I have this problem: I installed CUDA 10.1 update 1-it gives an error: “Black gem launch failed”. I reinstalled CUDA 10.0 and started getting the error “failed to load the dynamic library’ CUDART64_101.in DLL’”.
A GeForce GTX 1060 3GB video card is installed.
I have been trying to solve this problem for 2 days((( I dance with tambourines, a rabbit’s foot hangs around my neck - it Doesn’t help((
Question-why does the system ask cudart64_101.dll, although CUDA 10.0 is installed? but what about cudart64_100.dll? the system doesn’t need this file?
My OS: Windows 8.1
CUDA 10.0 installed (CUDA 10.1 was the same): nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 425.25 Driver Version: 425.25 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 106... WDDM | 00000000:08:00.0 On | N/A |
| 0% 40C P8 5W / 120W | 372MiB / 3072MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 752 C+G Insufficient Permissions N/A |
| 0 1924 C+G C:\Windows\Explorer.EXE N/A |
| 0 8156 C+G ...rogram Files\Microsoft VS Code\Code.exe N/A |
+-----------------------------------------------------------------------------
the error “Black gem launch failed” appears when I use keras-function: model.predict()
I did the cuda-installation according to the instructions:
https://stackoverflow.com/a/50862674/14390804
I compiled samples: deviceQuery, bandwidthTest and simpleCUBLAS. deviceQuery and simpleCUBLAS - pass ok. bandwidthTest - failed
print(tf.__version__): 2.3.1
I tried:
import tensorflow as tf
physical_devices = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
not working for me(