Python 实用宝典

Question 1

I have a plan to use distributed TensorFlow, and I saw TensorFlow can use GPUs for training and testing. In a cluster environment, each machine could have 0 or 1 or more GPUs, and I want to run my TensorFlow graph into GPUs on as many machines as possible.

I found that when running tf.Session() TensorFlow gives information about GPU in the log messages like below:

I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)

My question is how do I get information about current available GPU from TensorFlow? I can get loaded GPU information from the log, but I want to do it in a more sophisticated, programmatic way. I also could restrict GPUs intentionally using the CUDA_VISIBLE_DEVICES environment variable, so I don’t want to know a way of getting GPU information from OS kernel.

In short, I want a function like tf.get_available_gpus() that will return ['/gpu:0', '/gpu:1'] if there are two GPUs available in the machine. How can I implement this?

Question 2

There is an undocumented method called device_lib.list_local_devices() that enables you to list the devices available in the local process. (N.B. As an undocumented method, this is subject to backwards incompatible changes.) The function returns a list of DeviceAttributes protocol buffer objects. You can extract a list of string device names for the GPU devices as follows:

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

Note that (at least up to TensorFlow 1.4), calling device_lib.list_local_devices() will run some initialization code that, by default, will allocate all of the GPU memory on all of the devices (GitHub issue). To avoid this, first create a session with an explicitly small per_process_gpu_fraction, or allow_growth=True, to prevent all of the memory being allocated. See this question for more details.

Question 3

You can check all device list using following code:

from tensorflow.python.client import device_lib

device_lib.list_local_devices()

Question 4

There is also a method in the test util. So all that has to be done is:

tf.test.is_gpu_available()

and/or

tf.test.gpu_device_name()

Look up the Tensorflow docs for arguments.

Question 5

In TensorFlow 2.0, you can use tf.config.experimental.list_physical_devices('GPU'):

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    print("Name:", gpu.name, "  Type:", gpu.device_type)

If you have two GPUs installed, it outputs this:

Name: /physical_device:GPU:0   Type: GPU
Name: /physical_device:GPU:1   Type: GPU

From 2.1, you can drop experimental:

gpus = tf.config.list_physical_devices('GPU')

See:

Question 6

The accepted answer gives you the number of GPUs but it also allocates all the memory on those GPUs. You can avoid this by creating a session with fixed lower memory before calling device_lib.list_local_devices() which may be unwanted for some applications.

I ended up using nvidia-smi to get the number of GPUs without allocating any memory on them.

import subprocess

n = str(subprocess.check_output(["nvidia-smi", "-L"])).count('UUID')

Question 7

Apart from the excellent explanation by Mrry, where he suggested to use device_lib.list_local_devices() I can show you how you can check for GPU related information from the command line.

Because currently only Nvidia’s gpus work for NN frameworks, the answer covers only them. Nvidia has a page where they document how you can use the /proc filesystem interface to obtain run-time information about the driver, any installed NVIDIA graphics cards, and the AGP status.

/proc/driver/nvidia/gpus/0..N/information

Provide information about each of the installed NVIDIA graphics adapters (model name, IRQ, BIOS version, Bus Type). Note that the BIOS version is only available while X is running.

So you can run this from command line cat /proc/driver/nvidia/gpus/0/information and see information about your first GPU. It is easy to run this from python and also you can check second, third, fourth GPU till it will fail.

Definitely Mrry’s answer is more robust and I am not sure whether my answer will work on non-linux machine, but that Nvidia’s page provide other interesting information, which not many people know about.

Question 8

The following works in tensorflow 2:

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    print("Name:", gpu.name, "  Type:", gpu.device_type)

From 2.1, you can drop experimental:

    gpus = tf.config.list_physical_devices('GPU')

https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices

Question 9

I got a GPU called NVIDIA GTX GeForce 1650 Ti in my machine with tensorflow-gpu==2.2.0

Run the following two lines of code:

import tensorflow as tf
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

Output:

Num GPUs Available:  1

Question 10

Use this way and check all parts :

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_datasets as tfds


version = tf.__version__
executing_eagerly = tf.executing_eagerly()
hub_version = hub.__version__
available = tf.config.experimental.list_physical_devices("GPU")

print("Version: ", version)
print("Eager mode: ", executing_eagerly)
print("Hub Version: ", h_version)
print("GPU is", "available" if avai else "NOT AVAILABLE")

