Listen man... this can't be an "I made five videos and its done" kinda thing. You should pick this back up! This series is one of the best explanations of a topic like this I've ever seen on TH-cam. I know it probably took a lot of work to make these even though they're "simple" videos, but they are so well thought out and planned and executed. You have a talent for explaining this kind of stuff.
Awesome explanation! (Someone in the comments section said this is an understatement, but I can't think of any other adjective!😅) But seriously, great videos.
Hey Jonathan, this is great! Thank you! Quick question, the GH repo that you posted is not taking me to the one on your video, I also tried to look up the content using GH search but I can't find it!
sorry about that, I changed the repo name (so the video is outdated) but the code and examples should be the same as what is in github.com/jonathandinu/spark-ray-data-science. Let me know if that doesn't work
I believe that the default is to use Ray's native scheduling usually referred to as the autoscaler. In practice though you usually run Ray on a cloud provider (like GCP or AWS) or on-premise using Kubernetes. Ray has a nice built in module to manage all the complexity of this for you though docs.ray.io/en/latest/cluster/deploy.html
let me know if the Docker setup earlier in the video doesn't work for you,. Unfortunately with the dashboard it is somewhat dependent on your individual machine setup but hopefully Docker helps. I also setup a Discord server for general chat and Q&A: discord.gg/nbyZ6EpUum
@@JonathanDinu Actually I have had docker installed in my machine previously. But the process to install ray in docker seems too complicated for me. I don't have that much experience with docker tbh. So I was just using Ray on my anaconda server - jupyter notebook. Thanks for the Discord server!
![[TH] JFT vs RLF - VCT Ascension Pacific - Day 2](http://i.ytimg.com/vi/PuIH7w_jO6c/mqdefault.jpg)
Listen man... this can't be an "I made five videos and its done" kinda thing. You should pick this back up! This series is one of the best explanations of a topic like this I've ever seen on TH-cam. I know it probably took a lot of work to make these even though they're "simple" videos, but they are so well thought out and planned and executed. You have a talent for explaining this kind of stuff.
Saying this is awesome is an understatement. Absolutely fantastic explanation, can’t wait to try it out!
Awesome explanation! (Someone in the comments section said this is an understatement, but I can't think of any other adjective!😅) But seriously, great videos.
🙏🙏
Thx for this great video
Hey Jonathan, this is great! Thank you! Quick question, the GH repo that you posted is not taking me to the one on your video, I also tried to look up the content using GH search but I can't find it!
Also, based on your GH repo it seems like you have some content in O'reilly. Do you mind sharing the link here? Thanks!
sorry about that, I changed the repo name (so the video is outdated) but the code and examples should be the same as what is in github.com/jonathandinu/spark-ray-data-science. Let me know if that doesn't work
@@juanpabloguerra9512 sure thing! if you have an Oreilly subscription you can access it at learning.oreilly.com/videos/spark-ray-and/9780136805922/
for individual purchase you can get it at www.informit.com/store/spark-ray-and-python-for-scalable-data-science-video-9780136805946
@@JonathanDinu I do have a subscription thanks
earlier you had mentioned cluster manager in the Ray's layered cake, could you say, out of box which resource manager is used by Ray?
I believe that the default is to use Ray's native scheduling usually referred to as the autoscaler. In practice though you usually run Ray on a cloud provider (like GCP or AWS) or on-premise using Kubernetes. Ray has a nice built in module to manage all the complexity of this for you though docs.ray.io/en/latest/cluster/deploy.html
I cannot connect to the dashboard. Its showing can't reach site after opening the url. Please help.
are you using the Docker container? Sometimes depending on your local machine configuration the ports or port mappings need to be adjusted
@@JonathanDinu I wasn't using docker. I'll try that. Thank you!
let me know if the Docker setup earlier in the video doesn't work for you,. Unfortunately with the dashboard it is somewhat dependent on your individual machine setup but hopefully Docker helps.
I also setup a Discord server for general chat and Q&A: discord.gg/nbyZ6EpUum
@@JonathanDinu Actually I have had docker installed in my machine previously. But the process to install ray in docker seems too complicated for me. I don't have that much experience with docker tbh. So I was just using Ray on my anaconda server - jupyter notebook.
Thanks for the Discord server!