Implement interface to control CPU and GPU setup

njtierney commented 2 years ago

As discussed by @goldingn and @cboettig in https://github.com/greta-dev/greta/issues/306

I thought it might be good to have an issue open on this discussion so I can knock it off as a task.

What we require from this are:

CPU use by default
Coarse level control of control over either CPU or GPU
Finer level control of using CPU or GPU on specific operations.

The default interface would be something like:

(@goldingn - I'm not sure if we need to specify this for extra_samples as well? )

draws <- mcmc(
  ...,
  compute_options = cpu_only()
)

Which would have the output:

running 4 chains simultaneously on up to 8 CPU cores

samples <- calculate(
  ...,
  compute_options = cpu_only()
)
extra_samples <- extra_samples(
  ...,
  compute_options = cpu_only()
)
opt_res <- opt(
  ...,
  compute_options = cpu_only()
)

With the idea being that you could turn on the GPU like so:

draws <- mcmc(
  ...,
  compute_options = gpu_only()
)

And allow for finer grained use like so:

beta <- normal(0, 1, dim = 10)
eta <- X %*% beta
m <- model(eta)

draws <- mcmc(
  m,
  ...,
  compute_options = compute_setup(gpu_operations = list(
    with_gpu(0, eta),
    with_gpu(1, some_other_op)
  ))
)

Which would have output like:

running 4 chains simultaneously on up to 8 CPU cores and 2 GPU devices
GPU execution enabled for the operation greta array 'eta' (matrix multiply)
CPU execution enabled for all other greta operations
GPU devices in use: 0 and 1

To approach this API, I think the first release should probably focus on just getting the cpu_only() and gpu_only() approaches, but that we should design this so we can extend it easily - as Carl and Nick G both said.

In terms of order of operations, where's what I think we need:

[x] compute_options argument for mcmc, calculate, opt, and extra_samples
[x] device_used() (or similar) function which returns the name and type of the current device used - e.g., CPU or GPU. We can use this to capture the device used and then set it up at the end with on.exit().
[x] cpu_only() function - uses CPU and returns to default state after operation
[x] gpu_only() function - uses GPU and returns to default state after operation

Reading https://www.tensorflow.org/guide/gpu

they say:

If a TensorFlow operation has both CPU and GPU implementations, by default, the GPU device is prioritized when the operation is assigned.

They also say:

Note: Use tf.config.list_physical_devices('GPU') to confirm that TensorFlow is using the GPU.

So if we can confirm that CPU and GPU are available, then we can "safely" (I think?) return to using GPU

goldingn commented 2 years ago

We could allow it to change for extra samples. But in the first instance, we could just force it to reuse what was used in the first place. I don't think many people will want to change the compute in between those.

njtierney commented 2 years ago

OK cool!

I have nearly got a first pass at this working for MCMC

However I get this error

Error in py_call_impl(callable, dots$args, dots$keywords) : 
  RuntimeError: Visible devices cannot be modified after being initialized

Which is I believe from trying to set the device on exit...need to think about how to handle this

the_default_device <- default_device()
  on.exit(
    set_device(the_default_device)
  )

goldingn commented 2 years ago

Interesting. Maybe the solution is to run the TF code in a with() context? Worth looking at examples of how this is used in TF code

njtierney commented 2 years ago

Got it working!

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply
library(tictoc)
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#> 
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#> 
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#> 
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#> 
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
#> ℹ greta is ready to use!

x <- normal(0,1)
m <- model(x)
#> Loaded Tensorflow version 2.9.2
tic()
draws_cpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = cpu_only())
#> running 4 chains simultaneously on up to 8 cores
#> 
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta:  6s              warmup ========                                 100/500 | eta:  3s              warmup ============                             150/500 | eta:  2s              warmup ================                         200/500 | eta:  2s              warmup ====================                     250/500 | eta:  1s              warmup ========================                 300/500 | eta:  1s              warmup ============================             350/500 | eta:  1s              warmup ================================         400/500 | eta:  0s              warmup ====================================     450/500 | eta:  0s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta:  0s            sampling ========                                 100/500 | eta:  0s            sampling ============                             150/500 | eta:  0s            sampling ================                         200/500 | eta:  0s            sampling ====================                     250/500 | eta:  0s            sampling ========================                 300/500 | eta:  0s            sampling ============================             350/500 | eta:  0s            sampling ================================         400/500 | eta:  0s            sampling ====================================     450/500 | eta:  0s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 2.795 sec elapsed

