I get the same error when trying to run the task using clearml-agent services-mode with docker, so weird

interestingly, it works on one machine, but not on another one

Ok, now I get ERROR: No matching distribution found for conda==4.9.2 (from -r /tmp/cached-reqscaw2zzji.txt (line 13))

The file  /tmp/.clearml_agent_out.j7wo7ltp.txt   does not exist

yes

I think it comes from the web UI of the version 1.2.0 of clearml-server, because I didn’t change anything else

So two possible cases for trains-agent-1: either:
It picks a new experiment -> show randomly one of the two experiments in the "workers" tab no new experiment in default queue to start -> show randomly no experiment or the one that it is running

Thanks a lot for the solution SuccessfulKoala55 ! I’ll try that if the solution “delete old bucket, wait for its name to be available, recreate it with the other aws account, transfer the data back” fails

SuccessfulKoala55 I was able to make it work with use_credentials_chain: true in the clearml.conf and the following patch: https://github.com/allegroai/clearml/pull/478

same as the first one described

So it looks like the agent, from time to time thinks it is not running an experiment

When an experiment on trains-agent-1 is finished, I see randomly no experiment/long experiment and when two experiments are running, I see randomly one of the two experiments

by mistake I have two agents started in one machine

the latest version, but I think its normal: I set the TRAINS_WORKER_ID = "trains-agent":$DYNAMIC_INSTANCE_ID, where DYNAMIC_INSTANCE_ID is the ID of the machine

yes SparklingHedgehong28 🙂

Hi @<1523701205467926528:profile|AgitatedDove14> @<1537605940121964544:profile|EnthusiasticShrimp49> , the issue above seemed to be the memory leak and it looks like there is no problem from clearml side.
I trained successfully without mem leak with num_workers=0 and I am now testing with num_workers=8.
Sorry for the false positive :man-bowing:

I think that somehow somewhere a reference to the figure is still living, so plt.close("all") and gc cannot free the figure and it ends up accumulating. I don't know where yet

Is it exactly agg or something different?

Early debugging signals show that auto_connect_frameworks={'matplotlib': False, 'joblib': False} seem to have a positive impact - it is running now, I will confirm in a bit

clearml doesn't change the matplotlib backend under the hood, right? Just making sure 😄

There was no possible cache, the agent was running on a new ec2 instance

SuccessfulKoala55 I can try to make one, let’s see 🙂

Does that mean that agents do not read this parameter?

What will this parameter do?

so the task they execute must have clearml installed?

Disclaimer: I didn't check this will reproduce the bug, but that's all the components that should reproduce it: a for loop creating figures and clearml logging them

Yes, I would like to update all references to the old bucket unfortunately… I think I’ll simply delete the old s3 bucket, wait or his name to be available again and recreate it where on the other aws account and move the data there. This way I don’t have to mess with clearml data - I am afraid to do something wrong and loose data

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()
  ...

Hi SuccessfulKoala55 , will I be able to update all references to the old s3 bucket using this command?

Alright, I will try with that one