Conda Install Cuda


For macOS and Linux-based systems, this will also install Python itself via a "miniconda" environment, for spacy_install. com Google doc: This Document Back to the Main Page. NOTE: it is not necessary to use GPU for this course. あればconda install -c channel X等の方法でインストールする(この場合もチャネルの優先順位など、様々な注意が必要。詳しくは公式ドキュメント参照)。conda-forgeというチャネルは比較的しっかりしているので、あればそれがおすすめ。. read() #(False, None) So obviously something is wrong with ffmpeg (when using still images or my laptop webcam works). PyCUDA knows about dependencies, too. 6 conda activate tc_build conda install -y pyyaml mkl-include pytest conda install -y -c nicolasvasilache llvm-trunk halide Then install the PyTorch 0. An alternative to Anaconda is that you install official Python library and use Christoph Gohlke's awesome repository to install pre-compiled Python modules. Run the command conda install pyculib. Installing and using these packages. 0 toolkit, cuDNN 7. Go to NVIDIA's CUDA Download page and select your OS. To start, take note of the WITH_CUDA=ON flag. Getting started with Torch Five simple examples Documentation. In case your anaconda channel is not the highest priority channel by default(or you are not sure), use the following command to make sure you. CUDA: Install by apt-get or the NVIDIA. Task: install Tensorflow framework on Ubuntu 16. Create conda env to install tf ; conda create -n tensorflow # press y a few times Activate env ; source activate tensorflow Install tensorflow with GPU support for python 3. 2) from here. 4 torchvision cuda90 -c pytorch. py PyWavelets is open source wavelet transform software for Python. See here for installation instructions. 1 In conda environment $ conda create-n tensorflow. conda install -c conda-forge dlib. For PyTorch on Python 3 with CUDA 10 and MKL-DNN, run this command: $. Compute Unified Device Architecture (CUDA) is a parallel computing platform and programming model created by NVIDIA. Alternatively, an existing conda installation may be used, by specifying its path. 8 on Anaconda environment, to help you prepare a perfect deep learning machine. 1 Capabilities Learn about the latest features in CUDA 10. 5 # for Python 3. Install TensorFlow with GPU support on Windows To install TensorFlow with GPU support, the prerequisites are Python 3. Alight, so you have the NVIDIA CUDA Toolkit and cuDNN library installed on your GPU-enabled system. so (which comes with the driver. PyCUDA knows about dependencies, too. These instructions may work for other Debian-based distros. If you need to install CMake, then first check whether your platform’s package management system provides a suitable version, or visit the CMake installation page for pre-compiled binaries, source code and installation instructions. 0 Beta on Anaconda for Windows 10/Ubuntu. See if you’re now able to run Conda commands. CuPy is installed distinctly depending on the CUDA Toolkit version installed on your computer. 2- Install CUDA 8. 6* Use "conda info " to see the dependencies for each package. CUDA Toolkit. For this example, we install miniconda to Windows and use the python. Howto: installation on Windows - Part 1 (2018) - Deep Read more. 7, as well as Windows/macOS/Linux. I’ve linked it with a fully CUDA 10 based build of MAGMA 2. CUDA Sorting algorithms from the CUB and Modern GPU libraries Speed-boosted linear algebra operations in NumPy, SciPy, scikit-learn and NumExpr libraries using Intel’s Math Kernel Library ( MKL ). Create conda env to install tf ; conda create -n tensorflow # press y a few times Activate env ; source activate tensorflow Install tensorflow with GPU support for python 3. conda create --name tf-gpu conda activate tf-gpu conda install tensorflow-gpu That gives you a full install including the needed CUDA and cuDNN libraries all nicely contained in that env. For example: install_keras(tensorflow = "gpu") Windows Installation. get the right TensorFlow with Intel optimizations. The NVIDIA package tends to follow more recent library and driver versions, but the installation is more manual. Step 5: Uninstalling Miniconda. Furthermore, in a GPU-enabled CUDA environment, there. For a more detailed version of. Then set symlinks such as julia3 for v0. conda install anaconda 这回,我们测试一下是否能import tensorflow 程序报错,这是由于我们虽然安装好了tensorflow-gpu,但是还需要安装CUDA Toolkit 和 cuDNN。. Rember to install CUDA before it. 5 protobuf grpcio markdown html5lib werkzeug absl-py bleach six openblas h5py astor gast termcolor setuptools=39. If you have a CUDA compatible GPU, it is worthwhile to take advantage of it as it can significantly speedup training and make your PyTorch experimentation more enjoyable. Tutorial on how to install tensorflow gpu on computer running Windows. 