1641396900
TensorFlow ROCm port
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.
Tensorflow ROCm port
Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used:
alias drun='sudo docker run \
-it \
--network=host \
--device=/dev/kfd \
--device=/dev/dri \
--ipc=host \
--shm-size 16G \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-v $HOME/dockerx:/dockerx'
drun rocm/tensorflow:latest
We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:
# Install some ROCm dependencies
sudo apt install rocm-libs rccl
# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade
For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.
Install
See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add --upgrade flag to the above commands.
Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.
Try your first TensorFlow program
$ python>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'For more examples, see the TensorFlow tutorials.
Contribution guidelines
If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
Continuous build status
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
Official Builds
| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU | PyPI | |
| Linux GPU | PyPI | |
| Linux XLA | TBA | |
| macOS | PyPI | |
| Windows CPU | PyPI | |
| Windows GPU | PyPI | |
| Android | Download | |
| Raspberry Pi 0 and 1 | Py3 | |
| Raspberry Pi 2 and 3 | Py3 | |
| Libtensorflow MacOS CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Linux CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Linux GPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Windows CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Windows GPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
Resources
- TensorFlow.org
- TensorFlow Tutorials
- TensorFlow Official Models
- TensorFlow Examples
- DeepLearning.AI TensorFlow Developer Professional Certificate
- TensorFlow: Data and Deployment from Coursera
- Getting Started with TensorFlow 2 from Coursera
- TensorFlow: Advanced Techniques from Coursera
- TensorFlow 2 for Deep Learning Specialization from Coursera
- Intro to TensorFlow for A.I, M.L, and D.L from Coursera
- Intro to TensorFlow for Deep Learning from Udacity
- Introduction to TensorFlow Lite from Udacity
- Machine Learning with TensorFlow on GCP
- TensorFlow Codelabs
- TensorFlow Blog
- Learn ML with TensorFlow
- TensorFlow Twitter
- TensorFlow YouTube
- TensorFlow model optimization roadmap
- TensorFlow White Papers
- TensorBoard Visualization Toolkit
Learn more about the TensorFlow community and how to contribute.
Download Details:
Author: ROCmSoftwarePlatform
Source Code: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
License: Apache-2.0 License
#tensorflow #machine-learning #deep-learning
What is GEEK
Buddha Community
1615446243
Android App to iOS App Porting Services in Virginia, USA | SISGAIN
Want to port your android app to IOS ? The Android to iOS portion can be easy with SISGAIN. Our android to ios porting services make it easier to port android apps to iOS in Virginia, USA. With our remote team you can port your app today. Our dedicated android to iOS Porting developers will help you to run your business smoothly without any hassle. For more information call us at +18444455767 or email us at hello@sisgain.com
#android to ios porting #port android app to ios #porting android to ios #android to ios porting #android app to ios app porting in usa #dedicated android to ios porting developers
1623228736
5 Steps to Passing the TensorFlow Developer Certificate
Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!
- Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.
2. Do the course questions in parallel in PyCharm.
…
#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing
1641396900
TensorFlow ROCm port
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.
Tensorflow ROCm port
Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used:
alias drun='sudo docker run \
-it \
--network=host \
--device=/dev/kfd \
--device=/dev/dri \
--ipc=host \
--shm-size 16G \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-v $HOME/dockerx:/dockerx'
drun rocm/tensorflow:latest
We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:
# Install some ROCm dependencies
sudo apt install rocm-libs rccl
# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade
For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.
Install
See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add --upgrade flag to the above commands.
Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.
Try your first TensorFlow program
$ python>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'For more examples, see the TensorFlow tutorials.
Contribution guidelines
If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
Continuous build status
You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.
Official Builds
| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU | PyPI | |
| Linux GPU | PyPI | |
| Linux XLA | TBA | |
| macOS | PyPI | |
| Windows CPU | PyPI | |
| Windows GPU | PyPI | |
| Android | Download | |
| Raspberry Pi 0 and 1 | Py3 | |
| Raspberry Pi 2 and 3 | Py3 | |
| Libtensorflow MacOS CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Linux CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Linux GPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Windows CPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
| Libtensorflow Windows GPU | Status Temporarily Unavailable | Nightly Binary Official GCS |
Resources
- TensorFlow.org
- TensorFlow Tutorials
- TensorFlow Official Models
- TensorFlow Examples
- DeepLearning.AI TensorFlow Developer Professional Certificate
- TensorFlow: Data and Deployment from Coursera
- Getting Started with TensorFlow 2 from Coursera
- TensorFlow: Advanced Techniques from Coursera
- TensorFlow 2 for Deep Learning Specialization from Coursera
- Intro to TensorFlow for A.I, M.L, and D.L from Coursera
- Intro to TensorFlow for Deep Learning from Udacity
- Introduction to TensorFlow Lite from Udacity
- Machine Learning with TensorFlow on GCP
- TensorFlow Codelabs
- TensorFlow Blog
- Learn ML with TensorFlow
- TensorFlow Twitter
- TensorFlow YouTube
- TensorFlow model optimization roadmap
- TensorFlow White Papers
- TensorBoard Visualization Toolkit
Learn more about the TensorFlow community and how to contribute.
Download Details:
Author: ROCmSoftwarePlatform
Source Code: https://github.com/ROCmSoftwarePlatform/tensorflow-upstream
License: Apache-2.0 License
1623139838
Transfer Learning on Images with Tensorflow 2 – Predictive Hacks
In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.
The Data
We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.
#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2
1623737882
TensorFlow Lite Object Detection using Raspberry Pi and Pi Camera
I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!
Prerequisites:
-
I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)
-
I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)
#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera