Macbook vs macbook pro geekbench8/21/2023 ![]() RTX3060Ti from NVIDIA is a mid-tier GPU that does decently for beginner to intermediate deep learning tasks. The only way around it is renting a GPU in the cloud, but that’s not the option we explored today. Still, if you need decent deep learning performance, then going for a custom desktop configuration is mandatory. Nothing comes close if we compare the compute power per wat. Parting WordsĪpple’s M1 chip is remarkable - no arguing there. We knew right from the start that M1 doesn’t stand a chance. Still, these results are more than decent for an ultralight laptop that wasn’t designed for data science in the first place. For the augmented dataset, the difference drops to 3X faster in favor of the dedicated GPU. RTX3060Ti is 10X faster per epoch when training transfer learning models on a non-augmented image dataset. The results look more realistic this time. Image 6 - Transfer learning model results in seconds (M1: 395.2 M1 augmented: 442.4 RTX3060Ti: 39.4 RTX3060Ti augmented: 143) (image by author) Here are the results for the transfer learning models: Both are roughly the same on the augmented dataset.īut who writes CNN models from scratch these days? Transfer learning is always recommended if you have limited data and your images aren’t highly specialized. On the non-augmented dataset, RTX3060Ti is 4.7X faster than the M1 MacBook. Don’t get me wrong, I expected RTX3060Ti to be faster overall, but I can’t reason why it’s running so slow on the augmented dataset. One thing is certain - these results are unexpected. Image 5 - Custom model results in seconds (M1: 106.2 M1 augmented: 133.4 RTX3060Ti: 22.6 RTX3060Ti augmented: 134.6) (image by author) Keep in mind that two models were trained, one with and one without data augmentation: We’ll now compare the average training time per epoch for both M1 and custom PC on the custom model architecture. RTX3060Ti - Data Science Benchmark Results Print( f 'Duration: ')įinally, let’s see the results of the benchmarks. Conv2D(filters = 32, kernel_size =( 3, 3), activation = 'relu'), MaxPool2D(pool_size =( 2, 2), padding = 'same'), # USED ON A TEST WITH DATA AUGMENTATION train_datagen = tf. Data loading # USED ON A TEST WITHOUT DATA AUGMENTATION train_datagen = tf. TensorFlow for Image Classification - Top 3 Prerequisites for Deep Learning Projects Refer to the following article for detailed instructions on how to organize and preprocess it: Long story short, you can use it for free. Cats dataset from Kaggle, which is licensed under the Creative Commons License. Install TensorFLow with GPU support on WindowsĪlso, you’ll need an image dataset.Here’s an entire article dedicated to installing TensorFlow for both Apple M1 and Windows: You’ll need TensorFlow installed if you’re following along. ![]() We’ll have to see how these results translate to TensorFlow performance. Overall, M1 is comparable to AMD Ryzen 5 5600X in the CPU department, but falls short on GPU benchmarks. Heck, the GPU alone is bigger than the MacBook pro. RTX3060Ti scored around 6.3X higher than the Apple M1 chip on the OpenCL benchmark. ![]() Image 4 - Geekbench OpenCL performance (image by author) A thin and light laptop doesn’t stand a chance: Custom PC has a dedicated RTX3060Ti GPU with 8 GB of memory. We can conclude that both should perform about the same. Image 3 - Geekbench multi-core performance (image by author) M1 has 8 cores (4 performance and 4 efficiency), while Ryzen has 6: Let’s compare the multi-core performance next. Keep in mind that we’re comparing a mobile chip built into an ultra-thin laptop with a desktop CPU. Image 2 - Geekbench single-core performance (image by author)Īpple M1 is around 8% faster on a synthetical single-core test, which is an impressive result. Let’s first see how Apple M1 compares to AMD Ryzen 5 5600X in a single-core department: Synthetical benchmarks don’t necessarily portray real-world usage, but they’re a good place to start.
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