ColorfulBeetle67 you might need to configure use_credentials_chain
see here:
https://github.com/allegroai/clearml/blob/a9774c3842ea526d222044092172980ae505e24f/docs/clearml.conf#L85
Regrading the Token, I did not find any reference to "AWS_SESSION_TOKEN" in the clearml code, my guess it is used internally by boto?!
If this is the case and assuming you were able to use clearml to upload them, this means that adding the
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY
To your env file should just work
https://github.com/allegroai/clearml-serving/blob/main/docker/example.env
Make sense?
You can check the keras example, run it twice, on the second time it will continue from the previous checkpoint and you will have input and output model.
https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py
hmm, yes it should create the queue if it's missing (btw you could work around that and create it in the UI). Any chance you can open a github issue in the clearml helm chart repo so we do not forget ?
Hi @<1532532498972545024:profile|LittleReindeer37>
This is truly a great discussion to have. Personally I think the main difference is that software development is a somewhat linear process , and git captures it very well. But ML is a lot wider nonlinear process, which to me means that trying to conform the same workflow into a Dev tree will end up failing. The way ClearML thinks about it (and I think the analogy to source control is correct ) is probably closer to how you think about proj...
I think this is the discussion you are after:
https://clearml.slack.com/archives/C01H5VAUZ8R/p1612452197004900?thread_ts=1612273112.002400&cid=C01H5VAUZ8R
Hi @<1661542579272945664:profile|SaltySpider22>
Basically you need to put all of these files into a repository , which is always a good practice.
The reason is that the pipeline (and for that matter any Task on the system) can store wither a single script or a git reference, but not multiple scripts.
Hi ElegantCoyote26
What's the docker / docker-compose version?
What's the OS?
You need to mount it to ~/clearml.conf
(i.e. /root/clearml.conf)
Hmmm, what's your trains version ?
but I belive it should have work with 0.14.1 as well
Correct
Are you doing from keras import ...
or from tensorflow.keras import
?
Hi ShinyWhale52
This is just a suggestion, but this is what I would do:
- use
clearml-data
and create a dataset from the local CSV fileclearml-data create ... clearml-data sync --folder (where the csv file is)
2. Write a python code that takes the csv file from the dataset and creates a new dataset of the preprocessed data
` from clearml import Dataset
original_csv_folder = Dataset.get(dataset_id=args.dataset).get_local_copy()
process csv file -> generate a new csv
preproces...
2021-07-11 19:17:32,822 - clearml.Task - INFO - Waiting to finish uploads
I'm assuming a very large uncommitted changes 🙂
Hmm let check again something.
so I didn't have much time to upgrade all the packs because I have some issues with that but it is on my todo list
No worries 🙂
Quick question, if you run https://github.com/allegroai/trains/blob/master/examples/frameworks/keras/legacy/keras_tensorboard.py
Do you see models in the artifacts tab?
LudicrousDeer3 when using Logger you can provide 'iteration' argument, is this what you are looking for?
BTW:
======> WARNING! Git diff to large to store (1327kb), skipping uncommitted changes <======
This means all your git changes are stored as an artifact, which is consistent with the "wait for upload" message.
I cannot reproduce, tested with the same matplotlib version and python against the community server
Hi PlainSquid19
Any model stored by TF/Keras/PyTorch/Joblib will automatically appear in the artifact/models tab.
Are you asking on how to add one manually ?
PlainSquid19 I will also look into it as well.
maybe for some reason model.keras_model.save_weights
is not caught ...
BTW: What's the TF / Keras version?
Hmm there was this one:
https://github.com/allegroai/clearml/commit/f3d42d0a531db13b1bacbf0977de6480fedce7f6
Basically always caching steps (hence the skip), you can install from the main branch to verify this is the issue. an RC is due in a few days (it was already supposed to be out but got a bit delayed)
Hmm I see what you mean. It is on the roadmap (ETA the next version 0.17, 0.16 is due in a week or so) to add multiple models per Task so it is easier to see the connections in the UI. I'm assuming this will solve the problem?
What do you have in "server_info['url']" ?
And actually the slack thing is actually a good workaround this since people can just comment easily
Any reference for similar integration between Slack and other platforms ?
I'm thinking maybe the easiest way is Slack bot to you can @ task id ?
RoundMosquito25 actually you can 🙂# check the state every minute while an_optimizer.wait(timeout=1.0): running_tasks = an_optimizer.get_active_experiments() for task in running_tasks: task.get_last_scalar_metrics() # do something here
base line reference
https://github.com/allegroai/clearml/blob/f5700728837188d7d6005726c581c9d74fd91164/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py#L127
Also what's the additional p
doing at the last line if the screenshot ?
And how is the endpoint registered ?
Okay that makes sense.best_diabetes_detection
is different from your example curl -X POST "
None "
notice best_mage_diabetes_detection` ?