Comments (5)
Good catch. For some reason the logger_api file is inside the src.
can you change line 8
from logger_api import get_logger
to
from src.logger_api import get_logger
And see if it works? If yes, I'll edit the file in the github
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Its giving me a new error: ModuleNotFoundError: No module named 'src.logger_api'. Do I need to import anything prior to this line?
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I was able to resolve it using:
import logging
logger = logging.getLogger(name)
furthermore, exists() did not return True for the api_keys/kaggle.json path - I simply pasted kaggle.json in the root directory which finally worked.
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Glad you were able to resolve it. I've corrected the logger issue in the main branch
the kaggle.json issue shouldn't be occurring. I wonder why. It works well for me.
the code looks for the api key in api_keys/kaggle.json
. The folder you are in when you invoke the download_data.py should the root folder with api_keys
folder.
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Thanks. Yeah, I agree, the kaggle.json issue shouldn't be occurring- I think it might be some idiosyncrasy associated with interpreting a windows path although changing it to a string did not work either, moving it to the root directory was a last resort. The folder I was invoking download.py in was the root folder with api_keys.
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Related Issues (20)
- ModuleNotFoundError: No module named 'src' HOT 3
- Environment Issues on M1 pro HOT 1
- Chapter 04 - Kaboudan Metric HOT 1
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- Chapter 13) output tensor dimension error HOT 1
- sklearn.linear_model RidgeCV got an unexpected keyword argument 'normalize' HOT 2
- Issues installing environment on M2 Macbook Air
- Chapter 08 : 01-Forecasting with ML HOT 2
- Setting up environment: 404 pytorch HOT 1
- Reference to downloaded code in preface before download location is mentioned HOT 1
- Abstract method supports_multivariate not implemented in baselines.py HOT 2
- Target Transformation in ML_Forecast class not working (workaround solution included) HOT 1
- In implementation of Seasonal Decompose (STL) with FourierSeries, `max_cycle=np.max(seasonal_cycle)` is wrong? HOT 2
- For BoxCoxTransformer, `y = self._add_one(y)` twice in fit + transform?
- TypeError: Passing a set as an indexer is not supported. Use a list instead. Occured in Chapter08. HOT 2
- ValueError from `eval_model` function within Chapter 4: 02-Baseline Forecasts using darts.ipynb Cell 12
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- Chapter 4- 02-Baseline Forecasts using darts
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