Question 11

Ensure you have the latest TensorFlow 2.x GPU installed in your GPU supporting machine, Execute the following code in python,

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf 

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

Will get an output looks like,

2020-02-07 10:45:37.587838: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2020-02-07 10:45:37.588896: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0, 1, 2, 3, 4, 5, 6, 7 Num GPUs Available: 8

Question 12

I would like to know if pytorch is using my GPU. It’s possible to detect with nvidia-smi if there is any activity from the GPU during the process, but I want something written in a python script.

Is there a way to do so?

Question 13

This is going to work :

In [1]: import torch

In [2]: torch.cuda.current_device()
Out[2]: 0

In [3]: torch.cuda.device(0)
Out[3]: <torch.cuda.device at 0x7efce0b03be0>

In [4]: torch.cuda.device_count()
Out[4]: 1

In [5]: torch.cuda.get_device_name(0)
Out[5]: 'GeForce GTX 950M'

In [6]: torch.cuda.is_available()
Out[6]: True

This tells me the GPU GeForce GTX 950M is being used by PyTorch.

Question 14

As it hasn’t been proposed here, I’m adding a method using torch.device, as this is quite handy, also when initializing tensors on the correct device.

# setting device on GPU if available, else CPU
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Using device:', device)
print()

#Additional Info when using cuda
if device.type == 'cuda':
    print(torch.cuda.get_device_name(0))
    print('Memory Usage:')
    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
    print('Cached:   ', round(torch.cuda.memory_reserved(0)/1024**3,1), 'GB')

Edit: torch.cuda.memory_cached has been renamed to torch.cuda.memory_reserved. So use memory_cached for older versions.

Output:

Using device: cuda

Tesla K80
Memory Usage:
Allocated: 0.3 GB
Cached:    0.6 GB

As mentioned above, using device it is possible to:

To move tensors to the respective device:
```
  torch.rand(10).to(device)
```
To create a tensor directly on the device:
```
  torch.rand(10, device=device)
```

Which makes switching between CPU and GPU comfortable without changing the actual code.

Edit:

As there has been some questions and confusion about the cached and allocated memory I’m adding some additional information about it:

torch.cuda.max_memory_cached(device=None)

Returns the maximum GPU memory managed by the caching allocator in bytes for a given device.
torch.cuda.memory_allocated(device=None)

Returns the current GPU memory usage by tensors in bytes for a given device.

You can either directly hand over a device as specified further above in the post or you can leave it None and it will use the current_device().

Additional note: Old graphic cards with Cuda compute capability 3.0 or lower may be visible but cannot be used by Pytorch!
Thanks to hekimgil for pointing this out! – “Found GPU0 GeForce GT 750M which is of cuda capability 3.0. PyTorch no longer supports this GPU because it is too old. The minimum cuda capability that we support is 3.5.”

Question 15

After you start running the training loop, if you want to manually watch it from the terminal whether your program is utilizing the GPU resources and to what extent, then you can simply use watch as in:

$ watch -n 2 nvidia-smi

This will continuously update the usage stats for every 2 seconds until you press ctrl+c

If you need more control on more GPU stats you might need, you can use more sophisticated version of nvidia-smi with --query-gpu=.... Below is a simple illustration of this:

$ watch -n 3 nvidia-smi --query-gpu=index,gpu_name,memory.total,memory.used,memory.free,temperature.gpu,pstate,utilization.gpu,utilization.memory --format=csv

which would output the stats something like:

Note: There should not be any space between the comma separated query names in --query-gpu=.... Else those values will be ignored and no stats are returned.

Also, you can check whether your installation of PyTorch detects your CUDA installation correctly by doing:

In [13]: import  torch

In [14]: torch.cuda.is_available()
Out[14]: True

True status means that PyTorch is configured correctly and is using the GPU although you have to move/place the tensors with necessary statements in your code.

If you want to do this inside Python code, then look into this module:

https://github.com/jonsafari/nvidia-ml-py or in pypi here: https://pypi.python.org/pypi/nvidia-ml-py/

Question 16

On the office site and the get start page, check GPU for PyTorch as below:

import torch
torch.cuda.is_available()

Reference: PyTorch|Get Start

Question 17

From practical standpoint just one minor digression:

import torch
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

This dev now knows if cuda or cpu.

