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Learn MorePros of CuPy
Pros of PyTorch
Pros of CuPy
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Pros of PyTorch
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
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Cons of CuPy
Cons of PyTorch
Cons of CuPy
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Cons of PyTorch
- Lots of code3
- It eats poop1
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What is CuPy?
It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.
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.
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What companies use CuPy?
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What are some alternatives to CuPy and PyTorch?
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
Numba
It translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. It offers a range of options for parallelising Python code for CPUs and GPUs, often with only minor code changes.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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.
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.