You can clone it via the UI, enqueue it to a queue that has a worker running against that queue. You should get a perfect 1:1 reproduction
You can see my answer in channel
Hi @<1618418423996354560:profile|JealousMole49> , why not just use different datasets? Just to make sure I'm understanding correctly - you have a duplication of data on both s3 and local?
ReassuredTiger98 , nothing CLI based but you can do it programmatically via the API quite easily.
Also, what happens if you do clearml-data delete --id <TASK_ID> ? It's a bet but it could actually work as well 🙂
Hi @<1523701181375844352:profile|ExasperatedCrocodile76> , and now the worker clones the repo correctly?
JitteryCoyote63 , if you hit F12 and open the console in the webUI you should see some calls going out called events.get_task_log , can you take a peek and see if the logs are missing from there?
Hi @<1874626649442488320:profile|KindTurkey44> , can you elaborate on what is happening?
the question how does ClearML know to create env and what files does it copy to the task
Either automatically detecting the packages in requirements.txt OR using the packages listed in the task itself
Strange, let me check with the guys
Hi @<1659368250930106368:profile|ConfusedFlamingo31> , I second Jean 🙂
You should have a small cogwheel icon you can click to enable this behavior
Sharing the same workspace so it makes sense that you'd encounter the same issue being on the same network 🙂
@<1808672054950498304:profile|ElatedRaven55> , If you manually spin up the machines, does the issue reproduce? Did you try running the same exact VM setup manually?
@<1594863230964994048:profile|DangerousBee35> , I'd ask the DevOps to check if there might be something slowing communication from your new network in GCP to the app.clear.ml server
Hi @<1577468611524562944:profile|MagnificentBear85> , you can also use Task.init(... , output_uri=True) in the code as well
Hi @<1724960468822396928:profile|CumbersomeSealion22> , can you provide a log of such a run?
Hi @<1572395190897872896:profile|ShortWhale75> , that is not the correct way to use workers & queues.
First of all, Task.init will mark your task as running so this error makes sense.
The idea is first you run the code locally on your machine, once everything is logged (packages, repo, uncomitted changes & configurations) you can clone the task and then enqueue it into the agent.
Programmatically, you would watch to fetch an existing task in the system, clone it and then enqueue the n...
BTW, considering the lower costs of EC2, you could always use longer timeout times for the autoscaler to ensure better availability of machines
You can also set just specific commit/branch in the component object - None
VictoriousPenguin97 , can you please try with the latest version? 1.1.3 🙂
Hi @<1582542029752111104:profile|GorgeousWoodpecker69> , can you elaborate please on the exact steps you took?
Hi @<1526734383564722176:profile|BoredBat47> , do you see any errors in the elastic container?
Again, I'm telling you, please look at the documentation and what it says specifically on minio like solutions.
The host should behost: " our-host.com :<PORT>"
And NOThost: " s3.our-host.com "
Maybe you don't require a port I don't know your setup, but as I said, in the host settings you need to remove the s3 as this is reserved only to AWS S3.
SmallDeer34 , and they still have the same colors when you maximize the graph?
Hi @<1574931891478335488:profile|DizzyButterfly4> , I think if you have a pandas object pd then the usage would be something like ds.set_metadata(metadata=pd, metadata_name="my pandas object")
I think you would be referencing the entire thing using the metadata_name parameter
UnevenDolphin73 , I think I might have skipped a beat. Are you running the autoscaler through the code example in the repo?
The metadata would relate to the entire dataset.
For your use case I think what's relevant is HyperDatasets
Hi @<1523702547896864768:profile|FrightenedHippopotamus86> , debug samples are stored automatically on the files server. You can manipulate this by changing api.files_server in clearml.conf to connect to the bucket of your choice
SubstantialElk6 , I don't think anything like this currently exists in the API. Maybe add a feature request to github?
MoodyCentipede68 , I'm sorry. I meant inject a preconfigured ~/clearml.conf . Or as Jake mentioned, just use environment variables 🙂
You can see the version if you go to settings page. It is in the bottom right of the screen 🙂