DeepSpeed vs Pipelines

Overview

Pipelines

Stacks29

Followers72

Votes0

GitHub Stars4.0K

Forks1.8K

DeepSpeed

Stacks11

Followers16

Votes0

DeepSpeed vs Pipelines: What are the differences?

<Write Introduction here>

Model Parallelism: One key difference between DeepSpeed and Pipelines is that DeepSpeed focuses on model parallelism, allowing for the distribution of model components across different devices, while Pipelines focus on data parallelism which involves splitting the data to multiple devices for processing.
Optimization Techniques: DeepSpeed incorporates a wide range of optimization techniques such as gradient compression, tensor slicing, and tensor fusion to improve training efficiency and reduce memory footprint, whereas Pipelines primarily rely on data parallelism and model parallelism without specific optimization techniques.
Support for Large Models: DeepSpeed is designed to support training large-scale models with billions of parameters efficiently by optimizing memory usage and computation, while Pipelines may have limitations when dealing with extremely large models due to its data parallelism focus.
Resource Utilization: DeepSpeed provides ways to efficiently utilize resources like GPUs and accelerators through optimizations like model parallelism, while Pipelines might not offer the same level of resource utilization efficiency due to its focus on simpler parallelism techniques.
Community and Ecosystem: DeepSpeed has a strong community and ecosystem with contributions from researchers and developers working on cutting-edge deep learning projects, whereas Pipelines may have a more limited community and ecosystem support, potentially impacting its scalability and flexibility.
Ease of Integration: DeepSpeed offers a seamless integration with PyTorch for easy adoption and implementation of distributed training techniques, while Pipelines may require more manual setup and customization for integrating with different deep learning frameworks.

In Summary, DeepSpeed and Pipelines differ in their focus on model parallelism, optimization techniques, support for large models, resource utilization, community and ecosystem, and ease of integration.

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CLI (Node.js)

Manual

Detailed Comparison

Pipelines	DeepSpeed
Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.	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.
-	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 4.0K	GitHub Stars -
GitHub Forks 1.8K	GitHub Forks -
Stacks 29	Stacks 11
Followers 72	Followers 16
Votes 0	Votes 0
Integrations
Argo Kubernetes Kubeflow TensorFlow	PyTorch

What are some alternatives to Pipelines, 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.

Related Comparisons

DeepSpeed vs Pipelines: What are the differences?

<Write Introduction here>

Model Parallelism: One key difference between DeepSpeed and Pipelines is that DeepSpeed focuses on model parallelism, allowing for the distribution of model components across different devices, while Pipelines focus on data parallelism which involves splitting the data to multiple devices for processing.
Optimization Techniques: DeepSpeed incorporates a wide range of optimization techniques such as gradient compression, tensor slicing, and tensor fusion to improve training efficiency and reduce memory footprint, whereas Pipelines primarily rely on data parallelism and model parallelism without specific optimization techniques.
Support for Large Models: DeepSpeed is designed to support training large-scale models with billions of parameters efficiently by optimizing memory usage and computation, while Pipelines may have limitations when dealing with extremely large models due to its data parallelism focus.
Resource Utilization: DeepSpeed provides ways to efficiently utilize resources like GPUs and accelerators through optimizations like model parallelism, while Pipelines might not offer the same level of resource utilization efficiency due to its focus on simpler parallelism techniques.
Community and Ecosystem: DeepSpeed has a strong community and ecosystem with contributions from researchers and developers working on cutting-edge deep learning projects, whereas Pipelines may have a more limited community and ecosystem support, potentially impacting its scalability and flexibility.
Ease of Integration: DeepSpeed offers a seamless integration with PyTorch for easy adoption and implementation of distributed training techniques, while Pipelines may require more manual setup and customization for integrating with different deep learning frameworks.

DeepSpeed vs Pipelines

Overview