Your browser will warn you that the website you’re trying to open uses a self-signed certificate (which is fine, since we self-signed it) - just breeze through all those warnings and you should see the Jupyter Notebook login page. You should now be able to ssh into your AWS machine, run jupyter notebook and then connect to it from the browser via the following URL: where AWS-INSTANCE-IP is a public IP of your running AWS instance. As tutorial suggests, one way to do that would be using IPython:
INSTALL JUPYTER NOTEBOOK ON AWS PASSWORD
I highly encourage you to use an amazing script found in this tutorial:ĭon’t forget to set a password by providing SHA1 of your password in the script. Now you need to configure the Jupyter Notebook on your AWS instance and set a password. Now, select Inbound and click Edit, you can now add another custom TCP rule for port 8888. Select Security Groups in the side panel and choose the group you have added while following the tutorial ( my-sg one). All you need to do now is configure remote access to your Jupyter Notebook server. I mean really, it saved me a whole lot of time. Even better, the author provides you with a script that automates the whole process. Luckily, there is already an amazing tutorial on configuring your AWS instance and installing TensorFlow and Anaconda with Python and Jupyter Notebook. I mean, how cool is that? Oh boy oh boy oh boy! So basically it means that you work in exactly the same environment you do locally, running your notebooks in a browser, the only difference being that now they are backed by a powerful GPU.
Since I prefer working in Jupyter Notebook (previously known as IPython Notebook), AWS fits perfectly into this workflow: you fire up a cloud machine instance running Notebook kernel with TensorFlow installed, and connect to the Jupyter Notebook server remotely from your browser. You can also bid on so called spot instances that utilise Amazon’s unused resources (and therefore might suddenly get interrupted, although apparently it’s not happening too often) - for as little as ~$0.07-$0.10 per hour. The cheapest GPU-powered instance ( g2.2xlarge) would cost you ~$0.65 per hour. There is a wide range of machines you can, well, rent, and there are a couple of options with powerful NVIDIA GPUs that are of particular interest to any data scientist. AWS is a on-demand computing platform that lets you use their computational resources and only pay for what you use.
INSTALL JUPYTER NOTEBOOK ON AWS PRO
Since I’m stuck with my 2015 MacBook Pro with AMD GPU onboard, I can’t use TensorFlow with GPU on my machine, which, as one could imagine, is a huge bummer.Īnd that’s where Amazon comes in with their AWS service. You may be tempted to use TensorFlow with your own GPU, but not only a powerful GPU could cost you an arm and a leg, but you need to also make sure it’s a NVIDIA card, since TensorFlow currently only supports proprietary CUDA GPUs architecture standard (as opposed to open AMD’s OpenCL). Now, when it comes to a “lab” you still need a lot of computational resources in order to do the painful process of training your model.
You are still expected to train your model in a lab, tweak and improve it the best you can, and then deploy it on various platforms - including iOS devices, which, with all the native Metal support, should now work pretty fast. However, mobile support does not mean that you are going to use TensorFlow for learning on a mobile device. Not like there hasn’t been any attempts before to use Metal for machine learning, but receiving native support would be a huge leap forward. Not to mention a new set of machine learning tools in iOS 10 SDK, like specifically designated kernel Metal shaders for convolutional neural networks - meaning that they will use most of the device’s GPU for parallel computations with an interface allowing you to import TensorFlow models.
When it comes to iOS development it’s even more interesting! TensorFlow has recently received mobile support including an option to build it for iOS devices. So if you are into data science the chances are that you will at least give it a go sooner or later. Even though Google is said to use a slightly different version internally, and the current version of TensorFlow is somewhat behind its competitors performance wise, one can hardly argue that it has a lot of potential. Google’s open source TensorFlow is one of the most promising machine learning frameworks nowadays. Running TensorFlow with Jupyter Notebook on AWS
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