vanbasten23

vanbasten23's Repositories

• vanbasten23/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/

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• vanbasten23/AnsibleLabs

  Ansible on Azure Lab playbooks and documentation

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• vanbasten23/azure-docs

  Open source documentation of Microsoft Azure

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• vanbasten23/cluster-algorithms-java-alpha

  Automatically exported from code.google.com/p/cluster-algorithms-java-alpha

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• vanbasten23/Comp404-OOP-Design

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• vanbasten23/Coursera-Front-End-Javascript-Frameworks-AngularJS-Overview-Assignment-4

  Assignment 4 of the Front End Javascript Frameworks AngularJS Overview Course in Coursera.com

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• vanbasten23/Hello-World

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• vanbasten23/leetcode

  solution and idea for some of the problem in leetcode

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• vanbasten23/ml-testing-accelerators

  Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)

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• vanbasten23/pytorch

  Tensors and Dynamic neural networks in Python with strong GPU acceleration

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• vanbasten23/accelerate

  🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

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• vanbasten23/benchmark

  TorchBench is a collection of open source benchmarks used to evaluate PyTorch performance.

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• vanbasten23/cs231n.github.io

  Public facing notes page

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• vanbasten23/examples

  This repository contains example code to build models on TPUs

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• vanbasten23/fairseq

  Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

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• vanbasten23/hello_world_mantaray

  Project for testing mantaray

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• vanbasten23/jax

  Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

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• vanbasten23/kubernetes-engine-samples

  Sample applications for Google Kubernetes Engine (GKE)

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• vanbasten23/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.

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• vanbasten23/maskrcnn

  fork from Nick's maskrcnn repo (https://docs.google.com/document/d/1pd3XOvCeMugiZ6y3adLZo3pAgqiWHitkASuUawEbECY/edit#)

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• vanbasten23/maxtext

  A simple, performant and scalable Jax LLM!

• vanbasten23/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.

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• vanbasten23/ng-video-lecture

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• vanbasten23/Spoon-Knife

  This repo is for demonstration purposes only. Comments and issues may or may not be responded to.

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• vanbasten23/transformers

  🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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• vanbasten23/vision

  Datasets, Transforms and Models specific to Computer Vision

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• vanbasten23/vllm

  A high-throughput and memory-efficient inference and serving engine for LLMs

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• vanbasten23/xla_minimal_bug

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• vanbasten23/xw32Configs

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