A Community of Awesome Machine Learning Projects
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The dmlc organization on GitHub focuses on a wide range of machine learning projects, showcasing expertise in languages such as C++, Python, and Jupyter Notebook. Notable repositories include xgboost, a scalable library for gradient boosting, and dgl, which simplifies deep learning on graphs.
Top languages
Public repositories
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
dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
gluon-cv
Gluon CV Toolkit
gluon-nlp
NLP made easy
decord
An efficient video loader for deep learning with smart shuffling that's super easy to digest
nnvm
No description provided for this repository.
ps-lite
A lightweight parameter server interface
dlpack
common in-memory tensor structure
mshadow
Matrix Shadow:Lightweight CPU/GPU Matrix and Tensor Template Library in C++/CUDA for (Deep) Machine Learning
minpy
NumPy interface with mixed backend execution
cxxnet
move forward to https://github.com/dmlc/mxnet
dmlc-core
A common bricks library for building scalable and portable distributed machine learning.
treelite
Universal model exchange and serialization format for decision tree forests
minerva
Minerva: a fast and flexible tool for deep learning on multi-GPU. It provides ndarray programming interface, just like Numpy. Python bindings and C++ bindings are both available. The resulting code can be run on CPU or GPU. Multi-GPU support is very easy.
parameter_server
moved to https://github.com/dmlc/ps-lite
mxnet-notebooks
Notebooks for MXNet
rabit
Reliable Allreduce and Broadcast Interface for distributed machine learning
mxnet.js
MXNetJS: Javascript Package for Deep Learning in Browser (without server)
tensorboard
Standalone TensorBoard for visualizing in deep learning
MXNet.jl
MXNet Julia Package - flexible and efficient deep learning in Julia
wormhole
Deprecated
mxnet-memonger
Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets
XGBoost.jl
XGBoost Julia Package
difacto
Distributed Factorization Machines
mxnet-model-gallery
Pre-trained Models of DMLC Project
GNNLens2
Visualization tool for Graph Neural Networks
HalideIR
Symbolic Expression and Statement Module for new DSLs
mxnet-gtc-tutorial
MXNet Tutorial for NVidia GTC 2016.
experimental-lda
No description provided for this repository.
keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on MXNet, Theano or TensorFlow.
MXNet.cpp
C++ interface for mxnet
experimental-mf
cache-friendly multithread matrix factorization
web-data
The repo to host all the web data including images for documents in dmlc projects.
nnvm-fusion
Kernel Fusion and Runtime Compilation Based on NNVM
tl2cgen
TL2cgen (TreeLite 2 C GENerator) is a model compiler for decision tree models
dmlc.github.io
No description provided for this repository.
cub
No description provided for this repository.
xgboost-devops
Host custom actions; keep track of manual approval requests for CI jobs.
caffe
Caffe: a fast open framework for deep learning.
mxnet-deepmark
Benchmark speed and other issues internally, before push to deep-mark
mxnet-examples
MXNet Example
numpy-ml
Machine learning, in numpy
DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
drat
Drat Repository for DMLC R packages
xgboost-bench
No description provided for this repository.
ccache
ccache – a fast compiler cache
dmlc.r-universe.dev
No description provided for this repository.
nccl
Optimized primitives for collective multi-GPU communication
gluon-nlp-notebooks
No description provided for this repository.
docs-redirect-for-mxnet
redirect mxnet.readthedocs.io to mxnet.io
nn-examples
No description provided for this repository.
Frequently asked questions
What does dmlc build on GitHub?
dmlc builds various machine learning tools and libraries on GitHub, including xgboost for gradient boosting and dgl for deep learning on graphs. Their repository offerings cater to diverse needs in the machine learning community.
Which programming languages does dmlc use?
The primary programming languages used by dmlc include C++, Python, Jupyter Notebook, Cuda, JavaScript, and Julia. This diverse language usage supports a range of applications in machine learning and deep learning.
Are dmlc's repositories public?
Yes, all of dmlc's repositories are public on GitHub. This openness allows developers and researchers to access, contribute to, and utilize their machine learning projects freely.
Is this exposure intended?
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