This command may prompt us to create a ssh key, if we have not done it before on our local machine.

Once we ssh to the instance, we will need to use pip to install scikit-learn and some other packages:

# Run on the new instance
sudo apt-get install -y python-pip
pip install numpy pandas sklearn scipy tensorflow

Note: If you choose a different image for the instance, you may need to do something different to install pip and the required python packages.

Also note that we are not using tensorflow to train the model in this sample. We will however use gfile, a python class implemented in tensorflow which unifies how we access the local and remote files. An alternative to using gfile for accessing the files stored in GCS is Cloud Storage Client Libraries.

3. Running the Code

We need to make the dataset available to the training code. Here are two options for the code to access the dataset.

Option 1: Copying it to the instance

We can copy the dataset into the new instance and use it directly inside the instance and read it as a local file. Fortunately, gcloud makes this an easy step:

gcloud compute scp ./train.csv $MY_INSTANCE:~/

The same method can be used to copy any other file (e.g. the python code):

gcloud compute scp ./titanic.py $MY_INSTANCE:~/

We can then run our python code on the instance to create a model:

# Run on the new instance
python titanic.py --titanic-data-path ~/train.csv --model-output-path ~/model.pkl

which will generate the model and save it on the instance.

Option 2: Reading it from Google Cloud Storage

Another solution is to read the dataset directly from a bucket in GCS (assuming we created the instance with the right scope). We use gsutil to create the bucket in GCS, and then copy the dataset into it:

gsutil mb gs://$MY_BUCKET
gsutil cp ./train.csv  gs://$MY_BUCKET/$MY_FOLDER/train.csv

The advantage of using gfile in our python code is that we can use the exact same code and read the dataset from GCS:

# Run on the new instance
export $MY_BUCKET=THE_GIVEN_BUCKET_NAME_HERE
export $MY_FOLDER=THE_GIVEN_FOLDER_NAME_HERE
python titanic.py --titanic-data-path gs://$MY_BUCKET/$MY_FOLDER/train.csv --model-output-path gs://$MY_BUCKET/$MY_FOLDER/model.pkl

which will create the model and store it in the same location as our dataset in GCS.

4. Stopping or Deleting the Instance

The GCE instance that we created will be running indefinitely and incurring charges, unless we either stop or delete it. We are not charged for a stopped instance, but we may be charged for the resources attached to it. Stopping the instance will allow us to start it again later on, should we require to use it again, which may be the better choice:

gcloud compute instances stop $MY_INSTANCE

We can also stop the instance while we are logged into it:

# Run on the new instance
sudo shutdown -h now

Since the instance has only stopped, and not deleted, we can start it again:

gcloud compute instances start $MY_INSTANCE

However, if we want to completely delete the instance (and all the resources attached to it), we can use the following command:

gcloud compute instances delete $MY_INSTANCE

That is it!! We have successfully trained a classifier using scikit-learn on a GCE instance.

What is Next

Automating the Entire Process

It is possible to automate the entire process described in this tutorial by writing some scripts to be run periodically. The burst training sample has a detailed description of how to do this.

Preemptible VM Instances

You may sometimes require an instance with a high number of CPU's or a large amount of RAM which can become expensive, especially if you need this instance to train a model periodically.

An alternative to keep the cost down is to use Preemptible Instances which are short-lived instances suitable for fault-tolerant tasks. These instances cost a lot less than a normal instance, but they also have some limitations. For example, their availability is not guaranteed, and they may be terminated by Compute Engine.

To learn more about Preemptible Instances, click here.