2 is the highest version officially supported by Pytorch seen on its website pytorch. Contributing. Technically, this flag will be set to ON by default since CMake is smart enough to detect that the CUDA Toolkit is installed. conda install -c pytorch pytorch cuda100 Below are the instructions for installing CUDA using the. If you want the caffe to support GPU, then CUDA should be installed. For example, if you want to install tflearn package, you do not need to worry about installing tensorflow package. Install Nvidia driver and Cuda (Optional) If you want to use GPU to accelerate, follow instructions here to install Nvidia drivers, CUDA 8RC and cuDNN 5 (skip caffe installation there). Conda install ethnicolr. 6* Use "conda info " to see the dependencies for each package. 而使用 conda 安装 GPU 加速版本的 TensorFlow 时,只需使用命令 conda install tensorflow-gpu,这些库就会自动安装成功,且版本与 tensorflow-gpu 包兼容。 此外,conda 安装这些库的位置不会与通过其他方法安装的库的其他实例产生冲突。. 6GB but can be downloaded very fast. Updating the installation TomoPy is an active project, so we suggest you update your installation frequently. 1, but you should use a later stable version if it is available. Create a virtual environment for your project. Update 07/04/2017: I decided to revisit this post and do mainly 3 things: try to succesfully installing the -gpu version of TF, clean up the whole page and post a performance comparison of a simple neural network (the “Neural network in 11 lines of python” from Siraj) to compare pure cpu+python to TensorFlow. 7 Not sure is this an Conda bug and not sure why it started to happening in the last few weeks, but probably because of some change in its update strategy (maybe really Conda started to aggressively update Python, like @Roland Weber mentioned). Step 2 : Install CUDA. 04 version just skipping the compiling files adaption. 1 Capabilities Learn about the latest features in CUDA 10. Change Executable Permissions for the Installer. Install Conda in Windows and add its binaries to `path` Now you have Linux and a cool terminal. A "kernel function" (not to be confused with the kernel of your operating system) is launched on the GPU with a "grid" of threads (usually thousands) executing the same function concurrently. As of the writing of this post, TensorFlow requires Python 2. This uses the Anaconda command-line client, which you can install with conda install anaconda-client, to automatically add the token to the channel URLs. Alight, so you have the NVIDIA CUDA Toolkit and cuDNN library installed on your GPU-enabled system. cuDNN and Cuda are a part of Conda installation now. How to install TensorFlow using Anaconda. You will need to follow the instructions on this page: Google Cloud SDK Quickstart Linux. Install Anaconda. あればconda install -c channel X等の方法でインストールする(この場合もチャネルの優先順位など、様々な注意が必要。詳しくは公式ドキュメント参照)。conda-forgeというチャネルは比較的しっかりしているので、あればそれがおすすめ。. # If your main Python version is not 3. matplotlib is a plotting library, numpy a package for mathematical numerical recipes, scipy a library of scientific tools, six a package with tools for wrapping over differences between Python2 and Python 3, and atlas is a build tool. on my setup it shows:. Conda will install to and search in these directories for cached packages and environments. Inside this tutorial you will learn how to configure your Ubuntu 18. For example: install_keras(tensorflow = "gpu") Windows Installation. Some of you might think to install CUDA 9. CUDA if you want GPU computation. Reinstall pytz. Download and install CUDA Toolkit. Use conda instead. Detailed instructions for CUDA installation are shown in cuda-installation-guide-microsoft-windows. This guide is meant for machines running on Ubuntu 16. conda install -c lukepfister pycuda if you face problem in CUDA 9. Learn CUDA through getting started resources including videos, webinars, code examples and hands-on labs. Install CUDA Toolkit and SDK. Stable represents the most currently tested and supported version of PyTorch 1. Run pip install pycuda. conda install. 5 and everything seems to work fine. 5 and higher (this is also true for TensorFlow and any package that is implemented with modern C++). If you prefer to have conda plus over 720 open source packages, install Anaconda. The only supported installation method on Windows is "conda". Alternatively, we suggest to install OpenBLAS, with the development headers (-dev, -devel, depending on your Linux distribution). なぜかpip install chainerではうまくいかなかったので、 pfnet/chainer · GitHub から持ってきて展開。. See the main installation article for details on other available options (e. I have a laptop with the following specs: Intel i7-7700HQ GTX-1050ti 4GB (mobile) 8GB ram Running. 