Robocorp Lab changelog and release notes
Robocorp Lab (4.22.8)
October 25, 2021New features
- Dynamic Robot template updates
- Added Playwright -template and more coming.
- All templates also now visible in Portal
- Updated and curated Keyword Explorer content to match rpaframework v11.4.0
- RCC updated to v11.4.3 -->
rcc configuration speedtest
- In Lab, use
File > Terminal
and runrcc configuration speedtest
command to test the performance of your system. - System requirements are notoriously hard to write up as in virtualized environments, we cannot trust just the hardware specs.
- The speed test results are set so that zero is a "good enough" value, positive values are good and negative values indicate slower performance.
- The results are split into
operations over network
andoperations over filesystem
- These are not just transfer speeds, as in virtualized systems, you can have blazing fast internet, but the CPU actions related to those might slow down the result.
- Same goes for the filesystem measurement; the specs of the hardware mean nothing if the actual file storing and reading is slow due to some virtualization layer or a heavy virus scanner process.
- In Lab, use
Bug fixes
- Update to RCC v11.4.3
- Added retry mechanisms to attempt retries earlier and in smaller chunks.
- Added retries to removal processes as well (Windows file locks)
- Dropped minimum worker count to 2 to ease the load on minimal hardware.
- Lab kernel switching now jumps directly to the new one without an extra step to "backup kernel"
- Dependency updates for most extensions
- In Python the problem with
a={"b":"c"}
format fixed - Fixes to JupyterLab Tornado timing out, causing stuck states to
Restarting
orConnecting
states.
Known issues
- Having `defaults '-channel in conda.yaml will cause errors; now is the time to get rid of that.
- Parts of the Lab basement are no longer shipped via defaults -channel, causing weird errors.
- Templates and robots in Robocorp Portal have been updated since February, but basically, you only need to remove that one line.
- Simplest
conda.yaml
example is:
channels:
- conda-forge
dependencies:
- python=3.7.5
- pip=20.1
- pip:
- rpaframework==14.1.1
- "Lab is unresponsive" on some machines.
- Use
rcc configuration speedtest
to determine the cause and ping the result on Slack / Forum.- Disk speed seems to be the most common cause for slow environment setups
- A Windows machine with 2 CPU cores does struggle quite a lot as Windows Defender (or anti-virus scanners in general) pretty much occupy half of the processing power trying to scan the incoming files.
- Lab base dependencies are big, and we are working on getting a significant cut to those (via ipywidgets v8).
- Use
Robocorp Lab (4.21.1)
October 4, 2021New features
- Recent Robots listing implementation changed not to show deleted items
Set Global Variable
&Set Suite Variable
values are now held in cell-by-cell runningRestart kernel
-button added to the notebook button panel- All the kernel commands are still available behind
Ctrl+Shift+C > type restart
- Having a button there simplifies working with Python libraries.
- All the kernel commands are still available behind
Bug fixes
- Update to RCC v11.1.6
- Reduced use of
os.stat
as they seem to affect Windows usage - V11 migration guide at: RCC changelog
- Reduced use of
- Lab kernel environments are now rotated to avoid file lock when changing conda.yaml
- Note: If robot logic opens a process that freezes or does not shut down when Lab asks it to, you can still get file locks.
Known issues
- Having `defaults '-channel in conda.yaml will cause errors; now is the time to get rid of that.
- Parts of the Lab basement are no longer shipped via defaults -channel, causing weird errors.
- Templates and robots in Robocorp Portal have been updated since February, but basically, you only need to remove that one line.
- Simplest
conda.yaml
example is:
channels:
- conda-forge
dependencies:
- python=3.7.5
- pip=20.1
- pip:
- rpaframework==14.1.1
- "Lab is unresponsive" on some machines.
- RCC v11.1.6 improvements to performance.
- Kernel environments rotation now reduces the change of file locks which are compounding the issue.
- Big dependencies still take time on the first run of a unique conda.yaml.
- At the moment, pip for example, does not support any parallel actions, so machine utilization is really low during pip steps.
- A Windows machine with 2 CPU cores does struggle quite a lot as Windows Defender (or anti-virus scanners in general) pretty much occupy half of the processing power trying to scan the incoming files.