Would it also be possible to query based on

multiple

user properties

multiple key/value I think are currently not that easy to query,
but multiple tags are quite easy to do

tags=["__$all", "tag1", "tag2],

SmilingFrog76 this is not a weird mechanism at all , this is proper HPC scheduler 🙂
trains-agent is not actually aware of other nodes, it is responsible for launching a Task on its own hardware (with whatever configuration it was set). What can be done is to use the trains-agent inside a 3rd party scheduler and have the scheduler allocate the node and trains-agent spin the experiment. There is a k8s example here: basically pulling jobs for the trains-server queue and pushing ...

Where are they stored? I could not find a backend they work with, what am I missing?

my pleasure

I just tested the master with https://github.com/jkhenning/ignite/blob/fix_trains_checkpoint_n_saved/examples/contrib/mnist/mnist_with_trains_logger.py on the latest ignite master and Trains, it passed, but so did the previous commit...

RipeWhale0 I think this is installing older version of clearml, try to pull the latest chart 🙂

When you say status, what do you mean? Is it active? Running a task?

@<1523701099620470784:profile|ElegantCoyote26> what's the target upload? also how come you are uploading a local file and auto deleting it, and then uploading the same one as artifact ?

Hi GrittyKangaroo27

How could I turn off model logging when running this training step?

This is a good point! I think we cannot pass these arguments.
Would this make sense to you?
PipelineDecorator.component(..., auto_connect_frameworks)
wdyt?

Building the pipeline in runtime from external configuration is very cool!!
I think nested components is exactly the correct solution, and it is a great use case.

It's the safest way to run multiple processes and make sure they are cleaned afterwards ...

Here you go 🙂
(using trains_agent for easier all data access)
from trains_agent import APIClient client = APIClient() log_events = client.events.get_scalar_metric_data(task='11223344aabbcc', metric='valid_average_dice_epoch') print(log_events)

Hmm can you run:
docker run -it allegroai/clearml-agent-services:latest

DeliciousBluewhale87 basically any solution that is compliant with S3 protocol will work. An example:
output_uri=" :PORT/bucket/folder"Are you sure Nexus supports this protocol ?
I "think" nexus sits on top of a storage solution (like am object storage), meaning we can use the same storage solution Nexus is using.
Just to clarify we do not support the artifactory protocol Nexus provides for storing models/artifacts. But we do support it as a source for python packages used by the a...

Hi BurlySeagull48
you mean for the clearml-server ?

EnthusiasticCoyote30 you can register an existing Model with:
from clearml import InputModel model = InputModel.import_model(weights_url=" "...)

LovelyHamster1
Also you can use pip freeze instead of the static code analysis , on your development machines set:
detect_with_pip_freeze: false
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/docs/clearml.conf#L169

controller_object.start_locally()

. Only the pipelinecontroller should be running locally, right?

Correct, do notice that if you are using Pipeline decorator and calling run_locally() the actual the pipeline steps are also executed locally.
which of the two are you using (Tasks as steps, or functions as steps with decorator)?

is there something else in the conf that i should change ?

I'm assuming the google credentials?
https://github.com/allegroai/clearml/blob/d45ec5d3e2caf1af477b37fcb36a81595fb9759f/docs/clearml.conf#L113

should work as well 🙂

Yes that would work 🙂
You can also put it in the docker compose see TRAINS_AGENT_DEFAULT_BASE_DOCKER

Any comments/ideas on how to make it better will be more than welcomed 🙂

AbruptHedgehog21 could it be the console log itself is huge ?

So if I am not using remote machine can I disable this?

yes I think you can, add to your clearml.conf
sdk.development.store_jupyter_notebook_artifact = falseBTW: why would you turn it off ?

What do you have in the artifacts of this task id: 4a80b274007d4e969b71dd03c69d504c

Hi TrickyRaccoon92
BTW: checkout the HP optimization example, it might make things even easier 🙂 https://github.com/allegroai/trains/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py

The idea is that it is not necessary, using the trains-agent you can not only launch the experiment on a remote machine, you can override the parameters, not just cmd line arguments, but any dictionary you connected with the Task or configuration...

It's dead simple to install:
Pip install trains-agent
the.n you can simply do:
Trains-agent execute --id myexperimentid

This is an OS restriction 😞

Hi BroadMole98 ,
what's the current setup you have? And how do you launch jobs to Snakemake?