Based on your dockerfile, I built a container and launched it on my hardware with Quadro RTX 5000. I think the architecture is different. I'm sending the output. Can you tell me what to change in the build files to make it work for my architecture? I also have an Intel Xeon W-10885M processor, 4600 MHz, and he swears at it too.
==========
== CUDA ==
==========
CUDA Version 12.8.1
Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
root@1a0e81fcb860:/workspace# source /workspace/venv/bin/activate
(venv) root@1a0e81fcb860:/workspace# python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
2025-05-22 11:04:15.408282: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
AttributeError: 'MessageFactory' object has no attribute 'GetPrototype'
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1747911857.361172 31 gpu_device.cc:2429] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 7.5. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
W0000 00:00:1747911857.365960 31 gpu_device.cc:2429] TensorFlow was not built with CUDA kernel binaries compatible with compute capability 7.5. CUDA kernels will be jit-compiled from PTX, which could take 30 minutes or longer.
I0000 00:00:1747911857.504263 31 gpu_device.cc:2018] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 14215 MB memory: -> device: 0, name: Quadro RTX 5000 with Max-Q Design, pci bus id: 0000:01:00.0, compute capability: 7.5
F0000 00:00:1747911858.175191 31 random_op_gpu.h:247] Non-OK-status: GpuLaunchKernel(FillPhiloxRandomKernelLaunch<Distribution>, num_blocks, block_size, 0, d.stream(), key, counter, gen, data, size, dist)
Status: INTERNAL: no kernel image is available for execution on the device
*** Check failure stack trace: ***
@ 0x7f7446c913e4 absl::lts_20230802::log_internal::LogMessage::SendToLog()
@ 0x7f7446c90d5f absl::lts_20230802::log_internal::LogMessage::Flush()
@ 0x7f7446c91889 absl::lts_20230802::log_internal::LogMessageFatal::~LogMessageFatal()
@ 0x7f74308c7cc2 tensorflow::functor::FillPhiloxRandom<>::operator()()
@ 0x7f74308c2dce tensorflow::(anonymous namespace)::PhiloxRandomOp<>::Compute()
@ 0x7f7446e8ebaa tensorflow::BaseGPUDevice::Compute()
@ 0x7f7444e7c87b tensorflow::(anonymous namespace)::SingleThreadedExecutorImpl::Run()
@ 0x7f7444e48385 tensorflow::FunctionLibraryRuntimeImpl::RunSync()
@ 0x7f7444e53a04 tensorflow::ProcessFunctionLibraryRuntime::RunMultiDeviceSync()
@ 0x7f7444e59ad1 tensorflow::ProcessFunctionLibraryRuntime::RunSync()
@ 0x7f744048e7ba tensorflow::KernelAndDeviceFunc::Run()
@ 0x7f744043b55b tensorflow::EagerKernelExecute()
@ 0x7f744044534e tensorflow::ExecuteNode::Run()
@ 0x7f7440789976 tensorflow::EagerExecutor::SyncExecute()
@ 0x7f744043aef9 tensorflow::(anonymous namespace)::EagerLocalExecute()
@ 0x7f744043882a tensorflow::DoEagerExecute()
@ 0x7f744043c1c2 tensorflow::EagerExecute()
@ 0x7f7440032be7 tensorflow::EagerOperation::Execute()
@ 0x7f744048b23c tensorflow::CustomDeviceOpHandler::Execute()
@ 0x7f743de87925 TFE_Execute
@ 0x7f741aecfe82 TFE_Py_FastPathExecute_C()
@ 0x7f73f1dcaad3 pybind11::detail::argument_loader<>::call<>()
@ 0x7f73f1dcaa0f pybind11::cpp_function::initialize<>()::{lambda()#1}::__invoke()
@ 0x7f73f1da3327 pybind11::cpp_function::dispatcher()
@ 0x58208f (unknown)
@ 0x549185 _PyObject_MakeTpCall
@ 0x5d73c9 _PyEval_EvalFrameDefault
@ 0x5d58eb PyEval_EvalCode
@ 0x608a23 PyRun_StringFlags
@ 0x6b3e9e PyRun_SimpleStringFlags
@ 0x6bcb61 Py_RunMain
@ 0x6bc57d Py_BytesMain
@ 0x7f7447cab1ca (unknown)
@ 0x7f7447cab28b __libc_start_main
@ 0x657ce5 _start
Aborted (core dumped)
(venv) root@1a0e81fcb860:/workspace#
Based on your dockerfile, I built a container and launched it on my hardware with Quadro RTX 5000. I think the architecture is different. I'm sending the output. Can you tell me what to change in the build files to make it work for my architecture? I also have an Intel Xeon W-10885M processor, 4600 MHz, and he swears at it too.