AgitatedDove14 I tried your code snippet, something like this:
task = Task.init(...) if task.running_locally(): # wait for the repo detection and requirements update task._wait_for_repo_detection() # reset requirements task._update_requirements(None) task.execute_remotely(queue="default") ... rest of task here ...It’s able to create a draft successfully with no items in the installed packages section, but it only installed Cython and began the execution immediately....

Thanks! yeah that sounds good. Is there any way to modify the config aside from using the UI?

Awesome, thanks!

Yes

AgitatedDove14 So I ran a cell to create the task and execute it remotely. The cell (after execution) shows up with an error that says NameError: name 'exit' is not defined . Trains does store the task as a draft, and when I try to execute the task via trains agent, it aborts in the middle of installing all of the dependencies.

The notebook is contained within a git repo. I have the jupyter notebook pulled up and am running the cell for creating the task and executing it remotely. In the draft task, there is a git repo and uncommitted changes(which contains all of the cells of the notebook) and installed packages.

Yes, I am

Yeah, I’m trying to connect to my own server, and I installed trains-agent on a vm. How could I diagnose the issue?

i ran ping <IP HERE>:8008 , it returns
ping: <ip-here>:8008: Name or service not known

Let me look into that and I’ll let you know

Yes, I did both

If there a way to do this without manually editing installed packages? I'm trying to create a setup in which others can utilize the same repo and have their venvs be built similarly without having to do much work on their end (i.e. editing the installed packages). As for why it's failing to detect, I'm not sure.
from google.cloud import bigquery ImportError: cannot import name 'bigquery' from 'google.cloud' (unknown location)That's the error I get after the task makes use of the function ...

Since I'm running a pipeline, does this mean I have to execute each task of the pipeline individually and manually? Then uncomment task.execute_remotely() from each task and then run via trains-agent ?

This line: “if task.running_locally():” makes sure that when the code is executed by the agent it will not reset it’s own requirements (the agent updates the requirements/installed_packages after it installs them from the requiremenst.txt, so that later you know exactly which packages/versions were used)

got it! Thanks for explaining

Let me experiment with force_analyze_entire_repo and I’ll get back to you! It was missing google-cloud-bigquery , an import from a separate file in the repository. Hopefully that should resolve the problem. A flag would be really cool, just in case if theres any problem with the package analysis.

task = Task.init(...) if task.running_locally(): # wait for the repo detection and requirements update task._wait_for_repo_detection() # reset requirements task._update_requirements(None)Regarding this, does this work if the task is not running locally and is being executed by the trains agent?

If you have a few lines for resetting, I would love to have that as an option as well!

Since the package requirements vary from task to task, setting it up in trains.conf might be a little tedious to go back and change. Most of the time, the package analysis has worked very well, but in this particular use case it failed to detect. I think task.init flag would be great!

I did see that, just wanted to confirm that there's no way of deleting artifacts without deleting the experiment as well

Yes AgitatedDove14

SuccessfulKoala55 I looked back into my configuration settings for the VM(I already created a VM instance from the custom image yesterday) and thats where I got my external IP from. I checked "Allow HTTP access" but it's giving me a gateway timeout error now and telling me that the server at the IP is unreachable at the moment.

SuccessfulKoala55 At the time, yes it was v0.15.1. How do I actually launch trains server from the custom image? Do I use the external IP associated with the VM or are there any other steps I have to take?

SuccessfulKoala55 Actually- just resolved this. I had to allow access from all ports(not secure but will fix that asap) but everything seems to be running now, thanks for the help!

Regarding the trains server SDK, the end of the documentation for GCP says 'Next step, configuring trains for trains server' where the first step is running trains-init setup wizard

The VM ip is 153.73.X.Y and the trains-server IP is 34.74.X.Y

Yeah, the command is executed and nothing is returned.

SuccessfulKoala55 Getting an error with mounting on a read-only file system(Error response from daemon: error while creating mount source path '/opt/trains/logs': mkdir /opt/trains: read-only file system: RunContainerError), is there any workaround for this?

AgitatedDove14 Yes, I understand that, but as I mentioned earlier, I don't want to have to edit installed_packages manually, as others will also be running the same scripts from their own local development machines. This is why I was inquiring about the requirements.txt file, so that this manual editing would not have to be done by each person running the same scripts.

Additionally, since I'm running a pipeline, would I have to execute each task in the pipeline locally(but still...

AgitatedDove14 Thanks! That resolved a lot of my questions.

Which would mean 

trains

` will update the installed packages, no?

Can you explain what is meant by this?
I think what you're trying to say is that in the UI the installed packages will be determined through the code via the imports as usual and the users will have to manually clear the installed packages so that the requirements.txt is used instead.

I didn't execute the code manually, I executed it from trains-agent to begin with. So to conclude: it has to be executed manually first, then with trains agent?