And there is a difference how you deal with model and with tensors when moving to cuda. It is a bit strange at first.

import torch
import torch.nn as nn
dev = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
t1 = torch.randn(1,2)
t2 = torch.randn(1,2).to(dev)
print(t1)  # tensor([[-0.2678,  1.9252]])
print(t2)  # tensor([[ 0.5117, -3.6247]], device='cuda:0')
t1.to(dev) 
print(t1)  # tensor([[-0.2678,  1.9252]]) 
print(t1.is_cuda) # False
t1 = t1.to(dev)
print(t1)  # tensor([[-0.2678,  1.9252]], device='cuda:0') 
print(t1.is_cuda) # True

class M(nn.Module):
    def __init__(self):        
        super().__init__()        
        self.l1 = nn.Linear(1,2)

    def forward(self, x):                      
        x = self.l1(x)
        return x
model = M()   # not on cuda
model.to(dev) # is on cuda (all parameters)
print(next(model.parameters()).is_cuda) # True

This all is tricky and understanding it once, helps you to deal fast with less debugging.

Question 18

To check if there is a GPU available:

torch.cuda.is_available()

If the above function returns False,

you either have no GPU,
or the Nvidia drivers have not been installed so the OS does not see the GPU,
or the GPU is being hidden by the environmental variable CUDA_VISIBLE_DEVICES. When the value of CUDA_VISIBLE_DEVICES is -1, then all your devices are being hidden. You can check that value in code with this line: os.environ['CUDA_VISIBLE_DEVICES']

If the above function returns True that does not necessarily mean that you are using the GPU. In Pytorch you can allocate tensors to devices when you create them. By default, tensors get allocated to the cpu. To check where your tensor is allocated do:

# assuming that 'a' is a tensor created somewhere else
a.device  # returns the device where the tensor is allocated

Note that you cannot operate on tensors allocated in different devices. To see how to allocate a tensor to the GPU, see here: https://pytorch.org/docs/stable/notes/cuda.html

Question 19

Almost all answers here reference torch.cuda.is_available(). However, that’s only one part of the coin. It tells you whether the GPU (actually CUDA) is available, not whether it’s actually being used. In a typical setup, you would set your device with something like this:

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

but in larger environments (e.g. research) it is also common to give the user more options, so based on input they can disable CUDA, specify CUDA IDs, and so on. In such case, whether or not the GPU is used is not only based on whether it is available or not. After the device has been set to a torch device, you can get its type property to verify whether it’s CUDA or not.

if device.type == 'cuda':
    # do something

Question 20

Simply from command prompt or Linux environment run the following command.

python -c 'import torch; print(torch.cuda.is_available())'

The above should print True

python -c 'import torch; print(torch.rand(2,3).cuda())'

This one should print the following:

tensor([[0.7997, 0.6170, 0.7042], [0.4174, 0.1494, 0.0516]], device='cuda:0')

Question 21

If you are here because your pytorch always gives False for torch.cuda.is_available() that’s probably because you installed your pytorch version without GPU support. (Eg: you coded up in laptop then testing on server).

The solution is to uninstall and install pytorch again with the right command from pytorch downloads page. Also refer this pytorch issue.

Question 22

Create a tensor on the GPU as follows:

$ python
>>> import torch
>>> print(torch.rand(3,3).cuda())

Do not quit, open another terminal and check if the python process is using the GPU using:

$ nvidia-smi

Question 23

I have installed tensorflow in my ubuntu 16.04 using the second answer here with ubuntu’s builtin apt cuda installation.

Now my question is how can I test if tensorflow is really using gpu? I have a gtx 960m gpu. When I import tensorflow this is the output

I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally

Is this output enough to check if tensorflow is using gpu ?

Question 24

No, I don’t think “open CUDA library” is enough to tell, because different nodes of the graph may be on different devices.

To find out which device is used, you can enable log device placement like this:

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Check your console for this type of output.

Question 25

Apart from using sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) which is outlined in other answers as well as in the official TensorFlow documentation, you can try to assign a computation to the gpu and see whether you have an error.

