Vertex AI: Qwik Start
In this lab, you use BigQuery for data processing and exploratory data analysis and the Vertex AI platform to train and deploy a custom TensorFlow Regressor model to predict customer lifetime value. The goal of the lab is to introduce to Vertex AI through a high value real world use case - predictive CLV. You start with a local BigQuery and TensorFlow workflow that you may already be familiar with and progress toward training and deploying your model in the cloud with Vertex AI.
Vertex AI is Google Cloud’s next generation, unified platform for machine learning development and the successor to AI Platform announced at Google I/O in May 2021. By developing machine learning solutions on Vertex AI, you can leverage the latest ML pre-built components and AutoML to significantly enhance development productivity, the ability to scale your workflow and decision making with your data, and accelerate time to value.
- Train a TensorFlow model locally in a hosted Vertex Notebook.
- Create a managed Tabular dataset artifact for experiment tracking.
- Containerize your training code with Cloud Build and push it to Google Cloud Artifact Registry.
- Run a Vertex AI custom training job with your custom model container.
- Use Vertex TensorBoard to visualize model performance.
- Deploy your trained model to a Vertex Online Prediction Endpoint for serving predictions.
- Request an online prediction and explanation and see the response.
Enable Google Cloud services
- In Cloud Shell, use
gcloudto enable the services used in the lab:
Welcome to Cloud Shell! Type "help" to get started.
Your Cloud Platform project in this session is set to qwiklabs-gcp-02-aff506dc3787.
Use “gcloud config set project [PROJECT_ID]” to change to a different project.
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$ gcloud services enable \
compute.googleapis.com \
iam.googleapis.com \
iamcredentials.googleapis.com \
monitoring.googleapis.com \
logging.googleapis.com \
notebooks.googleapis.com \
aiplatform.googleapis.com \
bigquery.googleapis.com \
artifactregistry.googleapis.com \
cloudbuild.googleapis.com \
container.googleapis.com
Operation "operations/acat.p2-137820269452-727bc02e-7c3f-4415-963b-ba8e59c9d80d" finished successfully.
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$
Create Vertex AI custom service account for Vertex Tensorboard integration
- Create custom service account and Grant it access to Cloud Storage for writing and retrieving Tensorboard logs:
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$ SERVICE_ACCOUNT_ID=vertex-custom-training-sa
gcloud iam service-accounts create $SERVICE_ACCOUNT_ID \
--description="A custom service account for Vertex custom training with Tensorboard" \
--display-name="Vertex AI Custom Training"
Created service account [vertex-custom-training-sa].
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$ PROJECT_ID=$(gcloud config get-value core/project)
gcloud projects add-iam-policy-binding $PROJECT_ID \
--member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
--role="roles/storage.admin"
Your active configuration is: [cloudshell-21982]
Updated IAM policy for project [qwiklabs-gcp-02-aff506dc3787].
bindings:
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/aiplatform.admin
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/bigquery.admin
- members:
- serviceAccount:137820269452@cloudbuild.gserviceaccount.com
role: roles/cloudbuild.builds.builder
- members:
- serviceAccount:service-137820269452@gcp-sa-cloudbuild.iam.gserviceaccount.com
role: roles/cloudbuild.serviceAgent
- members:
- serviceAccount:service-137820269452@compute-system.iam.gserviceaccount.com
role: roles/compute.serviceAgent
- members:
- serviceAccount:service-137820269452@container-engine-robot.iam.gserviceaccount.com
role: roles/container.serviceAgent
- members:
- serviceAccount:137820269452-compute@developer.gserviceaccount.com
- serviceAccount:137820269452@cloudservices.gserviceaccount.com
role: roles/editor
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/iam.serviceAccountUser
- members:
- serviceAccount:service-137820269452@gcp-sa-notebooks.iam.gserviceaccount.com
role: roles/notebooks.serviceAgent
- members:
- serviceAccount:admiral@qwiklabs-services-prod.iam.gserviceaccount.com
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/owner
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/storage.admin
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/viewer
- members:
- serviceAccount:service-137820269452@gcp-sa-websecurityscanner.iam.gserviceaccount.com
role: roles/websecurityscanner.serviceAgent
etag: BwYcG_K1BAw=
version: 1
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$
Grant it access to your BigQuery data source to read data into your TensorFlow model:
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$ gcloud projects add-iam-policy-binding $PROJECT_ID \
--member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
--role="roles/bigquery.admin"
Updated IAM policy for project [qwiklabs-gcp-02-aff506dc3787].
