CUDA Toolkit
TensorFlow
PyTorch
Keras
Scikit-learn
MLKit
Neuton.AI
Kira
Deeplearning4j
TensorFlow
PyTorch
Keras
TFlearn
Scikit-learn
DeepPy
Darknet
CUDA ToolkitBased on our record, CUDA Toolkit should be more popular than Deeplearning4j. It has been mentiond 40 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.
Nvidia’s CUDA dominance is fading as developers embrace open-source alternatives like Triton and JAX, offering more flexibility, cross-hardware compatibility, and reducing reliance on proprietary software. - Source: dev.to / 3 months ago
Since I have a Nvidia graphics card I utilized CUDA to train on my GPU (which is much faster). - Source: dev.to / 5 months ago
In this post we continue our exploration of the opportunities for runtime optimization of machine learning (ML) workloads through custom operator development. This time, we focus on the tools provided by the AWS Neuron SDK for developing and running new kernels on AWS Trainium and AWS Inferentia. With the rapid development of the low-level model components (e.g., attention layers) driving the AI revolution, the... - Source: dev.to / 6 months ago
Install CUDA Toolkit (only the Base Installer). Download it and follow instructions from Https://developer.nvidia.com/cuda-downloads. - Source: dev.to / 11 months ago
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 / almost 2 years agogit clone https://github.com/ggerganov/llama.cpp.
DeepLearning4j Blockchain Integration is more than just a convergence of technologies; it’s a paradigm shift in how AI projects are developed, funded, and maintained. By utilizing the robust framework of DL4J, enhanced with secure blockchain features and an inclusive open source model, the project is not only pushing the boundaries for artificial intelligence but also establishing a resilient model for future... - Source: dev.to / about 2 months ago
While KotlinDL seems to be a good solution by Jetbrains, I would personally stick to Java frameworks like DL4J for a better community support and likely more features. Source: over 3 years ago
Would recommend taking a look at dl4j: https://deeplearning4j.org. Source: almost 4 years ago
We use DeepLearning4j in this chapter because it is written in Java and easy to use with Clojure. In a later chapter we will use the Clojure library libpython-clj to access other deep learning-based tools like the Hugging Face Transformer models for question answering systems as well as the spaCy Python library for NLP. Source: almost 4 years ago
FastAPI. Or even simpler: DL4J, to be used in Java when we need to communicate with the rest of the applications in real time. Source: about 4 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.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
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.
TFlearn - TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
MLKit - MLKit is a simple machine learning framework written in Swift.