similar to: TensorFlow in R

On Tue, Sep 10, 2019 at 1:40 PM David Greene via llvm-dev < llvm-dev at lists.llvm.org> wrote: > Renato Golin via llvm-dev <llvm-dev at lists.llvm.org> writes: > > > But perhaps more importantly, as Hal states clearly, is the need for > > an official specification, similar to the one for LLVM IR, as well as > > a formal document with the expected semantics into

Hi all, The TensorFlow team at Google has been leading the charge to build a new set of compiler infrastructure, known as the MLIR project <https://github.com/tensorflow/mlir>. The initial focus has been on machine learning infrastructure, high performance accelerators, heterogeneous compute, and HPC-style computations. That said, the implementation and design of this infrastructure is

Overall, I think it will be a good move. Maintenance wise, I'm expecting the existing community to move into LLVM (if not all in already), so I don't foresee any additional costs. Though, Hal's points are spot on... On Mon, 9 Sep 2019 at 18:47, Finkel, Hal J. via llvm-dev <llvm-dev at lists.llvm.org> wrote: > 3. As a specific example of the above, the current development

On Wed, Sep 11, 2019 at 1:54 PM David Greene <greened at obbligato.org> wrote: > Mehdi AMINI <joker.eph at gmail.com> writes: > > > Of course by its nature, MLIR doesn't lend itself to concrete semantic > >> descriptions, though I would expect the affine dialect (and others) to > >> have documentation on par with the LLVM IR. > > > > >

On Mon, 9 Sep 2019 at 22:22, Chris Lattner <clattner at google.com> wrote: > Including a bunch of content, eg a full langref doc: > https://github.com/tensorflow/mlir/blob/master/g3doc/LangRef.md Thanks Chris, that looks awesome! This one could perhaps be improved with time: https://github.com/tensorflow/mlir/blob/master/g3doc/ConversionToLLVMDialect.md Which I think was Hal's

Hi All, I'm planning to submit my first package to R, and although I read all the documentation, I'm not very clear on the following 2 items, from which I'd appreciate your guidance: 1)I understand it is suggested to use the R dev version to build the package. Which one specifically should I use to build a package on a Mac OS? How about package dependencies, which version should I

Dear List, I'm trying to solve this simple optimization problem in R. The parameters are the exponents to the matrix mm. The constraints specify that each row of the parameter matrix should sum to 1 and their product to 0. I don't understand why the constraints are not satisfied at the solution. I must be misinterpreting how to specify the constrains somehow. library(alabama) ff <-

Dear List, The gbm package on Win 7 produces different results for the relative importance of input variables in R 32-bit relative to R 64-bit. Any idea why? Any idea which one is correct? Based on this example, it looks like the relative importance of 2 perfectly correlated predictors is "diluted" by half in 32-bit, whereas in 64-bit, one of these predictors gets all the importance

Dear R users, I know this issue came up in the list several times. I’m currently running R on 32-bit on Windows and due to memory limitation problems would like to move to a 64-bit environment. I’m exploring my options and would appreciate your expertise: 1) Windows 64-bit: Prof. Brian Ripley recently posted the experimental built of R for win 64-bit. I’ll appreciate any feedback on

Hi, I'm looking to move from Windows into a 64-bit Linux environment. Which is the best Linux Flavor to use within R? To install R on this environment, do I need to do any compiling? Thanks all! Axel. [[alternative HTML version deleted]]

Dear LLVM devs, My GSoC project this year is about alias analysis, and I wrote a short article describing what I've done during the summer: https://github.com/grievejia/GSoC2016/raw/master/writeup.pdf In the report you can find an overview of what the current status of CFL-AA. There are also some numbers in the end, but please take those numbers with a grain of salt as they were rather

Dear List, I'm trying to add a function that calls a Shiny App in my R package. The issue is that within my function, I'm creating objects that I'd like to pass to the app. For instance, from the example below, I'm getting "Error: object 'xs' not found". How can I pass "xs" explicitly to shinyApp()? *Under R directory:* myApp <- function(x, ...) {

Dear Axel, I've used environment for such problems. assign("xs", xs, envir = my.env) in the myApp function get("xs", envir = my.env) in the server function Best regards, ir. Thierry Onkelinx Statisticus/ Statiscian Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie &

Dear list, For a reason it would take me long to explain, I need to do something along the lines of what's shown below -- i.e., create an object from dplyr::summarise, and then evaluate it on a data frame. I know I could directly do: df %>% dplyr::summarise(x1_mean = mean(x1)) but this is not what I'm looking for. library(dplyr) df <- data.frame(x1 = rnorm(100), x2 =

Dear List, This is an embarrassing question, but I can seem to make this work…How do I change the font size on the xlab and on the numbers shown in the x-axis on the time series plot below. The arguments cex.lab and cex.axis do not seem to be 'passing' to the plot function. plot(ts(rnorm(100), start=2004, freq=12), ylab="RQI", xlab="My X lab",

Dear List, I have a wrapper function that creates a Shiny App, as illustrated below. I'd like to include the function myApp() inside a package. I'd appreciate your guidance here, as I could not find good instructions on this online. myApp <- function(x) { require(shiny) shinyApp( ui = fluidPage( sidebarLayout( sidebarPanel(sliderInput("n",

Thank you for your answer! I agree with you except for the 3 (Error) example and I realize now I should have started with that in the explanation. >From my point of view parLapply(cl = clu, X = 1:2, fun = fun, c = 1) shouldn't give an error. This could be easily avoided by using all the argument names in the custerApply call of parLapply which means changing, parLapply <-

Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of

Thank you Thierry. I'm trying to following your suggestion in the example below, but getting: Error in get("xs", envir = my.env) : object 'my.env' not found. library(shiny) library(shinydashboard) myApp <- function(x, ...) { xs <- scale(x) my.env <- new.env() assign("xs", xs, envir = my.env) shiny::runApp(app) } app = shinyApp( ui =

Hi! I recognized that the argument matching of clusterApply (and therefore parLapply) goes wrong when one of the arguments of the function is called "c". In this case, the argument "c" is used as cluster and the functions give the following error message "Error in checkCluster(cl) : not a valid cluster". Of course, "c" is for many reasons an unfortunate