Can I use this for distributed training?

Yes, it provides templates and configurations specifically for distributed PyTorch training on Vertex AI using multiple GPUs or TPU pods.

How does this skill help with GCP costs?

It includes interactive estimation scripts and optimization tips to help you choose the most cost-effective machine types and avoid overspending on ML training.

How is authentication handled?

The skill provides scripts to configure service accounts, IAM roles, and workload identity, ensuring secure access to GCP resources without hardcoding keys.

Which GPUs and TPUs are supported?

It supports a wide range of hardware including NVIDIA T4, V100, A100, and L4 GPUs, as well as TPU v2, v3, v4, and the cost-optimized v5e configurations.

Does it support BigQuery ML?

Absolutely. It includes SQL templates for creating, evaluating, and predicting with BigQuery ML models directly from your data warehouse data.

Google Cloud ML Configs

Name: Google Cloud ML Configs
Author: vanman2024

byvanman2024

Data Science & ML

Configures and optimizes Google Cloud Platform environments for BigQuery ML and Vertex AI training workloads.

About

This skill provides a comprehensive suite of tools and templates for setting up, managing, and optimizing machine learning workflows on Google Cloud Platform. It simplifies the configuration of BigQuery ML for SQL-based models and Vertex AI for custom PyTorch or TensorFlow training, including specialized GPU and TPU hardware selections. Users can easily manage cloud authentication, estimate training costs, and deploy production-ready ML pipelines using standardized templates and best practices designed for both performance and cost-efficiency.

Key Features

Pre-configured templates for GPU (A100, T4, L4) and TPU (v3, v4, v5e) setups
SQL templates for BigQuery ML model training and evaluation
Python templates for distributed training on Vertex AI
Automated GCP authentication and IAM role configuration
Cost estimation tools for BigQuery ML and Vertex AI jobs
0 GitHub stars

Use Cases

Optimizing cloud infrastructure costs for machine learning through precise compute selection
Setting up a cost-effective SQL-based ML model using BigQuery ML
Deploying large-scale distributed PyTorch training jobs on Vertex AI

About

Key Features

Pre-configured templates for GPU (A100, T4, L4) and TPU (v3, v4, v5e) setups
SQL templates for BigQuery ML model training and evaluation
Python templates for distributed training on Vertex AI
Automated GCP authentication and IAM role configuration
Cost estimation tools for BigQuery ML and Vertex AI jobs
0 GitHub stars

Use Cases

Optimizing cloud infrastructure costs for machine learning through precise compute selection
Setting up a cost-effective SQL-based ML model using BigQuery ML
Deploying large-scale distributed PyTorch training jobs on Vertex AI