tic()
draws_gpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = gpu_only())
#> running 4 chains simultaneously on up to 8 cores
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta: 25s              warmup ========                                 100/500 | eta: 21s              warmup ============                             150/500 | eta: 17s              warmup ================                         200/500 | eta: 14s              warmup ====================                     250/500 | eta: 12s              warmup ========================                 300/500 | eta: 10s              warmup ============================             350/500 | eta:  7s              warmup ================================         400/500 | eta:  5s              warmup ====================================     450/500 | eta:  2s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta: 22s            sampling ========                                 100/500 | eta: 17s            sampling ============                             150/500 | eta: 16s            sampling ================                         200/500 | eta: 13s            sampling ====================                     250/500 | eta: 11s            sampling ========================                 300/500 | eta:  9s            sampling ============================             350/500 | eta:  7s            sampling ================================         400/500 | eta:  5s            sampling ====================================     450/500 | eta:  2s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 45.819 sec elapsed

library(coda)
#> 
#> Attaching package: 'coda'
#> 
#> The following object is masked from 'package:greta':
#> 
#>     mcmc
plot(draws_cpu)

plot(draws_gpu)

^{Created on 2022-08-10 by the reprex package (v2.0.1)}

Session info

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Remaining tasks

[x] Give objects some memory/slot of whether they used CPU or GPU
[x] Put in the printing message whether CPU or GPU is used
[x] Add tests for CPU and GPU usage

njtierney commented 2 years ago

OK, I got the message to display!

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply
library(tictoc)
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#> 
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#> 
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#> 
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#> 
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
#> ℹ greta is ready to use!

x <- normal(0,1)
m <- model(x)
#> Loaded Tensorflow version 2.9.2
tic()
draws_cpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = cpu_only())
#> running 4 chains simultaneously on up to 8 CPU cores
#> 
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta:  6s              warmup ========                                 100/500 | eta:  3s              warmup ============                             150/500 | eta:  2s              warmup ================                         200/500 | eta:  2s              warmup ====================                     250/500 | eta:  1s              warmup ========================                 300/500 | eta:  1s              warmup ============================             350/500 | eta:  1s              warmup ================================         400/500 | eta:  0s              warmup ====================================     450/500 | eta:  0s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta:  0s            sampling ========                                 100/500 | eta:  0s            sampling ============                             150/500 | eta:  0s            sampling ================                         200/500 | eta:  0s            sampling ====================                     250/500 | eta:  0s            sampling ========================                 300/500 | eta:  0s            sampling ============================             350/500 | eta:  0s            sampling ================================         400/500 | eta:  0s            sampling ====================================     450/500 | eta:  0s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 2.829 sec elapsed

tic()
draws_gpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = gpu_only())
#> running 4 chains simultaneously on up to 8 GPU cores
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta: 25s              warmup ========                                 100/500 | eta: 21s              warmup ============                             150/500 | eta: 18s              warmup ================                         200/500 | eta: 15s              warmup ====================                     250/500 | eta: 12s              warmup ========================                 300/500 | eta: 10s              warmup ============================             350/500 | eta:  7s              warmup ================================         400/500 | eta:  5s              warmup ====================================     450/500 | eta:  2s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta: 24s            sampling ========                                 100/500 | eta: 20s            sampling ============                             150/500 | eta: 16s            sampling ================                         200/500 | eta: 13s            sampling ====================                     250/500 | eta: 12s            sampling ========================                 300/500 | eta: 10s            sampling ============================             350/500 | eta:  8s            sampling ================================         400/500 | eta:  5s            sampling ====================================     450/500 | eta:  3s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 51.845 sec elapsed

^{Created on 2022-08-10 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-08-10 #> pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> callr 3.7.0 2021-04-20 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.25.0 2022-04-24 [1] CRAN (R 4.2.0) #> globals 0.15.0 2022-05-09 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> greta * 0.4.2.9000 2022-08-10 [1] local #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> parallelly 1.31.1 2022-04-22 [1] CRAN (R 4.2.0) #> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.6.1 2022-06-17 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0) #> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0) #> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.24-9000 2022-05-11 [1] Github (rstudio/reticulate@451fbff) #> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0) #> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0) #> tensorflow 2.9.0 2022-05-21 [1] CRAN (R 4.2.0) #> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.2.0) #> tfruns 1.5.0 2021-02-26 [1] CRAN (R 4.2.0) #> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0) #> tictoc * 1.0.1 2021-04-19 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> whisker 0.4 2019-08-28 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0) #> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ─ Python configuration ─────────────────────────────────────────────────────── #> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python #> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.8.dylib #> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2 #> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ] #> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/numpy #> numpy_version: 1.22.4 #> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/tensorflow #> #> NOTE: Python version was forced by use_python function #> #> ────────────────────────────────────────────────────────────────────────────── ```

