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@AlexandreAbraham
AlexandreAbraham / unsupervised_alt.py
Last active July 1, 2024 12:26
These are two implementations of the silhouette score. They are compatible with the scikit learn implementation but offers different drawbacks in term of complexity and memory usage. The slow version needs no memory but is painfully slow and should, I think, not be used. The second one is based on a block strategy: distance between samples and c…
""" Unsupervised evaluation metrics. """
# License: BSD Style.
from itertools import combinations
import numpy as np
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import distance_metrics
from sklearn.metrics.pairwise import pairwise_distances
@kmhofmann
kmhofmann / building_tensorflow.md
Last active March 2, 2024 18:37
Building TensorFlow from source

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht