While working on a computer we can monitor and research how different models are utilizing the computational power and resources in practical scenarios. This idea helps us understand the relation between GPU processor utilization and training batch size.
According to a study, nearly one-third of users who deal with machine learning or deep learning training and framework can only utilize up to 15 percent of their computational resources. In this article, we will try to find out the effects of deep learning model training on machines and how we can maximize the utilization of GPUs with increased batch size.
Know about the terminologies
Whenever we try to construct a machine learning model we always search for the most beneficial parameters which can provide proper configuration for our training models. This customization may include layer stack setup, data shape, optimizer for batch sizes, etc.
When training a neural natural network, an individual may run into batch sizing constraints and it can also affect the power of the GPU as well as its accessible memory. Here, we will try to discuss the relationship between maximizing batch size and GPU processor utilization but let us know about the terminology.
A single element of data is regarded as Sample. A sample has the output for measuring the errors and it also has the inputs for training the algorithms. Other names of the sample include the feature vector, observation, and the input vector.
The term batch size signifies the number of samples which is used to train any machine learning model. The batch of samples goes through each training step of the model and then it is used to decide the gradients of every sample.
Depending on the type of optimizers used, all the gradients of the sample are either added or averaged. This process will repeat itself subsequently whenever the parameters of the sample batch are updated.
Epoch is the hyper meter that can manage the number of times any training algorithm can undergo the full training Dataset. If internal model parameters have experienced a chance to be updated a single time for every sample in the training Dataset then it will be regarded as one epoch. The number of batches in a single epoch can differ according to the configuration.
The amount of batches required to finalize a single epoch is known as iteration. For example, if any dataset has 20000 samples which have been divided into 40 batches then the number of iterations would be 500 (500*40).
Effect of batch size on training models
First, we should analyze what kind of effect batch size has on GPU and GPU memory usage. Usually, researchers use 3 of the most used machine learning frameworks namely MXnet, TensorFlow and PyTorch to record the results.
Among these three frameworks, the GPU utilization of Mxnet is the largest whereas the GPU usage is lowest in the case of TensorFlow. If we are to increase the batch size then TensorFlow has the biggest GPU utilization rate and PyTorch has the lowest. With the increase in the size of the batch, GPU consumption increases dramatically.
In order to increase the GPU usage, increasing the batch size is a foolproof technique but it is not always triumphant. As long as there is enough memory to conduct the entire operation, a big batch size will be enough to enhance the performance of the GPU. Moreover, memory usage is also affected by different parameters, models, frameworks and every batch of data.
Accuracy and performance of the algorithm
Whenever we try to use small or huge batch sizes, it can primarily have two types of obstructive effects.
Overfitting is an incident where the neural network does not work properly on the sample which is outside of the exact training data set. This is the result of bad generalization where the models and batch sizes get stuck in the local minima.
With the use of lesser data in a single batch, the gradient estimation becomes less accurate and noisy. Due to the small batch size, the algorithms learn very slowly and produce mostly inaccurate data.
Methods to increase the batch size
Now after reading the previous sections we can understand the importance of the batch size which also heavily impacts GPU usage. To overcome the overfitting issue we can make use of particular strategies and take advantage of our GPU’s full potential. Some of the important strategies we can use to overcome this problem are:
- Data augmentation
- Conservative training
- Robust training
For every batch size, there is a particular threshold that we need to maintain otherwise the quality of the model will definitely decrease. Nevertheless, you can utilize the above-mentioned methodologies to overcome the degradation of the huge batch sizes and properly utilize the capabilities of your GPU.