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Julio Perez

jperez999

Nvidia

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Python86%Jupyter Notebook14%

Repos

20

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0

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0

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Python

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Repositories

20
JP
jperez999/nv-ingestFork

NVIDIA Ingest is a set of microservices for parsing hundreds of thousands of complex, messy unstructured PDFs and other enterprise documents into metadata and text to embed into retrieval systems.

Python00Updated 20 hours ago
JP
jperez999/pymilvusFork

Python SDK for Milvus.

00Updated 11 months ago
JP
jperez999/cuvsFork

cuVS - a library for vector search and clustering on the GPU

00Updated 1 year ago
JP
jperez999/nvtabular_triton_backendFork

Triton Backend for NVTabular

00Updated 1 year ago
JP
jperez999/raftFork

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

00Updated 10 months ago
JP
jperez999/dataloader-1Fork

No description provided.

00Updated 2 years ago
JP
jperez999/MerlinFork

NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.

Python00Updated 2 years ago
JP
jperez999/systems-1Fork

No description provided.

00Updated 1 year ago
JP
jperez999/models-1Fork

Merlin Models is a collection of deep learning recommender system model reference implementations

00Updated 2 years ago
JP
jperez999/coreFork

Core Utilities for NVIDIA Merlin

00Updated 1 year ago
JP
jperez999/Transformers4RecFork

Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation, available for both PyTorch and Tensorflow.

00Updated 2 years ago
JP
jperez999/NVTabularFork

A library that sits on top of RAPIDS cuDF library providing a range of benefits for processing extremely large tabular datasets, particularly those that don't fit in GPU or CPU memory. The highlights of NVTabular’s capabilities are fast terabyte-scale data preparation and accelerated tabular data loading, all on GPU, which streamline the first step for both training and inference to any deep recommender system pipelines.

Python00Updated 4 months ago
JP
jperez999/pytorchFork

Tensors and Dynamic neural networks in Python with strong GPU acceleration

00Updated 6 years ago
JP
jperez999/asvdbFork

No description provided.

00Updated 5 years ago
JP
jperez999/cudfFork

cuDF - GPU DataFrame Library

00Updated 5 years ago
JP
jperez999/blueocean-docker-imageFork

No description provided.

00Updated 5 years ago
JP
jperez999/dlrmFork

An implementation of a deep learning recommendation model (DLRM)

Python00Updated 6 years ago
JP
jperez999/docker-pythonFork

Kaggle Python docker image

Python00Updated 6 years ago
JP
jperez999/blockchain_iot

No description provided.

Python00Updated 7 years ago
JP
jperez999/deeplearning1

No description provided.

Jupyter Notebook00Updated 8 years ago

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