It might be a bit of a hack, but these two are the relevant commits

https://github.com/greta-dev/greta/pull/534/commits/40b984b9f7c61dc1e2af97d4941e14771436bfc2 https://github.com/greta-dev/greta/pull/534/commits/094a5e5cff3bbd59413d8d361347df472bf13d85

goldingn commented 2 years ago

Great! For GPU it shouldn't list the number of CPU cores though. So maybe just put 'on GPU '?

njtierney commented 2 years ago

OK, how's this?

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply
library(tictoc)
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#> 
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#> 
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#> 
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#> 
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
#> ℹ greta is ready to use!

x <- normal(0,1)
m <- model(x)
#> Loaded Tensorflow version 2.9.2
tic()
draws_cpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = cpu_only())
#> running 4 chains simultaneously on each on up to 8 CPU cores
#> 
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta:  6s              warmup ========                                 100/500 | eta:  3s              warmup ============                             150/500 | eta:  2s              warmup ================                         200/500 | eta:  2s              warmup ====================                     250/500 | eta:  1s              warmup ========================                 300/500 | eta:  1s              warmup ============================             350/500 | eta:  1s              warmup ================================         400/500 | eta:  0s              warmup ====================================     450/500 | eta:  0s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta:  0s            sampling ========                                 100/500 | eta:  0s            sampling ============                             150/500 | eta:  0s            sampling ================                         200/500 | eta:  0s            sampling ====================                     250/500 | eta:  0s            sampling ========================                 300/500 | eta:  0s            sampling ============================             350/500 | eta:  0s            sampling ================================         400/500 | eta:  0s            sampling ====================================     450/500 | eta:  0s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 2.798 sec elapsed

tic()
draws_gpu <- mcmc(m, n_samples = 500, warmup = 500, compute_options = gpu_only())
#> running 4 chains simultaneously on GPU
#>     warmup                                            0/500 | eta:  ?s              warmup ====                                      50/500 | eta: 25s              warmup ========                                 100/500 | eta: 20s              warmup ============                             150/500 | eta: 17s              warmup ================                         200/500 | eta: 15s              warmup ====================                     250/500 | eta: 12s              warmup ========================                 300/500 | eta: 10s              warmup ============================             350/500 | eta:  7s              warmup ================================         400/500 | eta:  5s              warmup ====================================     450/500 | eta:  2s              warmup ======================================== 500/500 | eta:  0s          
#>   sampling                                            0/500 | eta:  ?s            sampling ====                                      50/500 | eta: 22s            sampling ========                                 100/500 | eta: 19s            sampling ============                             150/500 | eta: 16s            sampling ================                         200/500 | eta: 13s            sampling ====================                     250/500 | eta: 11s            sampling ========================                 300/500 | eta:  9s            sampling ============================             350/500 | eta:  7s            sampling ================================         400/500 | eta:  5s            sampling ====================================     450/500 | eta:  2s            sampling ======================================== 500/500 | eta:  0s
toc()
#> 47.299 sec elapsed

^{Created on 2022-08-10 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-08-10 #> pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> callr 3.7.0 2021-04-20 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.25.0 2022-04-24 [1] CRAN (R 4.2.0) #> globals 0.15.0 2022-05-09 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> greta * 0.4.2.9000 2022-08-10 [1] local #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> parallelly 1.31.1 2022-04-22 [1] CRAN (R 4.2.0) #> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.6.1 2022-06-17 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0) #> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0) #> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.24-9000 2022-05-11 [1] Github (rstudio/reticulate@451fbff) #> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0) #> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0) #> tensorflow 2.9.0 2022-05-21 [1] CRAN (R 4.2.0) #> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.2.0) #> tfruns 1.5.0 2021-02-26 [1] CRAN (R 4.2.0) #> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0) #> tictoc * 1.0.1 2021-04-19 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> whisker 0.4 2019-08-28 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0) #> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ─ Python configuration ─────────────────────────────────────────────────────── #> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python #> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.8.dylib #> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2 #> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ] #> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/numpy #> numpy_version: 1.22.4 #> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/tensorflow #> #> NOTE: Python version was forced by use_python function #> #> ────────────────────────────────────────────────────────────────────────────── ```

goldingn commented 2 years ago

The CPU one is mangled? Should be the same as before. GPU one looks good

njtierney commented 2 years ago

Blarg, here we go

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#> 
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#> 
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#> 
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#> 
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
#> ℹ greta is ready to use!

x <- normal(0,1)
m <- model(x)
#> Loaded Tensorflow version 2.9.2
draws_cpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = cpu_only())
#> running 4 chains simultaneously each on up to 8 CPU cores
#> 
#>     warmup                                             0/50 | eta:  ?s              warmup ========================================== 50/50 | eta:  0s          
#>   sampling                                             0/50 | eta:  ?s            sampling ========================================== 50/50 | eta:  0s
draws_gpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = gpu_only())
#> running 4 chains simultaneously on GPU
#>     warmup                                             0/50 | eta:  ?s              warmup ========================================== 50/50 | eta:  0s          
#>   sampling                                             0/50 | eta:  ?s            sampling ========================================== 50/50 | eta:  0s

^{Created on 2022-08-10 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-08-10 #> pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> callr 3.7.0 2021-04-20 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.25.0 2022-04-24 [1] CRAN (R 4.2.0) #> globals 0.15.0 2022-05-09 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> greta * 0.4.2.9000 2022-08-10 [1] local #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> parallelly 1.31.1 2022-04-22 [1] CRAN (R 4.2.0) #> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.6.1 2022-06-17 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0) #> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0) #> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.24-9000 2022-05-11 [1] Github (rstudio/reticulate@451fbff) #> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0) #> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0) #> tensorflow 2.9.0 2022-05-21 [1] CRAN (R 4.2.0) #> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.2.0) #> tfruns 1.5.0 2021-02-26 [1] CRAN (R 4.2.0) #> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> whisker 0.4 2019-08-28 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.31 2022-05-10 [1] CRAN (R 4.2.0) #> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ─ Python configuration ─────────────────────────────────────────────────────── #> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python #> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.8.dylib #> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2 #> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ] #> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/numpy #> numpy_version: 1.22.4 #> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/tensorflow #> #> NOTE: Python version was forced by use_python function #> #> ────────────────────────────────────────────────────────────────────────────── ```

goldingn commented 2 years ago

Sorry Nick, it's not 'each on up to', that's not true, has to be the same as before for CPU

njtierney commented 2 years ago

Ack, sorry about that - does this look right?

library(greta)
#> 
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#> 
#>     binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#> 
#>     %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#>     eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#>     tapply
greta_sitrep()
#> ℹ checking if python available
#> ✔ python (v3.8) available
#> 
#> ℹ checking if TensorFlow available
#> ✔ TensorFlow (v2.9.2) available
#> 
#> ℹ checking if TensorFlow Probability available
#> ✔ TensorFlow Probability (v0.17.0) available
#> 
#> ℹ checking if greta conda environment available
#> ✔ greta conda environment available
#> 
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#> 
#> ℹ greta is ready to use!

x <- normal(0,1)
m <- model(x)
#> Loaded Tensorflow version 2.9.2
draws_cpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = cpu_only())
#> running 4 chains simultaneously on up to 8 CPU cores
#> 
#>     warmup                                             0/50 | eta:  ?s              warmup ========================================== 50/50 | eta:  0s          
#>   sampling                                             0/50 | eta:  ?s            sampling ========================================== 50/50 | eta:  0s
draws_gpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = gpu_only())
#> running 4 chains simultaneously on GPU
#>     warmup                                             0/50 | eta:  ?s              warmup ========================================== 50/50 | eta:  0s          
#>   sampling                                             0/50 | eta:  ?s            sampling ========================================== 50/50 | eta:  0s

