Refresh CI and fix TensorFlow compatibility issues

.github/workflows/R-CMD-check.yaml

@@ -65,7 +65,7 @@ jobs:
 
     steps:
 
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v7
 
       - uses: r-lib/actions/setup-pandoc@v2
 

.github/workflows/awaiting-response-close.yml

@@ -14,7 +14,7 @@ jobs:
       pull-requests: write
 
     steps:
-    - uses: actions/stale@v4
+    - uses: actions/stale@v11
       with:
         repo-token: ${{ secrets.GITHUB_TOKEN }}
 

.github/workflows/awaiting-response-remove-label.yml

@@ -11,8 +11,8 @@ jobs:
       - name: Remove 'awaiting response' label and maybe reopen
         if: contains(github.event.issue.labels.*.name, 'awaiting response')
         run: |
-          gh issue edit $ISSUE --remove-label 'awaiting response'
-          gh issue reopen $ISSUE
+          gh issue edit "$ISSUE" --remove-label 'awaiting response'
+          gh issue reopen "$ISSUE"
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
           ISSUE: ${{ github.event.issue.html_url }}

DESCRIPTION

@@ -49,7 +49,7 @@ Imports:
     lifecycle
 Roxygen: list(markdown = TRUE)
 Suggests:
-    testthat (>= 2.1.0),
+    testthat (>= 3.1.7),
     keras3,
     pillar,
     withr,

NEWS.md

@@ -1,5 +1,9 @@
 # tensorflow (development version)
 
+- `as_tensor()` preserves large numeric values when converting to `int64`,
+  `uint64`, or `uint32`, and avoids an integer overflow error on Windows.
+- `py_require_tensorflow()` no longer adds an obsolete NumPy constraint on
+  Windows, allowing TensorFlow to resolve with Python 3.13.
 - Updated docs to recommend `py_require_tensorflow()` for TensorFlow setup (#627).
 - Fixed `tensorboard()` startup with recent TensorBoard versions (#626).
 

R/generics.R

@@ -824,7 +824,9 @@ as_tensor.double <- function(x, dtype = NULL, ..., name = NULL) {
       # tf.dtypes.saturate_cast() is better, but still can be surprising.
       # Users must opt in to saturate cast (or overflow cast)
       # by casting in a separate call after converting to tensor.
-      if (dtype$max <= .Machine$integer.max)
+      # Compare width and signedness without converting dtype$max to an R
+      # integer, which overflows for int64, uint64, and uint32.
+      if (dtype$size < 4L || (dtype$size == 4L && !dtype$is_unsigned))
         storage.mode(x) <- "integer"
 
       if (anyNA(x))

R/install.R

@@ -40,11 +40,10 @@
 #' if `tensorflow-metal` is installed. There are known issues with random number
 #' generators like `tf$random$stateless_uniform()`, likely others as well.
 #'
-#' - On Windows: `"tensorflow"` and `"numpy<2"` are declared. Note that
-#' TensorFlow GPU usage on Windows is no longer supported (Since TensorFlow
-#' 2.10). To use a GPU on windows, use TensorFlow via WSL. `"numpy<2"` is
-#' declared because at the time of this publishing, the pre-built binaries of
-#' `tensorflow` for Windows are not compatible with `numpy>2`.
+#' - On Windows: `"tensorflow"` is declared. TensorFlow's package metadata
+#' determines the compatible NumPy versions. TensorFlow 2.10 was the last
+#' release with GPU support on native Windows. To use a GPU with newer
+#' versions, use TensorFlow via WSL.
 #'
 #' `install_tensorflow()` remains available for users who want to explicitly
 #' create a persistent virtual environment. It creates a new virtual environment
@@ -411,10 +410,6 @@ get_py_requirements <- function(use_gpu = NA) {
       packages <- c("tensorflow", "tensorflow-metal")
     }
 
-  } else if (is_windows()) {
-
-    packages <- c(packages, "numpy<2")
-
   }
 
   list(packages = packages, python_version = python_version)

tests/testthat/test-as_tensor.R

@@ -97,6 +97,22 @@ test_that("as_tensor works", {
 })
 
 
+test_that("as_tensor preserves values outside R's integer range", {
+  skip_if_no_tensorflow()
+
+  for (dtype in c("int64", "uint64", "uint32")) {
+    value <- if (dtype == "uint32") 2^31 else 2^40
+    for (x in list(value, c(value, value + 1))) {
+      tensor <- as_tensor(x, dtype)
+      expect_identical(tensor$dtype$name, dtype)
+      expect_equal(as.numeric(tensor), x)
+    }
+  }
+
+  expect_equal(as.numeric(as_tensor(-2^40, "int64")), -2^40)
+})
+
+
 test_that("conversion of tf.string dtype tensors", {
 
   xx <- list(array("foo"),

tests/testthat/test-requirements.R

@@ -0,0 +1,18 @@
+test_that("TensorFlow requirements allow NumPy 2 on Windows", {
+  requirements <- callr::r(function() {
+    library(tensorflow)
+    testthat::local_mocked_bindings(
+      is_linux = function() FALSE,
+      is_mac_arm64 = function() FALSE,
+      is_windows = function() TRUE,
+      .package = "tensorflow"
+    )
+
+    reticulate::py_require("numpy>=2", python_version = "3.13", action = "set")
+    tensorflow::py_require_tensorflow()
+    reticulate::py_require()
+  })
+
+  expect_setequal(requirements$packages, c("numpy>=2", "tensorflow"))
+  expect_identical(requirements$python_version, "3.13")
+})