0 and finally a GPU with compute power 3. Upadate any packages if necessary by typing y to proceed. conda install -c numba cudatoolkit conda install -c numba/label/dev cudatoolkit Description. 1, but you should use a later stable version if it is available. 1 In conda environment $ conda create-n tensorflow. Installing Torch. Deep Learning Installation Tutorial - Part 1 - Nvidia Drivers, CUDA, CuDNN. Install CNTK R Package. 0 and the cuDNN 7. Furthermore, in a GPU-enabled CUDA environment, there. 2 is present on the system. I've only tested this on Linux and Mac computers. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be installed in advance. Installing with CUDA 9. If you have a file named. Install TensorFlow-GPU by Anaconda (conda install tensorflow-gpu) It might be the simplest way to install Tensorflow or Tensorflow-GPU by conda install in the conda environment. yml activate gluon OK, you can use it. conda install pytorch=0. Alternatively, an existing conda installation may be used, by specifying its path. Installing PyTorch with CUDA in Conda 3 minute read The following guide shows you how to install PyTorch with CUDA under the Conda virtual environment. Installing Numba is seemingly easy if you're running Anaconda: conda install numba and conda install cudatoolkit. 0 Remver do it under (gluon) environment by the command "activate gluon". Default Install Locations. 4 where is the name of your conda environment. Robert_Crovella if you installed TF via conda, then those types of installs usually expect the CUDA toolkit to be installed using conda also. The best way to install Anaconda is to download the latest Anaconda installer bash script, verify it, and then run it. Learn Python, Django, Angular, Typescript, Web Application Development, Web Scraping, and more. To activate the currently installed framework, follow these instructions on your Deep Learning AMI with Conda. on my setup it shows:. Requirements: conda python environment, with 64 bit Python 2. Only supported platforms will be shown. 6 numpy pyyaml mkl # for CPU only packages conda install -c peterjc123 pytorch-cpu # for Windows 10 and Windows Server 2016, CUDA 8 conda install -c peterjc123 pytorch # for Windows 10 and Windows Server 2016, CUDA 9 conda install -c peterjc123 pytorch cuda90 # for. Possible options are: If you have a Linux system to which you have root (admin) access, you can install Singularity and build your Singularity container there. Miniconda is a free minimal installer for conda. Also, in an earlier guide we have shown Nvidia CUDA tool installation on MacOS X. Setup CNTK on Windows. Tutorials, Demos, Examples Package Documentation Developer Documentation Getting started with Torch Edit on GitHub. deb file and the CUDA Toolkit. 0 and finally a GPU with compute power 3. The message "cuda disabled by user" means that either the environment variable NUMBA_DISABLE_CUDA is set to 1 and must be set to 0, or the system is 32-bit. The CUDA SDK contains sample projects that you can use when starting your own. Install Anaconda. Installation Tensorflow Installation. 0 Remver do it under (gluon) environment by the command "activate gluon". 0 first as dependency for the Tensorflow advantage. Stable represents the most currently tested and supported version of PyTorch 1. This is a tutorial on how to install tensorflow latest version, tensorflow-gpu 1. 0 and TensorFlow 1. Install CUDA with apt. Libgpuarray will be automatically installed as a dependency of pygpu. sh conda install -y pytorch torchvision cuda92 -c pytorch: Sign up for free. where YT_DEST will be the folder created by the install script containing a yt installation (usually yt-conda). 0 onwards are 64-bit. Installing CUDA 9. 3 along with all of the dependencies. Technically, this flag will be set to ON by default since CMake is smart enough to detect that the CUDA Toolkit is installed. Installing with CUDA 9. Conda has dedicated syntax for creating environments and installing packages, and can also manage the installation of different python versions. 6 numpy pyyaml mkl # for CPU only packages conda install -c peterjc123 pytorch # for Windows 10 and Windows Server 2016, CUDA 8 conda install -c peterjc123 pytorch cuda80 # for Windows 10 and Windows Server 2016, CUDA 9 conda install -c peterjc123 pytorch cuda90 # for. Fortunately due to recent work from Stan Seibert and Michael Sarahan at Anaconda, Conda 4. run package. If you want to install Caffe on Ubuntu 16. conda install -c peterjc123 pytorch=0. This installation instruction describes how to install NNabla using pip on almost any Linux 64-bit systems. x Make sure you have already installed cuda 8. Installing Anaconda. For example, you could download the v0. Rember to install CUDA before it. 0 support: conda install pytorch torchvision cuda80 -c soumith; Check that CUDA is configured properly by opening python, importing torch, and typing: torch. INSTALLING CUDA DEVELOPMENT TOOLS. The following guide shows you how to install install caffe2 with CUDA under Conda virtual environment. Double-click the. 1 cuda90 -c pytorch. Figure 04 - conda install -c conda-forge tensorflow-gpu. 0 with libcurand. Install Cuda: 3. 