Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high-performance system.
Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off and the model diverged. With a good, solid GPU, one can quickly iterate over designs and parameters of deep networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make a choice that is right for you.
[Read more…] about Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning
On the computational side, there have been confusions about how TPUs and GPUs relate to BERT. BERT was done with 4 TPU pods (256 TPU chips) in 4 days. Does this mean only Google can train a BERT model? Does this mean that GPUs are dead? There are two fundamental things to understand here: (1) A TPU is a matrix multiplication engine — it does matrix multiplication and matrix operations, but not much else. It is fast at computing matrix multiplication, but one has to understand that (2) the slowest thing in matrix multiplication is to get the elements from the main memory and load it into the processing unit. In other words, the most expensive part in matrix multiplication is memory loads. Note the computational load for BERT should be about 90% for matrix multiplication. From these facts, we can do a small technical analysis on this topic.
With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now?
In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning. In this blog post I will focus on model parallelism.
In my last blog post I showed what to look out for when you build a GPU cluster. Most importantly, you want a fast network connection between your servers and using MPI in your programming will make things much easier than to use the options available in CUDA itself.
In this blog post I explain how to utilize such a cluster to parallelize neural networks in different ways and what the advantages and downfalls are for such algorithms. The two different algorithms are data and model parallelism. In this blog entry I will focus on data parallelism.
When I started using GPUs for deep learning my deep learning skills improved quickly. When you can run experiments of algorithms and algorithms with different parameters and gain rapid feedback you can just learn much more quickly. At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2nd place in the Crowdflower competition where the task was to predict weather labels from given tweets (sunny, raining etc.).
After this success I was tempted to use multiple GPUs in order to train deep learning algorithms even faster. I also took interest in learning very large models which do not fit into a single GPU. I thus wanted to build a little GPU cluster and explore the possibilities to speed up deep learning with multiple nodes with multiple GPUs. At the same time I was offered to do contract work as a data base developer through my old employer. This gave me opportunity to get the money to build the GPU cluster I thought of.