Comments (3)
This is the problem we had in the hackathon. Currently we tar and send all the files in the code folder to the backend. That does not work in case there are very large files. We need to add a warning to make sure that folder is not very large before taring, letting the user know that they are not supposed to have the dataset in this folder
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Can you provide more details ?
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Duplicated problem in #99 . Closing here
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Related Issues (20)
- Enhancement: save variant tabs order
- [Bug]: 'Create a New Variant' modal input data do not get cleared HOT 1
- [AGE-321] [Bug] Not all rows in evaluation comparison cannot be expanded
- [AGE-345] [bug] Some models are not working in the playground
- [Enhancement]: Table row's delete button placement and UI HOT 3
- [AGE-348] [bug] Errors are not correctly handled in the LLM applications HOT 4
- [AGE-357] [bug] Removing a variant used in an A/B test evaluation breaks the human evaluation view
- [AGE-365] Add new status for evaluation Queued HOT 2
- [AGE-370] Improve reproducibility of AI critique outputs HOT 1
- Add Exception handling in Agenta Observability HOT 1
- [AGE-391] [bug] LLM invocation errors are not correctly shown in the evaluation detail view HOT 1
- [AGE-399] [bug] Test creation in single model eval does not work in table view
- [AGE-112] [Evaluations] start new evaluation from previous evaluation
- Add encryption to LLM provider keys before saving it
- how can Agenta be integrated with ollama LLM platform HOT 1
- [AGE-433] [bug] New exact match and JSON evaluators are created with each served variant HOT 4
- Can I deploy without using port 80 ? HOT 5
- [AGE-451] Agent deployed by yourself, how to configure it when you want to Create a Custom App? HOT 3
- After local deployment, when creating an app through the page, there is still an error message when opening it http://0.0.0.0:80 How to modify this for accessing HOT 3
- [AGE-452] [bug] create_new_evaluator_config not setting default correct_answer value
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