Well Done. Good Job.
On Windows 10 -
(base) C:\WINDOWS\system32>pip show numpy
Name: numpy
Version: 1.20.2
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
Author-email: None
License: BSD
Location: c:\programdata\anaconda3\lib\site-packages
Requires:
Required-by: tifffile, tensorflow, tensorboard, tables, statsmodels, spherical, seaborn, scipy, scikit-learn, scikit-image, quaternionic, PyWavelets, pytools, pyrr, pyopencl, pyerfa, patsy, pandas, opt-einsum, numpy-quaternion, numexpr, numba, mkl-random, mkl-fft, matplotlib, Keras-Preprocessing, imageio, imagecodecs, h5py, cupy-cuda112, Bottleneck, bokeh, blis, bkcharts, astropy
everything is installed and working now fine
On WSL2
mabd@LAPTOP-T8DQ9UK0:~$ pip3 show numpy
Name: numpy
Version: 1.19.5
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
Author-email: None
License: BSD
Location: /home/mabd/.local/lib/python3.6/site-packages
Requires:
Required-by: torchvision, torch, tensorflow-gpu, tensorboard, scipy, pytools, opt-einsum, numba, Keras, Keras-Preprocessing, h5py, cupy-cuda112
everything: tensorflow- pycuda -cupy - tensorflow+directml
and torch torchvision as you can see
The Key Solution use cudatools 11.1 in wsl2 not cudatools 11.3
here my
mabd@LAPTOP-T8DQ9UK0:~$ ./deviceQuery
./deviceQuery Starting…
CUDA Device Query (Runtime API) version (CUDART static linking)
Detected 1 CUDA Capable device(s)
Device 0: “NVIDIA GeForce GTX 1660 Ti with Max-Q Design”
CUDA Driver Version / Runtime Version 11.3 / 11.1
CUDA Capability Major/Minor version number: 7.5
Total amount of global memory: 6144 MBytes (6442450944 bytes)
(024) Multiprocessors, (064) CUDA Cores/MP: 1536 CUDA Cores
GPU Max Clock rate: 1335 MHz (1.34 GHz)
Memory Clock rate: 6001 Mhz
Memory Bus Width: 192-bit
L2 Cache Size: 1572864 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: 65536 bytes
Total amount of shared memory per block: 49152 bytes
Total shared memory per multiprocessor: 65536 bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1024
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: 2147483647 bytes
Texture alignment: 512 bytes
Concurrent copy and kernel execution: Yes with 2 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
Device supports Unified Addressing (UVA): Yes
Device supports Managed Memory: Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: Yes
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 = 11.3, CUDA Runtime Version = 11.1, NumDevs = 1
Result = PASS
mabd@LAPTOP-T8DQ9UK0:~$
mabd@LAPTOP-T8DQ9UK0:~$ ./concurrentKernels
[./concurrentKernels] - Starting…
GPU Device 0: “Turing” with compute capability 7.5
Detected Compute SM 7.5 hardware with 24 multi-processors
Expected time for serial execution of 8 kernels = 0.080s
Expected time for concurrent execution of 8 kernels = 0.010s
Measured time for sample = 0.013s
Test passed
mabd@LAPTOP-T8DQ9UK0:~$
mabd@LAPTOP-T8DQ9UK0:~$ ./bandwidthTest
[CUDA Bandwidth Test] - Starting…
Running on…
Device 0: NVIDIA GeForce GTX 1660 Ti with Max-Q Design
Quick Mode
Host to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 6.7
Device to Host Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 6.6
Device to Device Bandwidth, 1 Device(s)
PINNED Memory Transfers
Transfer Size (Bytes) Bandwidth(GB/s)
32000000 251.4
Result = PASS
NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.
Now, we can talk. I will make some benchmarks and see the difference.
Stay Tuned