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.
Related
I am new with Deep learning. I have a A100 GPU installed with CUDA 11.6. I installed using Conda tensor flow-1.15 and tensorflow gpu - 1.15, cudatoolkit 10.0, python 3.7 but the code I am trying to run from github has given a note as below and it shows errors which I am finding difficult to interpret where I went wrong.The error is displayed as
failed to run cuBLAS routine: CUBLAS_STATUS_EXECUTION_FAILED 2022-06-30 09:37:12.049400: I tensorflow/stream_executor/stream.cc:4925] [stream=0x55d668879990,impl=0x55d668878ac0] did not memcpy device-to-host; source: 0x7f2fe2d0d400 2022-06-30 09:37:12.056385: W tensorflow/core/framework/op_kernel.cc:1651] OP_REQUIRES failed at iterator_ops.cc:867 : Cancelled: Operation was cancelled
tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found. (0) Internal: Blas GEMM launch failed : a.shape=(25, 25), b.shape=(25, 102400), m=25, n=102400, k=25 [[{{node Hyperprior/HyperAnalysis/layer_Hyperprior_1/MatMul}}]] (1) Internal: Blas GEMM launch failed : a.shape=(25, 25), b.shape=(25, 102400), m=25, n=102400, k=25 [[{{node Hyperprior/HyperAnalysis/layer_Hyperprior_1/MatMul}}]] [[Hyperprior/truediv_3/_3633]]
NOTE: At the moment, we only support CUDA 10.0, Python 3.6-3.7, TensorFlow 1.15, and Tensorflow Compression 1.3. TensorFlow must be installed via pip, not conda. Unfortunately, newer versions of Tensorflow or Python will not work due to various constraints in the dependencies and in the TF binary API.
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
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
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'm new to Tensorflow.
I am using a 64 bit version of Windows 10 and I would like to install Tensorflow for the CPU.
I don't remember the exact steps that I followed to install it, however when I checked for the installation using:
import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
I have the following output:
2017-10-18 09:56:21.656601: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
2017-10-18 09:56:21.656984: W C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\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.
b'Hello, TensorFlow!'
I am running python in Sublime Text 3 using the package SublimeREPL.
I tried to search these errors and found out that it means that the tensorflow is built without these instructions which could improve performances for the CPU. I also found the code to hide these warnings, but I actually I want to use these instructions.
The code that I found that enables this is:
bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-msse4.2 --copt=-msse4.1 --copt=-msse3 --copt=-mfma -k //tensorflow/tools/pip_package:build_pip_package
but I got this output:
ERROR: Skipping '//tensorflow/tools/pip_package:build_pip_package': no such package 'tensorflow/tools/pip_package': BUILD file not found on package path.
WARNING: Target pattern parsing failed. Continuing anyway.
INFO: Found 0 targets...
ERROR: command succeeded, but there were errors parsing the target pattern.
INFO: Elapsed time: 8,147s, Critical Path: 0,02s
How can I solve this problem?
Lastly, I don't understand what pip, wheel and bazel are so I need a step by step instructions.
Thank you a lot!
if you want to download TensorFlow source, compile+install, use this link. If you want to download binaries, then use this link.