You get all the features that are available for the hosted version such as experiment management, orchestration (with ClearML agent), data management (with ClearML Data), model serving (with ClearML serving) and more 🙂

Hi FierceHamster54 , this should be doable, actually we are adding this to other parts of the system so I'll make sure we update the autoscalers to be support this

OutrageousSheep60 The python package is in testing. Hopefully will be out Sunday \ Monday :)

As for the git, I'm no git expert but having your own git server is doable. I can't tell you what it means in terms of how does it work in your organization though as every one has their own limitations and rules. And as I said, you can use SVN but the connection between it and ClearML won't be as good as with git.

EnviousStarfish54 VivaciousPenguin66 Another question if we're in a sharing mood 😉 Do you think a video \ audio session with one of our experts, where you present a problem you're having (let's say large size of artifacts) and he tries to help you, or even can give some example code \ code skeleton. Would something like that be of interest? Would you spend some time in such monthly session?

JitteryCoyote63 Sorry to put the spotlight on you 😄 Have you had a chance to try and implement a dashboard yourself?

BTW, if you, by any chance, do deploy dashboards with any of these let's talk. I'll be happy to learn what worked for you and how does it serve you 🙂

Hi ResponsiveHedgehong88 , let me see if I get it straight, you have a my_config.yaml in your repo. Then you do something like task.add_configuration(my_config.yaml) and have it logged as a config object, then you modify the config object and rerun (so now it takes the configuration from the configuration object you modified in the UI, rather than the file in the repo). Am I understanding the setup correctly?

You can use pre \ post step callbacks.

If you return on a pre_execute_callback false (or 0, not 100% sure 🙂 ) the step just won't run.
Makes sense?

pipe._nodes['stage_data'].job.task.artifacts

That's right. once you call clearml-data close, the completed dataset is immutable. This is a very important feature if traceability is important as once an experiment uses a dataset version, we want to make sure it doesn't change without leaving a trace!

Hi SubstantialElk6 We moved to a new docs site here: https://clear.ml/docs/latest/docs/

Hmm, that indeed looks weird 😄 Let me reproduce. Just making sure, what version of the server \ SDK are you using?

EcstaticBaldeagle77 , Actually, these scalars and configurations are not saved locally to a file, but can be retrieved and saved manually. If you want to get metrics you can call task.get_reported_scalars() and if you want configuration then call task.get_configuration_object() with the configuration section as it appears in the web application

THANK YOU! It's because of user feedback that this feature was made so you can thank yourself and the community while you're at it 😄

Hi SubstantialElk6 For monitoring and production labelling, what we found is that there's no "one size fits all" so while we tried designing ClearML to be easily integrate-able. In the enterprise solution we do have a labeling solution but it's not meant to do production labeling and more to do R&D label fixes. We have customers that integrated 3rd party annotation services with Clearml.

the upload method (which has an SDK counterpart) allows you to specify where to upload the dataset to

got it 🙂

Am I doing something differently from you?

GiganticTurtle0 Got it, makes a lot of sense!

GiganticTurtle0 That is correct. ATM, you can store some things on the model (I think you can hack it by editing the network configuration and storing whatever you want there.

GiganticTurtle0 What about modifying the cleanup service to put all experiments with a specific tag into a subfolder? Then you'll have a subfolder for published experiments (or production models or whatever criteria you want to have 🙂 ). That would declutter your workspace automatically and still retain everything.

A new version should be available in a week and a half or so 😄

That's a good idea! In the autoscaler configuration you can specify an init script, and there you can copy the credentials from s3.

once integrating clearml it'll automatically report resource utilization (GPU \ CPU \ Memory \ Network \ Disk IO)

BTW, I suggest for new questions, just ask in the clearml-community. I'm really happy to help but I almost missed this message 😄

Sure 😄

Hi ZanyPig66 , do you want to have an agent per GPU? If so just add --gpus and specify the GPU number (0 or 1) that would be associated with this worker

ZanyPig66 , the 2 agents can run from the same ubuntu account and use the same clearml.conf. if you want each to have its own configuration file just add --config-file PATH_TO_CONF_FILE and it would take another config file. Makes sense?