Thank you for taking the time to respond; I really appreciate it. I want to clarify about the new environment: should I uninstall Python and reinstall it? Or is it enough to create a virtual environment? As for the environment, I set it up twice—once with pip and once with Anaconda. I installed it using the command from the website, but during testing, I received a message saying that there is no graphics card.
Or do I need to uninstall Python?
To understand the problem, I will show the test results I obtained using CUDA.
C:\Users\ASROCK>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_May__3_19:00:59_Pacific_Daylight_Time_2022
Cuda compilation tools, release 11.7, V11.7.64
Build cuda_11.7.r11.7/compiler.31294372_0
C:\Users\ASROCK>where nvcc
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\bin\nvcc.exe
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “NVIDIA GeForce GTX 1050 Ti”
CUDA Driver Version / Runtime Version 12.7 / 11.7
CUDA Capability Major/Minor version number: 6.1
Total amount of global memory: 4096 MBytes (4294705152 bytes)
( 6) Multiprocessors, (128) CUDA Cores/MP: 768 CUDA Cores
GPU Max Clock rate: 1392 MHz (1.39 GHz)
Memory Clock rate: 3504 Mhz
Memory Bus Width: 128-bit
L2 Cache Size: 1048576 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: zu bytes
Total amount of shared memory per block: zu bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 2048
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: zu bytes
Texture alignment: zu bytes
Concurrent copy and kernel execution: Yes with 5 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
CUDA Device Driver Mode (TCC or WDDM): WDDM (Windows Display Driver Model)
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 12.7, CUDA Runtime Version = 11.7, NumDevs = 1, Device0 = NVIDIA GeForce GTX 1050 Ti
Result = PASS
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.7\extras\demo_suite>bandwidthTest.exe > c:\1\222.txt
[CUDA Bandwidth Test] - Starting…
Running on…
Device 0: NVIDIA GeForce GTX 1050 Ti
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12643.2
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 12514.7
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(MB/s)
33554432 96155.2
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
System Information:
Graphics Card: NVIDIA GeForce GTX 1050 Ti
CUDA Driver Version: 12.7
CUDA Runtime Version: 11.7
Total Memory: 4096 MB
Testing Results:
Device Query:
Confirmed that the graphics card is functioning correctly and supports CUDA.
All information about the graphics card specifications was successfully retrieved.
Bandwidth Test:
Host to Device: 12,643.2 MB/s
Device to Host: 12,514.7 MB/s
Device to Device: 96,155.2 MB/s
The results show that the data transfer bandwidth between the host and device is within the normal range, indicating good system performance.
Conclusions:
Your system fully supports CUDA and is functioning correctly.
The data transfer bandwidth is at an acceptable level, making your graphics card suitable for computationally intensive tasks.
You can use CUDA for developing and running applications optimized for your graphics card.
I express my deep gratitude for the attention and time you have given.
