Broadcast Arrays in Numpy
Tips and Traps¶
The broadcast concept in numpy is essentially a way to "virtually" duplicate data in a numpy array so that it is "virtually" reshaped to be compatible with another numpy array for a certain operation. Do not confused yourself about it with the broadcast concept in Spark which sends a full copy of a (small) DataFrame to each work node for
BroadCastJoin
Kotlin Kernel for JupyterLab
Tips and Traps¶
There are multiple Kotlin kernels for JupyterLab. It is is suggested that you use official one released by JetBrains.
Jar Dependencies¶
You can add a JAR dependency using the following code.
Adaptive Pooling in PyTorch
Tips and Traps¶
- Adpative pooling an generate fixed-sized output, which enables a model to handle varying sized input. The models in torchvision are such examples.
References¶
Ways to Enable and Disable Gradient in PyTorch
set_grad_enable¶
Multiprocessing Issues in Docker
PyTorch Specific¶
Using num_workers > 0 for a DataLoader in Docker might causes issues ...
MULTIPROCESSING BEST PRACTICES
Runtime Error with DataLoader: exited unexpectedly #5301
https://forums.fast.ai/t/runtimeerror-dataloader-worker-pid-137-is-killed-by-signal-bus-error/27095