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84 × Eureka!It is documented at None ... super deep in the code. If you don't know that output_uri
in TASK's (!) init is relevant, you would never know...
I have already been trying to contribute (have three pull requests), but honestly I feel it is a bit weird, that I need to update a documentation about something I do not understand, while I actually try to evaluate if ClearML is the right tool for our company...
@<1523701087100473344:profile|SuccessfulKoala55> Also, I think that - in this case, but also in other cases - the issue is not just the documentation, but also the design of the SDK.
: What does
- The component code still needs to be self-composed (or, function component can also be quite complex)
Well it can address the additional repo (it will be automatically added to the PYTHONPATH), and you can add auxilary functions (as long as they are part of the initial pipeline script), by passing them to
helper_functions
mean? Is it not possible that I call code that is somewhere else on my local computer and/or in my code base? That makes thi...
The first scenario is you standard "the code stays the same, the configuration changes" for the second step. Here, I want
The second and third scenario is "the configuration stays the same, the code changes", this is the case, e.g., if code is refactored, but effectively does the same as before.
@<1523701083040387072:profile|UnevenDolphin73> , you wrote
About the third scenario I'm not sure. If the configuration has changed, shouldn't the relevant steps (the ones where the configuration...
But still, in the web app the task is considered to be still "running". I am not sure what to do, so that the task is considered to be "completed".
@<1523701087100473344:profile|SuccessfulKoala55> : I referenced this conversation in the issue None
Secondly, I do not understand this:
None says
Manually mark a Task as completed. This will close the running process and will change the Task’s status to Completed (Use this function to close and change status of remotely executed tasks). To simply change the Task’s status to completed, use task.close()
None says
Closes the current Task and cha...
Ok, I checked: A is terminated. This is not what I thought would happen and not what I intended with my documentation. I should clarify that.
@<1523701083040387072:profile|UnevenDolphin73>
It means "The syntax for the file name, folder name or volume label / disk is wrong" somthing along those lines. The [...] is the directory path to my project, which I opened in PyCharm and from which I run the commands in the Python Console.
@<1523701205467926528:profile|AgitatedDove14> : "does that make sense ?" Not really.
"you do not need to automatically Add/Log/Track things into the Task in the current process." - I do not need to automatically do [...]? You mean I can do it automatically, but alternatively I can do it manually? Do you mean I use close
within a process to prevent automatic logging/adding/tracking? But, as far as I know, after I used close
I am not able to log etc. manually either. So...
"Mark...
"using your method you may not reach the best set of hyperparameters."
Of course you are right. It is an efficiency trade-off of speed vs effectiveness. Whether this is worth it or not depends on the use-case. Here it is worth it, because the performance of the modelling is not sensitive to the parameter we search for first. Being in the ball-park is enough. And, for the second set of parameters, we need to do a full grid search (the parameters are booleans and strings); thus, this wo...
I mean those, that you see in the screen shot. The difference in code is - at least for me - to write
- parameters_data = {'custom1': 'no', 'custom2': False}; parameters_data = task.connect(parameters_data , name='data')
- task.set_user_properties(custom1='no', custom2=False)
Do you mean "exactly" as in "you finally got it" or in the sense of "yes, that was easy to miss"?
AgitatedDove14 : Not sure: They also have the feature store (data management), as mentioned, which is pretty MLOps-y 🙂 . Also, they do have workflows ( https://docs.mlrun.org/en/latest/concepts/multi-stage-workflows.html ) and artifacts/model management ( https://docs.mlrun.org/en/latest/store/artifacts.html ) and serving ( https://docs.mlrun.org/en/latest/serving/serving-graph.html ).
I just see the website that I linked to. I am not sure what is meant by "python environment". I cannot make a screen shot, because I do not know where to look for this in the first place.
My entire code is
from clearml import Task, TaskTypes
task = Task.init(project_name='FirstTrial', task_name='first_trial', task_type=TaskTypes.training)
PACKAGE_VERSION = '0.4.1'
dataset_name = "Demodata"
which I - now - also ran as a whole script.
@<1523701083040387072:profile|UnevenDolphin73> : From which URL is your most recent screenshot?
@<1523701205467926528:profile|AgitatedDove14> , you wrote
- Components anyway need to be available when you define the pipeline controller/decorator, i.e. same codebase
No you an specify a different code base, see here:
Is the code in this "other" repo downloaded to the agent's machine? Or is the component's code pushed to the machine on which the repository is?
If the second case is true: How is the other machine (on which the other repo is lying on) turned into an agent?
But, I guess @<1523701070390366208:profile|CostlyOstrich36> wrote that in a different chat, right?