To use TensorFlow Quantum on a local machine, install the TFQ package using.The easiest way to learn and use TFQ requires no installation-run the.You may determine if you will purchase a higher level graphics card to get faster training speed after you master the basics of TensorFlow.There are a few ways to set up your environment to use TensorFlow Quantum (TFQ): The beginner level models of TensorFlow do not require too much performance as the CPU version is adequate. Meanwhile, the acceleration rate is also influenced by the running task itself. NVIDIA GeForce GTX 1080 Ti or NVIDIA GeForce TITAN Series are powerful graphics card types when this handbook was being written). However, the acceleration rate may reach 10 or even higher under specific models if you have a powerful GPU (e.g. It won’t be satisfactory if you have a high performance CPU and a beginner level GPU where the acceleration rate will be like 1-2. The effect of acceleration is relative to the GPU performance. You can always download P圜harm Community version whose main functions do not differ that much from the former if you do not meet the aforementioned criteria. edu, you can apply for a free license here. If you are a student with an email address ended with. We recommend you to use P圜harm as your Python IDE. Relax, Python is easy to handle and advanced features of Python will be barely involved in TensorFlow. From now on we assume that readers are familiar with the basics of Python. It’s normal for the program to output some prompt messages when running. We can draw conclusions that TensorFlow was successfully installed. Tensor ( ], shape = ( 2, 2 ), dtype = int32 ) You have to copy the downloaded files of cuDNN to the installation directory of CUDA to complete cuDNN installation.The version of CUDA Toolkit and cuDNN must agree with the requirements on TensorFlow official website which does not always require the latest version.You can seek a more detailed guidance here You should disable the built-in graphics driver Nouveau and Secure Boot function of the motherboard. First click “Software & Updates” in “System Setting”, then toggle on “Using NVIDIA binary driver” option in “Additional Drivers” and click “Apply Changes” for system to install NVIDIA drivers automatically, otherwise, it won’t be peaceful for NVIDIA installation on Linux. There is a quite simple way to install drivers in Ubuntu.We recommend you install it through the following order: 1) latest NVIDIA graphics driver 2) CUDA (without selecting the built-in driver when installing since the built-in ones may be out-of-date) 3) cuDNN. ![]() (For GPU version installation) Install the NVIDIA graphics driver, CUDA Toolkit and cuDNN.You can always call them under the Anaconda Prompt started in the Start Menu. It enables you to call all Anaconda commands under command line or Powershell directly. You can choose to add the directory of Anaconda into the PATH (though not recommended by the installation wizard).Note that TensorFlow only supports Python Ver 3.X under Windows when we write this handbook. It is an open-source release version of Python that provides a full environment for scientific computation including common libraries such as NumPy and SciPy, or you can choose your favorite ones of course. To be more specific, the CUDA Computing Capability of your graphics card that you can check on NVIDIA official website should not be less than 3.0. Check if your computer has an NVIDIA graphics card and install the GPU version of TensorFlow in order to take advantages of its powerful capability of computation acceleration, or, just install CPU version if not so.Environment configuration before installation ¶īefore installing TensorFlow, we need to set up a proper environment with the following steps:
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