I enable GPU by going to >>runtime>> change runtime type >> then choose GPU.
But when I run my code I get this error:
usage: train.py [-h] [--pre PRETRAINED] TRAIN TEST GPU TASK
train.py: error: the following arguments are required: GPU, TASK
this is the part of the code that make error:
! python train.py part_A_train.json part_A_val.json
I also have this warning:
Warning: You are connected to a GPU runtime, but not utilising the GPU.
but by running this code looks like that GPU is active!
Related
I am using tensorflow-gpu 2.3.0 with CUDA_VERSION=8.0.61 and CUDNN_VERSION=6.0.21.
I just run a tensorflow code and get FailedPreconditionError:
'tensorflow.python.framework.errors_impl.FailedPreconditionError: Failed to allocate scratch buffer for device 0 [Op:VarHandleOp] name: Variable/'
What can I do to fix this?
Thanks
i'm using google colab for the detection of object with Yolo.
in the step of the Train Custom YOLOv4 Detector, i have this error
CUDA status Error: file: ./src/blas_kernels.cu : () : line: 841 : build time: Nov 26 2020 - 16:49:52
CUDA Error: no kernel image is available for execution on the device
CUDA Error: no kernel image is available for execution on the device: File exists
darknet: ./src/utils.c:325: error: Assertion `0' failed.
can you help me please
I am trying to run BERT model on Google Colab using TPU..
What is TPU_NAME exactly in:
!python run_classifier.py \
.
.
.
--use_tpu=True \
--tpu_name=$TPU_NAME
I tried to define it as:
TPU_NAME = os.environ['COLAB_TPU_ADDR']
But, I faced the following error:
INFO:tensorflow:Error recorded from training_loop: Cannot find any TPU
cores in the system. Please double check Tensorflow master address and
TPU worker(s). Available devices are
[_DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU,
268435456, 13626261043432226130),
_DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 240953179956275984)].
I am learning tesnorflow from this blog:
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/
The code i am running is :
https://github.com/dennybritz/cnn-text-classification-tf/blob/master/train.py
I have installed tensorflow from sourcse in a virtual enviroment,in CPU only enviroment using followinbg bazel build command: bazel build --config=mkl ...
here is the exact error:
"2018-01-16 03:15:27.783040: F tensorflow/core/kernels/mkl_maxpooling_op.cc:157] Check failed: dnnPoolingCreateForward_F32( &prim_pooling_fwd, primAttr, algorithm, lt_user_input, params.kernel_size, params.kernel_stride, params.in_offset, dnnBorderZerosAsymm) == E_SUCCESS (-127 vs. 0)
Aborted
"
I have debugged error to the line where sess.run is written, i have beleived it has something to do it mkl_maxpooling, as i had installed tensorflow with mkl optimization of INTEL cpu's
Given below are the steps that I followed:
Build tensorflow 1.4 from source with mkl as mentioned in the question
Cloned the git repo "https://github.com/dennybritz/cnn-text-classification-tf.git"
Ran "python train.py" from "cnn-text-classification-tf" directory(created from git clone)
Code ran without any errors. So it seems like the tensorflow was not properly built from the source. Please confirm that there were no errors while building tensorflow from source.
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.