After reading this tutorial https://www.tensorflow.org/guide/using_gpu I checked GPU session on this simple code
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
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(config=tf.ConfigProto(log_device_placement=True)) as sess:
x = sess.run(c)
print(x)
The output was
2018-08-07 18:44:59.019144: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX2
FMA Device mapping: no known devices. 2018-08-07 18:44:59.019536: I
tensorflow/core/common_runtime/direct_session.cc:288] Device mapping:
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:CPU:0
2018-08-07 18:44:59.019902: I
tensorflow/core/common_runtime/placer.cc:886] MatMul:
(MatMul)/job:localhost/replica:0/task:0/device:CPU:0 a: (Const):
/job:localhost/replica:0/task:0/device:CPU:0 2018-08-07
18:44:59.019926: I tensorflow/core/common_runtime/placer.cc:886] a:
(Const)/job:localhost/replica:0/task:0/device:CPU:0 b: (Const):
/job:localhost/replica:0/task:0/device:CPU:0 2018-08-07
18:44:59.019934: I tensorflow/core/common_runtime/placer.cc:886] b:
(Const)/job:localhost/replica:0/task:0/device:CPU:0 [[ 22. 28.] [
49. 64.]]
As you see there is no calculation done by GPU.
and when I changed the code to use GPU's configuration and process fraction:
conf = tf.ConfigProto()
conf.gpu_options.per_process_gpu_memory_fraction = 0.4
with tf.Session(config = conf) as sess:
x = sess.run(c)
print(x)
The output was
2018-08-07 18:52:22.681221: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX2
FMA [[ 22. 28.] [ 49. 64.]]
What can I do to run the session on GPU card? Thank you.
It is most certainly possible to run tensorflow on AMD GPUs. About 2 years back ROCm was released which gets things done. However, the is a caveat, that it runs only on Linux as of now owing to its open-source origins. So if you are willing to use Linux then you can most certainly train your DL models using AMD GPUs. That said the amount of support you will get is low as the community is still not large enough. Google search for ROCm and you can get instructions on how to get it set up and running on a Linux machine. May be it will work with WSL2 in windows, but I have not tried it yet and so cannot comment on that.
here is a link to ROCm installation docs
You can use TensorflowJS, the Javascript version of tensorflow.
TensorflowJS does not have any HW limitation and can run on all the gpu supporting webGL.
The api is pretty similar to tf in python and the project provides scripts to convert your models from python to JS
I believe TensorFlow-GPU only support GPU card with CUDA Compute Capability >= 3.0 of NVIDIA.
The following TensorFlow variants are available for installation:
TensorFlow with CPU support only. If your system does not have a NVIDIA® GPU, you must install this version. This version of TensorFlow is usually easier to install, so even if you have an NVIDIA GPU, we recommend installing this version first.
TensorFlow with GPU support. TensorFlow programs usually run much faster on a GPU instead of a CPU. If you run performance-critical applications and your system has an NVIDIA® GPU that meets the prerequisites, you should install this version. See TensorFlow GPU support for details.
https://www.tensorflow.org/install/install_linux
Related
I have this code to disable GPU usage:
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import tensorflow as tf
w = tf.Variable(
[
[1.],
[2.]
])
I get this output still, not sure why :
E:\MyTFProject\venv\Scripts\python.exe E:/MyTFProject/tfvariable.py
2021-11-03 14:09:16.971644: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2021-11-03 14:09:16.971644: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-11-03 14:09:19.563793: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-11-03 14:09:19.566793: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: newtonpc
2021-11-03 14:09:19.567793: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: mypc
2021-11-03 14:09:19.567793: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
TF Version: '2.6.1'
Not able to stop it from loading Cuda DLLs. I dont want to setup cuda just right now. Maybe later.
I am using the latest PyCharm and installed tensorflow as given in the site with pip.
