Hi Matias! 🙌

if I were to run an agent that would require to install pandas at some point I’d run it:
OPENBLAS="$(brew --prefix openblas)" clearml-agent daemon --queue default

for example I had to do a OPENBLAS="$(brew --prefix openblas)" pip install pandas to be able to install pandas on my M1 MAC

right, I’m saying I had to do that in my MAC. In your case you would have to point it to somewhere else. Please check where openblas is installed on your ubuntu

where is the dataset stored? maybe you deleted the credentials by mistake? or maybe you are not installing the libraries needed (for example if using AWS you need boto3, if GCP you need google-cloud-storage)

line 120 says unmark to enable venv caching (it comes commented by default, but since I’m copying my conf it isn’t commented there)

also I suggested to change TMPDIR env variable, since /tmp/ didn’t have a lot of space.

agent.environment.TMPDIR = ****

is it ok to see * ** * instead of the actual path?

just do:
import os.path as op dataset_folder = Dataset.get(dataset_id="...").get_local_copy() csv_file = op.join(dataset_folder, 'salary.csv')

so I can run the experiments, I can see them, but no plots are saved because there is an upload problem when uploading to localhost:8085

Thanks for your answer, but it didn’t work 😕

great! and I saw that there were some system packages needed for opencv that were installed automatically that could be turned off. Now I’m just wondering if I could remove the PIP install at the very beginning, so it starts straightaway

oh but docker-ps shows me 8081 ports for webserver, apiserver and fileserver containers
` CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
0b3f563d04af allegroai/clearml:latest "/opt/clearml/wrappe…" 7 minutes ago Up 7 minutes 8008/tcp, 8080-8081/tcp, 0.0.0.0:8080->80/tcp, :::8080->80/tcp clear...

mmm, can you try the following:
create a new folder with no git repo, and copy those two notebooks launch the notebook with the base task and copy the task id launch the notebook with the hyperopt task modifying the TEMPLATE_TASK_ID variable accordingly

would it be possible to change de dataset.add_files to some function that moves your files to a common folder (local or cloud), and then use the last step in the dag to create the dataset using that folder?

Hi ExasperatedCrocodile76 , I guess that you were able to install Scikit-learn and you were able to run it locally, and now you want to try it with an agent on the same machine.
The error is that it can’t find OpenBLAS:
` Run-time dependency openblas found: NO (tried pkgconfig and cmake)
Run-time dependency openblas found: NO (tried pkgconfig)

../../scipy/meson.build:130:0: ERROR: Dependency "OpenBLAS" not found, tried pkgconfig `My question is: did you export some env variabl...

Hi AgitatedDove14 , I’m talking about the following pip install.
After that pip install, it displays agent’s conf, shows installed packages, and launches the task (no installation)
` Running in Docker mode (v19.03 and above) - using default docker image: spoter ['-e CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1', '-e CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1']
Running task '3ebb680b17874cda8dc7878ddf6fa735'
Storing stdout and stderr log to '/tmp/.clearml_agent_out.tsu2tddl.txt', '/tmp/.clearml_agent_o...

how do I mount my local ssh folder into /root/.ssh/ docker when running clearml-agent?
also, is there a way for it to not install the requirements, and simply run the task?

There’s only a jupyter notebook

Thanks TimelyPenguin76 for your answer! So indeed it was mounting it, and how do I check that “CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL” is working in my agent in docker?

right, but it’d still try to install it

Thanks for the answer. You’re right. I forgot to add that this tasks runs inside a docker container and I’m currently only mapping the $PWD ( ml folder) into /app folder in the container.

there is no /usr/share/elasticsearch/logs/clearml.log file (neither inside the container nor in my server)

I’m suggesting MagnificentWorm7 to do that yes, instead of adding the files to a ClearML dataset in each step

I could map the root folder of the repo into the container, but that would mean everything ends up in there

another thing: I had to change 8081 to 8085 since it was already used

so when inside the docker, I don’t see the git repo and that’s why ClearML doesn’t see it

but would installing
git+ <user>/rmdatasetsinstall rmdatasets == 0.0.1 ?
Aren’t they redundant?

please remove rmdatasets == 0.0.1

that depends…would that only keep the latest version of each file?