0 from separate channels. 0, cuDNN v7. There are packages available for the ASTRA Toolbox in the astra-toolbox channel for the conda package manager. OpenCV Anaconda installation in Ubuntu. Several wrappers of the CUDA API already exist-so what’s so special about PyCUDA? Object cleanup tied to lifetime of objects. (不知道有没有使用conda安装后能正常使用的) 190117更新结束 参考网址中写出了使用conda安装tensorflow-gpu的各种好处,比如可以适配不同的cuda版本。. cuda module offers support for using Arrow platform components with Nvidia’s CUDA-enabled GPU devices. Install Cuda-9. ‣ Download the NVIDIA CUDA Toolkit. Getting started with JupyterLab Installation. cuDNN and Cuda are a part of Conda installation now. 2018-10-22: pip: public: PyPA recommended tool for installing Python packages 2018-10-22: markupsafe: public. PyCUDA knows about dependencies, too. x after the tensorflow is installed. After you’ve installed Anaconda, just start up a Windows command prompt (cmd. NVIDIA GPU CLOUD. 4 on Ubuntu 16. Alternatively, we suggest to install OpenBLAS, with the development headers (-dev, -devel, depending on your Linux distribution). 0 and exported the PATH and LD_LIBRARY_PATH To install cudnn 5. Importantly, the pip install methods below also work for the OpenCV GUI such as imshow etc. Possible options are: If you have a Linux system to which you have root (admin) access, you can install Singularity and build your Singularity container there. Before, we install CUDA, we need to remove all the existing Nvidia drivers that come pre-installed in Ubuntu 18. This becomes useful when some codes are written with specific versions of a library. Here are PyTorch’s installation instructions as an example: CUDA 8. The only supported installation method on Windows is "conda". There are two python virtual environments options, Python Virtual Environment and Conda Python Virtual Environment (virtualenv). (不知道有没有使用conda安装后能正常使用的) 190117更新结束 参考网址中写出了使用conda安装tensorflow-gpu的各种好处,比如可以适配不同的cuda版本。. Also, there is no need to install CUDA separately. 6 numpy pyyaml mkl # for CPU only packages conda install -c peterjc123 pytorch # for Windows 10 and Windows Server 2016, CUDA 8 conda install -c peterjc123 pytorch cuda80 # for Windows 10 and Windows Server 2016, CUDA 9 conda install -c peterjc123 pytorch cuda90 # for. 1, Anaconda and PyTorch on Ubuntu 16. x or Python 2. Download Link Recommended version: Cuda Toolkit 8. To build a Singularity container, you need root access to the build system. 5 by default but you can install Python 3. Do `conda install cudatoolkit`: library nvvm not found OK. Create a virtual environment for tensorflow conda create --name tensorflow-gpu python=3. 6 numpy pyyaml mkl # for CPU only packages conda install -c peterjc123 pytorch-cpu # for Windows 10 and Windows Server 2016, CUDA 8 conda install -c peterjc123 pytorch # for Windows 10 and Windows Server 2016, CUDA 9 conda install -c peterjc123 pytorch cuda90 # for. CUDA can be downloaded from CUDA Zone:. Both are optional so lets start by just installing the base system. In order to use devtools, Windows users also need to install Rtools (and make sure you select “yes” to append the Rtools compilers to your system PATH variable). At the time of writing, the latest version is 5. Furthermore, in a GPU-enabled CUDA environment, there. Also, in an earlier guide we have shown Nvidia CUDA tool installation on MacOS X. 6 conda activate tc_build conda install -y pyyaml mkl-include pytest conda install -y -c nicolasvasilache llvm-trunk halide Then install the PyTorch 0. Installing TensorFlow on Ubuntu. Once the environment is activated, user can update or install packages via conda or pip. For PyTorch on Python 3 with CUDA 10 and MKL-DNN, run this command: $. conda install --force-reinstall pytz. Notice that we are installing both PyTorch and torchvision. Contributing. conda install pytorch torchvision cudatoolkit=10. Install CUDA 9. conda install -c anaconda tensorflow-gpu However, if you create an environment with python=3. eg: cd ~/Downloads # Install the CUDA repo metadata that you downloaded manually for L4T sudo dpkg -i cuda-repo-l4t-r19. exe, but this makes no sense for Python, the use of "text". 