I also port forward 8889 on the Ubuntu machine to my local host as port 8888. The instructions will reflect this.įor my setup, running Ubuntu server, I do not use a browser on the Ubuntu machine, so I do not start Jupyter notebook with one. I like to keep all my cloned data in a sub directory called “repos”. Click y or yes or whatever as needed to add the items (from fastai github readme)Ĭonda install -c pytorch -c fastai fastaiĬonda install -c conda-forge libjpeg-turbo pillow=6.0.0Ĭ="cc -mavx2" pip install -no-cache-dir -U -force-reinstall -no-binary :all: -compile pillow-simdĬonda install -c conda-forge jupyter_contrib_nbextensions Run these commands to install fastaiv1 and any dependencies. The install will need a new shell access, so exit out and get a new terminal.Įxecute the following commands to create the fastaiv1 environment. You will have to press ENTER and space a few times to accept the licensing agreement. With the GPU recognized by Ubuntu, we can now install Anaconda. Version 440.64 is what was installed using these commands: sudo add-apt-repository ppa:graphics-drivers/ppaĪfter a reboot, running nvidia-smi should show an image like the followingĭo not proceed until this screen can appear. For this setup, I chose the version 440 drivers. As I had a clean install, there were no drivers installed. You can easily check to see if your drivers are installed by executing the nvidia-smi command at the command line. The first thing needed for Ubuntu are the drivers for the video card. If you run a Virtual Machine that enables GPU passthru, then you can also run these instructions.ġ. If you run bare metal Ubuntu, then you can use these instructions. I will post a blog/topic later on how to set that up as well. This install is actually running inside of a Virtual Machine within UNRAID with GPU passthru. The steps below were done using a clean Ubuntu 18.04.4 LTS server install. If you do not want or need the fastai version 1 code or the coursev3 notebooks, you can skip steps 3 and 4, and run step 5 later in conjunction with step 8. My intent is create a blog post using fastpages sometime in the future but with the course just started last night, I wanted to get the information out there as soon as possible. They will show how to get fastai versions 1 and 2 up and running independently with access to both the notebooks in course v3 and v4. I know Jeremy has recommended that new users focus on learning the deep learning methodologies over troubleshooting a local installation, but the instructions below should be easy enough to follow. My apologies in advance for the crudeness of this post, but I wanted to provide some setup instructions to those who intend on running the fastai code without using the cloud options.
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