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))

Here

“/cpu:0”: The CPU of your machine.
“/gpu:0”: The GPU of your machine, if you have one.

If you have a gpu and can use it, you will see the result. Otherwise you will see an error with a long stacktrace. In the end you will have something like this:

Cannot assign a device to node ‘MatMul’: Could not satisfy explicit device specification ‘/device:GPU:0’ because no devices matching that specification are registered in this process

Recently a few helpful functions appeared in TF:

tf.test.is_gpu_available tells if the gpu is available
tf.test.gpu_device_name returns the name of the gpu device

You can also check for available devices in the session:

with tf.Session() as sess:
  devices = sess.list_devices()

devices will return you something like

[_DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:CPU:0, CPU, -1, 4670268618893924978),
 _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 6127825144471676437),
 _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 16148453971365832732),
 _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 10003582050679337480),
 _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 5678397037036584928)

Question 26

Following piece of code should give you all devices available to tensorflow.

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

Sample Output

[name: “/cpu:0” device_type: “CPU” memory_limit: 268435456 locality { } incarnation: 4402277519343584096,

name: “/gpu:0” device_type: “GPU” memory_limit: 6772842168 locality { bus_id: 1 } incarnation: 7471795903849088328 physical_device_desc: “device: 0, name: GeForce GTX 1070, pci bus id: 0000:05:00.0” ]

Question 27

I think there is an easier way to achieve this.

import tensorflow as tf
if tf.test.gpu_device_name():
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
else:
    print("Please install GPU version of TF")

It usually prints like

Default GPU Device: /device:GPU:0

This seems easier to me rather than those verbose logs.

Question 28

Tensorflow 2.0

Sessions are no longer used in 2.0. Instead, one can use tf.test.is_gpu_available:

import tensorflow as tf

assert tf.test.is_gpu_available()
assert tf.test.is_built_with_cuda()

If you get an error, you need to check your installation.

Question 29

This will confirm that tensorflow using GPU while training also ?

Code

sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Output

I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GT 730
major: 3 minor: 5 memoryClockRate (GHz) 0.9015
pciBusID 0000:01:00.0
Total memory: 1.98GiB
Free memory: 1.72GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0
I tensorflow/core/common_runtime/direct_session.cc:255] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GT 730, pci bus id: 0000:01:00.0

Question 30

In addition to other answers, the following should help you to make sure that your version of tensorflow includes GPU support.

import tensorflow as tf
print(tf.test.is_built_with_cuda())

Question 31

Ok, first launch an ipython shell from the terminal and import TensorFlow:

$ ipython --pylab
Python 3.6.5 |Anaconda custom (64-bit)| (default, Apr 29 2018, 16:14:56) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.4.0 -- An enhanced Interactive Python. Type '?' for help.
Using matplotlib backend: Qt5Agg

In [1]: import tensorflow as tf

Now, we can watch the GPU memory usage in a console using the following command:

# realtime update for every 2s
$ watch -n 2 nvidia-smi

Since we’ve only imported TensorFlow but have not used any GPU yet, the usage stats will be:

Notice how the GPU memory usage is very less (~ 700MB); Sometimes the GPU memory usage might even be as low as 0 MB.

Now, let’s load the GPU in our code. As indicated in tf documentation, do:

In [2]: sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Now, the watch stats should show an updated GPU usage memory as below:

Observe now how our Python process from the ipython shell is using ~ 7 GB of the GPU memory.

P.S. You can continue watching these stats as the code is running, to see how intense the GPU usage is over time.

Question 32

This should give the list of devices available for Tensorflow (under Py-3.6):

tf = tf.Session(config=tf.ConfigProto(log_device_placement=True))
tf.list_devices()
# _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456)

Question 33

I prefer to use nvidia-smi to monitor GPU usage. if it goes up significantly when you start you program, it’s a strong sign that your tensorflow is using GPU.

Question 34

With the recent updates of Tensorflow, you can check it as follow :

tf.test.is_gpu_available( cuda_only=False, min_cuda_compute_capability=None)

This will return True if GPU is being used by Tensorflow, and return False otherwise.

