Pytorch vs. Weights & Biases

They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.

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No brainer to use it when doing ML experiments as it is very easy compared to any other open source tool. You don't have to host anything like in Tensorboard.

Experiment details can be shared very easily with public using the reports

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• flexibility
• Clean code, close to the algorithm.
• Fast
• Handles GPUs, multiple GPUs on a single machine, CPUs, and Mac.
• Versatile, can work efficiently on text/audio/image/tabular datasets.

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• Metrics Logging
• Hyperparmeters Sweeps
• Model Artifcats

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A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Since pythonic if developing an app with pytorch as backend the response can be substantially slow and support is less compares to Tensorflow

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• Dashboard lags when we log a lot of metrics
• Improved support for matplotlib charts and documentation of wandb custom charts is not straghtforward

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The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.

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Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.

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• The ability to make models as never before
• Being able to control the bias of models was not done before the arrival of Pytorch in our company

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• Made it very easy to track experiments
• Track ML and Business Metrics improvements across experiments
• Reproduce runs which is essential in ML modelling

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info