Conda Install Cuda 8

It exploits multicore CPUs, it is able to rely on MPI for distributing the workload in a cluster, and it can be accelerated by CUDA. Head node: atlas2. then link libcurand. 0 Toolkit? #y #Enter Toolkit Location: # /usr/local/cuda-8. pip install --ignore-installed --upgrade tensorflow-gpu. 1 pip install mxnet-cu101mkl. 0 and Anaconda, type the following commands; conda install pytorch cuda90 -c pytorch pip3 install torchvision It is about 500 MB, so be patient!. 4 torchvision -c pytorch conda install cuda80 -c soumith. Massively parallel self-organizing maps View on GitHub Download. 04 and finally download the runfile, which is 1. reboot or import cupy will fail with errors like: AttributeError: type object ‘cupy. 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. That's it! You now have TensorFlow with NVIDIA CUDA GPU support! This includes, TensorFlow, Keras, TensorBoard, CUDA 10. Click Download. Installing and using these packages. 55 - a Jupyter Notebook package on PyPI - Libraries. Keras is a high-level neural. 8 on Anaconda environment, to help you prepare a perfect deep learning machine. 3 builds that are generated nightly. 1 including updates to the programming model, computing libraries and development tools. conda install -c anaconda cudatoolkit Description. 0 버전을 설치하였다. To check what GPUs are in your system, source activate NK conda install pycuda = 2015. Setup CNTK on Linux. CUDA 8 will enable CUDA applications to get high performance on Tesla P100 out of the box. NET ML OpenCV Python PyTorch Qt5 scikit-learn Setup Shell T4 Template Engine TensorFlow Visualization Visual Studio VSCode VTK Windows. To install additional data tables for lemmatization in spaCy v2. 0 is enough for course use. To run the unit tests, the following packages are also required:. It only requires a few lines of code to leverage a GPU. checkingTensorflow website, we know that we have to install cuda9. Linux running on POWER 8, ARM v7 and v8 CPUs also works well. conda create -n envname python=2. An attempted Python upgrade that wiped out the operating system’s native b. 0 then link libcurand. conda install --name h5py=2. To install additional data tables for lemmatization in spaCy v2. 0_0 anaconda But i need 7. Next, download the code for this book and install and activate the Conda environment. 6 conda create -n test python=3. installed from Anaconda, so select \Conda" for package (choose other options only if you feel more comfortable with them). 0 的 cuDNN)。. 5 by opening up the Anaconda Prompt (look for it in the Anaconda folder in the Start menu) and running conda install python=3. #Do you accept the previously read EULA? #accept #Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 367. I expect this to be outdated when PyTorch 1. Great news! PyTorch now is supporting Windows! If you have a PC with suitable Nvidia graphics card and installed CUDA 9. and choose linux, then Ubuntu-16. We are going to perform benchmark on the CIFAR10 dataset to test just how faster is that in comparison to earlier CUDA 8 and cuDNN 6. is_available(). Therefore, if there are 4 GPUs (4 slots in the CUDA queue), they can be processed in parallel. 4 torchvision cuda90 -c pytorch if cuda 9 fails, install this way using cuda 8 conda install -y pytorch=0. If you feel like you can improve these instructions, please don't hesitate to do so. Writing massively parallel code for NVIDIA graphics cards (GPUs) with CUDA. If the tests below fail after installation, this is the first thing to check. * cuDNN: 6. Tensorflow 1. How to install Docker on Ubuntu 18. Some other versions of TensorFlow have been tested (i. Determine the Compute Capability of your model GPU and install the correct CUDA Toolkit version. 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. Installing Keras with Theano on Windows for Practical Deep Learning For Coders, Part 1 Posted July 31, 2017 September 22, 2017 ParallelVision The below instructions should have you set up with both Keras 1. Download Package 3. 