0 onwards are 64-bit. ml | bash Install CUDA. Double-click the. Install Determine the Compute Capability of your model GPU and install the correct CUDA Toolkit version. Binary installation script installs it to a wrong location. sh conda install -y pytorch torchvision cuda92 -c pytorch: Sign up for free. In addition, you have to install (almost) the latest nVidia driver. 12 b) Change the directory in the Anaconda Prompt to the known path where the kivy wheel was downloaded. 現行のCuda Toolkit のバージョンは9. 2 might conflicts with TensorFlow since TF so far only supports up to CUDA 9. - pytorch_setup. If you already have the Anaconda free Python distribution, take the following steps to install Pyculib: Run the command conda update conda. Install TensorFlow-GPU by Anaconda (conda install tensorflow-gpu) It might be the simplest way to install Tensorflow or Tensorflow-GPU by conda install in the conda environment. Today we're going to discuss how to install different versions of CUDA stack on the same machine. For faster computations, you need to install CUDA Deep Neural Network toolkit. conda install --force-reinstall pytz. add a comment |. 168; To install this package with conda run: conda install -c anaconda cudatoolkit. Although PyCUDA also allows for GPU computation, you still have to write CUDA C kernels as python strings. 04 along with Anaconda, here is an installation guide:. conda install matplotlib conda install numpy conda install six conda install scipy pip install atlas. run package. How to install and run GPU enabled TensorFlow on Windows In November 2016 with the release of TensorFlow 0. Install with GPU Support. ‣ Download the NVIDIA CUDA Toolkit. NOTE: For Ubuntu 11. 0 conda install -c anaconda tensorflow-gpu To validate the installation, try the following in python:. 168 trying to open library ok. Thus, you do not need to independently install tensorflow. To get GPU support without having to manually install the CUDA 10. It seems that your tensorflow needs cudnn 5. Currently supported versions include CUDA 8, 9. I recommend to follow the official Nvidia CUDA Installation Guide for Microsoft Windows and to chose the express full installation including the CUDA samples (which is the default setting). This idiom, often called RAII in C++, makes it much easier to write correct, leak- and crash-free code. Contributing. Preview is available if you want the latest, not fully tested and supported, 1. Click on the green buttons that describe your target platform. This should make it much easier for users in the future to install the correct package. 5 and everything seems to work fine. These packages are available via the Anaconda Repository, and installing them is as easy as running "conda install tensorflow" or "conda install tensorflow-gpu" from a command line interface. Includes popular frameworks such as TensorFlow, MXNet, PyTorch, Chainer, Keras, and debugging and hosting tools such as TensorBoard, TensorFlow Serving, and MXNet Model Server. 5 environment and install Anaconda into this environment. 7) Conda Installation - Cannot create session; conda installing tensorflow; Cuda and tensorflow in Conda; Installing OpenCV with Conda; Broken Matplotlib installation, Conda Update Did not Work; Issue with conda update anaconda; Conda Update fails with PermissionError; TensorFlow installation denied due to user permissions. Fortunatelly, Debian stable/Stretch provids ready-to-use NVidia drivers and CUDA toolkits. 1 cuda92 -c. You need to register an account on anaconda for uploading the package. 0 Remver do it under (gluon) environment by the command "activate gluon". See the Get RAPIDS version picker for more OS and version info. conda update conda conda create -n tensorflow_conda pip python = 2. conda install cudatoolkit=10. I recommend to follow the official Nvidia CUDA Installation Guide for Microsoft Windows and to chose the express full installation including the CUDA samples (which is the default setting). GPU version of tensorflow is a must for anyone going for deep learning as is it much better than CPU in handling large datasets. Assumptions. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. 3 along with all of the dependencies. Here is Practical Guide On How To Install PyTorch on Ubuntu 18. This becomes useful when some codes are written with specific versions of a library. How to install CUDA 9. It explains the step-wise method to setup CUDA toolkit, cuDNN and latest tensorflow-gpu version release 1. Please refer to pytorch’s github repository for compilation instructions. 