Please add it as a code snippet.

Can you add a code snippet that reproduces this for you please?

Hi @<1673501397007470592:profile|RelievedDuck3> , you simply need to integrate clearml into your code.

from clearml import Task
task = Task.init(...)

More info here:
None

Hi @<1673501397007470592:profile|RelievedDuck3> , there is some discussion of it in this video None

Hi @<1673501397007470592:profile|RelievedDuck3> , should be possible. What errors are u getting?

Hi @<1673501397007470592:profile|RelievedDuck3> , no you don't. The basics can be run with a docker compose 🙂

Hi @<1639799308809146368:profile|TritePigeon86> , I think the 1.16 refers to the version of the SDK. I'd suggest upgrading your server regardless 🙂

I meant that maybe you ran it with a newer version of the SDK

I'd suggest using the API directly, fetch a task and compare it's start to end time.

Hi @<1702130048917573632:profile|BlushingHedgehong95> , where did you see this?

Hi @<1547028121666981888:profile|AdventurousOwl31> , from my understanding this is technically supported but heavily unadvised due to differences between the two

Hi 🙂
When viewing the experiments tables you have a tab called 'models'. If the artifact was saved as a model, then you can find it there 🙂

Same repo as the private repo?

Can you share the entire log of the run?

Hi, ZippyWalrus56 , can you add a full print of your console log?

Also if possible provide a code snippet, that can help understand the problem 🙂

Hi TartSeagull57 ,

Which one is fig 1 and which one is fig 2?
How are you logging them?

TartSeagull57 , I couldn't make the sample you gave me work 😞

Can you please provide a self contained example that would reproduce the issue?

Hi @<1523703107031142400:profile|FlatOctopus65> , can you please elaborate on what exactly happens and when? Do you have a snippet to play with ?

Hi DrabCockroach54 , in the open source version there are no roles. You can set up users & passwords using this:
https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server_config/#web-login-authentication

I am not sure there is a simple way to delete users - I think you would need to edit MongoDB manually, which I would not recommend

Hi DrabCockroach54 , I think you are looking for these endpoints:
https://clear.ml/docs/latest/docs/references/api/workers#post-workersget_activity_report
https://clear.ml/docs/latest/docs/references/api/workers#post-workersget_stats

Regarding the api token - you can use the key/secret pair that you generated through the ClearML UI

When in table view (rows) there is a small icon next to the 'Started' column. There you can configure time periods you'd like to view 🙂

TimelyPenguin76 , MammothGoat53 , I think you shouldn't call Task.init() more than once inside a script

Can you try and specify the exact absolute path? I think that might be the problem.

OK, there appears to be a github issue relating this:
https://github.com/allegroai/clearml/issues/388
I was right regarding encountering this before. People have asked for this feature and I think it appears to be a priority to add as a feature.

You can circumvent auto logging with the following:
task = Task.init(..., auto_connect_frameworks={'pytorch': False})However you will need to log other models manually now. More information is in the github issue 🙂

I think I've encountered something related to this. Let me take a look at the docs

Can you gain access to the apiserver logs?

Hi @<1699593312320622592:profile|TeenyOtter72> , I think what you're looking for is the comparison. You can checkbox multiple experiments and then you will have a bar at the bottom with an option to compare. Let me know if this is what you were looking for

Basically the Agent automates the docker run command with everything that you need (this can become rather complex). You can see this in the third line of the console log:
` Executing: ['docker', 'run', '-t', '--gpus', 'all', '-l', 'clearml-worker-id=ip-172-31-28-179:0', '-l', 'clearml-parent-worker-id=ip-172-31-28-179:0', '-e', 'CLEARML_WORKER_ID=ip-172-31-28-179:0', '-e', 'CLEARML_DOCKER_IMAGE=nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04', '-e', 'CLEARML_TASK_ID=cdbfa9cda5ab4d86b012a87...

I can think of two solutions:
Fix local python environments and begin using virtual environments ( https://github.com/pyenv/pyenv for example) Use the agent in --docker mode. You won't need to worry about python versions but you will need to install Docker on that machine.

I'm not personally familiar, but I'm sure searching for docker images with the python version you need will yield the required results 🙂