04 # Install some basic utilities RUN apt-get update && apt-get install -y \ curl \ ca-certificates \ sudo \ git \ bzip2 \ libx11-6 \ tmux \ htop \ gcc \ xvfb \ python-opengl\ x11-xserver-utils\ && rm -rf /var/lib/apt/lists/* # Create a working directory RUN mkdir /app WORKDIR /app # Create a non-root. Home High Performance Computing CUDA Toolkit CUDA Toolkit Archive CUDA Toolkit 8. Cuda is needed needed to run TensorFlow with GPU support. Download and install CUDA pip install --ignore-installed --upgrade tensorflow update packages from ui install keras from ui conda install -n DL scikit-learn. 0 的 CUDA Toolkit 和版本为 7. /configure in cloned tensorflow repository. 1 according to some other people. Miniconda is a free minimal installer for conda. This is going to be a tutorial on how to install tensorflow 1. 0-beta0 1 然后在 Conda 中安装 cudatoolkit, cudnn, numba。Numba 用于支持 Anaconda 找到安装的 cudatoolkit 和 cudnn。 conda install cudnn cudatoolkit numba 1. 0 with CUDA (Cuda 8. CUDA 8 will enable CUDA applications to get high performance on Tesla P100 out of the box. Select the cuDNN version you want to install. Commands for Versions < 1. However, this process is much easier using the Homebrew package to install most of the prerequisites, since it will handle dependencies and other details for you. Anaconda uses a package manager called "conda" that has its own environment system similar to Virtualenv. Enter y to proceed when prompted. Stable represents the most currently tested and supported version of PyTorch. $ conda install -c conda-forge tomopy This will install TomoPy and all the dependencies from the conda-forge channel. conda install pytorch torchvision -c pytorch Start Via Cloud Partners Cloud platforms provide powerful hardware and infrastructure for training and deploying deep learning models. To install cuDNN, copy bin, include and lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v{CUDA_VERSION} See a list of compatible CUDNN versions of CUDA extension packages. Run the command conda install pyculib. GPU-enabled packages are built against a specific version of CUDA. Meet "Digital Ira", a glimpse of the realism we can look forward to in our favorite game characters. Miniconda¶. bat' could not be found for installation at 'E:. The remainder of the questions ask whether you want to install the CUDA 8. Binary installation script installs it to a wrong location. 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. Chose \None" for CUDA unless you have NVIDIA graphics card that has CUDA support. 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. /usr/local/cuda-10. Then add the conda-forge channel and install hoomd: $ conda config --add channels conda-forge $ conda install hoomd Source. Expand the CUDA tab, the CUDA_TOOLKIT_ROOT_DIR should point to your CUDA 8. driver as drv from pycuda. All the details on how to build and install galario can be found in the Setup page. Reinstall pytz. To install a previous version of PyTorch via Anaconda or Miniconda, replace "0. 5 by opening up the Anaconda Prompt (look for it in the Anaconda folder in the Start menu) and running conda install python=3. # conda conda install -c conda-forge pymapd # pip pip install pymapd If you have an NVIDIA GPU in the same machine where your pymapd code will be running, you’ll want to install cudf as well to return results sets into GPU memory as a cudf GPU DataFrame:. Be aware that the ToolKit contains more software than just the CUDA drivers. Select the cuDNN version you want to install. Note: If you upgraded from a previous release, repeat this step with RHEL 7. In case you missed it, TensorFlow is now available for Windows, as well as Mac and Linux. If you prefer to have conda plus over 720 open source packages, install Anaconda. Depends on the CUDA version that you’ve installed you should select the appropriate CuDNN version. Install CUDA v8. Ubuntu OS; NVIDIA GPU with CUDA support; Conda (see installation instructions here) CUDA (installed by system admin) Specifications. 0 toolkit installation, if you have more than one version of the toolkit installed and it has picked that one then simply change the path to point to CUDA 8. Common operations like linear algebra, random-number generation, and Fourier transforms run faster, and take advantage of multiple cores. Anaconda Cloud. 