@<1532532498972545024:profile|LittleReindeer37> nice!!! 😍
Do you want to PR? it will be relatively easy to merge and test, and I think that they might even push it to the next version (or worst case quick RC)

right now I can't figure out how to get the session in order to get the notebook path

you mean the code that fires "HTTPConnectionPool" ?

and this

server_info['url'] = f"http://{server_info['hostname']}:{server_info['port']}/{server_info['base_url']}/"

Hmm what do you have here?

os.system("cat /var/log/studio/kernel_gateway.log")

print(requests.get(url='

print(requests.get(url='

Basically two options, spin the clearml-k8s-glue, as a k8s service.
This service takes clearml jobs and creates k8s job on your cluster.
The second option is to spin agents inside pods statically, then inside the pods the agent work in venv model.
I know the enterprise edition has more sophisticated k8s integration where the glue also retains the clearml scheduling capabilities.
https://github.com/allegroai/clearml-agent/#kubernetes-integration-optional

@<1558986821491232768:profile|FunnyAlligator17> can you provide the full log ?

I assume it is reported into TB, right ?

Sorry I need the full log ... feel free to DM it to me

Hmm, you are correct
Which means this is some conda issue, basically when installing from env file, conda is not resolving the correct pytorch version 😞
Not sure why... Could you try to upgrade conda ?

WickedGoat98 until the next RC release (should not take long) this will solve it:
df = pd.concat([tickerDf.Close, tickerDf_Change.Close_pcent], axis=1) df = df[1:] df.index = df.index.astype(str) setattr(df, 'ticker', args.symbol)Basically removing the nan and converting the datetime to string representation (so plotly.js likes it)

WickedGoat98 Same for me, let me ask the UI guys, I think this is a UI bug.
Also maybe before you post the article we could release a fix to both, what do you think?
EDIT:
Never mind 🙂 i just saw the medium link, very cool!!!

WickedGoat98 this is awesome! Let me know how I could help 🙂
BTW: I checked regrading the plot comparison, this is a BE issue due to the size of the plot, I was told a fix will be deployed in a day or two.

WickedGoat98 give me a minute, I'm not sure it is not ClearML related

https://github.com/allegroai/clearml/blob/master/clearml/automation/trigger.py
Example coming soon, with docs :)

Hi JitteryCoyote63
Could it be a python mismatch ? can you send the full log?
BTW: when I do
pip3.8 install pytorch3d==I get the following versions:
pytorch3d== (from versions: 0.0.1, 0.1.1, 0.2.0, 0.2.5, 0.3.0)

I can raise this as an issue on the repo if that is useful?

I think this is a good idea, at least increased visibility 🙂
Please do 🙏

Notice the args will be set on the connect call, so the check on whether they are empty should come after

LazyLeopard18 you can point the artifact directly to your azure object storage and have StorageManager download and cache it for you:

using the docker-compose file for the

clearml-serving

pipeline, do we also have to mount it somehow?

oh yes, you are correct the values are passed using environment variables (easier when using docker compose)
You can in addition add a mount from the host machine to a conf file,

    volumes:
      - ${PWD}/clearml.conf:/root/clearml.conf

wdyt?

It’s only on this specific local machine that we’re facing this truncated download.

Yes that what the log says, make sense

Seems like this still doesn’t solve the problem, how can we verify this setting has been applied correctly?

hmm exec into the container? what did you put in clearml.conf?

Okay we got to the bottom of this. This was actually because of the load balancer timeout settings we had, which was also 30 seconds and confusing us.

Nice!
btw:

in the clearml.conf we put this:

for future reference, you are missing the sdk section:

sdk.http.timeout: 300

. notation works as well as {}

Hi GrotesqueOctopus42

Dispite having reuse_last_task_id=True on Task.init, it always creates a new task id. Anyone ever had this issue?

So the way "reuse_last_task_id=True" works is that if there are no artifacts on the Task it will reuse it, but when running inside jupyter it always has artifacts (the notebook itself), so it starts a new Task.
You can however pass a specific Task ID and it will reuse it "reuse_last_task_id=aabb11", would that help?

If you take a look here, the returned objects are automatically serialized and stored on the files server or object storage, and also deserialized when passed to the next step.
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_decorator.py

You can of course do the same manually

:param list(str) xlabels: Labels per entry in each bucket in the histogram (vector), creating a set of labels for each histogram bar on the x-axis. (Optional)

Yes, offline got broken in 1.3.0 😞 , RC fixed it:
pip install clearml==1.3.1rc0Stable release later this week

SmarmySeaurchin8
Something like this one:
vector_series = np.random.randint(10, size=10).reshape(2,5) logger.report_vector(title='vector example', series='vector series', values=vector_series, iteration=0, labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

RoundMole15 how does the Task.init look like?

None

It's the pip cache