DMTK vs Xcessiv
Overview
Detailed Comparison
DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces. | A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. |
DMTK Framework: a flexible framework that supports unified interface for data parallelization, hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.;
LightLDA, an extremely fast and scalable topic model algorithm, with a O(1) Gibbs sampler and an efficient distributed implementation.;
Distributed (Multisense) Word Embedding, a distributed version of (multi-sense) word embedding algorithm. | Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code;Any model following the Scikit-learn API can be used as a base learner;Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches;Direct integration with TPOT for automated pipeline construction;Automated hyperparameter search through Bayesian optimization;Easy management and comparison of hundreds of different model-hyperparameter combinations;Automatic saving of generated secondary meta-features;Stacked ensemble creation in a few clicks;Automated ensemble construction through greedy forward model selection;Export your stacked ensemble as a standalone Python file to support multiple levels of stacking |
Statistics | |
GitHub Stars 2.7K | GitHub Stars 1.3K |
GitHub Forks 559 | GitHub Forks 105 |
Stacks 4 | Stacks 0 |
Followers 18 | Followers 7 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to DMTK, Xcessiv?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
Polyaxon
An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
Streamlit
It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
H2O
H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.
