'True' should point to the files server
Hi @<1742355077231808512:profile|DisturbedLizard6> , you can use the output_uri
parameter of Task.init()
to specify where to upload models.
None
DeliciousSeal67 , something along these lines:task.upload_artifact('<ARTIFACT_NAME>', artifact_object=os.path.join('<FOLDER>', '<FILE_NAME>'))
So in your case it would be along the lines oftask.upload_artifact('trained_model', 'model_folder/best_mode.pt')
@<1577468638728818688:profile|DelightfulArcticwolf22> , in addition to @<1523701087100473344:profile|SuccessfulKoala55> ’s answer, you have data management, orchestration (Integration with SLURM), pipelines, reports and much more.
As I mentioned, provisioning resources according to different groups - i.e. role based access controls are an enterprise feature.
I suggest you watch the onboarding videos on the ClearML Youtube channel - None
@<1702492411105644544:profile|YummyGrasshopper29> , I suggest you take a look here - None
I'm assuming this is 8 gigs of ram?
I think it should suffice. To be entirely sure you can run one of the AMI's listed below and see it's specifications:
https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_aws_ec2_ami/#latest-version
Also, is it an AWS S3 or is it some similar storage solution like Minio?
How about when you view it in the datasets view? Also what version of clearml
package do you have?
EnormousWorm79 , are you working from different browsers / private windows?
VexedCat68 , how is your ~/clearml.conf
configured? And is there any chance you were having network problems at the time you tried to run it?
Hi @<1657556312684236800:profile|ManiacalSeaturtle63> , can you please elaborate specifically on the actions you took? Step by step
Can you add a screenshot of how you see them currently?
Do you have a code snippet that reproduces this?
Hmmm maybe SuccessfulKoala55 can help 🙂
Hi @<1623491856241266688:profile|TenseCrab59> , you need to mark output_uri = True
in Task.init()
@<1754676270102220800:profile|AlertReindeer55> , I think what @<1523701087100473344:profile|SuccessfulKoala55> means is that you can set the docker image on the experiment level itself as well. If you go into the "EXECUTION" tab of the experiment, in the container section you might see an image there
You would need to implement this logic yourself. For example you expose a pipeline argument for the controller (These are the configurations you can control via the UI as well) and then basically have if
logic in the controller code that will run/skip steps according to the step you'd like to start from.
Makes sense?
CurvedHedgehog15 , isn't the original experiment you selected to run against is the basic benchmark?
What actions did you take exactly to get to this state?
Hi ExuberantParrot61 , if it's on the same machine you can use different configuration files. for example, when running the agent you can use --config-file <PATH_TO_USER_CONFIG_FILE>
You'll need ES/Mongo to run the ClearML server
on /data/
Hi @<1570220858075516928:profile|SlipperySheep79> , I think it depends on your code. Can you provide a self contained code snippet that reproduces this?
Hi SkinnyPanda43 ,
Can you please add a log of the failure? I think if you add specific requirements manually it should override auto detection
Yes, for an enqueued task to run you require an agent to run against the task 🙂
What OS are you using?
Hi @<1749602873152376832:profile|EncouragingSquid4> , Welcome!
To address your questions:
- I think you can do routing for them but you'd have to set it up.
- I assume the models are being saved to the files server. Can you share a screenshot?