I have multiple enviroments of anaconda with different cuda toolkits installed on them.
env1 has cudatoolkit 10.0.130
env2 has cudatoolkit 10.1.168
env3 has cudatoolkit 10.2.89
I found these by running conda list on each environment.
When i do nvidia-smi i get the following output no matter which environment i am in
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 435.21 Driver Version: 435.21 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce RTX 208... Off | 00000000:01:00.0 On | N/A |
| 0% 42C P8 7W / 260W | 640MiB / 11016MiB | 2% Default |
+-------------------------------+----------------------+----------------------+
Is the cuda version shown above is same as cuda toolkit version? If so why is it same in all the enviroments?
In env3 which has cudatoolkit version 10.2.89, i tried installing cupy library using the command pip install cupy-cuda102 .
I get the following error when i try to do it.
ERROR: Could not find a version that satisfies the requirement cupy-cuda102 (from versions: none)
ERROR: No matching distribution found for cupy-cuda102
I was able to install using pip install cupy-cuda101 which is for cuda 10.1.
Why is it not able to find cudatoolkit 10.2?
The reason i am asking this question is because, i am getting an error cupy.cuda.cublas.CUBLASError: CUBLAS_STATUS_NOT_INITIALIZED when i am running a deep learning model. I am just wondering if cudatoolkit version has something to do with this error.Even if this error is not related to cudatoolkit version i want to know how anaconda uses cudatoolkit.
Is the cuda version shown above is same as cuda toolkit version?
It has nothing to do with CUDA toolkit versions.
If so why is it same in all the enviroments [sic]?
Because it is a property of the driver. It is the maximum CUDA version that the active driver in your system supports. And when you try and use CUDA 10.2, it is why nothing works. Your driver needs to be updated to support CUDA 10.2.