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Can you use 2 different types of GPU?
Yes, this can technically work—both cards will give you graphical output. However, different cards cannot be linked together to function as a GPU array (CrossFire or SLI), so you generally won’t be able to use them together to render graphics in games. The cards will operate independently of each other.
Can you use two GPUs for deep learning?
For deep learning it depends. If you plan on launching one experiment per gpu, then having different GPUs doesn’t matter. One experiment will be slower but both GPUs will be fully used.
Can a keras model be trained on multiple GPUs?
Synchronicity keeps the model convergence behavior identical to what you would see for single-device training. Specifically, this guide teaches you how to use the tf.distribute API to train Keras models on multiple GPUs, with minimal changes to your code, in the following two setups:
What kind of parallelism is used in keras?
Model parallelism, where different parts of a single model run on different devices, processing a single batch of data together. This works best with models that have a naturally-parallel architecture, such as models that feature multiple branches.
Why do we need Keras in deep learning?
Keras enables you to distribute your model training tasks over multiple resources, performing training tasks in parallel. Distributed training is an essential part of deep learning. It enables you to leverage multiple CPUs or GPUs and drastically reduces the amount of time needed to train models.
When to use global batch size in keras?
When creating your dataset, make sure it is batched with the global batch size. For instance, if each of your 8 GPUs is capable of running a batch of 64 samples, you call use a global batch size of 512. If you call .cache () on a dataset, its data will be cached after running through the first iteration over the data.