If you want device device_name you can type : tf.test.gpu_device_name(). Get more details from here

Question 35

Run the following in Jupyter,

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

If you’ve set up your environment properly, you’ll get the following output in the terminal where you ran “jupyter notebook”,

2017-10-05 14:51:46.335323: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro K620, pci bus id: 0000:02:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K620, pci bus id: 0000:02:00.0
2017-10-05 14:51:46.337418: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\direct_session.cc:265] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Quadro K620, pci bus id: 0000:02:00.0

You can see here I’m using TensorFlow with an Nvidia Quodro K620.

Question 36

I find just querying the gpu from the command line is easiest:

nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.98                 Driver Version: 384.98                    |
|-------------------------------+----------------------+----------------------+
| 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 GTX 980 Ti  Off  | 00000000:02:00.0  On |                  N/A |
| 22%   33C    P8    13W / 250W |   5817MiB /  6075MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0      1060      G   /usr/lib/xorg/Xorg                            53MiB |
|    0     25177      C   python                                      5751MiB |
+-----------------------------------------------------------------------------+

if your learning is a background process the pid from jobs -p should match the pid from nvidia-smi

Question 37

You can check if you are currently using the GPU by running the following code:

import tensorflow as tf
tf.test.gpu_device_name()

If the output is '', it means you are using CPU only;
If the output is something like that /device:GPU:0, it means GPU works.

And use the following code to check which GPU you are using:

from tensorflow.python.client import device_lib 
device_lib.list_local_devices()

Question 38

Put this near the top of your jupyter notebook. Comment out what you don’t need.

# confirm TensorFlow sees the GPU
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())

# confirm Keras sees the GPU (for TensorFlow 1.X + Keras)
from keras import backend
assert len(backend.tensorflow_backend._get_available_gpus()) > 0

# confirm PyTorch sees the GPU
from torch import cuda
assert cuda.is_available()
assert cuda.device_count() > 0
print(cuda.get_device_name(cuda.current_device()))

NOTE: With the release of TensorFlow 2.0, Keras is now included as part of the TF API.

Originally answerwed here.

Question 39

For Tensorflow 2.0

import tensorflow as tf

tf.test.is_gpu_available(
    cuda_only=False,
    min_cuda_compute_capability=None
)

source here

other option is:

tf.config.experimental.list_physical_devices('GPU')

Question 40

UPDATE FOR TENSORFLOW >= 2.1.

The recommended way in which to check if TensorFlow is using GPU is the following:

tf.config.list_physical_devices('GPU')

As of TensorFlow 2.1, tf.test.gpu_device_name() has been deprecated in favour of the aforementioned.

Then, in the terminal you can use nvidia-smi to check how much GPU memory has been alloted; at the same time, using watch -n K nvidia-smi would tell you for example every K seconds how much memory you are using (you may want to use K = 1 for real-time)

Question 41

This is the line I am using to list devices available to tf.session directly from bash:

python -c "import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'; import tensorflow as tf; sess = tf.Session(); [print(x) for x in sess.list_devices()]; print(tf.__version__);"

It will print available devices and tensorflow version, for example:

_DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 268435456, 10588614393916958794)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 12320120782636586575)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 13378821206986992411)
_DeviceAttributes(/job:localhost/replica:0/task:0/device:GPU:0, GPU, 32039954023, 12481654498215526877)
1.14.0

Question 42

I found below snippet is very handy to test the gpu ..

Tensorflow 2.0 Test

import tensorflow as tf
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
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))

Tensorflow 1 Test

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))

Question 43

The following will also return the name of your GPU devices.

import tensorflow as tf
tf.test.gpu_device_name()

Question 44

With tensorflow 2.0 >=

import tensorflow as tf
sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))

Question 45

You have some options to test whether GPU acceleration is being used by your TensorFlow installation.

