enter image description here
All gpu versions are the same.
enter image description here
But tensorflow recognizes only No.1 gpu.
I want to know why this is happening.
I use TensorFlow 2.5 version
We've looked at it in many ways, but we've found similar cases.
Related
I have a Deep Learning Code for Object Detection. What I did is that I ran the code on Google Colab and then Exported the model to use it locally. Now to run the model I have to again install whole Tensorflow package which is quite heavy for my system.
I want to ask if there is a way to download and run only specific parts of Tensorflow Library?
I am using Tensorflow at only 2 places in my code and I have to install whole Tensorflow library for it.
This is where I am loading the model.
detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)
This is where I am using Tensorflow 2nd time.
input_tensor = tf.convert_to_tensor(image_rgb)
These are the only 2 functions required to me from the Tensorflow Library and not the whole library... Thanks in anticipation.
Though I'm not entirely sure on the library as a whole, there is a Lite version of Tensorflow (I guess they realised 430MB is a bit much too).
Information regarding this can be found here:
https://www.tensorflow.org/lite/
A guide here seems to detail how to pick and choose parts of the Lite library and although not used myself, I should expect some degree of compatibility between the two...
https://www.tensorflow.org/lite/guide/reduce_binary_size
I'm working on an object detection project. I followed the instruction from Github.
But I used a different model.
I run this command
python train.py --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_inception_v2_coco.config
The error is
ValueError: ssd_inception_v2 is not supported. See `model_builder.py` for features extractors compatible with different versions of Tensorflow
I don't know why. I tried to change the model version but still error.
Please guide me. How to solve it?
I already knew how to solve it. I used model which is support Tensorflow 1., but I build my program with Tensorflow 2.. So, I changed the to use a model which support tensorflow 2.*
For those asking where to find v2 version of ssd_mobilenet_v1_coco trained models please visit and download appropriate model:
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
I am working on a Letter Recognition Application for a robot. I used my home PC for training the model and wanted the recognition to be on the RPI Zero W with the already trained model.
I got an HDF model. When I try to install Tensorflow on the RPI zero, it's throwing a hash error, as far as I found it this is due to TF beeing for 64bit machines. When I try to install Tensorflow Lite, the installation stocks and crashes.
For saving the model I use:
classifier.save('test2.h5')
That are the Prediction lines:
test_image = ks.preprocessing.image.load_img('image.jpg')
test_image = ks.preprocessing.image.img_to_array(test_image)
result = classifier.predict(test_image)
I also tried to compile the python script via Nuitka, but as the RPI is ARM and nuitka is not offering cross-compile, this possibility felt out.
You can use already available TFLite to solve your issue.
If that does not help, you can also build TFLite from source.
Please refer to below links:
https://www.tensorflow.org/lite/guide/build_rpi
https://medium.com/#haraldfernengel/compiling-tensorflow-lite-for-a-raspberry-pi-786b1b98e646
I am really looking for your help.
I have GTX-1070 which is 8vram.
I downloaded tensorflow-gpu, cuda 9.0, cudnn 7.0 for cuda 9.0.
and everything works fine with DNN. GPU is also working fine.
but whenever I try to train any model that has to do with image, it crashes.
Currently I am working with keras pre-trained VGG16.
I tried using smaller batch-size, resized image down to 64x64.
When I look at the process, GPU is used 0%, then spikes up to 100% then crashes.
Spyder says "kernel died, restarting".
Is gtx-1070 really that short of memory or am I missing something?
Thanks for reading
The first thing I would try is to download Cudnn 7.1.
These are good instructions to follow, and you may consider reinstalling Cuda 9 again. I had to do the same at one point, it was frustrating but haven't had a problem since I got it right.
Installation Instructions
I had a similar crashing problem before. The cause was my cudnn7.1 and tensorflow-gpu (precompiled with cudnn7.05) versions mismatched. Once taken care, there is no more problem.
I have questions to ask about why tensorflow with poets was not able to classify the image i want. I am using Ubuntu 14.04 with tensorflow installed using docker. Here is my story:
After a successful retrained on flower category following this link here. I wish to train on my own category as well, I have 10 classes of images and they are well organized according to the tutorial. My photos were also stored in the tf_files directory and following the guide i retrain the inception model on my category.
Everything on the retraining went well. However, as I tired to classify the image I want, I was unable to do so and I have this error. I also tried to look for py file in /usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/errors_impl.py but my dist-packages were empty! Can someone help me in this! Where can I find the py files? Thank you!
Your error indicates that it is unable to locate the file. I would suggest you to execute the following command in the directory where you have the graph and label files
python label_image.py exact_path_to_your_testimage_file.jpg