1+cuda8061-cp36-cp36m-win_amd64. Configure TF intallation:. L'installation de Micmac sous Archlinux n'est pas des plus simples. HOOMD-blue is available on conda-forge. NVIDIA CUDA Toolkit 5. Because the pre-built Windows libraries available for OpenCV v3. Home About Us Ubuntu QuickStart Linux Mint QuickStart Kali Linux QuickStart Terminal QuickStart Install Printer Driver Scanning QuickStart Photoshop Install Reduce Eye Strain Oracle DB Install VirtualBox Install VMware Player VMware Workstation Adobe Reader Install Google-Chrome Install. Since PyMOL 2. Expand the CUDA tab, the CUDA_TOOLKIT_ROOT_DIR should point to your CUDA 8. 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 Deep Learning AMI with Conda's CUDA version and the frameworks supported for each:. $ sudo apt-get install openjdk-8-jdk git python-dev python3-dev python-numpy python3-numpy build-essential python-pip python3-pip python-virtualenv swig python-wheel libcurl3-dev curl Nvidia drivers Then, go to the Nvidia drivers web page , and select your GPU model, Linux 64-bit, and English (US), then click SEARCH. Depends on the CUDA version that you’ve installed you should select the appropriate CuDNN version. 8 anaconda conda install -c anaconda tensorflow-gpu But if I then want to check the installation via python console: import tensorflow as tf sess = tf. Users can use CUDA_HOME to select specific versions. Install with Conda¶ conda is a package manager built for scientific Python. Discovered GPUs are listed with information for compute capability and whether it is supported by NumbaPro. How to install CUDA 9. You would need super user access. 5 on Ubuntu 14. Meet "Digital Ira", a glimpse of the realism we can look forward to in our favorite game characters. Create conda environment Create new environment, with the name tensorflow-gpu and python version 3. 0 -c https://mirrors. 0 が conda-forge でも menpo でも利用できるので、macOS 用にもその内パッケージが用意されるかもしれませんが、それまでは上のような方法をとる必要があります。. Follow our previous post Install OpenCV3 on Windows to complete Step 1, 2 and 3. Ensure that you have an Ubuntu 18. activate tensorflow-gpu. 1首先在所在系统中安装Anaconda。 1. 2 in conda? Stack Overflow Products. conda env create -f environment. x or higher to. Create virtual enviroment using conda, see here for more details. pymapd can be installed with conda using conda-forge or pip. conda install-y numpy scipy nose pip install pydot-ng pip install parameterized conda install-y theano pygpu For optimal Theano performance, enable the CUDA memory manager CNMeM. 04 comes with CUDA support through the repositories: install nvidia-cuda-toolkit, nvidia-cuda-dev and python-pycuda using apt-get. 0); in the Nature Protocols paper, we tested up through TensorFlow 1. The only supported installation method on Windows is "conda". 2 库。而 pip 包仅支持 CUDA 9. We currently recommend CUDA 9. 0 conda install -c nvidia/label/cuda10. 9 or later). 0 which requires NVIDIA Drivers 384. 3 was released on 03/08/2017, go to Building OpenCV 3. Package Actions. cuDNN and Cuda are a part of Conda installation now. 6 conda create -n test python=3. x released, there were no binary installer avaliable for Windows. 04 显卡:NVIDIA GTX970 安装显卡驱动 由于我们需要在Pytorch使用CUDA加速训练过程,因此第一步需要安装显卡驱动为安装CUDA做准备。. import pycuda. 0) are intentionally ignored. 1首先在所在系统中安装Anaconda。 1. (enter) # Do you wish to run the installation with ‚sudo'? # y # Do you want to install a symbolic link at /usr/local/cuda? # y # Install the CUDA 8. It has a Cuda-capable GPU, the NVIDIA GeForce GT 650M. To build with this support, pass -DARROW_CUDA=ON when building the C++ libraries, and set the following environment variable when building pyarrow:. 7 environment. 04 is not listed in the. It explains the step-wise method to setup CUDA toolkit, cuDNN and latest tensorflow-gpu version release 1. NVIDIA GPU CLOUD. If you do not have Anaconda installed, see Downloads. 