1. Do the dockers support RedHat Linux-based Systems, such as Amazon Linux 2?
We haven’t fully tested running on RedHat Linux-based Systems, given that we’ve tested our docker images on top of Ubuntu kernels, it should theoretically work on most Linux kernel variants, since they are sufficiently similar that applications will not notice.
2. Where should the participants store their data, if they want to use the UI? Local laptop, cloud VM, or other environments?
The participants can store their data on any of your given examples, contingent on your use case. Ideally, for beginner user though, we recommend starting off with the local laptop, for easier access configurations. Otherwise, for cloud VMs for example, you will need to mount the corresponding remote volume prior to docker container run. On abstraction, for a participant, all Synergos UI requires for data tags declaration, are its symbolic links to the data file locations.
3. For the logger, what is the difference between graylog and basic?
Basic
outputs on CLI, which is next streamed into docker logs. Use Basic
if you don’t wish to deploy anything else.
Use Graylog
if you want to consolidate all the stats into a centralised logger. It’s useful when you have multiple parties and it becomes important to monitor each one, since down time on any one of them causes more problems (eg. needing to restart all 100 machines again in a 100 party network because of 1 computer)
4. Could you share an example of the correct command to utilize selected gpu(s) on a multi-gpu machine?
At the moment, the current version of Synergos (v0.1.0) does not support GPU. However, release of this feature is in the foreseeable product roadmap.
5. Do Synergos support pretrained CV models, or custom-written layer/loss?
Synergos does not officially support pre-trained CV models (presumably as a global model) and custom-written layer/loss at user abstraction level.
While greatly not recommended, It is still possible for advance users/developers, by exploring substituting torch model files in orchestrator outputs for the run.