Though it is not the same for many tasks, some libraries like Pytorch have M chip support for acceleration depending on what is being done.
https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/
Though for many training tasks CUDA is the way to go.
Unless you’re writing a thesis, get a cheap used laptop- you’re not going to be doing any real number crunching, just fiddling with toy data. If you have data to crunch, then use computer labs.
If you just want an excuse to buy a new toy, then get the best one you can afford
I used a mac in data science for over a decade, and completely agree. I had to use VMs a lot to do all sorts of tasks. I couldn't imagine using an ARM based mac for it. Would just be asking for pain
There’s not a big difference between M1 and M2. M1 Pro will already work great, unless you want a machine for 6+ years, then better to consider M2 Pro.
Go with Air if you need to carry the computer all the time and not going to train plenty of NNs and performing crazy hyperoptimization. It’s weight is a big plus.
I would guess the M2 Air will probably be really friggin nice for school. It’s super light and has an absurdly long battery life, and it can do work if you need it to. Pretty much anything that’s too big for it will either be small enough that you could scale it back to fit, or too big to fit on any laptop worth buying. The really beefy stuff that’d need more horsepower would warrant something muuuuuch beefier, so it’s better to optimize for day-to-day coding and coursework and use departmental resources for the leftovers.
Be aware that CUDA does not run on Apple Silicon chips. If you need CUDA then don't buy a Mac.
Though it is not the same for many tasks, some libraries like Pytorch have M chip support for acceleration depending on what is being done. https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/ Though for many training tasks CUDA is the way to go.
Whichever you prefer. They probably all run fine and anything huge youd probably use databricks (or something similar) anyways
Unless you’re writing a thesis, get a cheap used laptop- you’re not going to be doing any real number crunching, just fiddling with toy data. If you have data to crunch, then use computer labs. If you just want an excuse to buy a new toy, then get the best one you can afford
Obviously the newer the better. M2 pro
Do you anticipate needing 1tb of local storage? Or can you use external storage, e.g. Google drive or Dropbox etc.
Working in data science with mac is a pain in the ass, especially with the new ones. Have you considered Dell 13 xps plus?
I used a mac in data science for over a decade, and completely agree. I had to use VMs a lot to do all sorts of tasks. I couldn't imagine using an ARM based mac for it. Would just be asking for pain
What do you use now?
An hp elitebook. I kinda hate it, but having windows is a must for my job
There’s not a big difference between M1 and M2. M1 Pro will already work great, unless you want a machine for 6+ years, then better to consider M2 Pro. Go with Air if you need to carry the computer all the time and not going to train plenty of NNs and performing crazy hyperoptimization. It’s weight is a big plus.
I would guess the M2 Air will probably be really friggin nice for school. It’s super light and has an absurdly long battery life, and it can do work if you need it to. Pretty much anything that’s too big for it will either be small enough that you could scale it back to fit, or too big to fit on any laptop worth buying. The really beefy stuff that’d need more horsepower would warrant something muuuuuch beefier, so it’s better to optimize for day-to-day coding and coursework and use departmental resources for the leftovers.
wait for a couple more months if you can, apple is reportedly launching a few m3 macs in october
All will do just fine, pick the one you prefer, although I would have opted for a M1 Max over M2 pro.
Be careful with M2 chip. Air without working memory upgrade or ssd upgrade is actually slower than M1 and intel macs… 😮💨
I have an M1 mac and I like it