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Posted by Abdullah • (edited)
Hi @stubbi , @paf , @antoine , @yhaddad and @grossim
Since yesterday me and @jayesh1211 were trying to upload the submission files to the aqora Platform as a Team
we were getting a lot of technical issues while uploading the file and ran into errors such VS code not found
project.toml not found, so we decided to mail our submission notebook to Jannes on his email @stubbi, just before the final deadline to ensure a fair evaluation,
. if the submission has to be reflected on the leaderboard either way, we can upload the same test notebook file on the aqora platform for the leaderboard which was sent to @stubbi via email if you guys can permit some extra time for the upload with some guidelines on how to upload the file
Since yesterday me and @jayesh1211 were trying to upload the submission files to the aqora Platform as a Team
we were getting a lot of technical issues while uploading the file and ran into errors such VS code not found
project.toml not found, so we decided to mail our submission notebook to Jannes on his email @stubbi, just before the final deadline to ensure a fair evaluation,
. if the submission has to be reflected on the leaderboard either way, we can upload the same test notebook file on the aqora platform for the leaderboard which was sent to @stubbi via email if you guys can permit some extra time for the upload with some guidelines on how to upload the file
For fair evaluation you can cross check the final uploaded leaderboard results with the file which was send over on email before the deadline
We have achieved a good R^2 R2 score of 0.8189 % along with the cross-section's values. R² Score: 0.8189 for our Hybrid quantum classical Model
Posted by stubbi •
Thanks for the submissions. Can you please provide the exact commands and error messages you are facing? It will help us understand what’s the issue
Posted by Abdullah •
Hi @stubbi
To explain the entire process
We followed the H2 Ground state energy tutorial docs section for the intial testing and upload
We successfully installed aqora cli using pip in the VS code
We also have preinstalled all the up to date libraries and dependencies
Then we tested and verified aqora using aqora help
And succesfully logged in
We were abled to download the templates and data files successfully
Everything is working well at this point
.now coming to the submission
We are not able to find the directory or the solution notebook template for running and uploading the solution
We are getting errors such as directory not found
Project.toml not found
To explain the entire process
We followed the H2 Ground state energy tutorial docs section for the intial testing and upload
We successfully installed aqora cli using pip in the VS code
We also have preinstalled all the up to date libraries and dependencies
Then we tested and verified aqora using aqora help
And succesfully logged in
We were abled to download the templates and data files successfully
Everything is working well at this point
.now coming to the submission
We are not able to find the directory or the solution notebook template for running and uploading the solution
We are getting errors such as directory not found
Project.toml not found
These are the errors we are getting
. We want to know in detail about the submission
Can you please guide us on how to test and upload the files
.also how to write our code in the solution.ipynb file template in which cell
And how to run it
Thank you
. We want to know in detail about the submission
Can you please guide us on how to test and upload the files
.also how to write our code in the solution.ipynb file template in which cell
And how to run it
Thank you
Posted by stubbi •
Thanks! Did you
cd into the correct directory and could run aqora test there?Posted by bhagya •
Yes I was facing the same error too.
Posted by antoine •
$HOME/.local/state/aqora/aqora.log*if you are on Linux,$HOME/Library/Caches/aqora/aqora.log*if you are on macOS,%USERPROFILE%\AppData\Local\aqora\aqora.log*if you are on Windows.
Posted by Abdullah •
Hi @stubbi @antoine and @paf
We can send our submissions but
Due to the extensive computational time required by our model, we attempted to run our code on a subset of the original dataset and successfully obtained the R² value : 81 .However, when we executed the same code using the “aqora test” command in the solution file, we encountered an error indicating a mismatch in the dimensions of the dataset. We understand why this error occurs: the testing command calculates the R² value (which is predefined in the backend) and any other metrics based on the entire dataset. Since our model only utilized a subset of the data, this discrepancy leads to the dimensionality mismatch error.
What can be done now
Any thing if you can suggest
We can send our submissions but
Due to the extensive computational time required by our model, we attempted to run our code on a subset of the original dataset and successfully obtained the R² value : 81 .However, when we executed the same code using the “aqora test” command in the solution file, we encountered an error indicating a mismatch in the dimensions of the dataset. We understand why this error occurs: the testing command calculates the R² value (which is predefined in the backend) and any other metrics based on the entire dataset. Since our model only utilized a subset of the data, this discrepancy leads to the dimensionality mismatch error.
