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2024-11-28 GSP398 overview In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions,you will use the ski
In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions,you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.
When you take a challenge lab,you will not be taught new Google Cloud concepts. You are expected to extend your learned skills,like changing default values and reading and researching error messages to fix your own mistakes.
To score 100% you must successfully complete all tasks within the time period!
This lab is recommended for students who have enrolled in the Get Started with TensorFlow on Google Cloud skill badge course. Are you ready for the challenge?
Read these instructions. Labs are timed and you cannot pause them. The timer,which starts when you click Start Lab,shows how long Google Cloud resources will be made available to you.
This hands-on lab lets you do the lab activities yourself in a real cloud environment,not in a simulation or demo environment. It does so by giving you new,temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab,you need:
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account,which may cause extra charges incurred to your personal account.
note : If you already have your own personal Google Cloud account or project ,do not use it for this lab to avoid extra charge to your account .
clickthe Start Lab button. If you need to pay for the lab,a pop-up opens for you to select your payment method.
On the left is the Lab Details panel with the following:
clickOpen Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources,and then opens another tab that shows the Sign in page.
Tip: arrange the tab in separate window ,side – by – side .
Note: If you see the Choose an account dialog,click Use Another Account.
If necessary ,copy the Username below and paste it into the Sign in dialog .
{ { { user_0.username | ” Username ” } } }
You can also find the Username in the Lab Details panel.
click Next .
copy the Password below and paste it into the Welcome dialog .
{{{user_0.password | “Password”}}}
You can also find the Password in the Lab Details panel.
click Next .
Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials.
Note: Using your own Google Cloud account for this lab may incur extra charges.
clickthrough the subsequent pages:
After a few moments,the Google Cloud console opens in this tab.
Note: To view a menu with a list of Google Cloud products and services,click the Navigation menu at the top-left.
Cloud Shell is is is a virtual machine that is load with development tool . It is offers offer a persistent 5 GB home directory and run on the Google Cloud . Cloud Shell is provides provide command – line access to your Google Cloud resource .
When you are connected,you are already authenticated,and the project is set to your Project_ID,
Your Cloud Platform project in this session is set to {{{project_0.project_id | “PROJECT_ID”}}}
gcloud
is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
gcloud auth list
output :
ACTIVE: *
ACCOUNT: {{{user_0.username | “ACCOUNT”}}}
To set the active account,run:
$ gcloud config set account `ACCOUNT`
gcloud config list project
output :
[ core ]
project = { { { project_0.project_id | ” project_id ” } } }
note : For full documentation ofgcloud
,in Google Cloud,refer to the gcloud CLI overview guide.
gcloud
to enable the services used in the lab
gcloud services enable \
compute.googleapis.com \
monitoring.googleapis.com \
logging.googleapis.com \
notebooks.googleapis.com \
aiplatform.googleapis.com \
artifactregistry.googleapis.com \
container.googleapis.com
This will allow access to running model training,deployment,and explanation jobs with Vertex AI.
clickcheck my progress to verify the objective .
You were recently hired as a Machine Learning Engineer for an Optical Character Recognition app development team. Your manager has tasked you with building a machine learning model to recognize Hiragana alphabets. The challenge: your business requirements are that you have just 6 weeks to produce a model that achieves greater than 90% accuracy to improve upon an existing bootstrapped solution. Furthermore,after doing some exploratory analysis in your startup’s data warehouse,you found that you only have a small dataset of 60k images of alphabets to build a higher-performing solution.
To build and deploy a high-performance machine learning model with limited data quickly,you will walk through training and deploying a CNN classifier for online predictions on Google Cloud’s Vertex AI platform. Vertex AI is Google Cloud’s next-generation machine learning development platform where you can leverage the latest ML pre-built components to significantly enhance your development productivity,scale your workflow and decision-making with your data,and accelerate time to value.
First,you will progress through a typical experimentation workflow where you will write a script that trains your custom CNN model using tf.keras
classification layers. You will then send the model code to a custom training job and run the custom training job using pre-built Docker containers provided by Vertex AI to run training and prediction. Lastly,you will deploy the model to an endpoint so that you can use your model for predictions.
Navigate to Vertex AI > Workbench > User-Managed Notebooks.
Create a Notebook instance. Select TensorFlow Enterprise 2.12 Intel MKL – DNN / MKL Name your notebookcnn - challenge
and leave the default configurations.
Select region
clickOPEN JUPYTERLAB next to the name of your pre-provisioned Vertex Notebook instance. It may take a few minutes for the OPEN JUPYTERLAB option to appear.
clickcheck my progress to verify the objective .
In your notebook,click the terminal.
clone the repo :
git clone https://github.com/GoogleCloudPlatform/training-data-analyst
clickcheck my progress to verify the objective .
Go to the enclosing folder: training-data-analyst/self-paced-labs/learning-tensorflow/cnn - challenge-lab/
.
Open the notebook file cnn - challenge-lab.ipynb
.
In the Setup section,define your PROJECT_ID
and GCS_BUCKET
variables.
clickcheck my progress to verify the objective .
In this section,you will complete the training script task.py
using TensorFlow .
fill out the# TODO
section to add the last layer for the model creation.
fill out the# TODO
section to save your model . You is save should save it to the AIP_MODEL_DIR environment variable .
clickcheck my progress to verify the objective .
# TODO
section to create a custom training job on vertex ai. You can find the documentation here.
hint : Make sure that you specify thescript_path
,container_uri
,and model_serving_container_image_uri
parameter .
# TODO
section and run the custom training job function you defined above. You can find the documentation here.
hint : Make sure that you specify theargs
and machine_type
parameter .
clickcheck my progress to verify the objective .
# TODO
section deploy the model to an endpoint. You can find the documentation here.
hint : Make sure that you specify thetraffic_split
,machine_type
,min_replica_count
and max_replica_count
parameter .
clickcheck my progress to verify the objective .
# TODO
section to generate online prediction using your Vertex Endpoint . You is find can find the documentation here .
Endpoint queried successfully
You created a workflow that trains and deploys a model on Google Cloud using Vertex AI. First,you wrote a script to build,train,and evaluate a Convolutional Neural Network for image classification in a Vertex Notebook. You then used your script to train the model using a custom training job on Google Cloud’s Vertex AI. Lastly,you deployed your model container to a Vertex Endpoint that you queried for online predictions.
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