if you have an automation process, then you should have the Task object, no?
then you have task.id
What am I missing here?

I'd prefer to use config_dict, I think it's cleaner

I'm definitely with you

Good news:

new 

best_model

 is saved, add a tag 

best

,

Already supported, (you just can't see the tag, but it is there :))

My question is, what do you think would be the easiest interface to tell (post/pre) store, tag/mark this model as best so far (btw, obviously if we know it's not good, why do we bother to store it in the first place...)

Task.enqueue will execute immediately, i need execute task to spesific time

Oh I see what you mean, trigger -> scheduled (cron alike) -> Task executed.
Is that correct?

ShakyOstrich31

I am reusing an old task ...

Which means that the old Task stores the requirements on the Task itself (see "Installed Packages" section), Notice it also stores the exact git commit to use.
When you are cloning the Task (i.e. in the pipeline), you should probably:
set the commit / branch to the latest in the branch clear the "installed packages" section, which would cause the agent to use the "requirements.txt" stored in the git repo itself.As far as I understand this s...

My question is what should be the path to the requirements.txt file?
Is it relative to the repo base?

This is actually in runtime (i.e. when running the code), so relative to the working directory. Make sense ? (you can specify absolute path, probably something I would avoid in the code base though...)

Thanks for checking @<1545216070686609408:profile|EnthusiasticCow4> stable release will be out soon

Sure thing 🙂

Could you test with 4.7.5 ?

Hi @<1523702932069945344:profile|CheerfulGorilla72>
I think more details re needed here:)

RoughTiger69 yes I think "Scale" tier covers it 😉

VictoriousPenguin97 I'm assuming the exact same server version ?

Can you send the full log ?

check the latest RC, it solved an issue with dataset uploading,
Let me check if it also solved this issue

Hmm whats the OS and python version?
Is this simple example working for you?
None

Hmm, let me check, there is a chance the level is dropped when manually reporting (it might be saved for internal critical reports). Regardless I can't see any reason we could not allow to control it.

Hmm that is odd. Let me take a look and ask the guys. Thank you for quickly testing the RC! I'm hoping a new RC with a fix will be there tomorrow, if we can quickly replicate

YEY 🙂

At the moment I'm querying by paging through the tasks as you recommended, and then filtering with standard python list-comprehension filters...Which is less than ideal.

At least let's do that better:
Use Task._query_tasks:
Task._query_tasks(order_by=['-started'], page_size=10, page=0, only_fields=['id', 'started'])You will get "lighter" objects returned, then you can filter them with code (but the request will be a lots faster)
SuccessfulKoala55 any suggestion on improving that ?

Hi JitteryCoyote63
Do you have a specific example in mind ?

https://github.com/googleapis/python-storage/issues/74

Hi SarcasticSparrow10

Is it better to post such questions on Stackoverflow so they benefit everybody?

Yes, I think you are correct it would please do 🙂

Try to do " reuse_last_task_id='task_id_here'" ,t o specify the exact Task to continue )click on the ID button next to the task name in the UI)
If this value is true it will try to continue the last task on the current machine (based on project/name, combination) if the task was executed on another machine, it will just start a ...

Hi GrotesqueOctopus42 ,

BTW: is it better to post the long error message on a reply to avoid polluting the channel?

Yes, that is appreciated 🙂
Basically logs in the thread of the initial message.

To fix this a had to spin the agent using --cpu-only flag (--docker --cpu-only)

Yes if you do not specify --cpu-only it will default to trying to access gpus
Nice!

Wait, let me double check

btw: what's the OS and python version?

When we enqueue the task using the web-ui we have the above error

ShallowGoldfish8 I think I understand the issue,
basically I think the issue is:
task.connect(model_params, 'model_params')Since this is a nested dict:
model_params = { "loss_function": "Logloss", "eval_metric": "AUC", "class_weights": {0: 1, 1: 60}, "learning_rate": 0.1 }The class_weights is stored as a String key, but catboost expects "int" key, hence it fails.
One op...

Thanks!
In the conf file, I guess this will be where ppl will look for it.

I can share some code

Please do 🙂

Number of entries in the dataset cache can be controlled via cleaml.conf : sdk.storage.cache.default_cache_manager_size

I'm assuming this is related to this thread:
None

@<1651395720067944448:profile|GiddyHedgehong81> just to be clear, Dataset.get_local_copy returns a path to your files,
You have to Manually add the additional path to the specific files you need to use. It does Not know that in advance.
That was the initial issue you had, and I assume it is the same one here. does that make sense ?