You can try to reinstall tensorflow with CPU-only version. The links are available here depending on your OS and your python version:
https://www.tensorflow.org/install/pip?hl=fr#windows_1
I am trying to run tensorflow with CPU support.
tensorflow:
Version: 1.14.0
Keras:
Version: 2.3.1
When I try to run the following piece of code :
def run_test_harness(trainX,trainY,testX,testY):
datagen=ImageDataGenerator(rescale=1.0/255.0)
train_it = datagen.flow(trainX, trainY, batch_size=1)
test_it = datagen.flow(testX, testY, batch_size=1)
model=define_model()
history = model.fit_generator(train_it, steps_per_epoch=len(train_it),
validation_data=test_it, validation_steps=len(test_it), epochs=1, verbose=0)
I get the following error as shown in image:
Image shows the error
I tried to configure bazel for the same but it was of no use. It would be helpful if someone could direct me to resources or help with the problem. Thank you
EDIT : (Warning messages)
WARNING:tensorflow:From /home/neha/valiance/kerascpu/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
WARNING:tensorflow:From /home/neha/valiance/kerascpu/lib/python3.6/site-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
2020-10-22 12:41:36.023849: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-10-22 12:41:36.326420: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2299965000 Hz
2020-10-22 12:41:36.327496: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5502350 executing computations on platform Host. Devices:
2020-10-22 12:41:36.327602: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2020-10-22 12:41:36.679930: W tensorflow/compiler/jit/mark_for_compilation_pass.cc:1412] (One-time warning): Not using XLA:CPU for cluster because envvar TF_XLA_FLAGS=--tf_xla_cpu_global_jit was not set. If you want XLA:CPU, either set that envvar, or use experimental_jit_scope to enable XLA:CPU. To confirm that XLA is active, pass --vmodule=xla_compilation_cache=1 (as a proper command-line flag, not via TF_XLA_FLAGS) or set the envvar XLA_FLAGS=--xla_hlo_profile.
2020-10-22 12:41:36.890241: W tensorflow/core/framework/allocator.cc:107] Allocation of 3406823424 exceeds 10% of system memory.
^Z
[1]+ Stopped python3 model.py
You should try running your code on google colab. I think there aren't enough resources available on your PC for the task you are trying to run even though you are using a batch_size of 1.
I install tensorflow gpu on my machine.
I install CUDA toolkit 9.0 and cuDNN 7.0 on my machine.
And when I go thru the steps
from https://www.tensorflow.org/install/install_windows to test my installation.
By entering the program
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
But I get the following error "Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2" error.
Can you please tell me how can I fix it?
>>> sess = tf.Session()
2018-07-25 23:27:54.477511: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-07-25 23:27:55.607237: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1392] Found device 0 with properties:
name: Quadro M2000 major: 5 minor: 2 memoryClockRate(GHz): 1.1625
pciBusID: 0000:03:00.0
totalMemory: 4.00GiB freeMemory: 3.34GiB
2018-07-25 23:27:55.612178: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1471] Adding visible gpu devices: 0
2018-07-25 23:27:55.977046: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-25 23:27:55.980238: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0
2018-07-25 23:27:55.982308: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N
2018-07-25 23:27:55.984488: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3069 MB memory) -> physical GPU (device: 0, name: Quadro M2000, pci bus id: 0000:03:00.0, compute capability: 5.2)
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
I have also been wondering what this warning means. After making a quick tour, here is what i ve found:
Adveance Vector Extensions are the instructions that extends integer operations to floating points numbers.
Eg: FUSE MULTIPLY ADD.
citing from the above source
"A fused multiply–add (sometimes known as FMA or fmadd) is a floating-point multiply–add operation performed in one step, with a single rounding.
That is, where an unfused multiply–add would compute the product b×c, round it to N significant bits, add the result to a, and round back to N significant bits, a fused multiply–add would compute the entire expression a+b×c to its full precision before rounding the final result down to N significant bits."
if AVX is not enabled in your compiler, the operation a+bxc would be done sequential steps wheras avx instructions executes it into one operation unit.
It seems by default, the build flags of tensorflow, doesn't include the support for AVX instructions as the configuration section states in on install from source page.
