![]() To install Anaconda on your system, visit this link. ![]() Once you are done with this, you can download Anaconda, and if you already have it, then create a Python 3.5 environment in it. C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\libnvvp.C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin.Add the following two paths to the path variable: Now click on the 'Environment Variables', Install cuDNN environment variablesĪnd under System Variables look for PATH, and select it and then click edit. Click on the search result and open the System Properties window and within it open the Advanced tab. Once you are done with the transfer of the contents, go to the start menu and search for ”edit the environment variables”. Similarly, transfer the contents of the include and lib folders. In this folder, you can see that you have the same three folders: bin, include and lib.Ĭopy the contents of the bin folder on your desktop to the bin folder in the v9.0 folder. Inside this, you will find a folder named CUDA which has a folder named v9.0. Go to the C drive, there you will find a folder named NVIDIA GPU Computing Toolkit. Extract these three files onto your desktop. Once you unzip the file, you will see three folders in it: bin, include and lib. This will download a zip file on to your system. Then choose the appropriate OS option for your system. Click on the cuDNN version 7.0 for CUDA 9.0.Once you create your login and agree to the terms and conditions, visit the archived cuDNN files.So, please go ahead and create your login if you do not have one. Here to download the required files, you need to have a developer's login.Once your installation is completed, you can download the cuDNN files. If you face any issue during installation, please check the Nvidia forums. Once the download is complete, install the base installer first followed by the patches starting from Patch 1 to Patch 4. Now download the base installer and all the available patches along with it.Please choose your OS, architecture (CPU type of the platform) and version of the OS correctly.Once you are certain that your GPU is compatible, download the CUDA Toolkit 9.0. So please check if you have a GPU on your system and if you do have it, check if it is a compatible version using the third link in the above screenshot. Install CUDAĪs it goes without saying, to install TensorFlow GPU you need to have an actual GPU in your system. I have a windows based system, so the corresponding link shows me that the latest supported version of CUDA is 9.0 and its corresponding cuDNN version is 7. This is a tricky step, and before you go ahead and install the latest version of CUDA (which is what I initially did), check the version of CUDA that is supported by the latest TensorFlow. Any other IDE or no IDE could be used for running TensorFlow with GPU as well. Note: Installing the Visual Studio Community is not a prerequisite. ![]() Once you have downloaded the Visual Studio, follow the setup process and complete the installation. Here, make sure that you select the community option. In the next step, we will install the visual studio community. Once you have removed all the programs, go to the C drive and check all the program files folders and delete any Nvidia folders in them. Do not worry if you have some drivers, they can be updated later once you finish the setup. Here, you uninstall all the Nvidia programs. Then scroll below to the section with programs that have been published by the Nvidia Corporation. Once you login to your system, go to the control panel, and then to the ‘Uninstall a program’ link. ![]() This may not look like a necessary step, but believe me, it will save you a lot of trouble if there are compatibility issues between your current driver and the CUDA. After a lot of trouble and a burnt motherboard (not due to TensorFlow), I learnt how to do it.Ī few days earlier I spoke to someone who was facing a similar issue, so I thought I might help people who are stuck in a similar situation, by writing down the steps that I followed to get it working. One of the basic problems that I initially faced was the installation of TensorFlow GPU. Later I heard about the superior performance of the GPUs, so I decided to get one for myself. When I started working on Deep Learning (DL) models, I found that the amount of time needed to train these models on a CPU was too high and it hinders your research work if you are creating multiple models in a day. Steps involved in the process of Tensorflow GPU installation are: In this blog, we will understand how to Tensorflow GPU installation on a Nvidia GPU system.
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