Exercise: Use RStudio and other preinstalled tools
Now that you're connected to your DSVM, you can begin using it to do data science.
The DSVM comes with tools preinstalled for:
You can add more programming languages, desktop applications, database tools, and so forth.
Run RStudio remotely
From the DSVM desktop running in your XFCE client, double-click the RStudio icon.
When RStudio finishes startup, enter
demo(graphics) in the console window to see a demonstration of RStudio's graphing capabilities.
The free Azure resources used for this module don't have permission to access the internet, unlike those you create with your own account. Because the DSVM can't access the internet in this context, it can't download the data files needed for the included Python Jupyter notebook tutorials.
In our scenario, your local data disk might be mounted as a remote disk on the virtual machine. If so, you can begin your analysis immediately.
List and activate a Python conda environment
The conda package manager is a popular way to manage Python programming environments. It's the default environment manager that the DSVM uses. The DSVM comes with several environments configured to work with popular data science and machine learning packages. As an example, let's run through initializing, activating, and using PyTorch running with Python 3.6.
To activate the desired conda environment:
Select the terminal emulator icon on the toolbar of the DSVM's desktop.
To initialize conda and activate it, run:
conda init && source ~/.bashrc
To list the installed conda environments, run:
conda info --envs
You should see output similar to:
# conda environments: # base * /anaconda azureml_py36_automl /anaconda/envs/azureml_py36_automl azureml_py36_pytorch /anaconda/envs/azureml_py36_pytorch azureml_py36_tensorflow /anaconda/envs/azureml_py36_tensorflow py37_default /anaconda/envs/py37_default py37_pytorch /anaconda/envs/py37_pytorch py37_tensorflow /anaconda/envs/py37_tensorflow
The asterisk indicates that the
baseenvironment is currently active.
python --version, and you should see that the base environment is running Python 3.7. You can see what packages are installed by running
conda list. To get a full list of dependencies, run
conda env export -n base.
Activate the environment that we want to use by running:
conda activate azureml_py36_pytorch
conda listagain to confirm that the Python environment has changed.
To start a Jupyter server, run:
After startup, the Firefox browser on your DSVM should start. It should open to
localhost:8888/tree and show the home directory of your DSVM user. Although the notebooks directory contains a number of tutorials on the various installed frameworks, almost all require downloading data from external sources. Because your DSVM is running in a free, restricted Azure environment in this module, you can't access that data at this time.
Feel free to try other software on the machine, although the sandbox restrictions will affect programs that rely on internet access.