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Pipelines vs PyTorch: What are the differences?

Developers describe Pipelines as "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. On the other hand, PyTorch is detailed as "A deep learning framework that puts Python first". 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.

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

Pipelines and PyTorch are both open source tools. PyTorch with 29.6K GitHub stars and 7.18K forks on GitHub appears to be more popular than Pipelines with 944 GitHub stars and 247 GitHub forks.

Decisions about Pipelines and PyTorch

Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.

I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.

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Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 25.8K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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Xi Huang
Developer at University of Toronto · | 8 upvotes · 60.1K views

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

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A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

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Pros of Pipelines
Pros of PyTorch
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    • 14
      Easy to use
    • 11
      Developer Friendly
    • 10
      Easy to debug
    • 7
      Sometimes faster than TensorFlow

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    Cons of Pipelines
    Cons of PyTorch
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      • 3
        Lots of code

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      What is Pipelines?

      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.

      What is 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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Pipelines?
      What companies use PyTorch?
      See which teams inside your own company are using Pipelines or PyTorch.
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      What tools integrate with Pipelines?
      What tools integrate with PyTorch?

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      What are some alternatives to Pipelines and PyTorch?
      AWS Data Pipeline
      AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
      AWS Glue
      A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
      Bamboo
      Focus on coding and count on Bamboo as your CI and build server! Create multi-stage build plans, set up triggers to start builds upon commits, and assign agents to your critical builds and deployments.
      Jenkins
      In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
      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.
      See all alternatives