How to prevent tensorflow from allocating the totality of a GPU memory?
问题 I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each. For small to moderate size models, the 12GB of the Titan X are usually enough for 2-3 people to run training concurrently on the same GPU. If the models are small enough that a single model does not take full advantage of all the computational units of the Titan X, this can actually result in a speedup compared with running one training