Popular GitHub Repositories for Gcp

Build, Manage and Deploy AI/ML Systems

dstack is a lightweight, open-source alternative to Kubernetes & Slurm, simplifying AI container orchestration with multi-cloud & on-prem support. It natively supports NVIDIA, AMD, TPU, and Intel accelerators.

A collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying GenAI agents.

LLMs for your CLI

Hopsworks - Data-Intensive AI platform with a Feature Store

Distribute and run AI workloads magically in Python, like PyTorch for ML infra.

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Google Cloud Platform Vertex AI end-to-end workflows for machine learning operations

The open-source policy-as-code software that provides analysis for Multi-Cloud and SaaS environments, you can get insight with natural language (powered by OpenAI).

Everything you want to know about Google Cloud TPU

Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required

MLOps tutorial using Python, Docker and Kubernetes.

Your Engineering Gen AI Team member ???

An end-to-end example of MLOps on Google Cloud using TensorFlow, TFX, and Vertex AI

The TensorFlow Cloud repository provides APIs that will allow to easily go from debugging and training your Keras and TensorFlow code in a local environment to distributed training in the cloud.

Fast model deployment on any cloud ?

[Book-2019] Pragmatic AI: An Introduction to Cloud-based Machine Learning

Build tensorflow keras model pipelines in a single line of code. Now with mlflow tracking. Created by Ram Seshadri. Collaborators welcome. Permission granted upon request.

Hands-On Python Deep Learning for Web by Anubhav Singh and Sayak Paul

A tool to deploy a mostly serverless MLflow tracking server on a GCP project with one command