Based on our record, CUDA seems to be a lot more popular than AutoGluon. While we know about 36 links to CUDA, we've tracked only 1 mention of AutoGluon. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For my fellow Windows shills, here's how you actually build it on windows: Before steps: 1. (For Nvidia GPU users) Install cuda toolkit https://developer.nvidia.com/cuda-downloads 2. Download the model somewhere: https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML/resolve/main/llama-2-13b-chat.ggmlv3.q4_0.bin In Windows Terminal with Powershell:- Source: Hacker News / 9 months agogit clone https://github.com/ggerganov/llama.cpp.
I use Ubuntu and configuring nvidia drivers is very easy installing from here https://developer.nvidia.com/cuda-downloads. Source: 10 months ago
You have posted almost no information about your Hardware and what exactly you have done. Do you actually have NVIDIA? Have you actually installed CUDA? Also when exactly do you get the error, while installed the python package or later? Source: 10 months ago
EDIT: LINK TO CUDA-toolkit: https://developer.nvidia.com/cuda-downloads. Source: 11 months ago
It's worth noting that you'll need a recent release of llama.cpp to run GGML models with GPU acceleration here is the latest build for CUDA 12.1), and you'll need to install a recent CUDA version if you haven't already (here is the CUDA 12.1 toolkit installer -- mind, it's over 3 GB). Source: 12 months ago
Hey Folks - I recently learned about AutoGluon (https://auto.gluon.ai) and was hoping to use it for HPO among other ML tasks! Using their quick quid, I can successfully use their TabularPredictor for my regression problem and get a number of models trained and have access to a number of details, e.g., performance, and hyperparameters used. However, using the same dataset I fail (with somewhat of a cryptic error... Source: almost 3 years ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
H2O.ai - Democratizing Generative AI. Own your models: generative and predictive. We bring both super powers together with h2oGPT.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
R Caret - Documentation for the caret package.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.