bindings:
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/aiplatform.admin
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/bigquery.admin
- members:
- serviceAccount:137820269452@cloudbuild.gserviceaccount.com
role: roles/cloudbuild.builds.builder
- members:
- serviceAccount:service-137820269452@gcp-sa-cloudbuild.iam.gserviceaccount.com
role: roles/cloudbuild.serviceAgent
- members:
- serviceAccount:service-137820269452@compute-system.iam.gserviceaccount.com
role: roles/compute.serviceAgent
- members:
- serviceAccount:service-137820269452@container-engine-robot.iam.gserviceaccount.com
role: roles/container.serviceAgent
- members:
- serviceAccount:137820269452-compute@developer.gserviceaccount.com
- serviceAccount:137820269452@cloudservices.gserviceaccount.com
role: roles/editor
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/iam.serviceAccountUser
- members:
- serviceAccount:service-137820269452@gcp-sa-notebooks.iam.gserviceaccount.com
role: roles/notebooks.serviceAgent
- members:
- serviceAccount:admiral@qwiklabs-services-prod.iam.gserviceaccount.com
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/owner
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/storage.admin
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/viewer
- members:
- serviceAccount:service-137820269452@gcp-sa-websecurityscanner.iam.gserviceaccount.com
role: roles/websecurityscanner.serviceAgent
etag: BwYcG_VQ70k=
version: 1
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$
Grant it access to Vertex AI for running model training, deployment, and explanation jobs:
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$ gcloud projects add-iam-policy-binding $PROJECT_ID \
--member=serviceAccount:$SERVICE_ACCOUNT_ID@$PROJECT_ID.iam.gserviceaccount.com \
--role="roles/aiplatform.user"
Updated IAM policy for project [qwiklabs-gcp-02-aff506dc3787].
bindings:
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/aiplatform.admin
- members:
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
role: roles/aiplatform.user
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/bigquery.admin
- members:
- serviceAccount:137820269452@cloudbuild.gserviceaccount.com
role: roles/cloudbuild.builds.builder
- members:
- serviceAccount:service-137820269452@gcp-sa-cloudbuild.iam.gserviceaccount.com
role: roles/cloudbuild.serviceAgent
- members:
- serviceAccount:service-137820269452@compute-system.iam.gserviceaccount.com
role: roles/compute.serviceAgent
- members:
- serviceAccount:service-137820269452@container-engine-robot.iam.gserviceaccount.com
role: roles/container.serviceAgent
- members:
- serviceAccount:137820269452-compute@developer.gserviceaccount.com
- serviceAccount:137820269452@cloudservices.gserviceaccount.com
role: roles/editor
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/iam.serviceAccountUser
- members:
- serviceAccount:service-137820269452@gcp-sa-notebooks.iam.gserviceaccount.com
role: roles/notebooks.serviceAgent
- members:
- serviceAccount:admiral@qwiklabs-services-prod.iam.gserviceaccount.com
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/owner
- members:
- serviceAccount:qwiklabs-gcp-02-aff506dc3787@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- serviceAccount:vertex-custom-training-sa@qwiklabs-gcp-02-aff506dc3787.iam.gserviceaccount.com
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/storage.admin
- members:
- user:student-01-932d053b64d1@qwiklabs.net
role: roles/viewer
- members:
- serviceAccount:service-137820269452@gcp-sa-websecurityscanner.iam.gserviceaccount.com
role: roles/websecurityscanner.serviceAgent
etag: BwYcG_cZt5I=
version: 1
student_01_932d053b64d1@cloudshell:~ (qwiklabs-gcp-02-aff506dc3787)$
Launch Vertex AI Workbench notebook
To create and launch a Vertex AI Workbench notebook:
- In the Navigation Menu
, click Vertex AI > Workbench.
- On the Workbench page, click Enable Notebooks API (if it isn’t enabled yet).
- Click on User-Managed Notebooks tab then, click Create New.
- Name the notebook.
Clone the lab repository
Next you’ll clone the training-data-analyst repo to your JupyterLab instance.
To clone the training-data-analyst repository in your JupyterLab instance:
- In JupyterLab, click the Terminal icon to open a new terminal.
