project-codeflare Goto Github PK

.github

adr

Home for CodeFlare Architecture Design Records (ADR)

appwrapper

AppWrapper controller for Kueue

codeflare

Simplifying the definition and execution, scaling and deployment of pipelines on the cloud.

codeflare-cli

codeflare-cli-1

codeflare-common

Common packages for use with CodeFlare Distributed Workload stack.

codeflare-operator

Operator for installation and lifecycle management of CodeFlare distributed workload stack, starting with MCAD and InstaScale

codeflare-sdk

An intuitive, easy-to-use python interface for batch resource requesting, access, job submission, and observation. Simplifying the developer's life while enabling access to high-performance compute resources, either in the cloud or on-prem.

codeflare-transfer-learning

community-operators-prod

community-operators metadata backing OpenShift OperatorHub

data-integration

Object Storage data processing for Ray framework

demo-images

ibm-ray-config

ibm-vpc-ray-connector

Enables Ray to use IBM Gen2 backend

instascale

On-demand Kubernetes/OpenShift cluster scaling and aggregated resource provisioning

instaslice

InstaSlice facilitates the use of Dynamic Resource Allocation (DRA) on Kubernetes clusters for GPU sharing

integration-tests

Integration tests verifying integration of all CodeFlare components as part of ODH.

mcad

MCAD v2

mcad-dashboard

Dashboard for MCAD

multi-cluster-app-dispatcher

Holistic job manager on Kubernetes

notebooks

Notebook images for ODH

project-codeflare-landing

pypi-cache

Simple implementation of PyPI cache server for offline use

ray-slurm-autoscaler

ray_lightning

Pytorch Lightning Distributed Accelerators using Ray

rayvens

Rayvens makes it possible for data scientists to access hundreds of data services within Ray with little effort.

serverless-distributed-dl-training

torchx

TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.

zero-copy-model-loading

In-depth code associated with my Medium blog post, "How to Load PyTorch Models 340 Times Faster with Ray"