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You can calculate the memory requirement analytically, but it's still not going to beat physical test in practice as there are so many unknown variables in the system which can takes the GPU memory. Maybe tensorflow will decide to store the gradients, then you have to take into account the memory usage of it also. The way I do it is by setting the GPU memory ...


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In fact, I do not know how to calculate GPU memory to run a neural network but I have a solution for allocation problems in GPUs while using tensorflow framework. import tensorflow as tf gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: # Restrict TensorFlow to only allocate 2GB * 2 of memory on the first GPU try: tf.config....


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Most of the algorithms seem accessing the training set sequentially so the images need not be loaded into memory all at the same time. It is technically fully possible to build a workstation with 1 Tb of RAM or more, using a server barebone in a tower form factor (see this, for instance, and would support multiple GPUs) but this only makes sense if the ...


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I suggest you fine-tune an existing model. Knowledge transfer models in many image processing tasks are now open sourced and you can build your model on top of them. Also, knowledge transfer models are trained on a large datasets and can quickly converge to your case-study with a little of task-specific extra training. This way you will use few data to ...


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Beyond the basic requirement of storage, it seems you are looking for some explicitly defined data organization providing a query interface which allows specifying, alongside the fundamental query params, a context and is able to use both these information items (instead of the first one only) to boost the query. If this interpretation of mine is correct, ...


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Solution In learning systems, the output of learning is a set of parameters. These parameters can be a tensor in the case of a CNN, a representation of a directed graph in the case of recursive network designs, or any other structure holding the results of training. The general case of compactly storing the result of training involves four process elements....


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