Catalog Details
CATEGORY
deploymentCREATED BY
UPDATED AT
November 23, 2024VERSION
0.0.1
What this pattern does:
JAX is a rapidly growing Python library for high-performance numerical computing and machine learning (ML) research. With applications in large language models, drug discovery, physics ML, reinforcement learning, and neural graphics, JAX has seen incredible adoption in the past few years. JAX offers numerous benefits for developers and researchers, including an easy-to-use NumPy API, auto differentiation and optimization. JAX also includes support for distributed processing across multi-node and multi-GPU systems in a few lines of code, with accelerated performance through XLA-optimized kernels on NVIDIA GPUs. We show how to run JAX multi-GPU-multi-node applications on GKE (Google Kubernetes Engine) using the A2 ultra machine series, powered by NVIDIA A100 80GB Tensor Core GPUs. It runs a simple Hello World application on 4 nodes with 8 processes and 8 GPUs each.
Caveats and Consideration:
Ensure networking is setup properly and correct annotation are applied to each resource
Compatibility:
Recent Discussions with "meshery" Tag
- Nov 22 | Meshery CI Maintainer: Sangram Rath
- Dec 04 | Link Meshery Integrations and Github workflow or local code
- Nov 20 | Meshery Development Meeting | Nov 20th 2024
- Nov 10 | Error in "make server" and "make ui-server"
- Nov 11 | Difference in dev Environments on port 9081 and 3000
- Nov 10 | npm run lint:fix error
- Oct 30 | Getting Meshery locally using Docker Desktop for Meshery UI contribution
- Nov 07 | Meshery + GCP Connector
- Oct 24 | Getting error when using utils.SetupContextEnv() when writing tests for relationship command
- Nov 16 | Where's the Cortex Integration of Meshmap?