What to do if GPU runs out of memory while training a CNN?

What to do if GPU runs out of memory while training a CNN?

If your GPU runs out of memory while training a CNN, what are five things you could try to solve the problem? Reduce the mini-batch size. Reduce dimensionality using a larger stride in one or more layers. Remove one or more layers.

Does GPU memory matter for machine learning?

Why choose GPUs for Deep Learning They have a large number of cores, which allows for better computation of multiple parallel processes. Additionally, computations in deep learning need to handle huge amounts of data — this makes a GPU’s memory bandwidth most suitable.

Should batch size always be power of 2?

The overall idea is to fit your mini-batch entirely in the the CPU/GPU. Since, all the CPU/GPU comes with a storage capacity in power of two, it is advised to keep mini-batch size a power of two.

Do you need a GPU for neural network?

A good GPU is indispensable for machine learning. Training models is a hardware intensive task, and a decent GPU will make sure the computation of neural networks goes smoothly. Compared to CPUs, GPUs are way better at handling machine learning tasks, thanks to their several thousand cores.

Why are large batch sizes bad?

the distribution of gradients for larger batch sizes has a much heavier tail. better solutions can be far away from the initial weights and if the loss is averaged over the batch then large batch sizes simply do not allow the model to travel far enough to reach the better solutions for the same number of training …

How do you break GPU memory boundaries even with large batch sizes?

Using larger batch sizes One way to overcome the GPU memory limitations and run large batch sizes is to split the batch of samples into smaller mini-batches, where each mini-batch requires an amount of GPU memory that can be satisfied.

Is 8GB RAM enough for neural network?

If it is on remote, then 8gb is sufficient, but if it is on cloud or on premise then, not less than 16gb. if you ‘re planning to try deep neural nets for heavy application like reinforcement learning or image nets, then don’t think of less than 16gb.

What is the impact of GPU memory on neural network performance?

It has an impact on the resulting accuracy of models, as well as on the performance of the training process. The range of possible val u es for the batch size is limited today by the available GPU memory. As the neural network gets larger, the maximum batch size that can be run on a single GPU gets smaller.

How much GPU memory do I need for training my model?

However, given the size of your model and the size of your batches, you can actually calculate how much GPU memory you need for training without actually running it. For example, training AlexNet with batch size of 128 requires 1.1GB of global memory, and that is just 5 convolutional layers plus 2 fully-connected layers.

How much memory does a convolutional neural network need?

If we look at a bigger model, say VGG-16, using a batch size of 128 will require about 14GB of global memory. The current state-of-the-art NVIDIA Titan X has a memory capacity of 12GB . VGG-16 has only 16 convolutional layers and 3 fully-connected layers, and is much smaller than the resnet model which could contain about one hundred layers.

How do you train a machine learning model on multiple GPUs?

There are two main ways to implement this: Data-parallelism — use multiple GPUs to train all mini-batches in parallel, each on a single GPU. The gradients from all mini-batches are accumulated and the result is used to update the model variables at the end of every step.