I see, so its a path. Another question, as far as i can tell, clearml-data will download entire datasets before starting training. This isn't very ideal when we are dealing with billions of datasets (E.g. WE might want to download a subset at a time, send to GPU for training and then use the CPU to concurrently pull another subset.). Any comments on this?

like create multiple datasets?
create parent (all) - upload to S3
create child1 (first 100k)
create child2 (second 100k)...blah blah

Then only pull indices from children. Technically workable but not sure if its best approach since different ppl have different batch sizes in mind.

From ClearML perspective, how would we enable this, considering we don't have direct control or even IP of the agents

yes, previously run experiments. I will just kill clearml-elastic container if that may solve the problem.

Alright, fingers crossed.

I used nvcr pytorch image and instruct clearml to inherit global dependencies. No need to install torch and work well.

Hi,
It did, nvidia/cuda:10.1-runtime-ubuntu18.04.

So if i need to set this every time, what is the following config for? And how do i pass in new env parameters?
` default_docker: {
# default docker image to use when running in docker mode
image: "dockerrepo/mydocker:custom"

    # optional arguments to pass to docker image
    # arguments: ["--ipc=host", ]
    arguments: ["--env GIT_SSL_NO_VERIFY=true",]
} `

Next step to figure out if i can do all that in the python code instead of UI.

Ok thanks, that worked.

Thank. Gonna try that out. But i hit another snag. Strangely, the Agent is not creating the right venv. This is what the Agent created.
` pip:

• asn1crypto==0.24.0
• attrs==20.3.0
• certifi==2020.12.5
• chardet==4.0.0
• cryptography==2.1.4
• Cython==0.29.22
• furl==2.1.0
• future==0.18.2
• humanfriendly==9.1
• idna==2.6
• importlib-metadata==3.7.0
• jsonschema==3.2.0
• keyring==10.6.0
• keyrings.alt==3.0
• orderedmultidict==1.0.1
• pathlib2==2.3.5
• psutil==5.8.0
• pycrypto==2.6.1
• pygobject...

Sorry i don't quite understand this. The task itself was submitted as I run the code on the client. I suppose the dependancies requirements would be copied over as the experiment is cloned?

To note, the latest codes have been pushed to the Gitlab repo.

running git diff on my terminal in this repo gave nothing. nothing at all.

Yes, as listed in the snippet. The torch library is torchvision.

Ok that worked. So every time i have changes in codes, i will have to rerun the experiment on my own machine that doesn't have any GPUs?

Kinda defeat the purpose of using ClearML Agent.

Thanks. That's easy to miss as its not quite apparent in the main docs. How should i pass in env variables with Task?

Ok that works. thanks.

It's a local deployment. I was only presented with username without a need to enter passwords. When I'm in, I don't see an option in my profile to set a password as well. Neither is there integration with ldap for example.

I see i understand better now. Thanks.

Ok thks.

After some churning, this is the answer. Change it in the clearml-agent init generated clearml.conf.

` default_docker: {
    # default docker image to use when running in docker mode
    image: "nvidia/cuda:10.1-runtime-ubuntu18.04"

    # optional arguments to pass to docker image
    # arguments: ["--ipc=host", ]
    arguments: ["--env GIT_SSL_NO_VERIFY=true",]
  } `

Hi thanks.
So i suppose ClearML make use of the information in .git folder at the root of the script folder to gather those info.

I have yet to go through thoroughly with ClearML agent. TimelyPenguin76 , so if i run a training with uncommited changes and didn't commit/push after. When i clone the task, isn't ClearML agent unable to pull that script from the git repo?

yup. in this case it wasn't root. Removing that USER and -u in pip solves the problem. However, in our production images, we are required to remove root access.
` FROM nvidia/cuda:10.1-cudnn7-devel

ENV DEBIAN_FRONTEND noninteractive
RUN apt-get update && apt-get install -y
python3-opencv ca-certificates python3-dev git wget sudo ninja-build
RUN ln -sv /usr/bin/python3 /usr/bin/python

create a non-root user

ARG USER_ID=1000
RUN useradd -m --no-log-init --system --uid ${USER_ID} a...

I did another test by running
kubectl exec pod-name -- echo $PIP_INDEX_URL and it returned nothing. So the env are not passed to the container at all.

That didn't work as well...

Hi, the problem is the same.

I noticed that its not checking out the latest version in gitlab. This latest version would contain the requirements.txt.
Using cached repository in "/root/.clearml/vcs-cache/pytorchmnist.f220373e7227ec760b28c7f4cd99b534/pytorchmnist" warning: redirecting to Note: checking out 'cfb833bcc70f3e10d3b6a96cfad3225ed682382b'.But i'm guessing this block below applied the diff..does it include the requirements.txt though?
` HEAD is now at cfb833b Upload New Fil...

Yeah me 3.

Hi this is the log. I didn't see any attempt from the agent to install virtualenv on the base image.
` 1618369068169 clearml-gpu-id-b926b4b809f544c49e99625380a1534b:gpuGPU-4ad68290-0daf-4634-6768-16fad73d47a3 DEBUG Current configuration (clearml_agent v0.17.2, location: /tmp/.clearml_agent.wgsmv2t9.cfg):

agent.worker_id = clearml-gpu-id-b926b4b809f544c49e99625380a1534b:gpuGPU-4ad68290-0daf-4634-6768-16fad73d47a3
agent.worker_name = clearml-gpu-id-b926b4b809f544c49e99625...

Hi, so you meant i need to installl virtualenv in my base image?

I'm using the k8s Glue.