Jesse Lopez (yosoyjay)

yosoyjay

Geek Repo

Company:@Microsoft

Location:Portland, Oregon

Home Page:https://jesse-lopez.com/about/

Github PK Tool:Github PK Tool


Organizations
Azure
hackoregon
ioos
microsoft

Jesse Lopez's repositories

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ai-gbb-bootcamp-2024

Artifacts for AI GBB bootcamp 2024

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benchmark-opt175b

Benchmark OPT-175 on Azure

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azhpc-images

Scripts for installing HPC/AI libraries, and tools used in building Azure HPC/AI images

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azure-cli-extensions

Public Repository for Extensions of Azure CLI.

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azure-docs

Open source documentation of Microsoft Azure

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azurehpc

This repository provides easy automation scripts for building a HPC environment in Azure. It also includes examples to build e2e environment and run some of the key HPC benchmarks and applications.

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azurehpc-health-checks

Health checks for Azure N- and H-series VMs.

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benchmark-fairseq-moe

Benchmark FAIRSEQ MoE models

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cyclecloud-container

Build files for creating a containerized Azure CycleCloud install

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cyclecloud-nodehealth

Node healthcheck extension for Azure CycleCloud clusters

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dask

Parallel computing with task scheduling

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deck.gl

WebGL2 powered geospatial visualization layers

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docker-mlflow-server

a docker image of the MLflow server component

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extract_model

Facilitates read-in and extraction for ocean model output.

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ifcb-analysis

custom resolver

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KeepIt

The most powerful minimal Hugo theme.

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llama-examples

Examples of training Llama2 with different frameworks

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metaseq

Repo for external large-scale work

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ocean-model-skill-assessor

A package to fully run the comparison between data and model to assess model skill.

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ocean_data_gateway

Your gateway to ocean data.

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oii

Ocean Imaging Informatics

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orcaal

Orca detection Active Learning tool

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project-classify-fish-sounds

An archive repo with collection of data, scripts, notebooks, and a model to detect fish sounds from spectrograms.

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spectral

Port of html5up’s spectral theme to hugo

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Thalassa

Large Scale Sea level visualizations of unstructured mesh data

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training

Reference implementations of MLPerf™ training benchmarks

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