Caffe2 vs PyTorch

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Caffe2

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78
+ 1
2
PyTorch

953
1K
+ 1
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Caffe2 vs PyTorch: What are the differences?

Caffe2: Open Source Cross-Platform Machine Learning Tools (by Facebook). Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile; PyTorch: 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.

Caffe2 and PyTorch belong to "Machine Learning Tools" category of the tech stack.

Caffe2 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 Caffe2 with 8.46K GitHub stars and 2.13K GitHub forks.

Decisions about Caffe2 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 · 19.2K 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 · 51.8K 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 Caffe2
Pros of PyTorch
  • 1
    Mobile deployment
  • 1
    Open Source
  • 14
    Easy to use
  • 11
    Developer Friendly
  • 10
    Easy to debug
  • 7
    Sometimes faster than TensorFlow

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Cons of Caffe2
Cons of PyTorch
    Be the first to leave a con
    • 3
      Lots of code

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    What is Caffe2?

    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.

    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!

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    What are some alternatives to Caffe2 and PyTorch?
    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.
    Caffe
    It is a deep learning framework made with expression, speed, and modularity in mind.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    Tensorflow Lite
    It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
    MXNet
    A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
    See all alternatives