This is what I just used:
` import os
from argparse import ArgumentParser

from tensorflow.keras import utils as np_utils
from tensorflow.keras.datasets import mnist
from tensorflow.keras.layers import Activation, Dense, Softmax
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint

from clearml import Task

parser = ArgumentParser()
parser.add_argument('--output-uri', type=str, required=False)
args =...

GrievingTurkey78 I have to admit I can't see the difference, can you help me out 🙂

GrievingTurkey78 are you able to reproduce it?

Hmm what do you mean? Isn't it under installed packages?

Okay that might explain the issue...
MysteriousBee56 so what you are saying is
python3 -m trains-agent --help does NOT work
but trains-agent --help does work?

Basically it gives it direct access to the host, this is why it is considered less safe (access on other levels as well, like network)

See the log:

Collecting keras-contrib==2.0.8
  File was already downloaded c:\users\mateus.ca\.clearml\pip-download-cache\cu0\keras_contrib-2.0.8-py3-none-any.whl

so it did download it, but it failed to pass it correctly ?!
Can you try with clearml-agent==1.5.3rc2 ?

WickedGoat98 until the next RC release (should not take long) this will solve it:
df = pd.concat([tickerDf.Close, tickerDf_Change.Close_pcent], axis=1) df = df[1:] df.index = df.index.astype(str) setattr(df, 'ticker', args.symbol)Basically removing the nan and converting the datetime to string representation (so plotly.js likes it)

Hi @<1695969549783928832:profile|ObedientTurkey46>

Why do tags only show on a version level, but not on the dataset-level? (see images)

Tags of datasets are tags on "all the dataset versions" i.e. to help someone locate datasets (think locating projects as an analogy). Dataset Version tags are tags on a specific version of the dataset, helping users to locate a specific version of the dataset. Does that make sense ?

Hi PungentLouse55 ,
Yes we have integration with hydra on the todo list since it was first released, we actually know the guy behind Hydra, he is awesome!
What are your thoughts on integration, we would love to get feedback and pointers (Hydra itself is quite capable, and we waiting until we have multiple configuration support, and with v0.16 it was added, so now it is actually possible)

TrickyRaccoon92 actually Click is on the to do list as well ...

Hi ProudMosquito87
My apologies there is still no concrete ETA ...
That said I think a good toy example would really help accelerate this process.
How about opening a PR with a nice hydra example, then we can start discussing implementation details based on the toy example ?

Oh my bad, post 0.17.5 😞
RC will be out soon, in the meantime you can install directly from github:
pip install git+

🤞

Hi SteadyFox10
I promised to mention here once we start working on ignite integration, you can check it here:
https://github.com/jkhenning/ignite/tree/trains-integration
Feel free to provide insights / requests 🙂

As for the model upload. The default behavior is
torch.save() calls will only be logged , nothing more. But, if you pass to the Task.init output_uri field, then all your models will be uploaded automatically. For example:
` task = Task.init('examples', 'model upload test', o...

I want to keep the above setup, the remote branch that will track my local will be on 

fork

 so it needs to pull from there. Currently it recognizes 

origin

 so it doesn’t work because the agent then can’t find the commit.

So you do not want to push the change set ?
You can basically add the entire change set (uncomitted changes) from the last pushed commit).
In your clearml.conf, set store_code_diff_from_remote: true
https://github.com/allegroai...

RoundMosquito25 how is that possible ? could it be they are connected to a different server ?

You’ll just need the user to 

name them

 as part of loading them in the code (in case they are loading multiple datasets/models).

Exactly! (and yes UI visualization is coming 🙂 )

DeterminedToad86 I suspect that since it was executed on sagemaker it registered a specific package that is unique for Sagemaker (no to worry installed packages can be edited after you clone/reset the Task)

Hi UpsetCrocodile10

First, I perform many experiments in one process, ...

How about this one:
https://github.com/allegroai/trains/issues/230#issuecomment-723503146
Basically you could utilize create_function_task
This means you have Task.init() on the mainn "controller" and each "train_in_subset" as a "function_task". Them the controller can wait on them, and collect the data (like the HPO does.

Basically:
` controller_task = Task.init(...)
children = []
for i, s in enumer...

maybe this can cause the issue?

Not likely.
In the original pipeline (the one executed from the Pycharm) do you see the "Pipeline" section under Configuration -> "Config objects" in the UI?

PompousBeetle71 a few questions:
is this like using PyTorch distributed , only manually? Why don't you use call trains.init in all the sub processes? We had a few threads on that, it seems like a recurring question, I'll make sure we have an example on GitHub. Basically trains will take care of passing the arg-parser commands to the sub processes, and also on torch node settings. It will also make sure they all report to the tame experiment.What do you think?

MoodyCentipede68 could it be that the model is on one account (workspace) and your credentials (the ones provided to the docker compose) are from another workspace?
The error itself point to the triton helper failing to get the model ID from the backend. The models are uploaded to a a specific workspace, and it looks like a mismatch (I.e. the model Id is nowhere to be found) wdyt?

Hi SillyPuppy19
I think I lost you half way through.

I have a single script that launches training jobs for various models.

Is this like the automation example on the Github, i.e. cloning/enqueue experiments?

flag which is the model name, and dynamically loading the module to train it.

a Model has a UUID in the system as well, so you can use that instead of name (which is not unique), would that solve the problem?

This didn't mesh well with Trains, because the project a...

Thanks!

more like testing especially before a pipeline

Hmm yes, that makes sense.
Any chance you can open a github issue on it?
Let me see if I understand, basically, do not limit the clone on execute_remotely, right ?

When did this PipelineDecorator come. Looks interesting 

A few days ago (I think)
It is very cool! checkout the full object proxy interaction on the actual pipeline logic This might be better for your workflow, https://github.com/allegroai/clearml/blob/c85c05ef6aaca4e...

Hi @<1573119962950668288:profile|ObliviousSealion5>

Hello, I don't really like the idea of providing my own github credentials to the ClearML agent. We have a local ClearML deployment.

if you own the agent, that should not be an issue,, no?

forward my SSH credentials using

ssh -A

and then starting the clearml agent?

When you are running the agent and you force git clonening with SSH, it will autmatically map the .ssh into the container for the git to use

Ba...

p.s. you should remove this line 🙂
extra_index_url: ["git@github.com:salimmj/xxxx"]

Hmm, let me check something

@<1564422644407734272:profile|DistressedCoyote60> could you open a GitHub issue on it in clearml-agent, just so we know of the problem and fix it for next version ?