Hi, it make sense if i only had to change hyperparameters, but it's not so when i am still changing the model architecture (training code) and train and repeat.

Hi. If we disable the API service, how will it affect the system? How do we disable?

From an efficiency perspective, we should be pulling data as we feed into training. That said, always a good idea to uncompress large zip files and store them as smaller ones that allow you to batch pull for training.

Thanks SuccessfulKoala55 , how might I do this clean up? Does this increase with more use of ClearML? And to add, we save all artifacts onto a remote S3 server.

Thanks CostlyOstrich36 , how do i know how is the parts indexed in the first place? Or rather, how is chunk and parts defined? Say in the context of images, videos, text documents...etc.

In the ClearML config that's being run by the ClearML container?

AgitatedDove14 , would you elaborate on this resolution process?

Hi SuccessfulKoala55 , just to add, my clearml.conf (client) and clearml.agent.conf (agent) can have differing values. I'm not sure which one takes precedence and if this could be the cause.

ah... thanks!

Sorry AgitatedDove14 can you bump me to that thread?

Sorry AgitatedDove14 i missed your reply. So this means that in the community version, when i have an experiment using clearml and it uses clearml datasets SDK, the dataset id that was used will not be reflected on the clearml experiment in any way, thus making it impossible to establish Data Lineage/Provenance. (E.g. Link data used to experiment). This feature is however available in the Enterprise Version as HyperDatasets. Am i correct?
Code example.
` from clearml import Task, Logger
tas...

Hi, the idea is to load the gituser and password into the --env by loading it via a env var so the client could access the resources without divulging the credentials in source code and it would be removed after completion since the container would be removed. Its actually doing well with ClearML except the part that the agent seems to print the content of docker_cmd on running the task.

I would like to note that this behaviour doesn't exist with the clearml-agent daemon though. It only exis...

Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?

Not sure how this will work when i can't supply the credentials to ClearML programatically.

I thought of another potential way but not sure if the SDK supports it.
We will perform manual save and upload of model using vanilla boto3 and credentials passed in as env var. Use ClearML SDK to update the Model Repo on the location of the model, without ClearML uploading it explicitly.Would the above work?

what feature on this paid roadmap are you referring to? I am indeed communicating with Noem on paid features.

Hi, scenario as follows.

client.py runs task.execute_remotely(queue='myqueue', exit_process=True) The API section of clearml.conf at client side is read in. client side calls clearml server and insert task into queue. K8S glue retrieves task from queue. Spawn a K8S pod. K8S pod performs git clone Error. ssh keys not found.
Each individual has their own key in the gitlab profile and gitlab is configured to only work via ssh.
We can't place the key in the image as this is as good as ...

Hi, this is the setup.

client
from clearml import Task, Logger task = Task.init(project_name='DETECTRON2',task_name='Train',task_type='training') task.set_base_docker("quay.io/fb/detectron2:v3 --env GIT_SSL_NO_VERIFY=true --env TRAINS_AGENT_GIT_USER=testuser --env TRAINS_AGENT_GIT_PASS=testuser" ) task.execute_remotely(queue_name="single_gpu", exit_process=True)
k8s_glue_example.py spawned a pod and starts running.

ClearML UI -> Experiment -> Results -> Console.
` At the top it will pri...

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.

Thanks TimelyPenguin76 , is there an env var for the S3 connection as well?

Hi thanks for the examples! I will look into them. Quite a fair bit of my teams uses tf datasets to pull data directly from object stores, so tfrecords and stuff are heavily involved. I'm trying to figure if they should version the raw data or the tfrecords with ClearML, and if downloading entire set of data to local can be avoided as tf datasets is able to handle batch downloading quite well.

Hi, this is what i got. No mention of the env variables.
` Current configuration (clearml_agent v0.17.2, location: /home/jax/clearml.conf):

api.version = 1.5
api.verify_certificate = true
api.default_version = 1.5
api.http.max_req_size = 15728640
api.http.retries.total = 240
api.http.retries.connect = 240
api.http.retries.read = 240
api.http.retries.redirect = 240
api.http.retries.status = 240
api.http.retries.backoff_factor = 1.0
api.http.retries.backoff_max = 120.0
ap...

Executing task id [228caa5d25d94ac5aa10fa7e1d02f03c]:
repository = https://192.168.50.88:18443/tkahsion/pytorchmnist
branch = master
version_num = cfb833bcc70f3e10d3b6a96cfad3225ed682382b
tag = 
docker_cmd = nvidia/cuda:10.1-runtime-ubuntu18.04
entry_point = pytorch_mnist.py
working_dir = .
Warning: could not locate requested Python version 3.9, reverting to version 3.6
Using base prefix '/usr'
New python executable in /root/.clearml/venvs-builds/3.6/bin/python3.6
Also creating executable i...

Hi CostlyOstrich36 , thanks. I will check with the Enterprise team then.

Thanks TimelyPenguin76 , let me try it out now.

i passed it through the yaml as follows.
apiVersion: v1 kind: Pod spec: containers: - image: clearml-agent:latest" env: - name: PIP_INDEX_URL value: " " - name: PIP_TRUSTED_HOST value: "192.168.56.253" - name: PIP_FIND_LINKS value: " ` "
- name: GIT_SSL_NO_VERIFY
value: true
resources:
requests:
cpu: "2"
...

Hi, i changed it, but it still point to https://files.pythonhosted.org/packages .

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...

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

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?

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.