iefgnoix's repositories
ansible
Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy. Avoid writing scripts or custom code to deploy and update your applications — automate in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com/ansible/
AnsibleLabs
Ansible on Azure Lab playbooks and documentation
azure-docs
Open source documentation of Microsoft Azure
cluster-algorithms-java-alpha
Automatically exported from code.google.com/p/cluster-algorithms-java-alpha
Coursera-Front-End-Javascript-Frameworks-AngularJS-Overview-Assignment-4
Assignment 4 of the Front End Javascript Frameworks AngularJS Overview Course in Coursera.com
ml-testing-accelerators
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
accelerate
🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision
benchmark
TorchBench is a collection of open source benchmarks used to evaluate PyTorch performance.
cs231n.github.io
Public facing notes page
hello_world_mantaray
Project for testing mantaray
jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
kubernetes-engine-samples
Sample applications for Google Kubernetes Engine (GKE)
lit-gpt
Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
maskrcnn
fork from Nick's maskrcnn repo (https://docs.google.com/document/d/1pd3XOvCeMugiZ6y3adLZo3pAgqiWHitkASuUawEbECY/edit#)
ml-auto-solutions
A simplified and automated orchestration workflow to perform ML end-to-end (E2E) model tests and benchmarking on Cloud VMs across different frameworks.
Spoon-Knife
This repo is for demonstration purposes only. Comments and issues may or may not be responded to.
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.