^{Created on 2022-08-11 by the reprex package (v2.0.1)}

Session info

``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.0 (2022-04-22) #> os macOS Monterey 12.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_AU.UTF-8 #> ctype en_AU.UTF-8 #> tz Australia/Perth #> date 2022-08-11 #> pandoc 2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.2.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.2.0) #> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.2.0) #> callr 3.7.0 2021-04-20 [1] CRAN (R 4.2.0) #> cli 3.3.0.9000 2022-06-15 [1] Github (r-lib/cli@31a5db5) #> coda 0.19-4 2020-09-30 [1] CRAN (R 4.2.0) #> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.2.0) #> crayon 1.5.1 2022-03-26 [1] CRAN (R 4.2.0) #> digest 0.6.29 2021-12-01 [1] CRAN (R 4.2.0) #> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.2.0) #> evaluate 0.15 2022-02-18 [1] CRAN (R 4.2.0) #> fansi 1.0.3 2022-03-24 [1] CRAN (R 4.2.0) #> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.0) #> fs 1.5.2 2021-12-08 [1] CRAN (R 4.2.0) #> future 1.25.0 2022-04-24 [1] CRAN (R 4.2.0) #> globals 0.15.0 2022-05-09 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> greta * 0.4.2.9000 2022-08-11 [1] local #> here 1.0.1 2020-12-13 [1] CRAN (R 4.2.0) #> highr 0.9 2021-04-16 [1] CRAN (R 4.2.0) #> hms 1.1.1 2021-09-26 [1] CRAN (R 4.2.0) #> htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.2.0) #> jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.2.0) #> knitr 1.39 2022-04-26 [1] CRAN (R 4.2.0) #> lattice 0.20-45 2021-09-22 [1] CRAN (R 4.2.0) #> lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.2.0) #> listenv 0.8.0 2019-12-05 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> Matrix 1.4-1 2022-03-23 [1] CRAN (R 4.2.0) #> parallelly 1.31.1 2022-04-22 [1] CRAN (R 4.2.0) #> pillar 1.7.0 2022-02-01 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> png 0.1-7 2013-12-03 [1] CRAN (R 4.2.0) #> prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.2.0) #> processx 3.6.1 2022-06-17 [1] CRAN (R 4.2.0) #> progress 1.2.2 2019-05-16 [1] CRAN (R 4.2.0) #> ps 1.7.1 2022-06-18 [1] CRAN (R 4.2.0) #> purrr 0.3.4 2020-04-17 [1] CRAN (R 4.2.0) #> R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.2.0) #> R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.2.0) #> R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> Rcpp 1.0.9 2022-07-08 [1] CRAN (R 4.2.0) #> reprex 2.0.1 2021-08-05 [1] CRAN (R 4.2.0) #> reticulate 1.24-9000 2022-05-11 [1] Github (rstudio/reticulate@451fbff) #> rlang 1.0.4 2022-07-12 [1] CRAN (R 4.2.0) #> rmarkdown 2.14 2022-04-25 [1] CRAN (R 4.2.0) #> rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.2.0) #> rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.6 2021-11-29 [1] CRAN (R 4.2.0) #> stringr 1.4.0 2019-02-10 [1] CRAN (R 4.2.0) #> styler 1.7.0 2022-03-13 [1] CRAN (R 4.2.0) #> tensorflow 2.9.0 2022-05-21 [1] CRAN (R 4.2.0) #> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.2.0) #> tfruns 1.5.0 2021-02-26 [1] CRAN (R 4.2.0) #> tibble 3.1.7 2022-05-03 [1] CRAN (R 4.2.0) #> utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0) #> vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.2.0) #> whisker 0.4 2019-08-28 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.32.1 2022-08-11 [1] https://yihui.r-universe.dev (R 4.2.0) #> yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0) #> #> [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ─ Python configuration ─────────────────────────────────────────────────────── #> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python #> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.8.dylib #> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2 #> version: 3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:16) [Clang 12.0.1 ] #> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/numpy #> numpy_version: 1.22.4 #> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.8/site-packages/tensorflow #> #> NOTE: Python version was forced by use_python function #> #> ────────────────────────────────────────────────────────────────────────────── ```

goldingn commented 2 years ago

That looks right to me! I guess you could check the CPU one against the snapshot?

njtierney commented 1 year ago

For the time being we have the interfaces

x <- normal(0,1)
m <- model(x)
draws_cpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = cpu_only())
draws_gpu <- mcmc(m, n_samples = 50, warmup = 50, compute_options = gpu_only())

But in the future we might explore more complex interfaces discussed by @cboettig and @goldingn here https://github.com/greta-dev/greta/issues/306

greta-dev / greta

Implement interface to control CPU and GPU setup #545