在www.example.com上使用rstanposit.cloud

j8ag8udp  于 2023-06-19  发布在  其他
关注(0)|答案(1)|浏览(423)

欢迎posit.cloud有rstan软件包经验的用户反馈。
我正在尝试在RStudio Cloud会话中使用rstan。install.packages("rstan")工作正常,也阅读了库。
但是在这个玩具示例之后它返回了下面的错误

options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
x=rnorm(1000,5,1)
my_data=list(N=1000,x~x)
stancode = 'data{ int N; real x[N];} parameters{real mu;real sigma;} 
            model{ x ~ normal(mu,sigma);}'
fit = stan(model_code = stancode,data=my_data)

它返回此错误

Error in compileCode(f, code, language = language, verbose = verbose) : 
  /cloud/lib/x86_64-pc-linux-gnu-library/4.2/RcppEigen/include/Eigen/src/Core/DenseCoeffsBase.h:55:30: warning: ignoring attributes on template argument ‘Eigen::internal::packet_traits<double>::type’ {aka ‘__vector(2) double’} [-Wignored-attributes]   55 |                      >::type PacketReturnType;      |                              ^~~~~~~~~~~~~~~~g++: fatal error: Killed signal terminated program cc1pluscompilation terminated.make: *** [/opt/R/4.2.2/lib/R/etc/Makeconf:178: file189644bbcfc.o] Error 1
Error in sink(type = "output") : invalid connection``

我的会话信息:

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] rstan_2.21.8         ggplot2_3.4.1        StanHeaders_2.21.0-7

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10        pillar_1.8.1       compiler_4.2.2     prettyunits_1.1.1  tools_4.2.2        digest_0.6.31      pkgbuild_1.4.0    
 [8] evaluate_0.20      lifecycle_1.0.3    tibble_3.1.8       gtable_0.3.1       pkgconfig_2.0.3    rlang_1.0.6        cli_3.6.0         
[15] rstudioapi_0.14    yaml_2.3.7         parallel_4.2.2     xfun_0.37          loo_2.5.1          fastmap_1.1.0      gridExtra_2.3     
[22] withr_2.5.0        dplyr_1.1.0        knitr_1.42         generics_0.1.3     vctrs_0.5.2        stats4_4.2.2       grid_4.2.2        
[29] tidyselect_1.2.0   glue_1.6.2         inline_0.3.19      R6_2.5.1           processx_3.8.0     fansi_1.0.4        rmarkdown_2.20    
[36] callr_3.7.3        magrittr_2.0.3     htmltools_0.5.4    codetools_0.2-18   matrixStats_0.63.0 scales_1.2.1       ps_1.7.2          
[43] colorspace_2.1-0   utf8_1.2.3         RcppParallel_5.1.6 munsell_0.5.0      crayon_1.5.2
wztqucjr

wztqucjr1#

编译C++代码需要相当多的内存。您可以看到右上角的RAM Jmeter 在进程被终止之前变红。

如果你点击RAM表,你可以进入“资源”选项卡,在那里你可以更改RAM的数量。对于您的示例,使用4 GB的RAM似乎就足够了。
顺便说一句,parallel::detectCores()在这样的情况下工作得不好。更好地使用parallelly::availableCores(),这也考虑到了由喉痹造成的限制。您可以在“资源”选项卡中更改可用CPU的数量。

相关问题