I ran using just the CPU, to improve performance, I wish to run using GPU, but received the following error:
Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.
The complete message is:
File "/home/claudino/Projetos/OpenSource/stable-diffusion-tensorflow/stable_diffusion_tf/stable_diffusion.py", line 270, in get_models
diffusion_model.load_weights(diffusion_model_weights_fpath)
File "/home/claudino/miniconda3/envs/stable-diffusion/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/claudino/miniconda3/envs/stable-diffusion/lib/python3.10/site-packages/keras/backend.py", line 4302, in batch_set_value
x.assign(np.asarray(value, dtype=dtype_numpy(x)))
tensorflow.python.framework.errors_impl.InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.
Cant determine the cause.
My environment:
(stable-diffusion) $> lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 22.04.1 LTS
Release: 22.04
Codename: jamm
(stable-diffusion) $> nvidia-smi
Tue Jan 17 17:31:00 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| N/A 56C P8 7W / N/A | 208MiB / 6144MiB | 35% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1572 G /usr/lib/xorg/Xorg 109MiB |
| 0 N/A N/A 3293 C+G ...014073573827879945,131072 96MiB |
+-----------------------------------------------------------------------------+
I ran using just the CPU, to improve performance, I wish to run using GPU, but received the following error:
The complete message is:
Cant determine the cause.
My environment:
Cuda 11.2, tensorflow 2.10.0, cudnn 8.1.0