Advanced Solutions Lab

Overview

This repository contains Jupyter notebooks meant to be run on Vertex AI. This is maintained by Google Cloud’s Advanced Solutions Lab (ASL) team. Vertex AI is the next generation AI Platform on the Google Cloud Platform. The material covered in this repo will take a software engineer with no exposure to machine learning to an advanced level.

In particular, the notebooks in this repository cover

• A wide range of model architectures (DNN, CNN, RNN, transformers, SNGP, etc.) targeting many data modalities (tabular, image, text, time-series) implemented mainly in Tensorflow and Keras.
• Tools on Google Cloud’s Vertex AI for operationalizing Tensorflow, Scikit-learn and PyTorch models at scale (e.g. Vertex training, tuning, and serving, TFX and Kubeflow pipelines).

If you are new to machine learning or Vertex AI start here: Introduction to TensorFlow

Repository Structure

All notebooks are in the notebooks folder. This folder is organized by different ML topics. Each folder contains a labs and a solutions folder. Use the labs notebooks to test your coding skills by filling in TODOs and refer to the notebooks in the solutions folder to verify your code.

We have three main folders described below:

├── kernels - contains kernel scripts needed for certain notebooks in lab folder
├── notebooks - contains labs and solutions notebook organized by topic
│   ├── bigquery
│   ├── building_production_ml_systems
│   ├── docker_and_kubernetes
│   ├── . . .
├── scripts - contains setup scripts for enabling and setting up services on Vertex AI

For a more detailed breakdown of the notebooks in this repo, please refer to this readme.

Environment Setup (Vertex AI)

First, open CloudShell and run the following instructions:

git clone https://github.com/GoogleCloudPlatform/asl-ml-immersion.git
cd asl-ml-immersion
./scripts/setup_on_cloudshell.sh

Second, follow the instruction of the official documentation to set up a JupyterLab instance on Vertex AI Workbench Instance.

Note: Accelerators (GPU/TPU) are not required in most of the labs, but some notebooks recommend using them.

After creating a Vertex Workbench Instance, open the terminal in your JupyterLab instance and run the following commands:

git clone https://github.com/GoogleCloudPlatform/asl-ml-immersion.git
cd asl-ml-immersion
export PATH=$PATH:~/.local/bin
make install

Note: Some notebooks might require additional setup, please refer to the instructions in specific notebooks.

After running these commands, you can open and execute a notebook on the base "Python 3" kernel.

Contributions

Currently, only Googlers can contribute to this repo. See CONTRIBUTING.md for more details on the contribution workflow.

Disclaimer

This is not an officially supported Google product. Usage of Google Cloud products will incur charges. Learn more about pricing here.

Licensing

All the code in this repo is licensed under the Apache License, Version 2.0 (the "License"). You may obtain a copy of the License here.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License