mlpack
mlpack: a fast, header-only C++ machine learning library (by mlpack)
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more (by apache)
| mlpack | mxnet | |
|---|---|---|
| 4 | 5 | |
| 5,349 | 20,644 | |
| 0.7% | - | |
| 9.7 | 4.1 | |
| 1 day ago | over 1 year ago | |
| C++ | C++ | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mlpack
Posts with mentions or reviews of mlpack.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-23.
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How much C++ is used when it comes to performing quant research?
Does C++ have the equivalent of Pandas or Apache Spark? Are there extensive libraries that exist/are being developed that allow you to perform operations with data? Or do people just use a combination of Python & its various libraries (NumPy etc)? If we leave aside the data bit, are there libraries that allow you to develop ML models in C++ (mlpack for instance ) faster & more efficiently compared to their Python counterparts (scikit-learn)? On a more general note, how does C++ fit into the routine of a Quant Researcher? And at what scale does an organization decide they need to start switching to other languages and spend more time developing the code ?
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What is the most used library for AI in C++ ?
mlpack is a great library for machine learning in C++. It's very fast and not too much of a learning curve.
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Ensmallen: A C++ Library for Efficient Numerical Optimization
This toolkit was originally part of the mlpack machine learning library (https://github.com/mlpack/mlpack) before it was split out into a separate, standalone effort.
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Top 10 Python Libraries for Machine Learning
Github Repository: https://github.com/mlpack/mlpack Developed By: Community, supported by Georgia Institute of technology Primary purpose: Multiple ML Models and Algorithms
mxnet
Posts with mentions or reviews of mxnet.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-08-21.
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Las 10 Mejores Herramientas de Inteligencia Artificial de Código Abierto
Apache MXNet: Rendimiento y Escalabilidad [(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/lkqk1iwnjb5a0spyygx3.png)](https://github.com/apache/mxnet)
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List of AI-Models
Click to Learn more...
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Introduction to deep learning hardware in the cloud
Build – Choose a machine learning framework (such as TensorFlow, PyTorch, Apache MXNet, etc.)
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just released my Clojure AI book
Clojure and Python also have bindings to the Apache MXNet library. Is there a reason why you didn't use them in some of your projects?
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Can Apple's M1 help you train models faster and cheaper than Nvidia's V100?
> But you still lose something, e.g. if you use half precision on V100 you get virtually double speed, if you do on a 1080 / 2080 you get... nothing because it's not supported.
That's not true. FP16 is supported and can be fast on 2080, although some frameworks fail to see the speed-up. I filed a bug report about this a year ago: https://github.com/apache/incubator-mxnet/issues/17665
What consumer GPUs lack is ECC and fast FP64.
What are some alternatives?
When comparing mlpack and mxnet you can also consider the following projects:
Dlib - A toolkit for making real world machine learning and data analysis applications in C++
Caffe - Caffe: a fast open framework for deep learning.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Caffe2
faiss-server - faiss serving :)