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TensorFlow contre PyTorch

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Difference entre TensorFlow et PyTorch

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TensorFlow Interview	PyTorch Interview
Written in Python, C++ and CUDA.	Written in Python, C++, CUDA and is based on Torch (written in Lua).
Developed by Google.	Developed by Facebook (now Meta AI).
API level: High and Low	API level: Low
Complex GPU installation.	Simple GPU installation.
Debugging: Difficult to conduct debugging and requires the TensorFlow debugger tool.	Debugging: Easy to debug as it uses dynamic computational process.
Architecture: TensorFlow is difficult to use/implement but with Keras, it becomes bit easier.	Architecture: Complex and difficult to read and understand.
Learning Curve: Steep and bit difficult to learn.	Learning Curve: Easy to learn.
Distributed Training: To allow distributed training, you must code manually and optimize every operation run on a specific device.	Distributed Training: By relying on native support for asynchronous execution through Python it gains optimal performance in the area of data parallelism.
APIs for Deployment/Serving Framework: TensorFlow serving.	APIs for Deployment/Serving Framework: TorchServe
Key Differentiator: Easy-to-develop models.	Key Differentiator: Highly 'Pythonic' and focuses on usability with careful performance considerations.
Widely used at the production level in Industry.	PyTorch is more popular in the research community.
Tools: TensorFlow Serving, TensorFlow Extended, TF Lite, TensorFlow.js, TensorFlow Cloud, Model Garden, MediaPipe and Coral.	Tools: TorchVision, TorchText, TorchAudio, PyTorch-XLA, PyTorch Hub, SpeechBrain, TorchX, TorchElastic and PyTorch Lightning.
Utilization: Large-scale deployment.	Utilization: Research-oriented and rapid prototype development.

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