Configuration

Uploading datasets and visualization to dstack.ai is done via the dstack package available for both Python and R. These packages can be used from Jupyter notebooks, RMarkdown, Python and R scripts and applications.

Once you've pushed your data to dstack.ai using the dstack packages for Python and R, you can combine your datasets and visualizations into a great-looking interactive dashboards with just a few clicks. Learn more on how to push your data to dstack.ai‚Äč

Installing dstack package

The dstack package can be easily installed on of the following ways:

pip
conda
R
pip
pip install dstack
conda
conda install dstack -c dstack.ai
R
install.packages("dstack")

Sometimes pip can't resolve PyYAML dependency, so it should be installed manually:pip install pyyaml

Note, the R CRAN package is still under review. In order to install it, please use the following commands:

install.packages(c('uuid', 'bit64', 'rjson', 'rlist'), repos = 'http://cran.us.r-project.org')
install.packages('https://drive.google.com/uc?export=download&id=1RREfEk_rZFvZN-

Configuring dstack.ai profile

Note, before using the dstack for Python or R, you have to configure it with your dstack.ai profile by specifying your dstack.ai username and token:

Bash
R
Bash
dstack config add --token <TOKEN> --user <USER>
R
dstack::configure(user = "<USER>", token = "<TOKEN>", persist = "global")

Configuring dstack profiles separately from your code, allows you to make the code safe and not include plain secret tokens.

Note, by default, the server stores all the data under .dstack in the user home directory. In case you'd like to store the .dstack folder in a different place, use the following command:

dstack server start --home <other_directory>

In this case, the server will store all the data in <other_directory>/.dstack/.