How to tell if tensorflow is using gpu acceleration from inside python shell?

Question

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 ?

You should see something like this in your log: I tensorflow/core/common_runtime/gpu/gpu_device.cc:838] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 980, pci bus id: 0000:03:00.0) — Yaroslav Bulatov
– Yaroslav Bulatov, Commented Jun 24, 2016 at 23:32
There's log_device_placement approach in answer. The most reliable way is to look at timeline as specified in this comment: github.com/tensorflow/tensorflow/issues/… — Yaroslav Bulatov
– Yaroslav Bulatov, Commented Jun 24, 2016 at 23:33
Yes , I have got this output following Yao Zhang's answer... — Tamim Addari
– Tamim Addari, Commented Jun 25, 2016 at 5:39

Martin · Accepted Answer · 2021-04-22 09:16:05Z

459

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

When using tensorflow2:

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

For tensorflow1, 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.

edited Apr 22, 2021 at 9:16

Martin

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answered Jun 24, 2016 at 18:07

Yao Zhang

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15 Comments

Qubix Over a year ago

I tried this and it prints absolutely nothing. Any idea why that might be?

Tamim Addari Over a year ago

Did you do it on a jupyter notebook ?

musically_ut Over a year ago

The output may be produced on the console from where you ran the Jupyter Notebook.

Roy Over a year ago

Can we get an updated answer for Tensorflow V2 (where tf.Sessions are not supported).

Vandan Revanur Over a year ago

@iyop45 For tensorflow V2, the command is a bit modified: sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))

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aspiring_sarge · Accepted Answer · 2018-11-06 01:28:44Z

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)

@GeorgePligor the result is not really important here. Either you have a result and the GPU was used or you have an error, which means that it was not used
This did not work for me. I ran this inside of my Docker Container that is exectued by the nvidia-docker and etcetc. However I get no error and the CPU is the one that does the work. I upped the matrices a bit (10k*10k) to ensure it calculates for a while. CPU util went up to 100% but the GPU stayed cool as always.
I got the "no devices matching" error when run it in console. In IDE like pycharm there is no error. I guess it's related to the Session I used, which is different in console.
dosen't seem to work for tensorflow 2.1 at all, even after replacing Session with tf.compat.v1.Session()

Sheraz · Accepted Answer · 2018-01-23 04:27:36Z

208

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" ]

edited Jan 23, 2018 at 4:27

answered Jan 23, 2018 at 4:22

Sheraz

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4 Comments

mercury0114 Over a year ago

and if this command does not return any entry with "GPU", does it mean my machine simply does have GPU, or tensorflow is not able to locate it?

jimijazz Over a year ago

@mercury0114 it may be either. for example, you may have a gpu but not have tensorflow-gpu properly installed.

jonas-eschle Over a year ago

I disagree, this does not answer the question: it's not about devices available but devises used. And that can be an entirely different story! (e.g. TF will only use 1 GPU by default.

kamran kausar Over a year ago

name: "/device:GPU:0" device_type: "GPU" memory_limit: 10711446324 locality { bus_id: 1 links { }} incarnation: 17935632445266485019 physical_device_desc: "device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5"]

Mateen Ulhaq · Accepted Answer · 2020-04-23 07:33:39Z

140

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.

edited Apr 23, 2020 at 7:33

Mateen Ulhaq

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answered Apr 10, 2019 at 8:54

ma3oun

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4 Comments

Overdrivr Over a year ago

That also works with TF 1.14 (maybe even a few older versions)!

Roy Shilkrot Over a year ago

It is now recommended to do tf.config.list_physical_devices('GPU')

joselquin Over a year ago

@Roy Shilkrot I think 'tf.config.list_physical_devices('GPU')' doesn't work in Tensorflow 2.0

Roy Shilkrot Over a year ago

@joselquin The TF v2.x docs state it should work: tensorflow.org/api_docs/python/tf/config/experimental/… , and I can verify it does work for me.

Ishan Bhatt · Accepted Answer · 2021-06-01 07:44:38Z

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.

Edit:- This was tested for TF 1.x versions. I never had a chance to do stuff with TF 2.0 or above so keep in mind.

Agreed. Easier than the approaches described above. Prints the list of GPUs its using. Thanks
This isn't a valid test. It will return into the else statement even if you have the GPU version of tensorflow installed.

Timbus Calin · Accepted Answer · 2022-12-20 08:57:23Z

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)

If you have multiple GPUs and you want to use multiple networks, each one on a separated GPU, you can use:

 with tf.device('/GPU:0'): neural_network_1 = initialize_network_1() with tf.device('/GPU:1'): neural_network_2 = initialize_network_2()

To see constantly how much GPU memory using , use nvidia-smi -l 10.

kmario23 · Accepted Answer · 2020-02-20 11:27:31Z

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.

