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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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.

Macbook pro m2 for machine learning

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I would appreciate any advice on this matter. In this article, we'll find out just that. It's a fantastic Python data science workstation, equipped with fast SSD and a great operating system, but To install PyTorch you can do:. You can install TensorFlow by running:. Average Samples per Second - Resnet50 Tensorflow. You can find code for the benchmarks here. Don't get me wrong, the performance per watt is good but we are still far behind what you get on any current Nvidia desktop GPU. 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. Never lose track of another ML project. Thomas Capelle. Average Samples per Second - Bert Tensorflow. Train BERT for one epoch. 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.

M2 Pro brings pro performance to Mac mini for the first time, while M2 Pro and M2 Max take the game-changing performance and capabilities of the inch and inch MacBook Pro even further.

Follow the on screen instructions and when prompted to initialise the terminal, say yes. I suspect that the point made in another reply about it not being the best solution for serious training is well taken, and installation of pytorch, tensorflow, and transformers is proving much trickier than I had hoped, but it seems to be performing well on basic vector operations such as cosine similarity. Login Signup. GPU Power W. Train BERT for one epoch. Average Samples per Second - Bert Tensorflow. There was an issue with latest tensorflow-metal and Adam optimiser compatibility, the solution was to fallback to tensorflow. Still, this is an improvement in performance over the M1 so if you're in the market for a workstation, definitely prioritize the newer models. But can you use it as a replacement for your power hungry workstation? The new Mac Mini equipped with the M2Pro processor is a silent little powerhouse. Copied to Clipboard.