1 버전이여서 CUDA Toolkit Archive에 가서 CUDA 9. In case you missed it, TensorFlow is now available for Windows, as well as Mac and Linux. Select your preferences and run the install command. Install Cuda toolkit. If during the installation of the CUDA Toolkit (see Install CUDA Toolkit) you selected the Express Installation option, then your GPU drivers will have been overwritten by those that come bundled with the CUDA toolkit. It has official pip binaries of all frameworks with CUDA 8, CUDA 9, CUDA 10, and CUDA 10. The main difference between them is that conda is a bit more full-featured. How to install Meld on Ubuntu Meld is popular cross-platform visual diff and merge tool. Here, you have to select your operating system, package, language, and CUDA version. 0_0 anaconda But i need 7. Hello Everyone, This post is a step by step tutorial on installing Theano for Windows 7, 8, and 10. Stable represents the most currently tested and supported version of PyTorch. 0 $ conda install pytorch torchvision cuda80 -c soumith 이 글은 Deep Learning , News , PyTorch 카테고리에 분류되었고 0. If your system has a NVIDIA® GPU meeting the prerequisites, you should install the GPU version. 아나콘다를 우선 설치하고, conda install -c anaconda cudatoolkit==[version] ex) conda install -c anaconda cudatoolkit==8. Therefore, if there are 4 GPUs (4 slots in the CUDA queue), they can be processed in parallel. Of course I could have used cloud services such as Amazon AWS GPU instances, but when I saw their pricing I realized that. Let's create a virtual environment specifically for tensorflow in Miniconda conda and install necessary packages. Since nearly all installation instructions assume that the operating system is Linux, I decided to write my own instructions for Windows, which I share with you. 1 + OpenCV 3. If you plan to use GPU instead of CPU only, then you should install NVIDIA CUDA 8 and cuDNN v5. Setting up Tensorflow for use with Unity. The first step to be able to use Cuda and cuDNN is having a nVidia graphic card. I am following this installation. -c rapidsai/label/cuda10. Great news! PyTorch now is supporting Windows! If you have a PC with suitable Nvidia graphics card and installed CUDA 9. Of course, the easiest way would be just buying an electronic darts board, but for a steel darts player this is not an option. Here goes the Dockerfile: FROM nvidia/cuda:10. 2 for tensorflow-gpu. If you want to install from source, using custom or optimized build options, the Deep Learning Base AMI's might be a better option for you. conda install [follows libraries name] • jupyter • h5py • pillow • pandas • scipy • matplotlib • scikit-learn • cython • opencv-python • keras •Install pydicom conda install -c conda-forge pydicom “ ” mark means to enter as a command. As root, 2. Please don't forget that this is a Wiki. Alternatively, we suggest to install OpenBLAS, with the development headers (-dev, -devel, depending on your Linux distribution). Home High Performance Computing CUDA Toolkit CUDA Toolkit Archive CUDA Toolkit 8. In the Nature Neuroscience paper, we used TensorFlow 1. astonzhang January 23, 2019, 1:13am #12 The original link is now redirected to:. How should a package maintainer specify a dependency on a specific CUDA version like 9. I'll go through how to install just the needed libraries (DLL's) from CUDA 9. Enter y to proceed when prompted. Click Download. 4 torchvision cuda90 -c pytorch if cuda 9 fails, install this way using cuda 8 conda install -y pytorch=0. 0 and Anaconda, type the following commands; conda install pytorch cuda90 -c pytorch pip3 install torchvision It is about 500 MB, so be patient!. 依存パッケージでついてくる CUDA Toolkit と cudnn は少し古いバージョンになる。(CUDA Toolkit 9. For example: install_keras(tensorflow = "gpu") Windows Installation. then link libcurand. 4 installation on Windows is still not as straightforward so here are quick steps:. 3 = np110py27_cuda75_0. This uses Conda, but pip should ideally be as easy. Anaconda Cloud. 7 source activate envname pip install numpy pillow lxml jupyter matplotlib dlib protobuf sudo apt -y install python-opencv conda install -c conda-forge opencv sudo snap install protobuf --classic pip install --upgrade tensorflow-gpu To KILL process and clear memory of GPU: nvidia-smi. Install CUDA 8. Pagination is the concept of constraining the number of returned rows in a recordset into separate, orderly pages to allow easy navigation between them, so when there is a large dataset you can configure your pagination to only return a specific number of rows on each page. 0); in the Nature Protocols paper, we tested up through TensorFlow 1. It has official pip binaries of all frameworks with CUDA 8, CUDA 9, CUDA 10, and CUDA 10. 