Robocorp Containers
Run your robot in Robocorp Hosted Cloud Worker
You need zero setups to run your Unattended Processes on our Cloud Workers.
👉 Select Cloud Worker in your process step configuration.
👉 Run the process.
Control Room handles the orchestration from there.
You can easily test things with our Portal examples most of them can run on the containers.
For example, RPA Form Challenge handles data from Excel, automates a website, and provides screenshots of the results.
🚀 Certificate Course I is also a good place to get familiar with this.
Details about the containers
Robocorp Hosted Cloud Containers run in secure containers on AWS EC2. A container is called up for the duration of the robot run and then deleted. Using the containers also means that scalability and parallel runs are there for you out of the box.
Containers are based on official Linux Ubuntu Docker images where we only add the basic required things like the Workforce Agent Core, Chromium browser, Git, and some basic tooling.
The containers are headless, meaning no GUI components are installed here. Still good to note that these containers can, for example, handle all browser automations and get screenshots in these cases. Linux does not need an active desktop GUI to render webpages.
Do not forget that you can load applications like Firefox within your robot that also work in the containers (example here). The applications listed in your conda.yaml get set up in isolated environments by RCC. Check out what you can find in conda-forge.
Global Environment Cache for Robocorp Hosted Cloud Workers
Because building new Python environments can take minutes, our Cloud Workers leverage the caching provided by RCC. The cloud containers where the Worker runs, gets a shared environment cache that builds up automatically as new unique conda.yaml files are encountered.
conda.yamlfiles that contain references to private packages are excluded from getting into the Global Cache to avoid any information leaks
The normal environments that only use publicly available dependencies get added to the cache automatically so that typically ~5 minutes after the first run of a new conda.yaml, the environment is available and loads quickly on container runs.