Install lab dependencies
- Run the following to go to the
training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstartfolder, thenpip3 installrequirements.txtto install lab dependencies:
(base) jupyter@instance-20240630-193809:~$ cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart
pip3 install --user -r requirements.txt
sudo apt -y install python3-pandas
pip uninstall openpyxl
pip install openpyxl
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Collecting werkzeug<2.1.0dev,>=2.0.0 (from google-cloud-aiplatform[tensorboard]>=1.8.0->-r requirements.txt (line 7))
Downloading Werkzeug-2.0.3-py3-none-any.whl.metadata (4.5 kB)
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Requirement already satisfied: google-cloud-core<3.0.0dev,>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from google-cloud-bigquery!=3.20.0,<4.0.0dev,>=1.15.0->google-cloud-aiplatform>=1.8.0->google-cloud-aiplatform[tensorboard]>=1.8.0->-r requirements.txt (line 7)) (2.4.1)
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Requirement already satisfied: docopt in /opt/conda/lib/python3.10/site-packages (from hdfs<3.0.0,>=2.1.0->apache-beam>=2.28.0->-r requirements.txt (line 6)) (0.6.2)
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Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow>=2.11.0->-r requirements.txt (line 1)) (3.2.2)
Downloading pyarrow-7.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (26.7 MB)
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Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)
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Downloading openpyxl-3.0.10-py2.py3-none-any.whl (242 kB)
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Downloading grpcio-1.64.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.6 MB)
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Downloading Werkzeug-2.0.3-py3-none-any.whl (289 kB)
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Downloading et_xmlfile-1.1.0-py3-none-any.whl (4.7 kB)
Building wheels for collected packages: wget
Building wheel for wget (setup.py) ... done
Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9656 sha256=a143f9ba84737015027950b6a871d00acad7d67e2a4d2df8b31c43ced476154d
Stored in directory: /home/jupyter/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769
Successfully built wget
Installing collected packages: wget, xlrd, werkzeug, pyarrow, grpcio, et-xmlfile, openpyxl
WARNING: The script plasma_store is installed in '/home/jupyter/.local/bin' which is not on PATH.
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed et-xmlfile-1.1.0 grpcio-1.64.1 openpyxl-3.0.10 pyarrow-7.0.0 werkzeug-2.0.3 wget-3.2 xlrd-2.0.1
Reading package lists... Done
Building dependency tree... Done
Reading state information... Done
The following additional packages will be installed:
binfmt-support fonts-font-awesome fonts-lyx libblosc1 libclang-cpp9 libffi-dev libimagequant0 libjs-jquery-ui libjs-sphinxdoc libjs-underscore liblbfgsb0 libllvm9 liblzo2-2
libncurses-dev libpfm4 libtbb2 libtinfo-dev libwebpdemux2 libwebpmux3 libxslt1.1 libz3-dev llvm-9 llvm-9-dev llvm-9-runtime llvm-9-tools mailcap mime-support numba-doc
python-matplotlib-data python-odf-doc python-odf-tools python-tables-data python3-attr python3-bottleneck python3-bs4 python3-cycler python3-dateutil python3-decorator
python3-defusedxml python3-et-xmlfile python3-html5lib python3-importlib-metadata python3-iniconfig python3-jdcal python3-jinja2 python3-kiwisolver python3-llvmlite
python3-lxml python3-markupsafe python3-matplotlib python3-more-itertools python3-numba python3-numexpr python3-numpy python3-odf python3-olefile python3-openpyxl
python3-packaging python3-pandas-lib python3-pil python3-pluggy python3-py python3-pygments python3-pyparsing python3-pytest python3-scipy python3-soupsieve python3-tables
python3-tables-lib python3-toml python3-tz python3-webencodings python3-xlwt python3-yaml python3-zipp sphinx-rtd-theme-common ttf-bitstream-vera
Suggested packages:
libjs-jquery-ui-docs ncurses-doc llvm-9-doc python-attr-doc python-bottleneck-doc python-cycler-doc python3-genshi python-jinja2-doc llvmlite-doc python3-lxml-dbg
python-lxml-doc dvipng ffmpeg ghostscript gir1.2-gtk-3.0 inkscape ipython3 librsvg2-common python-matplotlib-doc python3-cairocffi python3-gi-cairo python3-gobject