Nander Speerstra · Accepted Answer · 2016-12-06 08:24:35Z

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

Please add a little explanation to why your answer is working (what does the log_device_placement do and how to see CPU vs. GPU in the output?). That will improve the quality of your answer!

karaspd · Accepted Answer · 2018-06-05 22:07:51Z

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())

edited Jun 5, 2018 at 22:07

answered Jun 5, 2018 at 21:01

karaspd

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1 Comment

Ricardo Cruz Over a year ago

Warning: That tells you if TensorFlow is compiled with GPU. Not whether the GPU is being used. (If the drivers are not installed properly for example, then the CPU is used, even if "is_built_with_cuda()" is true.)

f0nzie · Accepted Answer · 2017-12-19 22:59:48Z

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)

scott huang · Accepted Answer · 2017-09-13 03:38:00Z

14

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.

answered Sep 13, 2017 at 3:38

scott huang

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5 Comments

papabiceps Over a year ago

This is an indirect way

Razin Over a year ago

How do you use nvdia-smi to monitor GPU usage?

scott huang Over a year ago

after you install cuda. nvidia-smi should be in your system. I usually use 'nvidia-smi -l ' to monitor the usage.

Perseus14 Over a year ago

You can also use watch nvidia-smi, updates the screen every 2 seconds

formica Over a year ago

watch nvidia-smi works well for me. I can also see in the output that my python process is using the GPU

Tim · Accepted Answer · 2017-12-26 17:07:20Z

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

smerllo · Accepted Answer · 2019-07-01 01:58:56Z

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

Timbus Calin · Accepted Answer · 2020-10-06 16:30:26Z

9

With tensorflow 2.0 >=

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

edited Oct 6, 2020 at 16:30

Timbus Calin

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answered Apr 15, 2020 at 14:49

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Comments

wafflecat · Accepted Answer · 2017-10-05 06:53:38Z

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.

Jupyter is not necessary at all, please don't add complexity to the question
Some users may want to ensure GPU is usable in Jupyter. Additionally, this can be run from a Python script.

Aryan · Accepted Answer · 2021-02-07 15:11:37Z

For TF2.4+ listed as the "official" way on tensorflow website to check if TF is using GPU or Not

>>> import tensorflow as tf >>> print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) Num GPUs Available: 2

Hu Xixi · Accepted Answer · 2019-03-27 15:42:04Z

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()

ajayramesh · Accepted Answer · 2019-11-02 18:26:06Z

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))

Paul Williams · Accepted Answer · 2019-11-17 02:25:45Z

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.

bLeDy · Accepted Answer · 2020-05-11 13:38:04Z

>>> 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')]

It would be useful if you point to the sections in the output that indicate whether a GPU is used.
@AmitaiIrron Thanks for the suggestion, i hope it is a bit more clear now.

Aadil Srivastava · Accepted Answer · 2021-02-20 03:16:56Z

In the new versions of TF(>2.1) the recommended way for checking whether TF is using GPU is:

tf.config.list_physical_devices('GPU')

Community · Accepted Answer · 2020-06-20 09:12:55Z

5

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')

edited Jun 20, 2020 at 9:12

CommunityBot

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answered Dec 31, 2019 at 12:43

ChaosPredictor

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1 Comment

Himanshu Teotia Over a year ago

is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.

Timbus Calin · Accepted Answer · 2020-03-10 12:21:45Z

5

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

import tensorflow as tf tf.test.gpu_device_name()

edited Mar 10, 2020 at 12:21

Timbus Calin

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answered Oct 30, 2019 at 3:32

Maz

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Mysterious Otter Over a year ago

If the output of this command is an empty string ... how I do debug?

cannin · Accepted Answer · 2020-04-23 12:13:13Z

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))

A nice test not only to check if tensorflow sees the GPU but if it will actually use one.

skulz00 · Accepted Answer · 2020-03-10 07:16:19Z

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

y.selivonchyk · Accepted Answer · 2019-07-31 23:48:36Z

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

simpleParadox · Accepted Answer · 2019-09-01 01:23:19Z

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()

Doug · Accepted Answer · 2019-11-20 04:28:48Z

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

Hari Krishnan · Accepted Answer · 2020-02-09 21:27:12Z

1

if you are using tensorflow 2.x use:

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

edited Feb 9, 2020 at 21:27

Hari Krishnan

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answered Dec 29, 2019 at 15:33

Cheptii

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w00dy · Accepted Answer · 2021-10-28 13:43:03Z

I found the most simple and comprehensive approach. Just set tf.debugging.set_log_device_placement(True) and you should see if ops are actually run on GPU e.g. Executing op _EagerConst in device /job:localhost/replica:0/task:0/device:GPU:0

More in the docs: https://www.tensorflow.org/guide/gpu#logging_device_placement

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