What can be done now
Any thing if you can suggest
Posted by Abdullah •
Hi @stubbi and @yhaddad
Good news we were finally able to upload the submission
We took a small subset of the training data and took the entire data for testing we got a good score of 85 for that then we tested the data for the final upload to check whether the training will crash or not we were able to successfully get a score of 81 on the final upload
I believe all the time we were trying to diagnose what was the error with testing the data with the entire dataset which was the reason for the crash we believe that due to taking all the test samples caused memory overload
We tried many methods to fix this until one proven method finally worked for us
.
Keeping the deadline in mind and for fair evaluation we did not made any changes to our QML and Classical model .
For reference we already have submitted our model notebook to @stubbi via email before deadline if there is need for cross checking the model
.
We just tried adjusting the data for training and applying methods to reduce testing time for our model which shows our model is robust and scalable
We thank you for the patience , advise and the opportunity for participating in this challenge
Awaiting the results
Thank you
Regards
Good news we were finally able to upload the submission
We took a small subset of the training data and took the entire data for testing we got a good score of 85 for that then we tested the data for the final upload to check whether the training will crash or not we were able to successfully get a score of 81 on the final upload
I believe all the time we were trying to diagnose what was the error with testing the data with the entire dataset which was the reason for the crash we believe that due to taking all the test samples caused memory overload
We tried many methods to fix this until one proven method finally worked for us
.
Keeping the deadline in mind and for fair evaluation we did not made any changes to our QML and Classical model .
For reference we already have submitted our model notebook to @stubbi via email before deadline if there is need for cross checking the model
.
We just tried adjusting the data for training and applying methods to reduce testing time for our model which shows our model is robust and scalable
We thank you for the patience , advise and the opportunity for participating in this challenge
Awaiting the results
Thank you
Regards
Posted by Abdullah • (edited)
Hi @yhaddad and @stubbi we are still facing the dimensionality error we did what was suggested by @yhaddad
and we also took less sample data for training but while uploading it throws an error
the only solution for us is to have some compute or HPC power to run the training for all the data samples because our device keeps crashing during the testing Or @stubbi we can send the file to you if you are able to upload it
Our model is working well with 20k data sample giving us a score of 82
thank you
and we also took less sample data for training but while uploading it throws an error
the only solution for us is to have some compute or HPC power to run the training for all the data samples because our device keeps crashing during the testing Or @stubbi we can send the file to you if you are able to upload it
Our model is working well with 20k data sample giving us a score of 82
thank you
Posted by Abdullah • (edited)
Hi @stubbi and @antoine
After a lot of debugging and testing we are able to test and run the code using the aqora test command
We are getting the same results which I mentioned above a R2 score of 81
After running the notebook with a subset of the original data which can seen in the image .
Given that we took only a subset of data due to less compute power of our machine we were able to get a good score
We believe if we take all the data there is a possibility of getting good score for the model but given that the training for the large amount of data can be time consuming which I believe is the issue we faced intially with each epoch taking a lot of time
One more thing is that we were able to successfully
Run and test our notebook and get a good score
But there is still issues while uploading the file to the leaderboard
Which we believe is due to evaluation and training metrics and data restrictions on the platform backend which takes into account all the data which is giving us errors like dimensionality mismatch
We would like to know your thoughts on how to proceed from here
Regards
After a lot of debugging and testing we are able to test and run the code using the aqora test command
We are getting the same results which I mentioned above a R2 score of 81
After running the notebook with a subset of the original data which can seen in the image .
Given that we took only a subset of data due to less compute power of our machine we were able to get a good score
We believe if we take all the data there is a possibility of getting good score for the model but given that the training for the large amount of data can be time consuming which I believe is the issue we faced intially with each epoch taking a lot of time
One more thing is that we were able to successfully
Run and test our notebook and get a good score
But there is still issues while uploading the file to the leaderboard
Which we believe is due to evaluation and training metrics and data restrictions on the platform backend which takes into account all the data which is giving us errors like dimensionality mismatch
We would like to know your thoughts on how to proceed from here
Regards
Posted by Abdullah •
Posted by stubbi •
hey @abdullah!
Thanks for sharing that picture, this looks alright! Can you run
aqora upload from there?Posted by Abdullah •
Hi @stubbi
Almost all the errors are resolved
We are able to test get a score
And even the uploading was working well
But later on an error pops up saying dimensionality error due to a small subset of data samples taken during our training
The metric or evaluation function backend takes into the whole data
And if we take the whole data in our code
It takes a lot of time for training
Almost all the errors are resolved
We are able to test get a score
And even the uploading was working well
But later on an error pops up saying dimensionality error due to a small subset of data samples taken during our training
The metric or evaluation function backend takes into the whole data
And if we take the whole data in our code
It takes a lot of time for training