python3-nose python3-pyqt5 python3-sip python3-tornado texlive-extra-utils ttf-staypuft nvidia-cuda-toolkit gfortran python-numpy-doc python3-dev python3-numpy-dbg
python-pandas-doc python3-statsmodels python-pil-doc python3-pil-dbg subversion python-pygments-doc python-pyparsing-doc python-scipy-doc python3-netcdf4 python-tables-doc
vitables python3-xlrd python-xlrt-doc
The following NEW packages will be installed:
binfmt-support fonts-font-awesome fonts-lyx libblosc1 libclang-cpp9 libffi-dev libimagequant0 libjs-jquery-ui libjs-sphinxdoc libjs-underscore liblbfgsb0 libllvm9 liblzo2-2
libncurses-dev libpfm4 libtbb2 libtinfo-dev libwebpdemux2 libwebpmux3 libxslt1.1 libz3-dev llvm-9 llvm-9-dev llvm-9-runtime llvm-9-tools mailcap mime-support numba-doc
python-matplotlib-data python-odf-doc python-odf-tools python-tables-data python3-attr python3-bottleneck python3-bs4 python3-cycler python3-dateutil python3-decorator
python3-defusedxml python3-et-xmlfile python3-html5lib python3-importlib-metadata python3-iniconfig python3-jdcal python3-jinja2 python3-kiwisolver python3-llvmlite
python3-lxml python3-markupsafe python3-matplotlib python3-more-itertools python3-numba python3-numexpr python3-numpy python3-odf python3-olefile python3-openpyxl
python3-packaging python3-pandas python3-pandas-lib python3-pil python3-pluggy python3-py python3-pygments python3-pyparsing python3-pytest python3-scipy python3-soupsieve
python3-tables python3-tables-lib python3-toml python3-tz python3-webencodings python3-xlwt python3-yaml python3-zipp sphinx-rtd-theme-common ttf-bitstream-vera
0 upgraded, 78 newly installed, 0 to remove and 2 not upgraded.
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Get:4 https://deb.debian.org/debian bullseye/main amd64 fonts-font-awesome all 5.0.10+really4.7.0~dfsg-4.1 [517 kB]
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Get:6 https://deb.debian.org/debian bullseye/main amd64 libblosc1 amd64 1.20.1+ds1-2 [48.7 kB]
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Created symlink /etc/systemd/system/multi-user.target.wants/binfmt-support.service → /lib/systemd/system/binfmt-support.service.
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update-alternatives: using /usr/share/python3-numba/numba to provide /usr/bin/numba (numba) in auto mode
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ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_train.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_train.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8 is not a symbolic link
ldconfig: /usr/local/cuda-11.3/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8 is not a symbolic link
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Found existing installation: openpyxl 3.0.10
Uninstalling openpyxl-3.0.10:
Would remove:
/home/jupyter/.local/lib/python3.10/site-packages/openpyxl-3.0.10.dist-info/*
/home/jupyter/.local/lib/python3.10/site-packages/openpyxl/*
Proceed (Y/n)? Y
Successfully uninstalled openpyxl-3.0.10
Collecting openpyxl
Downloading openpyxl-3.1.5-py2.py3-none-any.whl.metadata (2.5 kB)
Requirement already satisfied: et-xmlfile in /home/jupyter/.local/lib/python3.10/site-packages (from openpyxl) (1.1.0)
Downloading openpyxl-3.1.5-py2.py3-none-any.whl (250 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 250.9/250.9 kB 2.1 MB/s eta 0:00:00
Installing collected packages: openpyxl
Successfully installed openpyxl-3.1.5
(base) jupyter@instance-20240630-193809:~/training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart$ history
1 cd training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart
2 pip3 install --user -r requirements.txt
3 sudo apt -y install python3-pandas
4 pip uninstall openpyxl
5 pip install openpyxl
6 history
(base) jupyter@instance-20240630-193809:~/training-data-analyst/self-paced-labs/vertex-ai/vertex-ai-qwikstart$
In this lab, you ran a machine learning experimentation workflow using Google Cloud BigQuery for data storage and analysis and Vertex AI machine learning services to train and deploy a TensorFlow model to predict customer lifetime value. You progressed from training a TensorFlow model locally to training on the cloud with Vertex AI and leveraged several new unified platform capabilities such as Vertex TensorBoard and prediction feature attributions.