TensorFlow is still one of the popular Deep learning frameworks. It has been used in many different fields of applications including handwritten digit classification, image recognition, object detection, word embeddings, and natural language processing (NLP).
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In September last year, 2019, Google finally announced the availability of the final release of TensorFlow 2.0. With eager execution by default and tight integration with Keras, now TensorFlow 2.0 makes the development of machine learning applications much easier than before.
To work with the code examples in this course, we need to install the Python 3 programming language, the PyCharm development environment, and several software libraries, including TensorFlow. This video will cover installation on Mac OS. If you are using Windows, watch the separate video covering Windows installation instead. The method we'll use to install TensorFlow will only install the. TensorFlow. is a widely-used machine learning framework in the deep learning arena, demanding efficient utilization of computational resources. In order to take full advantage of Intel® architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning. This release version is built for Tensorflow 1.14.0rc1 for Python 2.7, 3.7 on Mac OS X 10.13 with GPU (CUDA) support. Supported CUDA compute capability: 3.0,3.5,5.0,5.2,6.1,7.0. (All possible capabilities on Mac OS. If you find anything missing, please open an issue.) CUDA 10.0 and CuDNN 7.4 are required.
We can now easily debug TensorFlow’s variables and print their values just like in the standard Python. That’s way, TensorFlow 2.0 is more friendly than the older version 1.x.
For those of you who don’t have prior experience with this topic, this post is special for you. Here, I’m going to show you how to install TensorFlow 2.0 in Anaconda.
What is Anaconda and why I recommend it?
Anaconda is a Python-based data processing built for data science. It comes with many useful built-in third-party libraries. Installing Anaconda meaning installing Python with some commonly used libraries such as Numpy, Pandas, Scrip, and Matplotlib.
For a Python developer or a data science researcher, using Anaconda has a lot of advantages, such as independently installing/updating packages without ruining the system. So, we no need to worry about the system library or anything like that. This can save time and energy for other things.
Anaconda can be used across different platforms, Windows, macOS, and Linux. If we want to use a different Python version or package libraries, just create a different environment and play around without any risk of crashing the system library.
Now, let’s install Anaconda first.
Installing Anaconda
Anaconda is available for Windows, Mac OS X, and Linux, you can find the installation file in the anaconda official site. I suggest you choose the Python version 3.7 64-bit installer if you have a 64-bit machine, otherwise choose the 32-bit installer, instead. If you need, you can easily install Python 2.7 versions later.
In case you have already installed Python on your computer, don’t worry, it won’t ruin anything. Instead, the default Python used by your programs will be the one that comes with Anaconda. Go ahead and choose the appropriate version, follow the instructions and install it.
I will let you explore it, but anyhow, if you have any problem, you can simply post a comment in the comment section and I will try to do my best for you.
(Note: For more details on how to use Anaconda, you can visit the Anaconda user guide here).
(Note: For more details on how to use Anaconda, you can visit the Anaconda user guide here).
Now, we’re going to create our first environment, but be sure that you’ve installed Anaconda on your computer.
Creating an Environment
Open Anaconda prompt, and create a new environment called yolov3_tf2 ( I gave this name because it relates to my next article about the implementation of YOLOv3 in TensorFlow 2.0). You can name it whatever you want. Just type or copy the following command to your Anaconda prompt and hit Enter.
After that, you will be prompted something like this, just type ‘y‘ and then hit the Enter.
Note: you might be prompted a bit different to this, it doesn’t matter just hit Enter, Anaconda will do the best for you.
Note: you might be prompted a bit different to this, it doesn’t matter just hit Enter, Anaconda will do the best for you.
Wait until all packages installed successfully, and then you can activate your new Anaconda environment.
Copy and paste this command to your Anaconda prompt and hit Enter.
Copy and paste this command to your Anaconda prompt and hit Enter.
Now, your Conda’s environment is ready to use. Let’s install TensorFlow 2.0.
Installing TensorFlow 2.0
When you are in the yolov3_tf2 environment, now you can install any package you want. To install TensorFlow 2.0, type this command and hit Enter.
GPU:
![Tensorflow Download Mac Tensorflow Download Mac](/uploads/1/2/4/3/124351825/393583484.jpg)
CPU:
Verify the Cuda toolkit and
cudnn
that will be installed, it must come with Cudatoolkit 10 and cudnn 7.6
. If everything goes right, just type ‘y’ and hit Enter. Basically, your TensorFlow has been installed now. Let’s check whether it’s installed correctly or not.
Type
Type
python
in Anaconda command prompt and hit Enter, your Python must be version 3.7, then type import tensorflow as tf
and hit Enter, followed by typing tf.__version__
and hit Enter. If you have TensorFlow installed on your environment, you’ll get no errors, otherwise, you’ll need to re-install it. If everything has been installed correctly, you’ll get the result as shown in the figure below. Your TF version must be ‘2.0.0’.
See you and check this out, my tutorial about YOLOv3 object detection.
![Tensorflow download mac app Tensorflow download mac app](/uploads/1/2/4/3/124351825/270266029.jpg)
#TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
Note: Currently we do not accept pull requests on github -- see CONTRIBUTING.md for information on how to contribute code changes to TensorFlow through tensorflow.googlesource.com
We use github issues for tracking requests and bugs, but please see Community for general questions and discussion.
Tensorflow Download Mac
To install the CPU version of TensorFlow using a binary package, see the instructions below. For more detailed installation instructions, including installing from source, GPU-enabled support, etc., see here.
Binary Installation
The TensorFlow Python API supports Python 2.7 and Python 3.3+.
The simplest way to install TensorFlow is using pip for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for more detailed installation instructions, see here.
Ubuntu/Linux 64-bit
Mac OS X
Tensorflow Download Mac App
Try your first TensorFlow program
Tensorflow Virtual Machine Download
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