How I use Bioconductor with Docker (part 2): More memory, faster Bioconductor with Docker
My previous posts showed “How I use Bioconductor with Docker”, think of this as an extension to that.
It was the case (way back when) that I found Docker containers “slow” and not a very efficient way of using R. This is because R is an in-memory model for computation. It is important not to allow a running container to consume too much of the host machine’s memory, but at the same time have enough to be able run my container efficiently, in this case R/Bioconductor.
On my 16Gb RAM available, the default ‘hard limit’ given to my containers by docker was 2Gb RAM. And R gets a little less than that. So, the solution to get my containers run faster, increase the memory available to them.
Step 1: Go to Preferences on docker desktop (Image 1)
Step 2: Go to the Advanced option (Image 2)
Increase the RAM to what you think is a sufficient amount based your host machine. Make sure you understand what these resources mean before changing them (here, also see the references section).
You’ll have to restart your docker engine after changing these settings.
Now, once you have set your resource limits, you can run each image with a specific amount of memory available if you wish. Or just let docker do it thing, and figure it out automatically based on the number of containers you are running.
Step 3: Run your image
Run your image, and set memory, cpu limits if you need.
docker run \
-rm \ ## Automatically remove the container when it exits
--memory=6g \ ## memory limit
--cpus=1.5 \ ## number of CPUs
-v /shared/data-store:/home/rstudio/data \
-v /shared/library-store:/usr/local/lib/R/host-site-library \
-e PASSWORD=bioc \
-p 8787:8787 \
As you can see, my docker container is taking up as much memory as needed up to the 6Gb memory limit I set which is container specific within the 8GB resource limit.
As a sanity check, my ‘Activity Monitor’ (Image 3) display shows that it is using about 4.94Gb of memory.
If I launch another image, they will both stay within the 8Gb resource limit.
References on customizations
Get started with Docker Desktop for Mac
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- Read about container resources and what each