Hi,
Thank you very much Martin and Hervé for your suggestions. I have reverted
my zzz.R on load function to that advised by ExperimentHub and had used the
ID look up (system.time(tt_alzh <- eh[["EH5373"]])) on internal functions
and unit tests. However, the check is still taking ~18 minutes so I need to
do a bit more work. Even with my new on load function, calling datasets by
name still takes substantially longer, see below for the example Hervé gave
on my new code:
a<-function(){
eh <- query(ExperimentHub(), "ewceData")
tt_alzh <- eh[["EH5373"]]
}
microbenchmark::microbenchmark(a,
tt_alzh <- ewceData::tt_alzh(),
times=20L,unit="s")
Unit: seconds
expr min lq
mean median uq max neval
a 0.00000003 0.000000031
0.0000002995 0.000000045 0.000000684 0.000001064 20
t>t_alzh <- ewceData::tt_alzh() 2.71135788 2.755388420 2.9922968274
2.993737666 3.144241330 3.842422679 20
My question is would it be acceptable to change my data load calls in my
examples and the vignette to reduce the runtime or is this against best
practice and should I look for improvements elsewhere? I ask because I feel
I'm running out of easy options at reducing the overall runtime.
Kind regards,
Alan.
________________________________
From: Martin Morgan <mtmorgan.b...@gmail.com>
Sent: 22 March 2021 18:17
To: Kern, Lori <lori.sheph...@roswellpark.org>; Murphy, Alan E <
a.mur...@imperial.ac.uk>; bioc-devel@r-project.org <
bioc-devel@r-project.org>
Subject: Re: [Bioc-devel] Methods to speed up R CMD Check
(sticking bioc-devel back in the recipient list so others can learn /
improve / disagree with this suggestion.)
my suggestion was to memorize the function in your package, not in the
example. Examples are not run independently, but collated into a single
file (EWCR-Ex.R in the EWCR.Rcheck directory, after running R CMD check)
and sourced. And the suggestion was not to solve the problem of examples
running slowly, but avoiding repeatedly calculating the same value. For
instance, from Hervé’s email ewceData::tt_alzh could be memorized in the
package. The first call would take several seconds, but subsequent calls
would be instantaneous. But as Hervé says that function should be cleaned
up anyway so that 'tricks' like memorization might not be necessary.
From: "Murphy, Alan E" <a.mur...@imperial.ac.uk>
Date: Monday, March 22, 2021 at 12:37 PM
To: Martin Morgan <mtmorgan.b...@gmail.com>
Subject: Re: [Bioc-devel] Methods to speed up R CMD Check
Hey Martin,
Thanks for the suggestion but how would I go about using this, let's say,
for the examples? If I redefine the memoise function in each example (as it
won't otherwise exist) would this not take the same amount of time?
Kind regards,
Alan.
From: Martin Morgan <mtmorgan.b...@gmail.com>
Sent: 22 March 2021 13:34
To: Kern, Lori <lori.sheph...@roswellpark.org>; Murphy, Alan E <
a.mur...@imperial.ac.uk>; bioc-devel@r-project.org <
bioc-devel@r-project.org>
Subject: Re: [Bioc-devel] Methods to speed up R CMD Check
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if your examples repeatedly calculate the same thing, and this is also
typical of how users use your package, it might make sense to 'memoise' key
functions in your package https://cran.r-project.org/package=memoise
Martin
On 3/22/21, 7:41 AM, "Bioc-devel on behalf of Kern, Lori" <
bioc-devel-boun...@r-project.org on behalf of
lori.sheph...@roswellpark.org> wrote:
If your data is using ExperimentHub, it should already be caching the
downloaded data. Once it is downloaded once, it should be using the cached
download for subsequent calls to the hub. We will investigate to ensure
that the caching mechanism is functioning properly on all of our
Bioconductor builders.
