TreeinRandomForest

Recently I was pointed to some llm-d benchmarking data here.

The three optimizations llm-d implements for large-scale distributed inference are: Prefill caching: Each node/worker/gpu Prefill/decode disaggregation (PD) Expert Parallelism (EP) for mixture-of-expert (MoE) models Let’s focus on expert parallelism.

Consider the symmetric block matrix \[M = \begin{pmatrix} A & C \\ C^T & B \\ \end{pmatrix}\] with $A = A^T$ and $B = B^T$ and $A, B$ are invertible.

Incomplete list.

Domain See: config_parser.py “float”: Needs (“min”, “max”).

%load_ext autoreload %autoreload 2 %matplotlib inline import torch import torch.nn as nn import torch.optim as optim import matplotlib.pylab as plt import pandas as pd import numpy as np from sklearn.neural_network import MLPClassifier, MLPRegressor i…

I was recently reading an old physics textbook and came across an interesting integral: \[\int_0^\infty \frac{y^3}{e^y - 1} dy = \frac{\pi^4}{15}\] This is pretty enough that one has to do the integral.

The last few years have shown an enormous rise in the use of neural networks for supervised-learning tasks.

Suppose we are given \(n\) independent, identically distributed (i.i.d.) random variables \(Y_1, Y_2, \ldots, Y_n\).