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That's all well explained in the anaconda test-drive page.
#Notepad++ download cuda how to#
Learn how to list avaiable environment, and switch from one to another. Make sure all your subsequent installs are done on a command console within the theano environment. Now your command console prompt should be preceded by "(theano) ". You now have two options for installing python packages. Test drive anaconda by running steps outlined in
#Notepad++ download cuda download#
There is an option to download a barebones version called miniconda, but if you don't have enough hard disk space on your computer, better invest in a new hard drive or a USB3 external, because Machine Learning entails Big Data. Realize that it is a big distribution, close to a third of a Gig. It is an enterprise-ready Python distribution, with packages for Big Data processing, predictive analytics, and scientific computing. I recognize you have some options here, but for Machine Learning, Anaconda Python ought to be your top choice. Python Distribution and your "theano" environment
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If you want to upgrade, you can upgrade to the g2.8xlarge instance in order to obtain four K520 GPUs for a grand total of 16GB of memory and for about $2 more per hour. This instance is named g2.2xlarge instance, costs about $0.65 per hour, and includes 4GB of memory and 1,526 CUDA cores on a K520 graphics card. Amazon offers an EC2 instance that provides access to the GPU for General Purpose GPU computing (GPGPU). Another option is to spin up a GPU-equipped Amazon Machine Instance (AMI).
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That is the starting block. The steps outlined in this article will get your computer up to speed for GPU-assisted Machine Learning with Theano on Windows 10. Asus, MSI, and AlienWare build some great laptops along this line. I'll assume you have a computer running Windows 10, equipped with an Nvidia GPU. We'll also learn how to test Theano with Keras, a very simple deep learning framework built on top of Theano. This is a love story of software meeting hardware. Along the way, we'll also learn a lot about GPUs, so this is a good introduction for GPU neophytes, along with GPU-equipped laptop recommendations. I am sure that Theano works equally well on Linux and OSX, but Windows is where I have the most experience in, so Windows it is for this article. So I'm writing this article to get you ramped up to Machine Learning on Windows with the minimum amount of pain. I need to say that we are experiencing a planet alignment phenomenon right now, because everything seems to be working fine with the latest from Microsoft and Nvidia, and that is a rare phenomenon. So i bought myself an ASUS laptop equipped with an Nvidia 1070 GPU, and started installing, prototyping, breaking, fixing, breaking again, and fixing again, till I got everything working. So I set out on a mini-odyssey to make it run on Windows 10, with the latest Visual Studio (2105 CE), the latest CUDA toolkit from Nvidia (CUDA 8), and the latest everything-related.
Well-intentioned advice, mind you, but hopelessly outdated and convoluted.
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There is however one huge issue with Theano, and that is the amount of junk advice out there about how to install and run it with a GPU. Whenever i see "simple", my heart lights up.
Third, there are other frameworks, like Keras, built on top of Theano with the goal to simplify building neural networks with it. Second, it's cross platform, with researchers running it on Linux, OSX, and Windows. First, it's essentially a graph-language but it's in Python, and I already speak Python.
The problem with TensorFlow is that you have to learn a new graph-based language, and Google being Google, they tend to not like Windows platforms very much, especially now that most elementary schools are busy upgrading their Chromebooks OS to Windows 10. I don't have much experience on Caffe, but there is nothing not to love about Theano. I already speack C#, Java, Python, and Javascript, so my brain is already kinda full. The problem with Torch, arguably the most used of all ML frameworks, is that it really prefers to run on Linux and you need to learn a new language (LUA). There are others, but these are the four that most University Machine Learning research is conducted on. There are 4 main Machine Learning (ML) frameworks out there: The University of Montreal's Theano, Facebook's Torch, Google's TensorFlow, and Berkeley's Caffe (Microsoft's Cognitive Toolkit, CNTK, is a bit more specialized).