To be able to suppress this warning, you have to build tensorflow from source and on the configuration part, use additional these additional flags
bazel build -c opt --copt=-mavx --copt=-mavx2
I suspect that these flags are omitted by default because not all cpus supports these instructions.
For more details, see this answer and this github issue.
EDIT
Here is an exaustive list of of build you can use depending on which warnings you are getting, including this one.
I have those warnings:
2017-09-26 14:50:45.956966: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:50:45.956986: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:50:45.956990: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:50:45.956996: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
So, based on what I found on internet, I then followed this link:
https://www.tensorflow.org/install/install_sources
However, when I try:
$ python
and :
# Python
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
The output should be : Hello, TensorFlow!
However, I don't get that at all...
emixam23#pt-mguittet:~/Workspace$ python
Python 3.6.2 (default, Sep 4 2017, 16:58:35)
[GCC 4.2.1 Compatible Apple LLVM 8.1.0 (clang-802.0.42)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
2017-09-26 14:56:33.905065: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:56:33.905096: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:56:33.905105: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-09-26 14:56:33.905112: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
>>> print(sess.run(hello))
b'Hello, TensorFlow!'
>>>
Even after I installed it, I still have the warnings, why so? I used Xcode 7.3 and I didn't put the GPU in the ./configuration process.
Any idea? :/ Thank in advance !
You can mask these warnings like so:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
Notice that your program still output 'Hello, TensorFlow!', just simply after all of the warnings regarding SSE4.2.
Why are you saying it doesn't seem to install?
You are getting what you expected (Hello, Tensorflow!) but you are also getting these warnings. Regarding the warnings, they have been discussed here: Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
Regarding the "b'" before Hello, Tensorflow!, see What does the 'b' character do in front of a string literal?.
Hope it helps.
So for example, if I run this piece of code:
import tensorflow as tf
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)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
Then I would get this:
2017-06-24 10:20:57.441289: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.442069: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.443010: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.444615: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.445662: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.446273: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.447475: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:20:57.448190: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
2017-06-24 10:21:01.548276: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties:
name: GeForce GTX 1060
major: 6 minor: 1 memoryClockRate (GHz) 1.6705
pciBusID 0000:01:00.0
Total memory: 6.00GiB
Free memory: 5.01GiB
2017-06-24 10:21:01.549636: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0
2017-06-24 10:21:01.550040: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: Y
2017-06-24 10:21:01.550479: 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: GeForce GTX 1060, pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0
2017-06-24 10:21:01.910806: 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: GeForce GTX 1060, pci bus id: 0000:01:00.0
MatMul: (MatMul): /job:localhost/replica:0/task:0/gpu:0
b: (Const): /job:localhost/replica:0/task:0/gpu:0
a: (Const): /job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.918147: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] MatMul: (MatMul)/job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.918794: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] b: (Const)/job:localhost/replica:0/task:0/gpu:0
2017-06-24 10:21:01.919403: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\simple_placer.cc:847] a: (Const)/job:localhost/replica:0/task:0/gpu:0
[[ 22. 28.]
[ 49. 64.]]
The warnings always come up and its extremely annoying. I have tried using other solution such as:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
But it still gave me the same warnings.
Two things, how can I get rid of the warnings? Is tensorflow using just GPU?
There warnings caused by building parameters. Recompile your tensorflow with parameter bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-msse --copt=-msse2 --copt=-msse3 --copt=-msse4.1 --copt=-msse4.2
--copt=-mfpmath=both --config=cuda -k //tensorflow/tools/pip_package:build_pip_package will solve the problem
You are getting this info dump, because you are starting your Session explicitly with the instruction to show them:
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
the log_device_placement=True bit is intended specifically to demonstrate which operations are performed on which devices (for example to demonstrate that the GPU is indeed utilized properly, etc.)
If you don't want to see this info, simply start the session without such options:
sess = tf.Session()
Having this bit is still helpful however:
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
Because TF might show you other warnings that you might find annoying/unnecessary, so I'd suggest keeping this line (but in my experience that doesn't have any effect on Windows, only on Mac and Linux)