0 is released (built with CUDA 10. yml This will make sure conda is up-to-date, install Git, get the latest gprMax source code from GitHub, and create an environment for gprMax with all the necessary Python packages. Install Ubuntu, prepare Nvidia Driver, Cuda 10 And Themes 1 minute read All instructions are for Dell Latitude 3550 laptop with graphic card Geforce 830M. So, in the next step, we will install the CUDA Toolkit. When I do, the following stack-trace accompanies a failure to import:. conda install -c lukepfister pycuda if you face problem in CUDA 9. The first step to be able to use Cuda and cuDNN is having a nVidia graphic card. 나는 여기에 맞춰서 CUDA 9. cuDNN and Cuda are a part of Conda installation now. Since macOS is, at its heart, a Unix system, one can, in principle compile and install Meep and all its prerequisites just as on any other Unix system. yml activate gluon OK, you can use it. 0 How to install tensorflow 1. We will install Anaconda as it helps us to easily manage separate environments for specific distributions of Python, without disturbing the version of python installed on your system. # If your main Python version is not 3. Install Ubuntu, prepare Nvidia Driver, Cuda 10 And Themes 1 minute read All instructions are for Dell Latitude 3550 laptop with graphic card Geforce 830M. When I do, the following stack-trace accompanies a failure to import:. CUDA Support. After installation, run source ~/. NET ML OpenCV Python PyTorch Qt5 scikit-learn Setup Shell T4 Template Engine TensorFlow Visualization Visual Studio VSCode VTK Windows. 0 版本,conda 包支持可用的 CUDA 8. conda install -c anaconda cudatoolkit Description. Install CUDA: install CUDA to your local machine. 2 do not include the CUDA modules, I have provided them for download here, and included the build instructions below for anyone who is interested. whl After I installed everything and finish modify some files mentioned on the instruction online. The documentation is out of date for some combination of OS/graphical-card requiring manual tweaks with are not always obvious. #do I really need matplotlib?. After extracting cuDNN, you will get three folders (bin, lib, include). CUDA is a parallel computing platform and programming model invented by NVIDIA. This guide is written for the following. How to install TensorFlow with GPU support on Windows 10 with Anaconda. How should a package maintainer specify a dependency on a specific CUDA version like 9. The installation will offer to install the NVIDIA. 5 and PyCUDA on windows (for testing theano with GPU) My previous installation of CUDA on Ubuntu 14. In order to use a particular environment you can click on that environment in the navigator or go to the Anaconda prompt and execute the following command. Next, download the correct version of the CUDA Toolkit and SDK for your system. Configure TF intallation:. Presumably you've got the latest NVIDIA drivers. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. Now let’s go through the steps to install Dlib. 1- Install Ubuntu 16. If you prefer to have conda plus over 720 open source packages, install Anaconda. 0 instead of Cuda 9. $ pip install conda $ sudo dpkg -i cuda-repo-ubuntu1604_8. Note: This works for Ubuntu users as. conda env create -f environment. For example, when I did this, I got a Anaconda3-5. 1 cudnn, which is not enough for me to run the CNN in tensorflow1. The only supported installation method on Windows is "conda". 1 pip install mxnet-cu101mkl. 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. How to install dlib Developed by Davis King , the dlib C++ library is a cross-platform package for threading, networking, numerical operations, machine learning, computer vision, and compression, placing a strong emphasis on extremely high-quality and portable code. Conda easily creates, saves, loads and switches between environments on your local computer. This is one of the 100+ free recipes of the IPython Cookbook, Second Edition, by Cyrille Rossant, a guide to numerical computing and data science in the Jupyter Notebook. conda create -n tensorflow python=3. 