Are the key/secret the one you see in the UI?

yes

And the server itself? is it http or https ?

http

my UI is also deployed on http server

make sure the API port is 8008 and the web 8080

yep. they are

Just making sure, the machine that you were running the "trains-init" on can access the API server ?

i.e. you can:
curl

I logged in to the UI from the local browser. is that enough?

Same machine running the trains-init ?

{"meta":{"id":"3b79f199a7fa42db8a30abbc67adbaa3","trx":"3b79f199a7fa42db8a30abbc67adbaa3","endpoint":{"name":"","requested_version":1.0,"actual_version":null},"result_code":400,"result_subcode":0,"result_msg":"Invalid request path /","error_stack":null},"data":{}}

that's the result

we have a cloud machine running the server UI and I want to log sessions from my local machine

Everything seems correct...
Let's try to set it manually.
create a file ~/trains.conf , then copy paste the credentials section from the UI, it should look something like:
api { web_server: http:127.0.0.1:8080 api_server: http:127.0.0.1:8008 files_server: http:127.0.0.1:8081 credentials { "access_key" = "access" "secret_key" = "secret" } }Let's see if that works

done

TRAINS-AGENT setup process
Configuration file already exists: ~/trains.conf
Leaving setup, feel free to edit the configuration file.

the code doesn't work

No that's okay

jwt.exceptions.DecodeError: It is required that you pass in a value for the "algorithms" argument when calling decode().

try: trains-init config

Ohh

yes its the JWT issue

trains-init: error: unrecognized arguments: config

pip install "pyjwt<2.0.0"

BTW: I think it was fixed in the latest trains package as well as the cleaml package

thanks. it seems to work now.

we are still considering whether to upgrade or not.

Any reason not to ?

if it ain't broke, don't fix it

if it ain't broke, don't fix it

😄

Up to you, just a few features & nicer UI.
BTW: everything is backwards compatible, there is no need to change anything all the previous trains/trains-agent packages will work without changing anything 🙂
(This even includes the configuration file, so you can keep the current ~/trains.conf and work with whatever combination you like of trains/clearml on the same machine)