Could it be the credentials are actually incorrect? because it seems like you can access the server? (I assume you were able to browse to it and generate credentials. right?)
Hi @<1533257411639382016:profile|RobustRat47>
sorry for the delay,
Hi when we try and sign up a user with github.
wait, where are you getting this link?
BTW: if you could implement _AzureBlobServiceStorageDriver with the new Azure package, it will be great:
Basically update this class:
https://github.com/allegroai/clearml/blob/6c96e6017403d4b3f991f7401e68c9aa71d55aa5/clearml/storage/helper.py#L1620
Correct 🙂
You can spin it in two modes, either venv or docker (notice that even in docker mode, it will still clone the code into the docker and install the packages inside the docker, but it also inherits from the docker preinstalled system packages, so that the installation process is a lot faster, but you have the ability to change packages without having to build an entire new docker image)
Decorators are good 🙂
Something along the lines of
` @PipelineDecorator.pipeline(...)
def pipeline(skip_a=False):
if not skip_a:
a = step_a()
else:
# somehow get a previous A?
# let's call it cached A
a = "replace with real'
step_b(a)
... `Is this the gist?
If it is, this looks like, "how can I control whether A is cached or not", is that correct?
I want to run only that sub-dag on all historical data in ad-hoc manner
But wouldn't that be covered by the caching mechanism ?
Can you try to manually install it and see what you are getting?python3.10 -m pip install /home/boris/.clearml/pip-download-cache/cu117/torch-1.12.1+cu116-cp310-cp310-linux_x86_64.whl
Hmm this is odd, could you provide the pipeline code maybe?
Bad news, there isn't a nice interface to get the table from the Optimizer object (I will make sure we add it, no reason not to).
But you can very easily get all the information you need and more:all_the_tasks = an_optimizer.get_top_experiments(top_k=100)Then for every task in the list you can get All the information:for task in all_the_tasks: task_params_as_dict = task.get_parameters() task_scalars = task.get_last_scalar_metrics()Basically the Task object enables you to que...
Hi GreasyPenguin14
Quick question, any reason not to use a 2D scatter ? or a histogram (or any other non time-series plot)?
I do expect it toÂ
pip
 install though which doesn’t root access I think
Correct, it is installed on a venv (exactly for that).
It will not fail if the apt-get fails (only warnings)
Let me know if it worked
Hi GreasyLeopard35
I try to resume a stopped or aborted parameter optimization experiment,
How are you continuing the HPO? are you runing everything locally? is this with an agent? are you seeing the '[0, 0]' value on the configuration when launching the HPO or when continuing it ?
Basically it gives it direct access to the host, this is why it is considered less safe (access on other levels as well, like network)
` Collecting inplace-abn==1.0.12
Downloading inplace-abn-1.0.12.tar.gz (137 kB)
ERROR: Command errored out with exit status 1:
command: /home/ubuntu/.clearml/venvs-builds/3.8/bin/python -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-xf3qf6et/inplace-abn_15b6998cb4af4199a7692be5d3a3538f/setup.py'"'"'; file='"'"'/tmp/pip-install-xf3qf6et/inplace-abn_15b6998cb4af4199a7692be5d3a3538f/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(file);code=f...
I guess I would need to put this in the extra_vm_bash_script param of the auto-scaler, but it will reboot in loop right? Isn’t there an easier way to achieve that?
You can edit the extra_vm_bash_script which means the next time the instance is booted you will have the bash script executed,
In the meantime, you can ssh to the running instance and change the ulimit manually, wdyt?
using caching where specified but the pipeline page doesn't show anything at all.
What do you mean by " the pipeline page doesn't show anything at all."? are you running the pipeline ? how ?
Notice PipelineDecorator.component needs to be Top level not nested inside the pipeline logic, like in the original example
@PipelineDecorator.component(
cache=True,
name=f'append_string_{x}',
)
Hi WickedGoat98 ,
I think you are correct 😞
I would guess it is something with the ingress configuration (i.e. ConfigMap)
VexedCat68 make sense, we could also (if implementing this feature) add a special Tag to the dataset , so you know it contains "external" links, wdyt?
creating a dataset with parents worked very well and produced great visuals on the UI!
woot woot!
I tried the squash solution, however this somehow caused a download of all the datasets into my
so this actually works, kind or like git squash, bottom line it will repackage the data from all the different versions into one new version. This means downloading the data from all squashed versions, then repackaging it into a single new version. Make sense ?
Hi @<1523702932069945344:profile|CheerfulGorilla72>
Please tell me what RAM metric is tracked by ClearML?
Free RAM is the entire machine free RAM
Yeah htop shows odd numbers as it doesn't "count" allocated buffers
specifically you can see the code here:
None
Any chance you can test with the latest RC ? 1.8.4rc2
Hi @<1523701337353621504:profile|FlutteringSheep58>
are you asking how to convert a worker IP into a dns resolved host name ?
MysteriousBee56 that is so weird ... last one, I promise 🙂docker run -t --rm nvidia/cuda:10.1-base-ubuntu18.04 bash -c "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean && apt-get update && apt-get install -y git python3-pip && python3 -m pip install trains-agent && echo \$(which python3) && echo \$(which trains-agent)"
I'll try to find the link...
You can control it with auto_ arguments in the Task.init call
https://clear.ml/docs/latest/docs/references/sdk/task#taskinit
Makes sense
we need to figure what would be the easiest way to have an "opt-in" for the demo server, that will still make it a breeze to quickly test code integration ...
Any suggestions are welcomed 🙂
and of course:task.set_parameters_as_dict(params)