Head to and download the Individual Edition for your operating system (Windows 圆4). Run the Anaconda installer and accept all the default settings. This proves to be extremely helpful when you want to run multiple versions of Python and TensorFlow side by side. While you could install TensorFlow directly on your system next to whatever Python version you wish, I recommend doing everything through Anaconda.Īnaconda provides a terminal prompt and can easily help you switch between Python environments. By looking at the table, we can see that it requires Python version 3.5-3.8. Head to this page to look at the available versions of TensorFlow.Īt the time of writing, the most recent version of TensorFlow available is 2.2.0. If the system outputs the following, means TensorFlow is successfully installed.We need to pay attention to version numbers, as TensorFlow works with only certain versions of Python. Hello = tf.constant( 'Hello, TensorFlow!') Type python from the shell as follows: Type the following program inside the python interactive shell: import tensorflow as tf If TensorFlow installed through Anaconda, activate Anaconda environment which created in previous part step 3. Start a terminal (install TensorFlow by windows command line) or a Anaconda Prompt (install TensorFlow by Anaconda). To install the GPU version of TensorFlow, type the following command: pip install -ignore-installed -upgrade tensorflow-gpu To install the CPU-only version of TensorFlow, type the following command: pip install -ignore-installed -upgrade tensorflow Type the appropriate command to install TensorFlow inside the conda environment. Follow the instructions on the Anaconda download site to download and install Anaconda.Ĭreate a conda environment named tensorflow (an arbitrary name) by using the following command: conda create -n tensorflow pip python=3.6Īctivate the conda environment by typing the following command: activate tensorflow.Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow team neither tests nor maintains the conda package. NOTE: The conda package is community supported, not officially supported. Within Anaconda, installing TensorFlow with the pip install command, not with the conda install command. In Anaconda, use the conda command to create a virtual environment. To use a different version of cuDNN must have to build from source. In particular, the cuDNN version must match exactly: TensorFlow will not load if it cannot find cuDNN64_7.dll. If the preceding packages version are different from this document, change to the specified versions. See NVIDIA documentation for a list of supported GPU cards. GPU card with CUDA Compute Capability 3.0 or higher for building from source, and 3.5 or higher for TensorFlow binaries.Ensure the cuDNN DLL directory path is added to the %PATH% environment variable. cuDNN is typically installed in a different location from the other CUDA DLLs. The NVIDIA drivers associated with CUDA Toolkit 9.0.For details, see NVIDIA's documentation to ensure the Cuda pathnames is appended to the %PATH% environment variable as described in the NVIDIA documentation. If installing TensorFlow with GPU support, then the following NVIDIA software must be installed on the system: Requirements to run TensorFlow with GPU support If the system has a NVIDIA GPU meeting the prerequistites shown below, this version run performance-critical applications is better. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Prebuilt binaries will use AVX instructions. If the system does not have a NVIDIA GPU, must install this version. Choose CPU/GPU version TensorFlow to installĬhoose one type of TensorFlow to install: Machine requirements are 64-bit, x86 desktops or laptops, and Windows 7 or later.
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