Hi HappyDove3
Are you passing it this way?
task.upload_artifact(name="my artifact", artifact_object=np.eye(3,3))
https://github.com/allegroai/clearml/blob/5953dc6eefadcdfcc2bdbb6a0da32be58823a5af/examples/reporting/artifacts.py#L38

Thanks IntriguedRat44 !
I'll follow up on GitHub 🙂

BTW: is this on the community server or self-hosted (aka docker-compose)?

confirmed that the change had been added by

Make sure you see them in the Task log in the UI (the agent print it when it starts)

Any insight on how we can reproduce the issue?

Can this be reproducible using a simple script that we can also run?

I think you can force it to be started, let me check (I pretty sure you can on aborted Task).

Hi @<1532532498972545024:profile|LittleReindeer37>
You mean something like login in the DAG? or just DAG?
None
None

I see, you can manually do that with add steps, i.e.

for elem in map:
  pipeline.add_step(..., elem)

or you can do that with full logic:

@PipelineDecorator.component(...)
def square_num(num):
    return num**2

@PipelineDecorator.pipeline(...)
def map_flow(nums):
    res = []
    # This will run in parallel
    for num in nums:
      res.append(square_num)
    # this is where we actually wait for the results
    for r in res:
        print_nums(r)

map_flow([1,2,3,5,8,13])
`...

I see, so in theory you could call add_step with a pipeline parameter (i.e. pipe.add_parameter etc.)
But currently the implementation is such that if you are starting the pipeline from the UI
(i.e. rerunning it with a different argument), the pipeline DAG is deserialized from the Pipeline Task (the idea that one could control the entire DAG externally without changing the code)
I think a good idea would be to actually allow the pipeline class to have an argument saying always create from cod...

seems like the network inside the running code cannot access the localhost (even though you have --network=host . Could you test it with the machine's IP?
(Actually the best practice is to add a name to the machine (in your hosts file), so that if later you move the server, all the links will be valid)

TenseOstrich47 this looks like elasticserach is out of space...

Hi @<1541954607595393024:profile|BattyCrocodile47> and @<1523701225533476864:profile|ObedientDolphin41>

"we're already on AWS, why not use SageMaker?"

TBH, I've never gone through the ML workflow with SageMaker.

LOL I'm assuming this is why you are asking 🙂

• First, you can use SageMaker and still log everything to ClearML (2 lines integration). At least you will have visibility to everything that is running/failing 🙂
• SageMaker job is a container, which means for ...

compression=ZIP_DEFLATED if compression is None else compressionwdyt?

Hi SmugOx94
Hmm are you creating the environment manually, or is it done by Task.init ?
(Basically Task.init will store the entire environment of conda, and if the agent is working with conda package manager it will use it to restore it)
https://github.com/allegroai/clearml-agent/blob/77d6ff6630e97ec9a322e6d265cd874d0ab00c87/docs/clearml.conf#L50

Hi ObnoxiousStork61

Is it possible to report ie. validation scalars but shifted by 1/2 iteration?

No 😞 these are integers

What's the reason for the shift?

I'm also curious 🙂

How about this one:
https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_toy.py

BTW: I think it was fixed in the latest trains package as well as the cleaml package

Ohhh I see, yes this is regexp matching, if you want the exact match:
'^{}$'.format(name)

Could it be the Args section of the task it clones does not have the "input_train_data" argument ?

DilapidatedDucks58 trains-agent adds the artifactory URL as --extra-index-url , are you sure you are getting the correct torch version in the container? because the torch html is not an artifactory html, it is a list of links, I just want to make sure you are getting the correct version, because otherwise it can default to the CPU version, which we don't want 🙂 anyhow you can use the direct link in the "installed packages and just put there " https://download.pytorch.org/whl/nightly/cu101...

SillyPuppy19 I think this is a great idea, basically having the ability to have a callback function called before aborting/exiting the process.

Unfortunately today abort will give the process 2 seconds to gracefully quit and then it kills the process. It was not designed to just send an abort signal, as these will more often than not, will not actually terminate the process.

Any chance I can ask you to open a GitHub Issue and suggest the callback feature. I have a feeling a few more users ...

ReassuredTiger98 in theory it should work, do you know what is actually stored ? (I mean reencoding it means you have to have opencv / ffmpeg which might be too much to ask)

Sure just setup clearml-agent on any machine 🙂
(The app.community server is the control plane)

It is configured as CPU (i.e. no CUDA)

Hi SubstantialElk6 ,
Are you still getting SSL errors ?

Hi WickedGoat98

I try to write an article on medium about ClearML and face some a problem with plotly figures.

This is awesome !

I ran the plotly_reporting.py example locally and the uploaded plot was ok.

So are you saying the same example code from the repository worked okay on your server but showed nothing on the hosted server ?

The problem is not really for the agents to wait (this is easily solved by additional high priority queue) the problem is will you have a "free" agent... you see my point ?

Yey!

docker mode. they do share the same folder with the training data mounted as a volume, but only for reading the data.

Any chance they try to store the TensorBoard on this folder ? This could lead to "No such file or directory: 'runs'" if one is deleting it, and the other is trying to access, or similar scenarios

Hi MagnificentSeaurchin79
Could you test with the tesnorflow toy example?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorboard_toy.py

Hi SuperiorDucks36
Could you post the entire log?
(could not resolve host seems to be coming from the "git clone" call).
Are you able to manually clone the repository on the machine running trains-agent