What is the difference between waitForIdle() and awaitIdle() in android's compose testing API?
Does anyone have examples of when they would use one over the other?
Related
The title says it all. I'm wondering if anyone has ever built a Docker container (for any purpose on a high performance computing cluster) that uses intel/17.4 and impi/17.3 -- or any other version of intel 17. I'm currently working on developing a container that will require these, and I'm wondering if anyone would be so generous as to let me use their's as a template.
Thanks in advance!
I know there is a spell checker built into textviews (XML), but I can't seem to find an equivalent in Jetpack Compose. Is there a way to use the old one, was I just unable to find it, or is this a feature that needs to be added?
I found this ticket so I believe there is currently no way to do so, so is there a way to get this done with what we currently have available in android? (no 3rd party library)
I came across the below docker image for spark. The image also comes with some of the connectors to some of the popular cloud services. An example of how to use the inbuilt connectors(say Azure storage gen2) in pyspark application will be of great help.
link to dockerhub image
: https://hub.docker.com/r/datamechanics/spark
I looked into the below example that was provided but it didn't help much in understanding how to use the connector that comes with the default image
https://github.com/datamechanics/examples/blob/main/pyspark-example/main.py
There is some more documentation at https://docs.datamechanics.co/docs/docker-images but it is not very helpful to understand how to use the images indeed..
The point that there is no Dockerfile and also no response to reported issues makes it very difficult.
It looks like https://g1thubhub.github.io/docker.html is helpful, although the versions of the images that are used are older.
I would like to understand if,
- it makes sense to have Karate integration with SauceLabs
- Benefits if that is an Yes
Also, if i want to implement the integration what is the approach i should use.
I am planning to mimic the similar implementation using Selenium remote web driver. Please let me know if this is the only approach that can be taken or there is any other way out for this.
Yes, if SauceLabs conforms to the spec - refer to the documentation for webDriverSession: https://github.com/intuit/karate/tree/master/karate-core#webdriversession
Note that this has been proven to work with Zalenium: https://github.com/intuit/karate/tree/master/karate-core#webdriverurl
And also AWS Device Farm: https://twitter.com/ptrthomas/status/1222790566598991873
Please note that if you are ok to use only Chrome, the Docker approach may give you a better experience, but you can decide: https://github.com/intuit/karate/tree/master/karate-core#karate-chrome
EDIT - also see:
https://stackoverflow.com/a/64682293/143475
https://stackoverflow.com/a/63270092/143475
https://stackoverflow.com/a/65644566/143475
I'm very new to Ray RLlib and have an issue with using a custom simulator my team made.
We're trying to integrate a custom Python-based simulator into Ray RLlib to do a single-agent DQN training. However, I'm uncertain about how to integrate the simulator into RLlib as an environment.
According to the image below from Ray documentation, it seems like I have two different options:
Standard environment: according to the Carla simulator example, it seems like I can just simply use the gym.Env class API to wrap my custom simulator and register as an environment using ray.tune.registry.register_env function.
External environment: however, the image below and RLlib documentation gave me more confusion since it's suggesting that external simulators that can run independently outside the control of RLlib should be used via the ExternalEnv class.
If anyone can suggest what I should do, it will be very much appreciated! Thanks!
If your environment is indeed can be made to structurized to fit Gym style (init,reset,step functions) you can use first one.
External environment is mostly for RL environments that doesn't fit this style for example Web Browser(test automation etc) based application or any continual finance app etc.
Since you wrote that you work with a custom Python-based simulator, I would say that you can employ PolicyClient and PolicyServerInput API. Implement the PolicyClient on your simulator (env) side and provide the PolicyClient with data from the simulator (observations, rewards etc.). This is what I think may help you.