What does model.eval() do in pytorch?
When should I use .eval() ? I understand it is supposed to allow me to "evaluate my model". How do I turn it back off for training? Example training code using .eval() .
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pytorchKernel termination during Kaggle Notebook timeout prevents state persistence
Kaggle Notebooks enforce hard execution limits—typically 12 hours for CPU and 9 hours for GPU instances. When these limits are reached, or a session is manually cancelled, the kernel process terminates immediately, resulting in the loss of all volatile memory state not explicitly committed to the /kaggle/working directory. Because the environment may not rel
python
kaggleDoes it make sense to use Conda + Poetry?
Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me: As far as I understand, Conda and Poetry have different purposes but are largely redundant: Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and depe
Pipeline memory overhead during large-scale transformations
Memory Constraints in Sequential Pipelines The scikit-learn Pipeline utility ensures repeatable workflows by encapsulating preprocessing steps and estimators. While this prevents data leakage during cross-validation, the sequential application of fit_transform across multiple intermediate steps can lead to significant memory consumption. When handling large
scikitlearnConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT
I have a dataset consisting of both numeric and categorical data and I want to predict adverse outcomes for patients based on their medical characteristics. I defined a prediction pipeline for my dataset like so: X = dataset.drop(columns=['target']) y = dataset['target'] # define categorical and numeric transformers numeric_transformer = Pipeline(steps=[ ('k