In that event, you can explicitly constrain its version (say 10.0): conda install some_gpu_package cudatoolkit=10.0 It's worth mentioning that the installed cudatoolkit does not always match your driver. The system's CUDA Toolkit, if there's any, is by default ignored due to this linkage, unless the package (such as Numba) has its own way to look up CUDA libraries in runtime. Then, GPU packages are compiled, linked to cudatoolkit, and packaged, which is the reason you only need the CUDA driver to be installed and nothing else. The latter is packed in the cudatoolkit package, is made as a run-dependency (in conda's terminology), and is installed when you install GPU packages like PyTorch. It is made in such a way that nvcc and friends are split from the rest of runtime libraries (cuFFT, cuSPARSE, etc) in CUDA Toolkit. In general, GPU packages on Anaconda/Conda-Forge are built using Anaconda's new CUDA compiler toolchain. In this case nvcc is not installed still it works fine. Tensorflow and pytorch can be installed directly through anaconda without explicitly downloading the cudatoolkit from nvidia. This isn't really answering the original question, but the follow up ones: 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. The reason i am asking this question is because, i am getting an error : CUBLAS_STATUS_NOT_INITIALIZED when i am running a deep learning model. Why is it not able to find cudatoolkit 10.2? I was able to install using pip install cupy-cuda101 which is for cuda 10.1. ERROR: Could not find a version that satisfies the requirement cupy-cuda102 (from versions: none)ĮRROR: No matching distribution found for cupy-cuda102 ![]() I get the following error when i try to do it. In env3 which has cudatoolkit version 10.2.89, i tried installing cupy library using the command pip install cupy-cuda102. ![]() If so why is it same in all the enviroments? Is the cuda version shown above is same as cuda toolkit version? | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. When i do nvidia-smi i get the following output no matter which environment i am in +-+ I found these by running conda list on each environment. I have multiple enviroments of anaconda with different cuda toolkits installed on them.
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