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List Docker volumes by creation data

Listing all docker volumes and their creation date without any additional tools required.

https://stackoverflow.com/a/77819947/11687201

1. