Check version of tensorflow

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Check version of tensorflow

To check your TensorFlow version in your Jupyter Notebook such as Google's Colab, use the following two commands:import tensorflow as tf This imports the TensorFlow library and stores it in the variable named tf.print(tf.__version__) This prints the installed TensorFlow version number in the format x.y.z.The following code example uses the dunder attribute __version__ on the tf module. Libraries commonly maintain their version information in this dunder attribute.import tensorflow as tf print(tf.__version__)You can check this out in the following online Jupyter Notebook I've prepared for you using a shareable Google Colab notebook:The interactive Jupyter Notebook opens in a new tab if you click on the image!How to Switch the TensorFlow version on Colab?Colab has two TensorFlow versions pre-installed:2.x version, and for legacy reasons,1.x version.Per default, Colab uses TensorFlow version 2.x but you can switch to another version by using a bit of "TensorFlow magic" as a percentage-prefixed tensorflow_version expression in any of your cells:%tensorflow_version 1.xAfter evaluating this statement, the Colab notebook will switch to a state where the TensorFlow version 1.x is used rather than 2.x as per default.Here's how this will look like in a cell:%tensorflow_version 1.x import tensorflow as tf print(tf.__version__)And the output in my Colab Notebook is:TensorFlow 1.x selected. 1.15.2Note that if you've already run any cell that imports the TensorFlow library, you need to restart the notebook by choosing Runtime > Restart runtime in your Colab notebook:As an alternative to check the TensorFlow version, you can also use the tf.version.VERSION attribute like so:import tensorflow as tf print(tf.version.VERSION)This doesn't work for some older versions of TensorFlow but the alternative tf.__version__ should work for all! While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.To help students reach higher levels of Python success, he founded the programming education website . He's author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here. Hu nh mi g?i th?ng thng trong python u g?n bin .__version__hoc VERSIONcho phi?n bn hin ti. V? vy, nu bn mun t?m phi?n bn ca mt s g?i, bn c? th l?m nh sau import a a.__version__ # or a.VERSION i vi d?ng chy, n? s l? import tensorflow as tf tf.VERSION i vi c?c phi?n bn c ca tenorflow (di 0.10), h?y s dng tf.__version__ BTW, nu bn nh c?i t tf, h?y c?i t n? bng conda, kh?ng phi pip NEWBEDEVPythonJavascriptLinuxCheat sheet Introduction TensorFlow is one of the most prominent machine learning packages. Knowing which version is on the system is vital as different builds have different options. There are multiple ways to check the TensorFlow version depending on the installation method. This article shows how to check the TensorFlow version in six different ways. Prerequisites The simplest way to check the TensorFlow version is through a Python IDE or code editor. The library has builtin methods for displaying basic information. To print the TensorFlow version in Python, enter: import tensorflow as tf print(tf.__version__) The TensorFlow 2.x versions provide a method for printing the TensorFlow version. To check which one is on your system, use: import tensorflow as tf print(tf.version.VERSION) TensorFlow 1.x has a slightly different method for checking the version of the library. Print the version for older TensorFlow builds in Python by running: import tensorflow as tf print(tf.VERSION) Display the TensorFlow version through Python invocation in the CLI with the python command. Using the -c option executes code. If your machine has multiple instances of Python installed, use the python command. Print the TensorFlow version in the terminal by running: python -c 'import tensorflow as tf; print(tf.__version__)' If there are multiple instances of Python on the system, use: python -c 'import tensorflow as tf; print(tf.__version__)' For example: Show the TensorFlow version in the command line by running: python -c "import tensorflow as tf; print(tf.__version__)" Check with a specific version of Python by adding the version number to the python command: python -c "import tensorflow as tf; print(tf.__version__)" The most common way to install Python libraries is using the pip package manager. There are two ways to print the version with pip. The pip show command prints information for any installed package. To show the TensorFlow data, run this command: pip show tensorflow The pip list command shows all the packages installed using pip install. In Linux, use the grep command to filter out the results: pip list | grep tensorflow For Windows, use findstr to filter the pip list results: pip list | findstr "tensorflow" The TensorFlow documentation recommends installing the platform through a virtual environment. Activate the virtual environment before checking the version. To activate the virtual environment, use the appropriate command for your OS: For Linux, run: virtualenv For Windows, use: \Scripts\activate The environment shows up in the CLI as active: Check the version inside the environment using the python -c or pip show command. For example: pip show tensorflow Anaconda uses the conda package manager for installation. conda list shows all the libraries installed using conda install. For Linux, filter the results with the grep command: conda list | grep tensorflow For Windows, combine the conda list and findstr commands to print the TensorFlow version: conda list | findstr "tensorflow" The Jupyter Notebook runs commands and Python code directly in the environment. There are two ways to check the TensorFlow version in Jupyter Notebooks. Import the TensorFlow library and print the version by running the following code: import tensorflow as tf print(tf.__version__) Show the TensorFlow version using the pip command with an exclamation point: !pip show tensorflow Conclusion This tutorial explains how to check the TensorFlow version for different cases in different environments. For additional TensorFlow material, check out our comparison of PyTorch vs TensorFlow. Prior to using the tensorflow R package you need to install a version of TensorFlow on your system. Below we describe how to install TensorFlow as well the various options available for customizing your installation.Note that this article principally covers the use of the R install_tensorflow() function, which provides an easy to use wrapper for the various steps required to install TensorFlow.You can also choose to install TensorFlow manually (as described at . In that case the Custom Installation section covers how to arrange for the tensorflow R package to use the version you installed.TensorFlow is tested and supported on the following 64-bit systems:Ubuntu 16.04 or laterWindows 7 or latermacOS 10.12.6 (Sierra) or later (no GPU support)TensorFlow is distributed as a Python package and so needs to be installed within a Python environment on your system. By default, the install_tensorflow() function attempts to install TensorFlow within an isolated Python environment ("r-reticulate").These are the available methods and their behavior:autoAutomatically choose an appropriate default for the current platform.virtualenvInstall into a Python virtual environment at ~/.virtualenvs/r-reticulatecondaInstall into an Anaconda Python environment named r-reticulatesystemInstall into the system Python environmentThe "virtualenv" and "conda" methods are available on Linux and OS X and only the "conda" method is available on Windows.install_tensorflow is a wraper around reticulate::py_install. Please refer to `Installing Python Packages' for more information. Here you will learn how to check CUDA version for TensorFlow. The 3 methods are CUDA toolkit's nvcc, NVIDIA driver's nvidia-smi, and simply checking a file. PrerequisiteBefore we begin, you should have installed NVIDIA driver on your system as well as Nvidia CUDA toolkit. We also assume you have TensorFlow installed.To check if TensorFlow is using GPU and how many GPUs are available in your system, runimport tensorflow as tf print("# GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))You should be able to see something similar:# GPUs Available: 1Method 1 -- Use nvcc to check CUDA version for TensorFlowIf you have installed the cuda-toolkit package either from Ubuntu's or NVIDIA's official Ubuntu repository through sudo apt install nvidia-cuda-toolkit, or by downloading from NVIDIA's official website and install it manually, you will have nvcc in your path ($PATH) and its location would be /usr/bin/nvcc (by running which nvcc).To check the CUDA version with nvcc for TensorFlow, executenvcc --versionYou can see similar output in the screenshot below. The last line shows your version. The version here is 10.1. Yours can vary, and may be either 10.0 or 10.2. After the screenshot you can find the full text output too.vh@varhowto-com:~$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Sun_Jul_28_19:07:16_PDT_2019 Cuda compilation tools, release 10.1, V10.1.243What is nvcc?nvcc is the NVIDIA CUDA Compiler, thus the name. It is the main wrapper for the CUDA compiler suite. For nvcc`s other usage, you can use it to compile and link both host and GPU code.Check out the manpage of nvcc for more information.Method 2 -- Use nvidia-smi from Nvidia Linux driverThe second way to check CUDA version for TensorFlow is to run nvidia-smi that comes from your NVIDIA driver installation, specifically the NVIDIA-utils package. You can either install Nvidia driver from Ubuntu's official repository or NVIDIA website. $ which nvidia-smi /usr/bin/nvidia-smiTo use nvidia-smi to check CUDA version, directly runnvidia-smiYou will see output similar to the following screenshot. The CUDA version information is on the top right of the output. Here my version is 10.2. Again, yours might vary if you installed 10.0, 10.1 or even have the older 9.0.Interestingly, you can also find more detail from nvidia-smi, except for the CUDA version, such as driver version (440.100), GPU name, GPU fan ratio, power consumption / capability, memory use. You can also find the processes which use the GPU at present. This is helpful if you want to see whether GPU is included in your TensorFlow model or software.Here is the full text output:Mon Aug 10 23:22:16 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 440.64 Driver Version: 440.64 CUDA Version: 10.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 GeForce GTX 1070 Off | 00000000:01:00.0 On | N/A | | 33% 47C P0 29W / 151W | 1914MiB / 8116MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 2032 G /usr/lib/xorg/Xorg 73MiB | | 0 2156 G /usr/bin/gnome-shell 179MiB | | 0 4259 G /usr/lib/xorg/Xorg 951MiB | | 0 4376 G /usr/bin/gnome-shell 268MiB | | 0 7919 G ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files 146MiB | | 0 10277 G ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files 290MiB | +----------------------------------------------------------------------------+What is nvidia-smi?nvidia-smi (NVSMI) is NVIDIA System Management Interface program. It is also known as NVSMI. nvidia-smi provides tracking and maintenance features for all of the Tesla, Quadro, GRID and GeForce NVIDIA GPUs and higher architectural families in Fermi. For most functions, GeForce Titan Series products are supported with only a limited amount of detail provided for the rest of the Geforce range.NVSMI is also a cross-platform program that supports all popular Linux distros supported by the NVIDIA driver and 64-bit Windows versions beginning with Windows Server 2008 R2. Metrics may be used by users directly through stdout, or stored for scripting purposes via CSV and XML formats.For more information, check out nvidia-smi`s manpage.Method 3 -- cat /usr/local/cuda/version.txtcat /usr/local/cuda/version.txtNote that this method might not work on Ubuntu 18.04 if you install Nvidia driver and CUDA from Ubuntu 18.04's own official repository.What is TensorFlowTensorFlow 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 has 3 major features: Easy model building. Create and train ML models with eager execution, using intuitive high-level APIs such as Keras. The eager execution makes for immediate model iteration and makes it easy to debug.Robust ML production anywhere. Models can be quickly trained and distributed in the cloud, on-prem, on the web, or on-device irrespective of the language you use.Powerful experimentation for research. A easy and scalable framework for bringing new ideas faster from concept to code, to state-of-the-art models and to publish.3 ways to check CUDA version for TensorFlowTime Needed : 5 minutesThere are three ways to identify the CUDA version, which isn't only for TensorFlow.The best way is possibly to test a fileRun cat /usr/local/cuda/version.txtNote: this may not work on Ubuntu 18.04Another solution is through the cuda-toolkit command nvcc.nvcc ?versionThe other way is by the NVIDIA driver's nvidia-smi command you may have installed.Simply run nvidia-smiMaterialsRelated This video will show you how to find out which version of TensorFlow is installed in your system by printing the TensorFlow version. First, we assume that you have already installed TensorFlow into your system. If you have installed TensorFlow correctly, then you will be able to import the package while in a Python interpreter session. import tensorflow as tf So you can do the import TensorFlow as tf. And it worked. There were no errors. Next, let's import a fake module or package to show you what error you would get. So we import superfakeunicorn. import superfakeunicorn When we evaluate it, we see that we get ModuleNotFoundError: No module named `superfakeunicorn'. So now that we know that we have TensorFlow installed correctly, let's figure out which version of TensorFlow is installed in our system by printing tf ? remember that we imported TensorFlow as tf ? so (tf.__version__). print(tf.__version__) When we evaluate that, we see that the TensorFlow package that is currently installed and being used in our Python interpreter session is TensorFlow 1.10.0. Perfect - We were able to find out which version of TensorFlow is installed in our system by printing the TensorFlow version.

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