Although, some correction here: While the secret is indeed hidden in the logs, it is still visible in the “execution” tab of the experiment, see two screenshots below.
One again I set them with
task.set_base_docker(docker_arguments=["..."])

The remaining problem is that this way, they are visible in the ClearML web UI which is potentially unsafe / bad practice, see screenshot below.

Ohhh that makes sense now, thank you 🙂
Assuming this is a one time credntials for every agent, you can add these arguments in the "extra_docker_arguments" in clearml.conf
Then make sure they are also listed in: hide_docker_command_env_vars which should cover the console log as well
https://github.com/allegroai/clearml-agent/blob/26e62da1a8fb4b66361a5d6a4d852d50b1e0a158/docs/clearml.conf#L239

ScantChimpanzee51 if you set these in the agent's configuration under agent.extra_docker_arguments and specify the keys in the agent.hide_docker_command_env_vars they should only appear hidden in the console log. If you set them by code ( task.set_base_docker(docker_arguments=["..."] ...) they would still appear in the WebUI (BTW we have a feature coming up that will hide them in the UI until you actually want to edit them, to prevent some passer-by to see them 🙂 )

Yes for example, or some other way to get credentials over to the container safely without them showing up in the checked-in code or web UI

Won’t they be printed out as well in the Web UI? That shows the full Docker command for running the task right…

Won’t they be printed out as well in the Web UI?

They would in the log, but it will not be stored back on the Task (the idea is these are "agent specific" additions no need for them to go with the Task)

So I’ve tried the approach and it does work,

ScantChimpanzee51 What do you mean it does not work? what exatcly are you trying with task.connect and does not work?

Is there a way to inject environment variables into a Task or into its container?

Yes you can with:
task.set_base_docker(docker_image="mycontainer", docker_arguments="-e KEY=VAL")what am I missing?

You mean getting them somehow into the container by specifying them in the clearml.conf file you provide to the agent?

SuccessfulKoala55 AgitatedDove14 So I’ve tried the approach and it does work, however, this of course results in the credentials being visible in the ClearML web interface output, which comes close to just hard-coding them in…
Is there any way to send the secrets safely?
Is there any way to access the clearml.conf file contents from within code? (afaik, the file does not get send over to the container - otherwise I could just yml-read it myself…)

Sorry to ask again, but the values are still showing up in the WebUI console logs this way (see screenshot.)
Here is the config that I paste into the EC2 Autoscaler Setup:
` agent {
extra_docker_arguments: ["-e AWS_ACCESS_KEY_ID=XXXXXX", "-e AWS_SECRET_ACCESS_KEY=XXXXXX"]

hide_docker_command_env_vars {
    enabled: true
    extra_keys: ["AWS_SECRET_ACCESS_KEY"]
    parse_embedded_urls: true
}    

} Never mind, it came from setting the options wrong, it has to be ["-e", "key=...", ...] ` . Everything working now ✔

Hey guys, really appreciating the help here!
So what I meant by “it does work” is that the environment variables go through to the container, I can use them there, everything runs.

The remaining problem is that this way, they are visible in the ClearML web UI which is potentially unsafe / bad practice, see screenshot below.

when I duplicate the experiment and clone it remote, the call is ignored and the recorded values are used?

Yes ScantChimpanzee51 exactly.
Think of it as the inital value you want to put on the Task when you are running the code on your machine, later when you clone the Task, you can edit the base docker image in the UI (or with the API), of course the new value is used when the agent spins this Task, and to avoid the actual docker (the one you changed in the UI) to be overwritten by this call (the set_base_docker call), it is essentially skipped over

Ahhh, ok got it! Thanks 👍

You could use the agent.extra_docker_arguments configuration section in the clearml.conf file to get the same result you've reached before... this should work, I think 🙂 - see here https://github.com/allegroai/clearml-agent/blob/26e62da1a8fb4b66361a5d6a4d852d50b1e0a158/docs/clearml.conf#L155

That was the missing piece - thank you!
Awesome to all the details you have considered in ClearML 😉

Hi SuccessfulKoala55 , thanks for getting back to me!
In the docs of Task.set_base_docker() it says “When running remotely the call is ignored”. Does that mean that this function call is executed when running locally to “record” the arguments and then when I duplicate the experiment and clone it remote, the call is ignored and the recorded values are used?

Hi ScantChimpanzee51 , you can set the task's container's arguments, which can include any docker run command line arguments. For your case, you can use -e KEY=VALUE as many times as you need. These will be used in the docker command and will be available inside the container as env vars