I am planning to deploy a model of TensorFlow 1.10.0 using TensorRT. But the version of TensorFlow is too early, so TensorRT 5.0.0Rc is required. Because the version is too early, NVIDIA's official website and GitHub do not provide download sources. Is there any other way to download it? Or is there any other way to deploy TensorFlow 1.10.0? Thank you.
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I am trying to run code that was written with tensorflow v1 and I am struggling to migrate it to tensorflow v2. I thought it might be easiest to install tensorflow v1 but I couldn't find a tutorial on how to do that. Is it even still possible to install Tensorflow 1?
Code written using Tensorflow V1 can be easily upgraded to Tensorflow v2 by following Tensorflow migrate guide.
And also convert Tensorflow v1 based code to Tensorflow V2 just by running upgrade code, to know more about this library see here.
To install Tensorflow v1 version,
pip install tensorflow==1.15
Follow the instructions mentioned to install Tensorflow.
I want to convert my tensorflow 1.1 based model into tensorflow lite in order to serve the model locally and remotely for a PWA. The official guide only offers Python APIs for 1.11 at the earliest. Command line tools only seem to work starting at 1.7. Is it possible to convert a 1.1 model to tensorflow lite? Has anyone had experience with this?
The tf module is an out-of-the-box pre-trained model using BIDAF. I am having difficulty serving the full tf app on Heroku, which is unable to run it. I would like to try a tf lite app to see if hosting it locally will make it faster, and easier to set up as a PWA.
I would like to use Tensorflow 1.3 (and maybe 1.4) on Cloud ML. Im running jobs on multi-GPU machines on Cloud ML
I do that by specifying the tensorflow version in the setup.py as shown below:
from setuptools import setup
REQUIRED_PACKAGES = ['tensorflow==1.3.0']
setup(
name='my-image-classification',
install_requires=REQUIRED_PACKAGES,
version='1.0',
packages=['my_image_classification',
'my_image_classification/foo',
'my_image_classification/bar',
'my_image_classification/utils'],
)
What is the cudnn library that is installed on Cloud ML? Is it compatible with tensorflow 1.3 and tensorflow 1.3+ ?
I was able to start the jobs, but the performance is 10X lower than the expected value, and I'm curious if there is a problem with the underlying linking of Libraries
Edit:
I'm pretty confident now that the Cudnn versions on Cloud ML dont match what is required for Tensorflow 1.3. I noticed that Tensorflow 1.3 jobs are missing the "Creating Tensorflow device (/gpu:0...) " Logs which appear when I run a job with the default available Tensorflow on cloud ml
DISCLAIMER: using anything but 1.0, 1.2 is not officially supported as of 2017/11/01.
You need to specify the GPU-enabled version of TensorFlow:
REQUIRED_PACKAGES = ['tensorflow-gpu==1.3.0']
But the version of pip is out-of-date so you need to force that to update first.
I want to use the tensorflow in a QNX operating system? The very first step is to integrate the tensorflow into QNX. Any suggestions?
There is an issue on that on GitHub, unfortunately w/o a result but it's a starting point: https://github.com/tensorflow/tensorflow/issues/14753
Depending on your objective, NVIDIA's TensorRT can load TensorFlow models and provides binaries for QNX, see for example https://docs.nvidia.com/deeplearning/sdk/pdf/TensorRT-Release-Notes.pdf
How can I report an issue on Tensorflow website? I am not talking about the API, but everything else, e.g. installation instructions and tutorials.
For instance, installation instructions indicate that Tensorflow for Python 3.5 and GPU requires CuDNN v5, but that is incorrect, as with CuDNN v5 it doesn't work, and it requires CuDNN v5.1 instead.
Thanks!
Please open an issue at the TensorFlow GitHub Issues page: https://github.com/tensorflow/tensorflow/issues