PyTorch Review

Good interface, free of cost, and quite stable


What is our primary use case?

It is mostly used for everything that is connected to machine learning, data science, training, and other similar things.

What is most valuable?

Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful. 

What needs improvement?

There is not enough documentation about some methods and parameters. It is sometimes difficult to find information.

For how long have I used the solution?

I have been using PyTorch for almost two years.

What do I think about the stability of the solution?

It is quite stable. Currently, I am the only user.

What do I think about the scalability of the solution?

It is scalable.

How are customer service and technical support?

They don't have support. It is an open-source kind of thing. It is not really open source, but openly available.

How was the initial setup?

It is more or less straightforward. I sometimes have problems installing it. The deployment duration depends on the size of the model. It could take one hour. Sometimes, the whole process can take more than several weeks.

What about the implementation team?

It was an in-house installation.

What's my experience with pricing, setup cost, and licensing?

It is free.

Which other solutions did I evaluate?

I also considered TensorFlow. I don't remember why exactly I chose PyTorch instead of TensorFlow, but I think I didn't evaluate much. It was just like one of the two options, so I went for PyTorch.

What other advice do I have?

I would definitely recommend this solution. I would rate PyTorch an eight out of ten.

Which version of this solution are you currently using?

1.7.0
**Disclosure: I am a real user, and this review is based on my own experience and opinions.
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