Sure! Here are the relevant parts:
` ...
Current configuration (clearml_agent v1.2.3, location: /tmp/.clearml_agent.3m6hdm1_.cfg):

...
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version = ==20.2.3
agent.package_manager.system_site_packages = false
agent.package_manager.force_upgrade = false
agent.package_manager.conda_channels.0 = pytorch
agent.package_manager.conda_channels.1 = conda-forge
agent.package_manager.conda_channels.2 ...

This is not the case, I downloaded it and I got a cuda error at runtime

I made sure before deleting the old index that the number of docs matched

It could be: I am running the clearml aws autoscaler in an ec2 instance having iam roles allowing for creating/deleting instances, but I get Warning! exception occurred: An error occurred (UnauthorizedOperation) when calling the RunInstances operation: You are not authorized to perform this operation. Encoded authorization failure message: ...
I suspect that since the agent is running in docker mode, the boto3 lib doesn’t automatically get the right permissions from the ec2-instance. To...

Awesome, thanks!

it also happens without hitting F5 after some time (~hours)

We would be super happy to have the possibility of documenting experiments (new tab in experiments UI) with a markdown editor!

No 🙂

Hi SuccessfulKoala55 , Yes it’s for the same host/bucket - I’ll try with a different browser

What do you mean by aws scalar?

ok yea now I see it

I had this problem before

Awesome! Thanks! 🙏

now how to adapt to do it from extra_vm_bash_script ?

I carry this code from older versions of trains to be honest, I don't remember precisely why I did that

yes

Thanks! (Maybe could be added to the docs ?) 🙂

with the RC version

For me it is definitely reproducible 😄 But the codebase is quite large, I cannot share. The gist is the following:

import matplotlib.pyplot as plt
import numpy as np
from clearml import Task
from tqdm import tqdm

task = Task.init("Debug memory leak", "reproduce")

def plot_data():
    fig, ax = plt.subplots(1, 1)
    t = np.arange(0., 5., 0.2)
    ax.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
    return fig

for i in tqdm(range(1000), total=1000):
    fig = plot_data()
  ...

Ha nice, good one! Thanks!

SuccessfulKoala55 Here is the trains-elastic error

` # Set the python version to use when creating the virtual environment and launching the experiment
# Example values: "/usr/bin/python3" or "/usr/local/bin/python3.6"
# The default is the python executing the clearml_agent
python_binary: ""
# ignore any requested python version (Default: False, if a Task was using a
# specific python version and the system supports multiple python the agent will use the requested python version)
# ignore_requested_python_version: ...

Nice, the preview param will do 🙂 btw, I love the new docs layout!

I understand, but then why the docker mode is an option of the CLI if we always have to use it so that it works?

venv mode

I am doing:
try: score = get_score_for_task(subtask) except: score = pd.NA finally: df_scores = df_scores.append(dict(task=subtask.id, score=score, ignore_index=True) task.upload_artifact("metric_summary", df_scores)

"Can only use wildcard queries on keyword and text fields - not on [iter] which is of type [long]"

I will try to isolate the bug, if I can, I will open an issue in trains-agent 🙂

trains==0.16.4

Ho and also use the colors of the series. That would be a killer feature. Then I simply need to match the color of the series to the name to check the tags