Tutorial to install NVIDIA Drivers, CUDA 11.4 and cuDNN for deep learning programming on Ubuntu 20.04.
-
Updated
Aug 21, 2022
#
cuda11
Here are 7 public repositories matching this topic...
One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.
natural-language-processing
nlu
transformer
gpu-acceleration
transfer-learning
ner
flask-api
bert
sequence-classification
slot-filling
joint-models
conversational-ai
intent-classification
multitask-learning
nlu-engine
tensorflow2
cuda11
bert-small
slot-classification
huggingface-fast-tokenizer
-
Updated
Jun 22, 2022 - Python
This repository contains Docker Image files, which support the common frameworks required for Deep learning implementation. The images support both the latest GPU (Nvidia CUDA) and CPU processors.
docker
dockerfile
opencv
deep-learning
gpu
docker-image
scikit-learn
cuda
pytorch
cudnn
pytorch-cnn
tensorflow2
kersa
cudnn8
cuda11
-
Updated
Feb 16, 2022 - Shell
Docker compose for your Jupyter notebooks + CUDA 11.6 (Pytorch, Tensorflow)
-
Updated
Apr 22, 2023 - Jupyter Notebook
Tensorflow 1.15.5, Py3.8 Ubuntu 22.04
-
Updated
Apr 20, 2023 - Shell
Using the https://github.com/BVLC/caffe with Python 3, OpenCV 4, CUDA 11.X and cuDNN 8.
-
Updated
Oct 9, 2023 - C++
Improve this page
Add a description, image, and links to the cuda11 topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the cuda11 topic, visit your repo's landing page and select "manage topics."