0需要driver的最低版本为367,所以如果已经够用,在安装cuda的时候保险点的话就不用更新驱动。 如果更新驱动后不幸中招,如循环登录或无法进入图形界面等问题,可以到字符终端(CTL+ALT+F1)先尝试清除已有驱动,禁用Nvidia开源驱动nouveau. TensorFlow를 설치한 컴퓨터의 환경입니다. The general install instructions are on tensorflow. CuPy is an open-source matrix library accelerated with NVIDIA CUDA. Therefore, Numba has another important set of features that make up what is unofficially known as “CUDA Python”. It looks like a few users at Anaconda Cloud have made PyTorch 0. 4 installation on Windows is still not as straightforward so here are quick steps:. Before you install the NVIDIA components, the udev Memory Auto-Onlining Rule must be disabled for the CUDA driver to function properly. with libcurand. 2, and compiled Tensorflow from source well enough that I can train a Resnet on Imagenet-100 in a barely decent amount of time by 2018 standards. Before you install the NVIDIA components, the udev Memory Auto-Onlining Rule must be disabled for the CUDA driver to function properly. Master Tests Status: If you are a developer of Theano, then check out the Developer Start Guide. 12 keras-gpu=2. 2 Step 3: Install Anaconda 3 Step 4: Download Dlib. We will install Anaconda as it helps us to easily manage separate environments for specific distributions of Python, without disturbing the version of python installed on your system. 04 is not listed in the. Indexer' has no attribute 'reduce_cython. $ conda install -c conda-forge opencv=3 Ubuntu では既に Python 3. Since we have created the Anaconda Python 2. 04 安装 tensorflow-gpu 包括 CUDA ,CUDNN,CONDA. conda install numpy scipy pandas matplotlib hdf5 pillow scikit-learn jupyterlab tensorflow-gpu=1. Some other versions of TensorFlow have been tested (i. reboot or import cupy will fail with errors like: AttributeError: type object 'cupy. conda install msvc_runtime I’ve run through the importing of tensorflow and deeplabcut a few time and this seems to work. My business case involved running GPU accelerated deep learning jobs on a set of local desktops, and was looking for installation instructions to provide the administrators. PyCUDA knows about dependencies. Install Cmake and add it to system path. Singa packages for other CUDA versions are also available. Install Dlib on Windows. 例如,对于 TensorFlow 1. 0, therefore CUDA8 will be installed in /usr/local/cuda-8, CUDA9. 5 on Ubuntu 14. Conda also controls non-Python packages like MKL or HDF5. Installing Keras, Theano and TensorFlow with GPU on Windows 8. cuda module offers support for using Arrow platform components with Nvidia’s CUDA-enabled GPU devices. 0); in the Nature Protocols paper, we tested up through TensorFlow 1. # Linux CUDA 7. conda install conda-build -c cryoem -c defaults -c conda-forge. 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. 0 onwards are 64-bit. 4 개발 환경 설치(Windows 10, CUDA 8. 0 or higher. Virtual packages are not real packages and not displayed by conda list. Hi all, I've recently setup a CentOS 7 and was trying to install CUDA toolkit on it but realised that there's no official rpm for 7. conda install numpy scipy pandas matplotlib hdf5 pillow scikit-learn jupyterlab tensorflow-gpu=1. For instance, for using 8 GPUs, run as follows: bedpostx -NJOBS 8 [options]. developerWorks blogs allow community members to share thoughts and expertise on topics that matter to them, and engage in conversations with each other. 3 builds that are generated nightly. condarc in your home directory, temporarily rename or move it for the following instructions to work properly. $ conda install -c conda-forge opencv=3 Ubuntu では既に Python 3. fastai makes deep learning with PyTorch faster, more accurate, and easier - 1. conda install --force-reinstall pytz. We currently recommend CUDA 9. 0 (depending on the Tensorflow version being used). 7 environment. Anaconda Cloud. For cuda 8, If you use conda, you can directly install both theano and pygpu. BLAS installation (with Level 3 functionality) Recommended: MKL, which is free through Conda.