DeepSpeed vs Gluon
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Detailed Comparison
A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. | It is a deep learning optimization library that makes distributed training easy, efficient, and effective. It can train DL models with over a hundred billion parameters on the current generation of GPU clusters while achieving over 5x in system performance compared to the state-of-art. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category. |
Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.;Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.;Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.;High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides. | Distributed Training with Mixed Precision; Model Parallelism; Memory and Bandwidth Optimizations; Simplified training API;
Gradient Clipping;
Automatic loss scaling with mixed precision; Simplified Data Loader; Performance Analysis and Debugging |
Statistics | |
GitHub Stars 2.3K | GitHub Stars - |
GitHub Forks 219 | GitHub Forks - |
Stacks 29 | Stacks 11 |
Followers 80 | Followers 16 |
Votes 3 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to Gluon, DeepSpeed?
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.
