Can I update cuda version through conda?

Question

My current:

nvidia-smi
Wed Aug  4 01:40:39 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.79       Driver Version: 410.79       CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla V100-SXM2...  On   | 00000000:00:0C.0 Off |                    0 |
| N/A   34C    P0    37W / 300W |      0MiB / 16130MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  Tesla V100-SXM2...  On   | 00000000:00:0D.0 Off |                    0 |
| N/A   34C    P0    36W / 300W |      0MiB / 16130MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   2  Tesla V100-SXM2...  On   | 00000000:00:0E.0 Off |                    0 |
| N/A   33C    P0    39W / 300W |      0MiB / 16130MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   3  Tesla V100-SXM2...  On   | 00000000:00:0F.0 Off |                    0 |
| N/A   37C    P0    41W / 300W |      0MiB / 16130MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

I want to install Tensorflow 2.3/2.4, so I need to upgrade cuda to 10.1 at least in Conda. I know how to install cudakit in conda:

conda install cudatoolkit=10.1

But this seems not enough:

Status: CUDA driver version is insufficient for CUDA runtime version

If I want to keep the old version cuda 10.0, can I update cuda to 10.1 through Conda? This won't work:

conda install cuda=10.1

I am using Python 3.8. If I can't keep cuda 10.0, how to directly upgrade cuda to 10.1 with or without conda? It's best if I can upgrade in Conda.

ADDITION:

I installed cudatoolkit=10.1, but the cuda driver still not good. My conda env list shows:

cudatoolkit               10.1.243             h6bb024c_0  
tensorflow-gpu            2.3.0                    pypi_0    pypi

The following test is good:

import tensorflow as tf
2021-08-04 04:21:31.110443: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1

In [3]: print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
2021-08-04 04:21:34.499432: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcuda.so.1
2021-08-04 04:21:34.665738: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.666369: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 0 with properties: 
pciBusID: 0000:00:0c.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-04 04:21:34.666459: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.667017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 1 with properties: 
pciBusID: 0000:00:0d.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-04 04:21:34.667064: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.667613: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1716] Found device 2 with properties: 
pciBusID: 0000:00:0e.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.75GiB deviceMemoryBandwidth: 836.37GiB/s
2021-08-04 04:21:34.667644: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
2021-08-04 04:21:34.670275: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcublas.so.10
2021-08-04 04:21:34.672971: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcufft.so.10
2021-08-04 04:21:34.673378: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcurand.so.10
2021-08-04 04:21:34.676043: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusolver.so.10
2021-08-04 04:21:34.677370: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcusparse.so.10
2021-08-04 04:21:34.681850: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudnn.so.7
2021-08-04 04:21:34.681989: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.682604: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.683196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.683782: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.684353: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.684961: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:982] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-08-04 04:21:34.685513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1, 2
Num GPUs Available:  3

But the following test failed:

import tensorflow as tf
with tf.device('/gpu:0'):
    a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
    b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
    c = tf.matmul(a, b)

with tf.Session() as sess:
    print (sess.run(c))

The error message:

2021-08-04 04:27:30.934969: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1858] Adding visible gpu devices: 0, 1, 2
2021-08-04 04:27:30.935028: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
---------------------------------------------------------------------------
InternalError                             Traceback (most recent call last)
......
InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version

If this statement is true, why my installation is still bad, because I already installed cudatoolkit=10.1 in Conda:

If you want to install a GPU driver, you could install a newer CUDA toolkit, which will have a newer GPU driver (installer) bundled with it. 

cudatoolkit and cuda driver still not match?

Answer 1

No, you can't update the GPU driver via conda, and that is what is needed in your case to support CUDA 10.1 or something newer. See here:

Anaconda requires that the user has installed a recent NVIDIA driver that meets the version requirements in the table below.

(the up-to-date table is here)

If you want to install a GPU driver, you could install a newer CUDA toolkit, which will have a newer GPU driver (installer) bundled with it. Or you can retrieve a driver here and install it. By newer CUDA toolkit, I mean the CUDA toolkit installers provided by NVIDIA, which are available here, not via conda. You cannot do the driver update via conda.

I suggest you study the CUDA linux install guide, because the methodology used to install the previous driver (runfile or package manager) is probably the one you want to use for your next driver.

As an alternative (for example if you don't have or can't get admin access to the system), you can investigate CUDA forward compatibility. (This may also be of interest regarding compatibility.)