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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Pipelines vs Tensorpack

Pipelines vs Tensorpack

OverviewComparisonAlternatives

Overview

Pipelines
Pipelines
Stacks29
Followers72
Votes0
GitHub Stars4.0K
Forks1.8K
Tensorpack
Tensorpack
Stacks1
Followers7
Votes0
GitHub Stars6.3K
Forks1.8K

Pipelines vs Tensorpack: What are the differences?

Pipelines: Machine Learning Pipelines for Kubeflow. 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; Tensorpack: A neural network training interface based on TensorFlow. It is a Neural Net Training Interface on TensorFlow, with focus on speed + flexibility. It is a training interface based on TensorFlow, which means: you’ll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.

Pipelines and Tensorpack can be primarily classified as "Machine Learning" tools.

Pipelines and Tensorpack are both open source tools. Tensorpack with 5.36K GitHub stars and 1.64K forks on GitHub appears to be more popular than Pipelines with 1.47K GitHub stars and 500 GitHub forks.

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Detailed Comparison

Pipelines
Pipelines
Tensorpack
Tensorpack

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 Neural Net Training Interface on TensorFlow, with focus on speed + flexibility. It is a training interface based on TensorFlow, which means: you’ll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.

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Training interface based on TensorFlow; Focus on training speed; Focus on large datasets
Statistics
GitHub Stars
4.0K
GitHub Stars
6.3K
GitHub Forks
1.8K
GitHub Forks
1.8K
Stacks
29
Stacks
1
Followers
72
Followers
7
Votes
0
Votes
0
Integrations
Argo
Argo
Kubernetes
Kubernetes
Kubeflow
Kubeflow
TensorFlow
TensorFlow
Python
Python
TensorFlow
TensorFlow

What are some alternatives to Pipelines, Tensorpack?

TensorFlow

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

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

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

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

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

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

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

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

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.

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