PyTorch Vs TensorFlow

A Framework is an interface which allows Engineers and Developers to build and deploy machine learning models faster and easier. Using this helps in making the process more efficient for a cycle of training process. There are many frameworks out there for machine learning models like TensorFlow, PyTorch, Theano, Keras etc… which use different languages. But most preferred one is TensorFlow and PyTorch. Because not only training, we also need better deployment for those models. So these frameworks provide better deployment of those models in back end and has detailed documentation. So let’s compare these frameworks.

PyTorch Vs Tensorflow

Here, We need to consider few parameters for comparing these frameworks.
And at Each parameter we can consider which one is better by referring it in a bracket

1. Ramp Up Time: (PyTorch is Better)
   PyTorch — Faster and Efficient in Execution and No Additional concepts are required to be learnt. GPU Enabled Drop in is used for mathematical calculations.
   Tensor flow — Not as Fast as PyTorch as it used Tf execution engine for compiling the graph functions and extra concepts like session, placeholder,variable scope are used. So more Boiler plate code is needed.
2. Graph Construction and Debugging: ( PyTorch is Better)
   PyTorch — Because of Dynamic Computations of Graphs at runtime it is more effective and uses python debugger.
   Tensor flow — Graph construction is Static and need to be compiled first. For debugging it used tensor flow debugger and request variables from session to inspect. So it much long process.
3. Coverage: (Tensor flow is Better)
   PyTorch — Less Feature Coverage. So less preferred among free lancers.
   Tensor flow — Supports higher level functionalities. Has contrib package for more models.
4. Serialization: (Tensor flow is Better)
   PyTorch — Simple API, saves weights of models but not has all other features as tf provides.
   Tensor flow — Here entire graph can be saved as .pb ( a protobuf model) which includes all the parameters and operations. This graph can be loaded in environments with other languages.
5. Deployment: (Tensor flow is Better)
   * For small scale application both are easy to wrap. ex: In a Flask Web Browser.
   * In Mobile Deployments Tensor flow is better because of its easy understanding of data flow.
   In Android and IOS no need to write entire inference in java/c++ because of availability of Tensor board.
   *Tensor flow serving models can be used for server side deployments and with latest functionalities of tensor flow the models can even be swapped easily without bringing down.
6. Documentation: (Both has Official and Detailed Documentation)
7. Device Management: (Tensor flow is Better)
   Tensor flow has default usage of GPU functionality and it is preferred for seamless integration of models. So there is no need of manual intervention for moving the device for enabling CUDA ( Compute Unified Device Architecture). But one downside is that it consumes all the memory of GPU even if one being used.
8. Customized Extensions: (PyTorch is Better)
   Building Customized Extensions written in C, C++ is doable in both frameworks depending on the preference .
   PyTorch — Need to write inference and implementation for each of GPU and CPU versions.
   Tensor flow — It requires more complex code for inference but supports for multiple devices and all types.

So, Overall considering all the points PyTorch is better for rapid prototyping in Research and for small scale projects for better utilization of GPU. and Tensor flow is for large scale deployments with bigger community base with many users makes it a good cross platform for embedded deployment.

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