Survive torch installed without lantern

R/vram.R

@@ -58,8 +58,13 @@ is_blackwell_gpu <- function() {
         return(mb / 1024)
     }
 
-    # Fallback: check if CUDA available but can't determine VRAM
-    if (torch::cuda_is_available()) {
+    # Fallback: check if CUDA available but can't determine VRAM.
+    # cuda_is_available() ERRORS (not FALSE) when torch is installed
+    # without its lantern binaries - fresh installs, win-builder, CRAN -
+    # and this branch is exactly where those machines land, since they
+    # have no nvidia-smi either. Probe soft, same as is_blackwell_gpu().
+    if (isTRUE(tryCatch(torch::cuda_is_available(),
+                        error = function(e) FALSE))) {
         # Conservative estimate - assume 8GB if we can't detect
         message("Could not detect VRAM via nvidia-smi; assuming 8 GB.")
         return(8)

cran-comments.md

@@ -18,9 +18,11 @@ addressed:
 ## Test environments
 
 * Ubuntu 24.04 (local), R 4.6.x: R CMD check --as-cran
-* Windows 10, R 4.6.0 (full test suite against installed torch backend)
-* Windows 10, R-devel, torch installed without its lantern backend
-  (tests skip gracefully)
+* Windows 10, R 4.6.0 and R-devel: R CMD check --as-cran, with torch's
+  lantern backend installed (so the full suite runs)
+* Ubuntu, with lantern deliberately absent, to exercise the path
+  win-builder and CRAN take: examples and tests skip gracefully rather
+  than erroring
 * win-builder, R-devel
 
 ## R CMD check results
@@ -71,4 +73,6 @@ package.
   under tools::R_user_dir("diffuseR", "data").
 * torch is in Imports; all tests, examples, and vignette code degrade
   gracefully when torch's backend (lantern) is not installed, as on
-  win-builder.
+  win-builder. Note that `torch::cuda_is_available()` raises an error
+  rather than returning FALSE in that state, so the package probes it
+  through `tryCatch()` everywhere it is reachable without a GPU.

inst/tinytest/test_resident.R

@@ -19,8 +19,19 @@ fake_pipeline <- function() {
               class = "test_pipeline")
 }
 
+# Everything from here to the "state machine" section builds real
+# nn_modules or torch dtypes, so it needs a working lantern. torch
+# installed WITHOUT its lantern binaries is the normal state on
+# win-builder and CRAN, and there `torch::nn_linear()` errors rather
+# than returning. The state-machine sections below are pure R and run
+# everywhere.
+have_torch <- requireNamespace("torch", quietly = TRUE) &&
+    torch::torch_is_installed()
+
 # --- component discovery ----------------------------------------------------------
 
+if (have_torch) {
+
 pipe <- fake_pipeline()
 comps <- diffuseR:::.resident_components(pipe)
 expect_equal(sort(names(comps)), c("decoder", "text_encoder", "transformer"))
@@ -40,6 +51,8 @@ expect_equal(diffuseR:::.dtype_bytes(torch::torch_bfloat16()), 2)
 expect_equal(diffuseR:::.dtype_bytes(torch::torch_uint8()), 1)
 expect_equal(diffuseR:::.dtype_bytes(torch::torch_int64()), 8)
 
+} # end have_torch
+
 # --- byte formatting --------------------------------------------------------------
 
 expect_equal(diffuseR:::.fmt_gb(0), "0 GB")
@@ -140,7 +153,7 @@ expect_equal(resident_status(u)$state, "unloaded")
 
 # --- CUDA round trip --------------------------------------------------------------
 
-if (at_home() && torch::cuda_is_available()) {
+if (have_torch && at_home() && torch::cuda_is_available()) {
     pipe <- fake_pipeline()
     staging <- diffuseR:::.resident_pin(pipe, verbose = FALSE)
     expect_equal(sort(names(staging)),