![]() I’ll try to reduce batch size bs=6 and try again after stopping the running process to clear GPU memory. OK after reboot GPU 1080i is being activated, but now I am back to CUDA out of memory at the learn.fit_one_cycle(1, 2e-2) cell in the Fine tuning the language model of 10_nlp.ipynb again. While writing this I thought I must go to basics so I shall reboot and see if that changes anything. to_fp16() which I removed and now none of the notebooks seem to activate the GPU it’s as if it’s not there, Even after many redeployments of latest core and 2 each day with the pip dev install. First all it’s memory was grabbed and the next call to it failed as no memory available. I am having issues with this but I can’t pin point the problem with my 1080i when running 10_nlp.ipynb. My 1080ti card and I believe the 20 series cards can use this method, but other 10 series cards cannot, so be careful just running these notebooks as is. ![]() The key difference is that some of the learn commands use the. Optional - increase swap 2G > 8G sudo swapoff -aĭd if=/dev/zero of=/swapfile bs=1G count=8 Setup fastai2 source ~/tools/python3.7_venv/bin/activate Virtualenv -python python3.7 ~/tools/python3.7_venv Sudo apt-get install python3.7 git virtualenv unzip python3-dev Sudo apt install libnvinfer-plugin6=6.0.1-1+cuda10.2 ![]() Sudo bash -c 'echo "deb /" > /etc/apt//cuda.list' Sudo bash -c 'echo "deb /" > /etc/apt//machine-learning.list' Sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 Anyway here are my notes… Install cuda, cudnn & tensorrt Some of these steps might not be necessary since I was playing around running order versions of tensorflow and fastai. Note that I tried first with Debian 10 but ran into dependency isssues installing cuda 10.2. Thanks for sharing, i’m doing similar but using Ubuntu 18.04 but using virtualenv instead of conda. Once the server is running, I can copy the link shown and paste it into my browser. So the following command takes that into account: 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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