Submitted by ConsciousInsects t3_103azcq in deeplearning
I was just starting out with ML and had no clue what I was doing. But eventually, I caught on and started reading about best practices and other stuff and I was reading an article about why most people won’t use newer software frameworks. Specifically, the ones that are still hosted on Github.
I have a feeling people don't want to learn new things because they already worked hard to learn something else. Changing to a new way of doing things can be hard if it doesn't work with what you are already using. And it's hard to convince people to switch unless it’s a mandatory switch.
But I also think choosing a framework depends on people’s needs for their projects. If that project could benefit from that new framework or just use it out of curiosity, people would definitely try it out. If not, they’ll just stick to the popular frameworks (i.e. TensorFlow or PyTorch).
So far what are your thoughts? Do you also use frameworks aside from the popular ones?
Nater5000 t1_j2y3k0e wrote
>Do you also use frameworks aside from the popular ones?
No, and this isn't just limited to deep learning. Anybody with even a small amount of engineering experience will have learned the pitfalls of trying to work with immature frameworks, libraries, concepts, etc. When you're building anything non-trivial, you're not just choosing a framework- you're investing in it. If the framework ends up being trash, or it becomes obsolete/abandoned, or you have a hard time finding resources for it, then you're risking the success of your entire project. As such, most engineers prefer the safety of maturity.
To illustrate: If I get some weird, cryptic error from PyTorch, I can usually just copy it, paste it into Google, and find a result immediately describing it and how to resolve it. Try doing that for some new, immature framework and quickly become disappointed as you realize that not only are you on your own in terms of understanding the issue and troubleshooting it, but you may even be the one responsible for fixing it. That's a tough sell for anyone who values their time.
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>I have a feeling people don't want to learn new things because they already worked hard to learn something else.
This may be a factor, but it's relatively minor compared to what I described above. I used to be all-in on TensorFlow. Once it became clear, at least in the domain I was focused on, that PyTorch was "winning," I switched over. There was some time I had to dedicate to figuring it out, but the switch was easy since I knew PyTorch was also mature. In fact, the reason I had originally invested in TensorFlow over PyTorch was because, at the time, PyTorch wasn't very mature and it seemed risky to invest anytime into it.
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>But I also think choosing a framework depends on people’s needs for their projects. If that project could benefit from that new framework or just use it out of curiosity, people would definitely try it out.
In a vacuum this makes sense. But, like I said, there's a cost to adopting new frameworks/libraries. If there's a new framework that does something that'd take me weeks to develop on my own, I may try it out. But if it's a critical component and that new framework isn't very mature, I won't even touch it (although I may look through it for inspiration). I don't want my project to be dependent on something that may break/become obsolete/be full of bugs/etc., even at the cost of my time and effort.
Odds are if you're making a new framework, you'd be better off contributing to an existing framework.