5 disadvantages of pytorch you should know in 2023

There are a few potential disadvantages to using PyTorch for machine learning tasks:

While PyTorch has support for mobile and embedded devices, but it is not as comprehensive as some other libraries, such as TensorFlow

[{"selector":"#anim-5de8a714-b2f0-48b5-9106-ec76f9156c69 [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}] [{"selector":"#anim-efc3f2a7-be57-4f17-b975-4b325f06697b","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-46e9f178-8626-4225-87eb-a905b7585459","keyframes":{"transform":["translate3d(-115.18988%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

PyTorch models can sometimes be larger in size compared to models trained using other libraries, such as TensorFlow.

[{"selector":"#anim-de0f87ac-73a5-4850-9354-fe0ba9f55d55 [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}] [{"selector":"#anim-80df41e9-af24-41aa-b189-dd6dc10e09ae","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-b40e5377-cb91-4c7f-a8d9-f0d1426f48bf","keyframes":{"transform":["translate3d(-115.18988%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

While PyTorch is widely used in research, it is not as production-ready as some other libraries, such as TensorFlow

[{"selector":"#anim-abb6d298-4630-4947-979a-cc9454f5e22f [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}] [{"selector":"#anim-d87d1218-0e2c-4569-acdd-bea0e96e9723","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-39a2c8d2-c400-4add-bdbc-dce9dd741eef","keyframes":{"transform":["translate3d(-115.18988%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

PyTorch does not have as many built-in visualization tools which makes it more difficult to debug and understand

[{"selector":"#anim-385ce140-fbbd-4441-828e-6a926df53ff9 [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}] [{"selector":"#anim-f07c645c-9ed8-494c-92a3-323c7c2bf7af","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-f5679774-76da-470a-a04f-94c0f93a1aa2","keyframes":{"transform":["translate3d(-115.18988%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

PyTorch does not have official support for some programming languages, such as Julia

[{"selector":"#anim-8a4f63ca-0cbb-4830-9e76-27c2cd470176 [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}] [{"selector":"#anim-52219312-bb18-49cf-a96b-f0eeea12b820","keyframes":{"opacity":[0,1]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}] [{"selector":"#anim-a6e7e137-634b-458d-9f35-2506931084d5","keyframes":{"transform":["translate3d(-115.18988%, 0px, 0)","translate3d(0px, 0px, 0)"]},"delay":0,"duration":600,"easing":"cubic-bezier(0.2, 0.6, 0.0, 1)","fill":"both"}]

Advantages of pytorch

[{"selector":"#anim-464f8a93-2042-43a9-a2ee-20016d34f754 [data-leaf-element=\"true\"]","keyframes":{"transform":["translate(0%, 0%) scale(1.5)","translate(0%, 0%) scale(1)"]},"delay":0,"duration":2000,"easing":"cubic-bezier(.3,0,.55,1)","fill":"forwards"}]

There are a few potential disadvantages to using PyTorch for machine learning tasks:

While PyTorch has support for mobile and embedded devices, but it is not as comprehensive as some other libraries, such as TensorFlow

Limited support

PyTorch models can sometimes be larger in size compared to models trained using other libraries, such as TensorFlow.

Larger model size

While PyTorch is widely used in research, it is not as production-ready as some other libraries, such as TensorFlow

Less production ready

PyTorch does not have as many built-in visualization tools which makes it more difficult to debug and understand

Limited visualizations

PyTorch does not have official support for some programming languages, such as Julia

Support for programming languages

Advantages of pytorch