Yes - for upgrading this is what we do. The thing is the first server was a pilot for my team and now we have a proper server for the company (but it was a pilot too so we didn't migrated all the old experiments). To make sure I understand the paid service - do you want to have a short phone call to explain the different options - is it a complete support or can we pay for specific support tasks? It worth mentioning that I'm not the decision maker in the company but I would like to have some ...

Yes - it's the second case 🙂

It's automatically set in the user's .clearml - I think that /opt/ml is persistent (this is where you are supposed to save checkpoints in sagemaker)

In the version we have I don't see that the plots are resizable - we are running 1.1.0, I believe

Thanks SuccessfulKoala55 for your prompt response, as always

We want to have many people working on a cluster of machines and we want to be able to allocate fraction of GPU to specific jobs, to avoid starvation

maybe I missed it in the documentation - but I could use also something like set_offline_dir() (to make sure it's pointing opt/ml or something) and then get_offline_file() and upload it myself

Ok - I will look into it

Sure - the problem is that many of our trainings in sagemaker are not exposed to the company's server

but what happens if the script is terminated? maybe a spot termination, ctrl+c, this means I loose track of the training?

Hi I mean something like what runai are doing, or how would you work together with http://run.ai ?

Great! I'll update to this version and will verify the issue is solved

I'll try it

makes sense.. I currently aws s3 sync every n iterations and then I saw that there is an option to load a dir rather than a zip

thanks for the quick response! and also - your library/product is really cool and impressive

Is it supported in other versions? 🙂

just add description="test" to one of the tf.summary.image calls and see that it silently doesn't get logged

https://github.com/allegroai/clearml/issues/436

Sure

Ok - thanks AgitatedDove14

Yes! https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_toy.py

Hi AgitatedDove14 - I think it's the one before 1.1.1, client is latest 1.0.5. Testing now on tensorflow-cpu 2.5.0

AgitatedDove14 , thanks for the quick response!

https://github.com/allegroai/clearml-server/issues/127

We can use VPC (which we use, but then the entire bringup of the training would be different)

You can also replace image_open = Image.open(os.path.join('..', '..', 'reporting', 'data_samples', 'picasso.jpg'))
    image = np.asarray(image_open) with image = np.random.random( size =[512, 512, 3])

Thanks!