nvidia-cuda-nvcc-cu12>=12.5.82,<13.0
nvidia-cuda-nvrtc-cu12>=12.5.82,<13.0
nvidia-cuda-runtime-cu12>=12.5.82,<13.0
# The upper bound is set for the CUDNN API compatibility.
# See
# https://docs.nvidia.com/deeplearning/cudnn/backend/latest/developer/forward-compatibility.html#cudnn-api-compatibility
nvidia-cudnn-cu12>=9.3.0.75,<10.0
nvidia-cufft-cu12>=11.2.3.61,<12.0
nvidia-curand-cu12>=10.3.6.82,<11.0
nvidia-cusolver-cu12>=11.6.3.83,<12.0

            if (stage.specificBuilds.contains(SpecificBuild.SanityCheck)) {
                buildReportTab(
                    "API Compatibility Report",
                    "$HIDDEN_ARTIFACT_DESTINATION/report-architecture-test-binary-compatibility-report.html",
                )
                buildReportTab("Incubating APIs Report", "incubation-reports/all-incubating.html")
            }

      - run: ./gradlew sanityCheck -DdisableLocalCache=true ${{ needs.build.outputs.sys-prop-args }}
      - name: Upload Compatibility Report
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: binary-compatibility-report
          path: testing/architecture-test/build/reports/binary-compatibility/report.html

  unit-test:
    name: "${{ matrix.bucket.name }} (Unit Test)"
    permissions:

---
title: "Go, Backwards Compatibility, and GODEBUG"
layout: article
---

<!--
This document is kept in the Go repo, not x/website,
because it documents the full list of known GODEBUG settings,
which are tied to a specific release.
-->

## Introduction {#intro}

Go's emphasis on backwards compatibility is one of its key strengths.
There are, however, times when we cannot maintain complete compatibility.

   
   **1. Installing **TensorFlow-GPU** (TF) prebuilt binaries**
   
   
   Make sure you are using compatible TF and CUDA versions.
   Please refer following TF version and CUDA version compatibility table.
   
   | TF  | CUDA |
   
   | :-------------: | :-------------: |
   
   | 2.5.0  | 11.2 |
   
   | 2.4.0  | 11.0 |
   
   | 2.1.0 - 2.3.0  | 10.1 |
   
   | 1.13.1 - 2.0  | 10.0  |

  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# Import tf nightly wheel built with numpy2 from PyPI in numpy1 env for testing.
# This aims to maintain TF compatibility with NumPy 1.x until 2025 b/361369076.
if [[ "$TFCI_WHL_NUMPY_VERSION" == 1 ]]; then
  # Uninstall tf nightly wheel built with numpy1.
  "$python" -m pip uninstall -y tf_nightly_numpy1

        subprojects/internal-build-reports/build/reports/incubation/all-incubating.html => incubation-reports
        testing/architecture-test/build/reports/binary-compatibility/report.html => binary-compatibility-reports
        build/reports/dependency-verification/** => dependency-verification-reports
        build/reports/problems/problems-report.html
        """.trimIndent()

				// here.
			} else if portRemoved(feature) {
				// okay.
			} else if featureSet[featureWithoutContext(feature)] {
				// okay.
			} else {
				fmt.Fprintf(w, "-%s\n", feature)
				ok = false // broke compatibility
			}
		case len(required) == 0 || (len(features) > 0 && required[0] > features[0]):
			newFeature := take(&features)
			fmt.Fprintf(w, "+%s\n", newFeature)
			ok = false // feature not in api/next/*
		default:

# Build with nvcc for CUDA and clang for host
build:cuda_nvcc --config=cuda
build:cuda_nvcc --action_env=TF_NVCC_CLANG="1"
build:cuda_nvcc --@local_config_cuda//:cuda_compiler=nvcc
# Old config for backward compatibility
build:nvcc_clang --config=cuda_nvcc

# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to

        try {
            final ACE[] aces = file.getSecurity(resolveSids);
            if (aces != null) {
                responseData.addMetaData(SMB_ACCESS_CONTROL_ENTRIES, aces); // backward compatibility
                final Set<SID> sidAllowSet = new HashSet<>();
                final Set<SID> sidDenySet = new HashSet<>();
                for (final ACE ace : aces) {
                    if (logger.isDebugEnabled()) {