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Do you need SLI for multi GPU deep learning?
If you’re using it for deep learning, you don’t need SLI. a motherboard with 2 PCI-E slots will work fine. Make sure it’s compatible with the CPU you get.
Is SLI worth it for deep learning?
Deep learning doesn’t use SLI generally because the frameworks for learning like tensorflow already support using multiple GPUs. What that means is that if you have 2 GPUs in your PC, a game would leverage SLI to use them like a single super GPU, however, tensorflow would use them as 2 GPUs.
Can you SLI with 2 different GPUs?
You cannot sli 2 different graphics cards. ATI/AMD cards are a bit more relaxed with crossfire, but Nvidia is very strict on this.
Is SLI good for machine learning?
No. NVIDIA SLI is used for distributing graphics rasterization across multiple GPUs. SLI cannot be used to achieve performance gains in Machine Learning / Deep Learning applications. All machine learning libraries (e.g. TensorFlow, PyTorch, and Caffe) interface with the GPU through CUDA, which has no concept of SLI.
Do you need to connect GPUs with SLI?
You need not connect GPUs via SLI. Keras and TensorFlow will take care of distributing batches across GPUs Instead of SLI, if you use NV-link, Keras can use use GPU for merge as well. https://www.nvidia.com/en-us/data-center/nvlink/
Is there a difference between CPU and GPU in keras?
I would really like to see some very basic Keras model trained on GPU vs CPU to have a better feeling about the difference in performance. Since TensorFlow 2.1, GPU and CPU packages are together in the same package, tensorflow, not like in previous versions which had separate versions for CPU and GPU : tensorflow and tensorflow-gpu.
Why is TensorFlow running on two different GPU?
This means, if you want to run two process on different GPU the easier way is to open two console and do: My guess is that your CUDA_VISIBLE_DEVICE is somehow set to O (or 1) which indeed would be cause problem. Tensorflow tries to allocate some space on every GPU it sees.
How to run Python on two GPUs at once?
Apply a model copy on each sub-batch. Every model copy is executed on a dedicated GPU. Concatenate the results (on CPU) into one big batch. E.g. if your batch_size is 64 and you use gpus=2, then we will divide the input into 2 sub-batches of 32 samples, process each sub-batch on one GPU, then return the full batch of 64 processed samples.