我可以通过conda更新cuda版本吗？
Can I update cuda version through conda?
我现在的：
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，所以我至少需要在Conda中将cuda升级到10.1。我知道如何在 conda 中安装 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更新到10.1吗？这行不通：
conda install cuda=10.1
我正在使用 Python 3.8。如果我不能保留 cuda 10.0，如何使用或不使用 conda 直接将 cuda 升级到 10.1？能在conda里升级就更好了
添加：
我安装了cudatoolkit=10.1，但是cuda驱动还是不行。我的 conda env 列表显示：
cudatoolkit 10.1.243 h6bb024c_0
tensorflow-gpu 2.3.0 pypi_0 pypi
下面的测试不错：
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
但以下测试失败：
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
如果这个说法是正确的，为什么我的安装还是不好，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 驱动仍然不匹配？
不行，你不能通过conda更新GPU驱动，那就是 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.
（最新的 table 是 here）
如果您想安装 GPU 驱动程序，您可以安装更新的 CUDA 工具包，它会捆绑更新的 GPU 驱动程序（安装程序）。或者您可以检索驱动程序 here and install it. By newer CUDA toolkit, I mean the CUDA toolkit installers provided by NVIDIA, which are available here，而不是通过 conda。您不能通过 conda 更新驱动程序。
我建议您学习 CUDA linux install guide，因为用于安装上一个驱动程序（运行文件或程序包管理器）的方法可能是您要用于下一个驱动程序的方法。
作为替代方案（例如，如果您没有或无法获得对系统的管理员访问权限），您可以调查 CUDA forward compatibility. ( 也可能对兼容性感兴趣。）
我现在的：
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，所以我至少需要在Conda中将cuda升级到10.1。我知道如何在 conda 中安装 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更新到10.1吗？这行不通：
conda install cuda=10.1
我正在使用 Python 3.8。如果我不能保留 cuda 10.0，如何使用或不使用 conda 直接将 cuda 升级到 10.1？能在conda里升级就更好了
添加：
我安装了cudatoolkit=10.1，但是cuda驱动还是不行。我的 conda env 列表显示：
cudatoolkit 10.1.243 h6bb024c_0
tensorflow-gpu 2.3.0 pypi_0 pypi
下面的测试不错：
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
但以下测试失败：
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
如果这个说法是正确的，为什么我的安装还是不好，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 驱动仍然不匹配？
不行，你不能通过conda更新GPU驱动，那就是
Anaconda requires that the user has installed a recent NVIDIA driver that meets the version requirements in the table below.
（最新的 table 是 here）
如果您想安装 GPU 驱动程序，您可以安装更新的 CUDA 工具包，它会捆绑更新的 GPU 驱动程序（安装程序）。或者您可以检索驱动程序 here and install it. By newer CUDA toolkit, I mean the CUDA toolkit installers provided by NVIDIA, which are available here，而不是通过 conda。您不能通过 conda 更新驱动程序。
我建议您学习 CUDA linux install guide，因为用于安装上一个驱动程序（运行文件或程序包管理器）的方法可能是您要用于下一个驱动程序的方法。
作为替代方案（例如，如果您没有或无法获得对系统的管理员访问权限），您可以调查 CUDA forward compatibility. (