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In case of stopping, send SIGINT and wait until the job finishes.
In case of aborting, immediately kill the job.

Also, make sure we don't wait extra time when cleaning up job resources.

It will be great to have an option either to specify the time to launch for the run or just say "this run to start in 2 hours"

If a run or a job is restarted on the same runner, the runner tries to apply the Git patch (repo diff) and fails because of a conflict as it's trying to apply it to the folder where it has already applied the patch.

Steps to reproduce:

1. Make sure you have only one runner (e.g. disable on-demand runners, and start a runner locally)
2. Submit a run (or job, and wait when it finishes (you can stop if if needed)
3. Restart the run.

Expected:

1. The run is executed exactly as the first time

Actual:

1. There is an error

Log:

ERRO[2022-05-25T11:21:58Z] diff applier error                            ae=ApplyError{Fragment: 1, FragmentLine: 3, Line: 3} run_name=odd-rabbit-1 job_id=e7fa162e70b1 workflow=train-mnist filename=.dstack/variables.yaml err=conflict: fragment line does not match src line
ERRO[2022-05-25T11:21:58Z] run job is finished with error                job_id=e7fa162e70b1 err=conflict: fragment line does not match src line workflow=train-mnist run_name=odd-rabbit-1
INFO[2022-05-25T11:24:57Z] New job submitted                             job_id=e7fa162e70b1 workflow=train-mnist run_name=odd-rabbit-1
WARN[2022-05-25T11:24:57Z] count of log arguments must be odd            job_id=e7fa162e70b1 workflow=train-mnist run_name=odd-rabbit-1 count=1
INFO[2022-05-25T11:24:58Z] git checkout                                  path=/root/.dstack/tmp/runs/odd-rabbit-1/e7fa162e70b1 workflow=train-mnist run_name=odd-rabbit-1 url=https://github.com/dstackai/dstack-examples.git branch=main hash=f219066b2379c69263f281f65167c8f6046874a2 job_id=e7fa162e70b1 auth=*http.BasicAuth
WARN[2022-05-25T11:24:58Z] git clone ref==nil                            branch=main hash=f219066b2379c69263f281f65167c8f6046874a2 job_id=e7fa162e70b1 path=/root/.dstack/tmp/runs/odd-rabbit-1/e7fa162e70b1 workflow=train-mnist run_name=odd-rabbit-1 url=https://github.com/dstackai/dstack-examples.git
INFO[2022-05-25T11:24:58Z] apply diff start                              run_name=odd-rabbit-1 dir=/root/.dstack/tmp/runs/odd-rabbit-1/e7fa162e70b1 job_id=e7fa162e70b1 workflow=train-mnist
ERRO[2022-05-25T11:24:58Z] diff applier error                            job_id=e7fa162e70b1 workflow=train-mnist run_name=odd-rabbit-1 filename=.dstack/variables.yaml err=conflict: fragment line does not match src line ae=ApplyError{Fragment: 1, FragmentLine: 3, Line: 3}
ERRO[2022-05-25T11:24:58Z] run job is finished with error                run_name=odd-rabbit-1 job_id=e7fa162e70b1 err=conflict: fragment line does not match src line workflow=train-mnist

Questions:

1. How to avoid requiring the user to add limits manually through the UI?
2. How to determine in what regions and what instance types it's allowed to use?
3. Is it possible to let the user configure it through the code and not UI?
4. How to make AWS configuration as easy as possible?

Currently, if the dif is larger than 400KB, dstack CLI fails to submit the run.

Currently, in order to use dstack, the user needs to either have an existing cloud account or own hardware.
It would be great if dstack provided its own compute provider and allow users to use dstack without having their own cloud account or hardware.
On one hand, dstack could provide a number of free GPU hours for the trial.
On the other hand, dstack could provide a way to pay for the spent hours, e.g. via a card.

using dstack artifacts upload I've provided the tag I wanted to assign to the data. Unfortunately, further runs depending on this data were failing without any logs. I've just removed the tag from the data (after it was successfully uploaded) and assigned it (the same tag) once again. Without any changes in the local repo, the code was resurrected and launched easily on dstack.

Here's one way to do it:

1. Allow dstack-runner to read the CUDA version from the runner.yaml. Add config --cuda <...> argument to dstack-runner.
2. Allow dstack-runner to replace ${{ cuda }} within jobs' image_namewith the configured CUDA version. Do the same for the docker image that is used to runnvidia-smi`.

Currently, the user doesn't see why a run is failing...

Examples:

• workflows.yaml is missing
• Specified workflow cannot be found
• Specified provider cannot be found
• Specified tag cannot be found
• Can’t fetch the repo
• Can’t apply the diff + error message
• Can’t download the provider
• Can’t create/start Docker container
• Can’t find/mount the artifact

etc

Regularly happens if you work with ipynb notebooks locally and going to submit a python file regardless if the latter was changed or not.
sometimes fails on the stage or dstack run with requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://api.dstack.ai/runs/submit
what is worse, sometimes this successfully goes to the servers but fails there without any notification

I am unable to add key, value pair in Secrets.

I go through the following steps. In the end, I do not see any key, value pair (see last image).

In the tutorial, the data is downloaded via the library which is not customisable enough.
It would be nice to have an option to pass the data to the execution environment. For example, it may be a tag in the workflows for the data to be taken from the specified path to the aws instance.

Thank you in advance!

Now, the /runners/ping response inside users provides secrets with the list of secrets to pass as environment variables to the jobs.

dstack artifacts download doesn't tell you anything if there is a typo in a run name. would be nice to make some warning

Would be nice to be able to set a tag for the run right from the console like
dstack run train-model --tag latest

If I use dstack init on a repository that is using SSH, dstack should be able to parse ~/.ssh/config automatically.

There are tons of dependencies apart from the ones passed by the user. These dependencies are installed each time the run is submitted. It would be nice to optimize this part.
Ideas:

1. Select really necessary libraries
2. Make several pre-built sets for the most common use-cases

Containers from colab\kaggle would be really nice as they are +- classical and have expected behaviour regarding popular libraries

The command should work similarly to dstack run but instead of creating new jobs, it should change the existing jobs to theSubmitted status.

Now, every job may have its own environment variables set by the provider – see the property environment in the job. It's a map of string to string. The runner should pass these environment variables to the job container.

This relates to the new UI, where we hide jobs.

Currently, the user sees the logs from the provider in the run logs.

We should treat them as runner logs and not as run logs so the user doesn't see them.

The Secrets feature in Settings tab is not visible just after I login. For example, the screen looks like below.

Once, I refresh the screen, it looks like below (Secrets feature is visible now!).