DM me the entire log, I would assume this is something with the configuration

Different question. How can I pass PYTHONPATH env variable to a task, run by agent (so python can find classes inside m subdirectories)?

Hi HelpfulHare30
By default the working directory will be added to the python path, this means if I have under execution:
Working Dir: "." Script: "src/script.py"The root git repo will be added to the python path.
BTW: next RC you could add a flag to the agent to always add the git repo

can the ClearML File server be configured to any kind of storage ? Example hdfs or even a database etc..

DeliciousBluewhale87 long story short, no 🙂 the file server, will just store/retrieve/delete files from a local/mounted folder

Is there any ways , we can scale this file server when our data volume explodes. Maybe it wouldnt be an issue in the K8s environment anyways. Or can it also be configured such that all data is stored in the hdfs (which helps with scalablity).I would su...

You might need to play around a bit, it might be that StorageHelper.get(' gs://bucket ') and then helper.list('folder/*')
Let me know what worked 🙂

👍

Hmm so is the problem having the gituser inside the code? or the k8s_glue print ?

🤞

You can always force it with environment variable CUDA_VERSION=10.1

Hi JitteryCoyote63 ,
I remember seeing something similar on our GitHub...
The error itself is pip failing to run "git clone" , seems like a weird network connection error (TLS is the HTTPS security layer)

Here you go:
` @PipelineDecorator.pipeline(name='training', project='kgraph', version='1.2')
def pipeline(...):
return

if name == 'main':
Task.force_requirements_env_freeze(requirements_file="./requirements.txt")
pipeline(...) If you need anything for the pipeline component you can do: @PipelineDecorator.component(packages="./requirements.txt")
def step(data):

some stuff `

For setting trains-server I would recommend the docker-compose, it is very easy to setup, and you just need a single fixed compute instance, details https://github.com/allegroai/trains-server/blob/master/docs/install_linux_mac.md With regards to the "low prio clusters", are you asking how they could be connected with the trains-agent or if running code that uses trains will work on them?

Hi @<1610083503607648256:profile|DiminutiveToad80>
Yes, it does. They are also cached by default (on the machine with the agent)
None

So basically development on a "shared" GPU?

I think my question is more about design, is a ModelPipeline class a self contained pipeline? (i.e. containing all the different steps or is it a single step in a pipeline)

Which means you currently save the argument after resolving and I'm looking to save them explicitly so the user will not forget to change some dependencies.

That is correct

I'm looking to save them explicitly so the user will not forget to change some dependencies.

Hmm interesting point. What's the use case for storing the values before the resolving ?
Do we want to store both ?
The main reason for storing the post resolve values, is that you have full visibility to the actual...

Thanks CleanPigeon16
Could you verify Task "d1d361d1059c4f0981200f59d7683773" exists (and not archived)?

Hi SmugLizard25 I was able to test and it seems that style is being ignored by the FE 😞
I passed to FE guys to make sure it is fixed in the next version.
Notice this is just for tables, anything else works as expected (i.e. styling any other type of plot)

In theory yes, in practice you will be using the same docker image for all the services, and they will never interfere with one another. and you have the option to do more sophisticated stuff, like map the file-server data for a clean up service (should be out in a few days :)) so a balance. Also remember that relatively speaking docker are quite light weight, this is not like saying a VM per service...

WittyOwl57 what about? vm.max_map_count echo "vm.max_map_count=262144" > /tmp/99-clearml.conf
sudo mv /tmp/99-clearml.conf /etc/sysctl.d/99-clearml.conf
sudo sysctl -w vm.max_map_count=262144
sudo service docker restart `https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_linux_mac (5)

Hi GracefulDog98

Are argument parameters to the script not passed on to the workers, or am I missing something?

The arguments are passed directly when the code is executed (i.e. the argparser parse_args is called).
If the code fails, I'm assuming the argparse is called before clearml is imported, could that be the case ?

Could it be there is a Task.init being called Before this code snippet ?

right click on the experiment, select Reset, now you can edit it.

GrittyHawk31
what are you getting when you are running:
docker psand what are you getting with:
netstat -natp | grep LISTEN

I mean to use a function decorated with 

PipelineDecorator.pipeline

 inside another pipeline decorated in the same way.

Ohh... so would it make sense to add "helper_functions" so that a function will be available in the step's context ?
Or maybe we need a new to support "standalone" decorator?! Currently to actually "launch" the function step, you have to call it from the "pipeline" main logic function, but, at least in theory, one could do without the Pipeline itself.....

FierceHamster54 what you are saying that Inside the container it took 20 min to run? or that spinning the GCP instance until it registered as an Agent took 20min ?

Most of the time is took by building wheels for

nympy

and

pandas

...

BTW: This happens if there is a version mismatch and pip decides it needs to build the numpy from source, Can you send the full logs of that? Maybe we can somehow avoid that?

Hi @<1561885941545570304:profile|PunyKangaroo87>
What do mean by store data locally?
Like clearml-data? I.e Dataset?
You can always use file:///root/path/folder as destination, this will store everything into the local folder, is that it?

Hi @<1526371965655322624:profile|NuttyCamel41>

. I do that because I do not know how to get the pickle file into the docker container

What would the pickle file do?

and load the MinMaxScaler within the script, as the sklearn dependency is missing

what do you mean by that? are you getting an error when loading your model ?

When I give my Minio to output_uri argument, it uploads 500 KB /sec as before.

But it worked well when using StorageManager and uploading to the minio directly, is that correct?

.. I give my Minio to output_uri argument

How long did it take to run the demo code I posted?
(The one you mentioned took 0.16s to run locally)

Thanks!!

What's the difference between the example pipeeline and this code ?
Could it be the "parents" argument ? what is it?