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CUDA VS Datatron

Compare CUDA VS Datatron and see what are their differences

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CUDA logo CUDA

Select Target Platform Click on the green buttons that describe your target platform.

Datatron logo Datatron

Datatron automates the deployment, monitoring, governance, and validation of your machine learning models in scikit-learn, TensorFlow, Keras, Pytorch, R, H20 and SAS
  • CUDA Landing page
    Landing page //
    2023-05-23
  • Datatron Landing page
    Landing page //
    2023-02-11

CUDA videos

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1971 Plymouth Cuda 440: Regular Car Reviews

More videos:

  • Review - Jackson Kayak Cuda Review
  • Review - Great First Effort! The New $249 Signum Cuda

Datatron videos

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Harish Doddi demos Datatron @SFNewTech on 1 Mar 2017 #SFNT @getdatatron

More videos:

  • Review - Virtual Records Management from Datatron

Category Popularity

0-100% (relative to CUDA and Datatron)

CUDA

Datatron

Data Science And Machine Learning
59 59%
Data Science And Machine Learning
41% 41
Business & Commerce
100 100%
Business & Commerce
0% 0
Machine Learning Tools
39 39%
Machine Learning Tools
61% 61
AI
100 100%
AI
0% 0

User comments

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Social recommendations and mentions

Based on our record, CUDA seems to be more popular. It has been mentiond 36 times since March 2021. 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.

CUDA mentions (36)

  • A comprehensive guide to running Llama 2 locally
    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: 
        git clone https://github.com/ggerganov/llama.cpp.
    - Source: Hacker News / 10 months ago
  • Nvidia with linux....... not a good combination
    I use Ubuntu and configuring nvidia drivers is very easy installing from here https://developer.nvidia.com/cuda-downloads. Source: 10 months ago
  • Can't get CLBLAST working on oobabooga
    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
  • NEW NVIDIA 535.98 DRIVER!!- INCREASE SPEED, POWER, IMAGE SIZE AN WHO KNOW WHAT ELSE MORE!
    EDIT: LINK TO CUDA-toolkit: https://developer.nvidia.com/cuda-downloads. Source: 11 months ago
  • WizardLM-30B-Uncensored
    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
View more

Datatron mentions (0)

We have not tracked any mentions of Datatron yet. Tracking of Datatron recommendations started around Mar 2021.

What are some alternatives?

When comparing CUDA and Datatron, you can also consider the following products

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.

Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

MCenter - Machine Learning Operationalization

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

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