Hi PanickyMoth78 an RC with a fix is out, let me know if it works (notice you can now set the max_workers from CLI or Dataset functions) pip install clearml==1.8.1rc1

I can see all the steps like git clone,

git clone has nothing to do with "env setup" this is brining the code, you cannot skip that one, that said, this is why the git itself is cached on the host machine, so it is fast

... There may be some odd package that need to be installed because one of our DS is experimenting ... But all that we can see what is happening.

even if everything is preinstalled, it Verifies the packages match, this might take a long time. It's just pip being ...

Yes you can 🙂 (though not on the open-source version)

Sorry ScaryLeopard77 I missed the reply,

the tutorial in the readme of clearml-serving repo doesn't mention it though. Where should I set it?

oh dear ... you are right (I think it was there in previous versions)
clearml-serving --helphttps://github.com/allegroai/clearml-serving/blob/ce6ec847b1e01c6f5bf35d638e6ceb8148db8a7a/clearml_serving/main.py#L142
This is the equivalent of what is created here in the example:
https://github.com/allegroai/clearml-serving/blob/ce6ec847b...

But adding a simple 

force_download

 flag to the 

get_local_copy

That's sounds like a good idea

If this is the case why not have the stream process call the rest api, then move forward with the result? This way it scales out of the box, the main "conceptual" difference is that the restapi is used internally, and the upside is the event streaming processing becomes part of the application layer, not tied with the compute cost of the model , wdyt?

Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy

Hmm MiniatureHawk42 how many files in the zip ?

a bit sad that there is no working integration with one of the leading time series framework...

You mean a series darts reports ? if it does report it, where does it do so? are you suggesting we have Darts integration (which sounds like a good idea) ?

Hi @<1610083503607648256:profile|DiminutiveToad80>

<h1>Request Entity Too Large</h1>

What's the size of the file? how are you running your clearml-server?

Regrading the missing packages, you might want to test with:
force_analyze_entire_repo: falsehttps://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L162

Or if you have a full venv you like to store instead:
https://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L169

BTW:
What is the missed package?

We were able to find a stable, free, open source, multiplatform way to do this

You mean to move the data from the gdrive to object storage ? or to just mount the gdrive ?

SUCCESS!!!

Hmm, what's the clearml-agent version ?

It should preserve the order as the order of the update back (i.e. when executed by the agent) is the same as the order of the keys (obviously py3.7+ becuase it creates dict not Ordered Dicts)

Hmm, it seems as if the task.set_initial_iteration(0) is ignored...
What's the clearml version you are using ?
Is it the same one you have on the local machine ?

DeliciousBluewhale87 Yes I think so, do notice that you might end up with maximum of 12 pods.
You can also do the following with max 10 nodes: (notice --queue can always get a list of nodes it will pull based on the order of the queues)
python k8s_glue_example.py --queue high_priority_q low_priority_q --ports-mode --num-of-services 10

. So i'd like to use the command line argument it in the first argparse, and then hide/delete/override before running the second argparse.

Nice, hack!

Hi ColossalDeer61 ,
the next trains-agent RC (solving the #196 issue) will also solve the double install issue 🙂

The main issue is applying the patch requires git clone and that would fail on local (not pushed) commits.
What's the use case itself ?
(btw, if you copy the uncommitted changed into a file and git apply it, it will work)

Hi SkinnyPanda43

I realized that the params are not being saved anymore

Could you test with clearml==1.0.4 ?

Actually it would be interesting to combine the two, feast is fully open-source and supported by the linux foundation, so I cannot see the harm in that.
wdyt?

Hmm, I think it is this line:

WARNING - Model configuration only supports dictionary or string objects
done

Let me check something.

Hi ScaryKoala63
Sure, add the following to your clearml.conf:
sdk.storage.cache.default_cache_manager_size = 400I think you are correct, it seems like for some reason you hit the cache limit, and a previous entry was deleted

Internally we use blob.upload_from_file it has a default 60sec timeout on the connection (I'm assuming the upload could take longer).

Hi RipeGoose2
Are you continuing the Task, i.e. passing Task.init(..., continue_last_task=True)

ElegantCoyote26 I don't think Keras logs it anywhere unless you have TB, so nowhere to take the data from...
In short, yes you have to have TB :)

Besides that, what are your impressions on these serving engines? Are they much better than just creating my own API + ONNX or even my own API + normal Pytorch inference?

I would separate ML frameworks from DL frameworks.
With ML frameworks, the main advantage is multi-model serving on a single container, which is more cost effective when it comes to multiple model serving. As well as the ability to quickly update models from the clearml model repository (just tag + publish and the end...

I see what you mean.
an_optimizer = HyperParameterOptimizer( base_task_id='39d2c27baa8145929b2e21f686a17046', hyper_parameters=[], objective_metric_title='epoch_accuracy', objective_metric_series='epoch_accuracy', objective_metric_sign='max', optimizer_class=aSearchStrategy, max_iteration_per_job=0, total_max_jobs=0, auto_connect_task=False, ) print(an_optimizer.get_top_experiments(top_k=5))

not really 😞
Why would you want to set it up manually ? makes sense to have it in the cache folder, no?