我可以通过conda更新cuda版本吗？
我可以通过conda更新cuda版本吗？
提问于 2021-08-03 17:46:45
我现在：
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 |
+-----------------------------------------------------------------------------+我想安装Tensorflow 2.3/2.4，所以我需要至少在Conda中将cuda升级到10.1。我知道如何在conda安装cudakit：
conda install cudatoolkit=10.1但这似乎还不够：
Status: CUDA driver version is insufficient for CUDA runtime version如果我想保留旧版本的cuda 10.0，我可以通过Conda将cuda更新为10.1吗？这不管用：
conda install cuda=10.1我正在使用Python3.8。如果我不能保持库达10.0，如何直接升级库达10.1与或不使用康达？最好我能升级到康达。
增添：
我安装了cudatoolkit=10.1，但库达的驱动程序仍然不好。我的conda env列表显示：
cudatoolkit 10.1.243 h6bb024c_0
tensorflow-gpu 2.3.0 pypi_0 pypi以下测试是好的：
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但下列测试失败：
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))错误信息：
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如果这个语句是正确的，那么为什么我的安装仍然很糟糕，因为我已经在Conda中安装了cudatoolkit=10.1：
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和cuda司机还不匹配吗？
回答 1
Stack Overflow用户
回答已采纳
发布于 2021-08-03 19:44:59
不，您不能通过conda更新GPU驱动程序，这就是支持CUDA10.1或其他更新的在你的情况下需要什么。请参阅这里
Anaconda要求用户最近安装了一个符合下表中版本要求的NVIDIA驱动程序。
(最新的表是这里)
如果您想安装GPU驱动程序，您可以安装一个较新的CUDA工具包，它将有一个新的GPU驱动程序(安装程序)与它捆绑在一起。或者您可以检索驱动程序这里并安装它。所谓更新的CUDA工具包，我指的是由NVIDIA提供的CUDA工具包安装程序，它们是可用的这里，而不是通过conda提供的。您不能通过conda进行驱动程序更新。
我建议您学习CUDA linux安装指南，因为用于安装前一个驱动程序(runfile或包管理器)的方法可能是您想要用于下一个驱动程序的方法。
作为另一种选择(例如，如果您没有或无法获得系统的管理员访问权限)，您可以调查CUDA 前向兼容性。(这也可能对兼容性感兴趣。)
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原文链接：
https://stackoverflow.com/questions/68640658
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