others from the local environment and this causes a conflict when importing the attr module
Inside the docker ? " local environment" ?
This is all under "root" no?
I think the main risk is ClearML upgrades to MongoDB vX.Y, and mongo changed the API (which they did because of amazon), and now the API call (aka the mongo driver) stops working.
Long story short, I would not recommend it 🙂
Hi ContemplativeCockroach39
Assuming you wrap your model with a flask app (or using any other serving solution), usually you need:
Get the model Add some metrics on runtime performance package in a dockerGetting a pretrained model is straight forward one you know either the creating Task or the Model ID
` from clearml import Task, Model
model_file_from_task = Task.get_task(task_id).models['output'][-1].get_local_copy()
or
model_file_from_model = Model(model_id=<moedl_id>).get_local_copy()...
Long story short, work in progress.
BTW: are you referring to manual execution or trains-agent ?
AntsySeagull45 kudos on sorting it out 🙂
quick note, trains-agent will try to run the python version specified by the original Task. i.e. if you were running python3.7 it will first try to look for python 3.7 then if it is not there it will run the default python3. This allows a system with multiple python versions to run exactly the python version you had on your original machine. The fact that it was trying to run python2 is quite odd, one explanation I can think of is if the original e...
Yep I think you are correct, you should have had the same output as a local jupyter notebook, and it seems that in sagemaker studio it is not working 😞
Let me check something
MistakenDragonfly51 just making sure I understand, on Your machine (the one running the pytorch example),
you have set " CLEARML_DEFAULT_OUTPUT_URI " / configured the "clearml.conf" file with default_output_uri , yet the model checkpoint was Not uploaded?
Hmm so the SaaS service ? and when you delete (not archive) a Task it does not ask for S3 credentials when you select delete artifacts ?
is the base Task a file or a notebook ?
Hi SmallDeer34
Hmm I'm not sure you can, the code will by default use rglob with the last part of the path as wildcard selection
😞
You can of course manually create a zip file...
How would you change the interface to support it ?
Hi LittleShrimp86
just to login into your clearml app (demo or server) so I can run python files related to clearml.
I think this amounts to creating a Task and enqueueing it, am I understanding correctly ?
You mean parameters of the pipeline? Is this a pipeline from Tasks or from function decorator?
LittleShrimp86 did you try to run the pipeline form the UI on remote machines (i.e. with the agents)? Did that work?
No worries, you should probably change it to pipe.start(queue= 'queue') not start locally
s it working when you are calling it with start locally ?
I mean test with:pipe.start_locally(run_pipeline_steps_locally=False)This actually creates the steps as Tasks and launches them on remote machines
LittleShrimp86 can you post the full log of the pipeline? (something is odd here)
LittleShrimp86 what do you have in the Configuration Tab of the Cloned Pipeline?
(I think that it has empty configuration -> which means empty DAG, so it does nothing and leaves)
BTW: Can you also please test with the latest clearml version , 1.7.2
MelancholyBeetle72 I think we collect them in Issue 81 on GitHub, feel free to add it if it is missing 🙂
https://github.com/allegroai/clearml/issues/81
The configuration tab -> configuration objects -> pipeline is empty
That's the reason it is doing nothing 😞
How come it is empty if you Cloned the local one?
Wait. are you saying it is disappearing ? meaning when you cloned the Pipeline (i.e. in draft mode) the configuration was there, then the configuration disappeared ?
oh dear ...
ScrawnyLion96 let me check with front-end guys 😞
Hurray conda.
Notice it does include cudatoolkit , but conda ignores it
cudatoolkit~=11.1.1
Can you test the same one only serach and replace ~= with == ?
No worries 🙂 glad to hear it worked out 🙂
Hi @<1523701260895653888:profile|QuaintJellyfish58>
Based on the docs
None
I think this should have worked, are you running the actual task_scheduler on yout machine? on the services queue ? what's the console output you see there ?
In order for the sample to work you have to run the template experiment once. Then the HP optimizer will find the best HP for it.
ElegantCoyote26 I don't think Keras logs it anywhere unless you have TB, so nowhere to take the data from...
In short, yes you have to have TB :)
you are correct, I was referring to the template experiment