A Short Guide to PyTorch DDP
In this blog post, we explore what
torchrun
and
DistributedDataParallel
are and how they can be used to speed up your neural network training by using
multiple GPUs.
In this blog post, we explore what
torchrun
and
DistributedDataParallel
are and how they can be used to speed up your neural network training by using
multiple GPUs.
We still encounter jobs on the HPC cluster that try to use all the cores on the node on which they're running, regardless of how many cores they requested, leading to node alarms. Sometimes, jobs try to use exactly twice or one-and-a-half the allocated cores, or even that number squared. This was a little perplexing at first. In your enthusiasm to parallelize your code, make sure someone else hasn't already done so.
In a previous blog, we discussed ways we could use multiprocessing
and
mpi4py
together to use multiple nodes of GPUs. We will cover some machine
learning principles and two examples of pleasingly parallel machine learning
problems. Also known as embarrassingly parallel problems, I rather call them
pleasingly because there isn't anything embarrassing when you design your
problem to be run in parallel. When doing so, you could launch very similar
functions to each GPU and collate their results when needed.
Using multiple GPUs is one option to speed up your code. On Apocrita, we have V100, A100 and H100 GPUs available, with up to 4 GPUs per node. On other compute clusters, JADE2 has 8 V100 GPUs per node and Sulis has 3 A100 GPUs per node. If your problem is pleasingly parallel, you can distribute identical or similar tasks to each GPU on a node, or even on multiple nodes.
Nowadays, there seems to be an R package for anything and everything. While this makes starting a project in R seem quick and easy, there are considerations to take into account that will make your life easier in the long run.
Living with Machines is a funded project at The Alan Turing Institute (aka the Turing), bringing together academics from different disciplines, to answer research questions such as how did historical newspapers tell the political landscape, how were accidents in factories reported, how did road and settlement names change, how did people change occupations during the industrial revolution...
There are many strategies and tools for improving the performance of Python code, for a comprehensive treatment see High Performance Python by Gorelick and Ozsvald (institutional access is available to QM staff). However, there are some subtleties when using them in an HPC environment. More bluntly, requesting processor cores does not automatically mean your code will use them effectively, and that cannot happen if it doesn't know how many of them there are!
As the complexity of HPC applications increases, the management of memory and threading scopes becomes increasingly important. Tools like Intel Inspector are crucial in this context, to effectively identify and resolve a wide array of memory errors and thread synchronisation issues.
The RSE team in ITS Research has had a busy few years since we started sharing our work in this blog. In this post we look at some highlights of recent activity and what we have to look forward to.
Polytomous variables can be used to model data that has two or more possible outcomes. For example, a survey with multiple-choice questions is polytomous. The R package, poLCA, does statistical clustering of polytomous variables. For example, grouping together survey results that are similar to each other.