Lori Shepherd
Bioconductor Core Team
Roswell Park Comprehensive Cancer Center
Department of Biostatistics & Bioinformatics
Elm & Carlton Streets
Buffalo, New York 14263
________________________________
From: Bioc-devel <bioc-devel-boun...@r-project.org> on behalf of
Murphy, Alan E <a.mur...@imperial.ac.uk>
Sent: Monday, March 22, 2021 5:38 AM
To: bioc-devel@r-project.org <bioc-devel@r-project.org>
Subject: [Bioc-devel] Methods to speed up R CMD Check
Hi all,
I am working on the development of [EWCE](
https://secure-web.cisco.com/1uG0LGgCjdg85VowwaeRHk2fMjXFkOtQWsgL8p2MQD2j2PZFh_tqvJWaCHJfArA8O4B2WLG1JOwn31NISgSrPW3syUdiPlWNi7cHAMCWKZUQ8d9RrlR-d81LDXXx0xtfCI5ZjjTyFS2xxM2tDea27Y51bWk4Y7jpSnC8Bx768AHBeaJAg3YAK_HTxR6hMzFW99X6Pg8bETgPYi92ccneqdgAJcDBIdfwZnd9OMaM4JS0kY9kYT3F58ho2jM_k0n6EqMzhuXl3HEM7uneL7twMxTTxSZ-vFC1U1eFSkAr0sp38AyD3g6gTbf-vUbghaGV-JBKoybZto3ZDmHhs8OE6cQ/https%3A%2F%2Fgithub.com%2FNathanSkene%2FEWCE)
but have hit an issue with R CMD check's runtime. I have been informed this
test needs to be completed in 15 minutes but mine is currently running in
~24 minutes and I am looking for methods to speed this up. The main
culprits for the runtime issue are:
checking examples (5m 49.8s)
Running �testthat.R� [308s/469s] (7m 49.1s)
checking for unstated dependencies in vignettes (7m 49.4s)
checking re-building of vignette outputs (5m 12s)
With the exception of using smaller datasets which I will consider
myself, is there known ways of speeding these up? EWCE derives data from an
Experimenthub package [ewceData](
https://secure-web.cisco.com/1r4B8NJkUGCpdQsdBW8RWLwGvwEA9TlvXY7VUYgAKS-TBmT7s-6a3zMLfS6rXRVUUxG4x8SCYzXUXZKYMtZ_ysyEzk56tVxfvju-9mo6l11KLQ7CzEpFMikVqdyT25f0G3SQK5u9b0_5JK2gNhR4l0j_5_b_B-uPxzyFF0jtLCZFHKW2-pD7e2P4RVOfbgRALwBXM-hQvhcoaxxrR8tWz3JLjKxWqNIhTrsJdATsAnUO0EnQ5U8JNXClmS9LvWwyTf-0ZqokYXTkjdfYDUAm6KiAGNJo4oX99GUBQZllyiIDprF07KeqjsMNMg4dbmMh0t6jl-UEiUaV3j1xRG8UyyA/https%3A%2F%2Fgithub.com%2Fneurogenomics%2FewceData)
for its examples, tests and vignette. This is run repeatedly and I have
noted this takes a significant amount of time to load a dataset. Is there
anyway of caching the datasets for all the checks or more generally of
speeding this up?
I have heard of the use of [long tests](
http://secure-web.cisco.com/1yfwFXFFfUKBuFTwUeuS8XGYbh53YduG9ZGKMVmVU9Yrgxg4DbKA0_prEIOCNcgc8uANWYzUw115x_8njawa33mjqM5ZBEvTPTJhmXRzttl1eaRVu3Pa0FTA-d-wPRK3Xxa4miiXob79k_exN0isifYlHPTK7WRxh9_LbFye17PwVVOGsfxjEFKi8WF27D6LWJynf8k-L7iEqB2MSDkf_1zWmfA2qJByna147_Jkaa-nLx9FFl4VhsosBoNDE_qnC939XrCLLCT7RgV0jPukrVdahccxXfT6bgtGBR8ZKfj25BoCeE1_hTJXFgGP0CGmegMYqqmsbd3pGTbo63vTW-A/http://bioconductor.org/developers/how-to/long-tests/)
which aren't run daily by Bioconductor but are these still checked in R CMD
Check? Is there any other way to exclude my tests from the R CMD Check
given they aren't a necessity from Bioconductor?
Does checking for unstated dependencies in vignettes have a long
runtime based on the number of package dependencies? If I just export
specific functions from packages will this check time reduce?
Lastly, is there any way to get an exception of the 15 minute maximum?
I may be ill-informed but is the max time for packages on Bioconductor's
daily check 40 minutes which my code in its current state would complete by.
Kind regards,
Alan.
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