GPU-powered Deep Learning for Time Series with modeltime.gluonts on Saturn Cloud

saturn_cloud_modeltime_gluonts_startup_script.sh

# Saturn Cloud Startup Script for Running GPU-enabled modeltime.gluonts
#
# How to use:
#   1. Create an account on saturncloud.io
#   2. Create a new project in Saturn Cloud
#   3. Select image "saturncloud/saturn-gpu:2020.12.23" as image for your project
#   4. Copy and paste code below into "Advanced Settings (optional)" > "Start Script (Bash)".
#      (This code will install R, a Jupyter R kernel, modeltime.gluonts, and prepare a Python environment
#      for executing GluonTS.)
#   5. The startup script takes about 40 min to run. After that, you can start Jupyter Lab in your Saturn Cloud
#      project and run GPU-enabled modeltime.gluonts in a new Jupyter Notebook that uses the "R" kernel.
#
# More info about GPUs in modeltime.gluonts: https://business-science.github.io/modeltime.gluonts/articles/using-gpus.html

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9
sudo su -c "echo 'deb https://cran.rstudio.com/bin/linux/ubuntu bionic-cran40/' >>/etc/apt/sources.list"
sudo apt update
sudo apt -y install r-base libv8-dev libcurl4-openssl-dev libssl-dev
mkdir -p ~/.local/lib/R
export R_LIBS_USER=~/.local/lib/R
R -e "install.packages(c('IRkernel','tidymodels','tidyverse','devtools'))"
R -e "IRkernel::installspec()"
R -e "devtools::install_github('business-science/modeltime.gluonts')"
conda run -n saturn pip install gluonts pandas numpy pathlib mxnet-cu101

try_it_yourself_example.ipynb

It turns out SaturnCloud has turned this into an image so we don't need to wait the 40 minutes for an image to build.
514354729342.dkr.ecr.us-east-2.amazonaws.com/saturn-r-modeltime-gpu:2021.03.01.02.45