1 including updates to the programming model, computing libraries and development tools. conda install -c anaconda cudatoolkit Description. installed from Anaconda, so select \Conda" for package (choose other options only if you feel more comfortable with them). /" # [anaconda root directory] # Install basic dependencies conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing conda install -c mingfeima mkldnn # Add LAPACK support for the GPU conda install -c cpbotha magma-cuda10 #thanks u/cpbotha. I’ve linked it with a fully CUDA 10 based build of MAGMA 2. The download can be verified by comparing the posted MD5 checksum with that of the downloaded file. If you have a CUDA compatible GPU, it is worthwhile to take advantage of it as it can significantly speedup training and make your PyTorch experimentation more enjoyable. GPU version of tensorflow is a must for anyone going for deep learning as is it much better than CPU in handling large datasets. These drivers are typically NOT the latest drivers and, thus, you may wish to updte your drivers. 12 If you fail to import torch, try to install it in a new virtual environment like this: conda create -n test python=3. Let's starts by installing CUDA on Colab. In this post we will explain how to prepare Machine Learning / Deep Learning / Reinforcement Learning environment in Ubuntu (16. In my case with CUDA 8. Tensorflow for example, took 10 to 15 seconds to perform recognition tasks when running on cpu, while it took 2 to 5 seconds for the same recognition tasks when running on a GPU with Cuda installed. Alternatively, an existing conda installation may be used, by specifying its path. 1 works with Python 2. Why Array-oriented computing. Install Jupyter notebook and other packages. Also, there is no need to install CUDA separately. Tensorflow-gpu(1. The goal of RAPIDS is not only to accelerate the individual parts of the typical data science workflow, but to accelerate the complete end-to-end workflow. The following guide shows you how to install install caffe2 with CUDA under Conda virtual environment. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. 4 to work with CUDA 3. Both are optional so lets start by just installing the base system. CUDA Installation. Add in your ~/. conda install anaconda 这回,我们测试一下是否能import tensorflow 程序报错,这是由于我们虽然安装好了tensorflow-gpu,但是还需要安装CUDA Toolkit 和 cuDNN。. This will generate a new environment name webdev and install all the packages that were installed by conda command. CUDA Education does not guarantee the accuracy of this code in any way. Breaking it down into separate commands, it looks like: conda create --name tf_gpu activate tf_gpu conda install tensorflow-gpu. $ sudo easy_install--upgrade pip $ sudo easy_install--upgrade six. For macOS and Linux-based systems, this will also install Python itself via a "miniconda" environment, for spacy_install. conda install-c conda-forge spacy For the feedstock including the build recipe and configuration, check out this repository. Installing using conda on x86/x86_64/POWER Platforms¶. conda install cudatoolkit=10. Install Tensorflow with Gpu support. This should make it much easier for users in the future to install the correct package. conda install tensorflow-gpu==1. Deep Learning Installation Tutorial - Part 1 - Nvidia Drivers, CUDA, CuDNN. Download the Cuda Toolkit, in this case we will install CUDA Toolkit 8. If you have a CUDA compatible GPU, it is worthwhile to take advantage of it as it can significantly speedup training and make your PyTorch experimentation more enjoyable. The CUDA Toolkit will let you compile CUDA programs. No install necessary—run the TensorFlow tutorials directly in the browser with Colaboratory, a Google research project created to help disseminate machine learning education and research. I am following this installation. Install CUDA with apt. Update 07/04/2017: I decided to revisit this post and do mainly 3 things: try to succesfully installing the -gpu version of TF, clean up the whole page and post a performance comparison of a simple neural network (the “Neural network in 11 lines of python” from Siraj) to compare pure cpu+python to TensorFlow. Uninstall all the old versions of Pytorch : conda uninstall pytorch conda uninstall pytorch-nightly conda uninstall cuda92 # 91, whatever version you have # do twice pip uninstall pytorch pip uninstall pytorch. Notes: Yes, there is the possibility to install it via apt-get install cuda. We will also be installing CUDA 10. Ensure that you have met all installation prerequisites including installation of the CUDA and cuDNN libraries as described in TensorFlow GPU Prerequistes. He says just create a conda env like conda create --name tf_gpu tensorflow-gpu where tf_gpu is the env name and tensorflow-gpu is to include cuda and cudnn in that env.