Hi TrickySheep9
Hmm I think you are correct, exit remotely will not work inside a jupyter notebook because it will not be able to close it.

I was just revising workflows that might be similar, wdyt?
https://clearml.slack.com/archives/CTK20V944/p1620506210463400?thread_ts=1614234125.066600&cid=CTK20V944

I think you are onto a good flow, quick iterations / discussions here, then if we need more support or an action-item then we can switch to GitHub. For example with feature requests we usually wait to see if different people find them useful, then we bump their priority internally, this is best done using GitHub Issues 🙂

Hi WickedStarfish97

As a result, I don’t want the Agent to parse what imports are being used / install dependencies whatsoever

Nothing to worry about here, even if the agent detects the python packages, they are installed on top of the preexisting packages inside the docker. That said if you want to over ride it, you can also pass packages=[]

AstonishingRabbit13
https://github.com/googleapis/google-cloud-python/issues/4941#issuecomment-369472576
check the openssl and the date, this seems like SSL low level error (even before authentication)

Thread is discussed here: None

I can't think of any actual difference in flow ...
Can you try the following?
task._setup_reporter() task.set_initial_iteration(0)

RipeGoose2

HTML file is not a standalone and has some dependencies that require networking..

Really? I thought that when jupyter converts its own notebook it packages everything into a single html, no?

JealousParrot68 yes this seems like a correct description.
The main diff between 1 & 2 is what is the actual data, if this is training/testing data, then Dataset would make sense, if this is a part of a preprocessing pipeline, then artifacts make more sense (notice we added pipeline step caching in the artifacts, so that you can reuse steps if they have the same parameters/code, which means you are able to clone a pipeline and rerun without repeating unnecessary data processing.

LOl my pleasure - I guess we should have a link in the doc string of add_requirements to set_packages , I will tell the guys

I still think the issue is getting boto3 credentials

It might be the case
Are you using clearml-agent or are you running it manually ?

Hi CrookedWalrus33
the python version is auto detected and register in "manual execution" time (i.e. when you run your code on your machine).
That said this is a suggestion for the agent, and only if it can actually find the matching Python version it will use it, otherwise it will use whatever is
available (i.e. Look through the PATH environment for a matching pythonX.Y executable)
The easiest way to support would just make sure the python binary's path is added to the PATH env.
Does...

LOL, great minds and so on 🙂

So a bit of explanation on how conda is supported. First conda is not recommended, reason is, is it very easy to create a setup on conda that is un-reproducible by conda (yes, exactly that). So what trains-agent does, it tries to install all the packages it can first with conda (not one by one, because that will break conda dependencies), then the packages that it failed to install from conda, it will install using pip.

notice that even inside docker the venv is cached on the host machine 🙂

Hi SmallDeer34
Is the Dataset in clearml-data ? If it is then Dataset.get().get_local_copy() will get you a cached local copy of the entire dataset.
If it is not, then you can use StorageManager.get_local_copy(url_here) to download the dataset.

2. Any Argparser is automatically logged (and later can be overridden from the UI). Specifically HfArgumentParser will be automatically logged https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/examples/pytorc...

I double checked the code it's always being passed 😞

Hi @<1545216070686609408:profile|EnthusiasticCow4>

Now that it's running how does one add a new task or remove an existing task from the scheduler?

Did you notice the scheduler stores its own configuration as a config object on the Task?
Notice that you can abort/reset the scheduler, change it's configuration in the UI and relaunch it (i.e. enqueue it). It will use the configuration from the UI (backend) and not the original code that created it. Does that make sense?

Hi SubstantialElk6
clearml-agent was just updated, it should solve the issue.2. Notice that "torch" / "torchvision" packages are resolved by the agent based on the pytorch compatibility table. Is there a way to reproduce the issue where it fails resolving the torch version? could you send a full log?
3. If you want a specific torch version , you can put a direct link to the torch wheel, for example: https://download.pytorch.org/whl/cu102/torch-1.6.0-cp37-cp37m-linux_x86_64.whl

Hi CynicalBee90
Always great to have people joining the conversation, especially if they are the decision makers a.k.a can amend mistakes 🙂
If I can summarize a few points here (and feel free to fill in / edit any mistake or leftovers)
Open-Source license: This is basically the mongodb license, which is as open as possible with the ability to, at the end, offer some protection against Amazon giants stealing APIs (like they did for both mongodb and elastic search) Platform & language agno...

SoreDragonfly16 as SmallDeer34 mentioned, you can iterate over the Tasks, pull metrics (with either task.get_last_scalar_metrics or task.get_reported_scalar ) then report them on the Task that runs the Loop itself with the Logger.
wdyt?

thanks!

Hi VexedCat68
(sorry I just saw the message)

I wanted to ask, how to run pipeline steps conditionally? E.g if step returns a specific value, exit the pipeline or run another step instead of the sequential step

So do do so you can do:
` def pre_execute_callback_example(a_pipeline, a_node, current_param_override):
# if we want to skip this node (and subtree of this node) we return False
...
# ew decided to skip so we return False
return False

pipe.add_step(name='...

Are you saying it only records the last 3 epochs or is it the first three epochs ?
Can you see scalars logged from other epochs ?

@<1546303254386708480:profile|DisgustedBear75> is think this was a UI bug, they are just releasing a new version that fixes that (i.e. server version), are you running a self-hosted server?

Hi SubstantialElk6
No need for that, you can use the helm chart (or spin them once with kubctl) then they take care of scheduling by themselves.
You can also use the k8s glue (basically spinning kubernetes pods automatically for you, based on the Tasks that you push into the ClearML queue)
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py

In short, two possible deployments
Static k8s pod running the agent (then the agent runs all the experiments inside t...

RoundMosquito25 do notice the agent is pulling the code from the remote repo, so you do need to push the local commits, but the uncommitted changes clearml will do for you. Make sense?

'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

This basically means there is no configuration on how to serve the mode, i.e. size/type of lower (input) layer and output layer.
You can wither store the configuration on the creating Task, like is done here:
https://github.com/allegroai/clearml-serving/blob/b5f5d72046f878bd09505606ca1147d93a5df069/examples/keras/keras_mnist.py#L51
Or you can provide it as standalone file when registering the mo...

do you have a video showing the use case for clearml-session

I totally think we should, I'll pass it along 🙂

what is the difference between vscode via clearml-session and vscode via remote ssh extension ?

Nice! remote vscode is usually thought of as SSH, basically you have your vscode running on your machine, and using SSH vscode automatically connects to the remote machine.

Clearml-Session also ads a new capability VSCode inside your browser, where the VSCode itself as well...

RipeGoose2 you mean to have the preview html on S3 work as expected (i.e. click on it add credentials , open in a new tab) ?

the services queue (where the scaler runs) will be automatically exposed to new EC2 instance?

Yes, using this extra_clearml_conf parameter you can add configuration that will be passed to the clearml.conf of the instances it will spin.
Now an example to the values you want to add :
agent.extra_docker_arguments: ["-e", "ENV=value"]https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a516cab60e83/docs/clearml.conf#L149
wdyt?