You can type in the following commands in three different platforms.

import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Jupyter Notebook – Check the console which is running the Jupyter Notebook. You will be able to see the GPU being used.
Python Shell – You will be able to directly see the output. (Note- do not assign the output of the second command to the variable ‘sess’; if that helps).
Spyder – Type in the following command in the console.

import tensorflow as tf tf.test.is_gpu_available()

Question 46

Tensorflow 2.1

A simple calculation that can be verified with nvidia-smi for memory usage on the GPU.

import tensorflow as tf 

c1 = []
n = 10

def matpow(M, n):
    if n < 1: #Abstract cases where n < 1
        return M
    else:
        return tf.matmul(M, matpow(M, n-1))

with tf.device('/gpu:0'):
    a = tf.Variable(tf.random.uniform(shape=(10000, 10000)), name="a")
    b = tf.Variable(tf.random.uniform(shape=(10000, 10000)), name="b")
    c1.append(matpow(a, n))
    c1.append(matpow(b, n))

Question 47

>>> import tensorflow as tf 
>>> tf.config.list_physical_devices('GPU')

2020-05-10 14:58:16.243814: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2020-05-10 14:58:16.262675: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.263119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: GeForce GTX 1060 6GB computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s
2020-05-10 14:58:16.263143: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2020-05-10 14:58:16.263188: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2020-05-10 14:58:16.264289: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2020-05-10 14:58:16.264495: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2020-05-10 14:58:16.265644: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2020-05-10 14:58:16.266329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2020-05-10 14:58:16.266357: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2020-05-10 14:58:16.266478: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.266823: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2020-05-10 14:58:16.267107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

As suggested by @AmitaiIrron:

This section indicates that a gpu was found

2020-05-10 14:58:16.263119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1555] Found device 0 with properties:

pciBusID: 0000:01:00.0 name: GeForce GTX 1060 6GB computeCapability: 6.1
coreClock: 1.7715GHz coreCount: 10 deviceMemorySize: 5.93GiB deviceMemoryBandwidth: 178.99GiB/s

And here that it got added as an available physical device

2020-05-10 14:58:16.267107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1697] Adding visible gpu devices: 0

[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

Question 48

If you are using TensorFlow 2.0, you can use this for loop to show the devices:

with tf.compat.v1.Session() as sess:
  devices = sess.list_devices()
devices

Question 49

if you are using tensorflow 2.x use:

sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))

Question 50

Run this command in Jupyter or your IDE to check if Tensorflow is using a GPU or not: tf.config.list_physical_devices('GPU')

组件	描述
火炬	像NumPy这样的张量器库，具有强大的GPU支持
torch.autograd	基于磁带的自动区分库，支持TORCH中的所有可微分张量操作
torch.jit	从PyTorch代码创建可序列化和可优化模型的编译堆栈(TorchScript)
torch.nn	与Autograd深度集成的神经网络库，旨在实现最大的灵活性
torch.multiprocessing	Python多处理，但具有跨进程共享火炬张量的神奇内存。适用于数据加载和HogWild培训
torch.utils	为方便起见，DataLoader和其他实用程序功能

CUDA版	Xcode版本
10.0	Xcode 9.4
10.1	Xcode 10.1

系统	3.6	3.8
Linux CPU		–
Linux GPU		–
Windows CPU/GPU	–	–
Linux(Ppc64le)CPU	–	–
Linux(Ppc64le)GPU	–	–
Linux(Aarch64)CPU

问题：如何在张量流中获取当前可用的GPU？

回答 0

回答 1

回答 2

回答 3

回答 4

回答 5

回答 6

回答 7

回答 8

回答 9

问题：如何检查pytorch是否正在使用GPU？

回答 0

回答 1

编辑：

Edit:

回答 2

回答 3

回答 4

回答 5

回答 6

回答 7

回答 8

回答 9

问题：如何判断tensorflow是否在python shell中使用gpu加速？

回答 0

回答 1

回答 2

回答 3

回答 4

回答 5

回答 6

回答 7

回答 8

回答 9

回答 10

回答 11

回答 12

回答 13

回答 14

回答 15

对于Tensorflow 2.0

For Tensorflow 2.0

回答 16

回答 17

回答 18

Tensorflow 2.0测试

Tensorflow 1测试

Tensorflow 2.0 Test

Tensorflow 1 Test

回答 19

回答 20

回答 21

回答 22

Tensorflow 2.1

Tensorflow 2.1

回答 23

回答 24

回答 25

回答 26

欢迎光临Fastai

正在安装

学习速度快

关于FAST

从其他库迁移

Windows支持

测试

贡献

码头集装箱

更多关于PyTorch的信息

一种支持GPU的张量库

动态神经网络：基于磁带的自动评分

Python优先

势在必行的经验

快速精益

无痛延长

安装

二进制文件

NVIDIA Jetson平台

来自源