Macbook pro m2 for machine learning

Login Signup. In this article, we explore whether the recent addition of the M2Pro chipset to the Apple Mac Mini family works as a replacement for your power hungry workstation. Thomas Capelle. But can you use it as a replacement for your power hungry workstation?

Based on my research and use case, it seems that 32GB should be sufficient for most tasks, including the 4K video rendering I occasionally do. However, I'm concerned about the longevity of the device, as I'd like to keep the MacBook up-to-date for at least five years. Additionally, considering the core GPU, I wonder if 32GB of unified memory might be insufficient, particularly when I need to train Machine Learning models or run docker or even kubernetes cluster. I would appreciate any advice on this matter. Thanks in advance! MPS on PyTorch is handicapped, you need cuda to play around some models. So do you recommend I stay with the 32gb unified memory and that should be enough for good long five years with the usecase?

Macbook pro m2 for machine learning

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After setting up the usual Apple stuff like the AppleID, username, and password and waiting almost 30 minutes for the OS updateI was ready to install the libraries to test this baby. Inside the box, you'll find only a power cable, nothing more. You will be prompted to install developer tools.

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While I appreciate their research on this topic, I think they have yet actually to work in data science or machine learning. The laptops you will see here will be all based on one premise, not just randomly researched laptops with good specs. In the last 15 years, laptops have really blossomed into computation powerhouses. Now, the actual difference between a laptop and a desktop computer is the GPU. While some laptops offer decent GPUs that can help speed up some of the heavier computations, those can be expensive and require custom fitting. With these services, you have all the power of a desktop with the mobility of a laptop — for a much lower cost. You do not need a desktop computer in for machine learning. Suppose you ever run into a situation where the computation is too much for your laptop which will sometimes happen.

Macbook pro m2 for machine learning

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I guess that Docker and K8s would be no problem, and that small-scale training might be OK. Last time I got a Mac laptop, it was a Mabook Air, 1. Good luck with whatever you decide on! Since PyTorch 1. MPS on PyTorch is handicapped, you need cuda to play around some models. Copy to clipboard Share this post. You can find code for the benchmarks here. There was an issue with latest tensorflow-metal and Adam optimiser compatibility, the solution was to fallback to tensorflow. You can grab it directly from the website or running this on a terminal:. Posted by heickxopelk. Additionally, considering the core GPU, I wonder if 32GB of unified memory might be insufficient, particularly when I need to train Machine Learning models or run docker or even kubernetes cluster. I expect that future applications will use this kind of processing, and am hoping that this macbook won't run out of steam for such applications for another 10 years. I would typically install more things on a new machine, but as I will return this one, I won't bother to install all my configurations and tools.

With the release of the MacBook Pro and the Mac mini , the shape of the second generation of Apple silicon on Mac has been revealed. It is, unsurprisingly, a bit of a replay of the first generation: Apple has segmented its chips into a few different varieties. As with the M1 generation , the new M2 Pro and M2 Max chips are closely related to each other and to the M2 chip introduced last summer.

The new Mac Mini equipped with the M2Pro processor is a silent little powerhouse. I expect that future applications will use this kind of processing, and am hoping that this macbook won't run out of steam for such applications for another 10 years. Next, you'll need to install the developer utilities from Apple. Based on my research and use case, it seems that 32GB should be sufficient for most tasks, including the 4K video rendering I occasionally do. However, I'm concerned about the longevity of the device, as I'd like to keep the MacBook up-to-date for at least five years. But it's a bit of a "finger in the air" decision. Sign up or log in to create reports like this one. I guess that Docker and K8s would be no problem, and that small-scale training might be OK. So do you recommend I stay with the 32gb unified memory and that should be enough for good long five years with the usecase? Posted by Aditya-ai. MPS on PyTorch is handicapped, you need cuda to play around some models. Average Samples per Second - Bert Tensorflow. The easiest way to grab Python and an environment manager for me is using Anaconda.