Hi Kieran,
Note that you can execute arbitrary code at load time by defining
an .onLoad() hook in your package. So you *could* put something
like this in your package:
.onUnload <- function(libpath)
{
if (!reticulate::py_module_available("tensorflow"))
tensorflow::install_tensorflow()
}
However, having things being automatically downloaded/installed
on the user machine at package load-time is not a good idea. There
are just too many things that can go wrong.
For example, I just tried to run tensorflow::install_tensorflow()
on my laptop (Ubuntu 16.04) and was successful only after the 3rd
attempt (I had to make some changes/adjustments to my system between
each attempt). And Debian Linux is probably the easiest target!
Also note that install.packages() tries to load the package at the
end of the installation when installing from source so if the
.onUnload() hook fails, install.packages() considers that
the installation of the package failed and it removes it.
Finally note that this installation needs to download hundreds of
Mb of Python stuff.
So this is probably the reasons why the authors of the tensorflow
CRAN package chose to separate installation of the tensorflow Python
module from the installation of the package itself. There are plenty
of good reasons for doing that.
What I would suggest instead is that you start your vignette with a
note reminding the user to run tensorflow::install_tensorflow() if
s/he didn't already do it. As a side note: I couldn't find a way to
programmatically figure out whether the tensorflow Python module is
already installed in the man page for tensorflow::install_tensorflow(),
I had to dig in the source code of the unit tests to find
reticulate::py_module_available("tensorflow")).
In addition, you could also start each of your functions that rely on
the tensorflow Python module with a check to see whether the module is
available, and fail gracefully (with an informative error message) if
it's not.
We'll figure out a way to install the tensorflow Python module on our
build machines.
Hope this helps,
H.
On 03/28/2018 09:23 AM, Kieran Campbell wrote:
Hi all,
Rstudio have released the Tensorflow package for R -
https://urldefense.proofpoint.com/v2/url?u=https-3A__tensorflow.rstudio.com_tensorflow_&d=DwICAg&c=eRAMFD45gAfqt84VtBcfhQ&r=BK7q3XeAvimeWdGbWY_wJYbW0WYiZvSXAJJKaaPhzWA&m=w2p-VnxwECq9u90RNv_B6yCOpXxDkcIPAjcgcpbEeBE&s=AchAIWmKzcnyw9VXJ7eH5M4dqnTAS0SACVMigCPusHk&e=
- and we have started
incorporating it into some of our genomics packages for the heavy
numerical computation.
We would ideally like these to be submitted to Bioconductor, but
there's a custom line required for Tensorflow installation in that
after calling
install.packages("tensorflow")
then Tensorflow must be installed via
tensorflow::install_tensorflow()
which would break package testing if tensorflow was simply imported
into the R package and wasn't already installed. Is there any way to
customise a package installation within Bioconductor to trigger the
tensorflow::install_tensorflow() ?
As more people use tensorflow / deep learning in genomics I can see
this being a problem so it would be good to have a solution in place.
Many thanks,
Kieran Campbell
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--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M1-B514
P.O. Box 19024
Seattle, WA 98109-1024
E-mail: hpa...@fredhutch.org
Phone: (206) 667-5791
Fax: (206) 667-1319
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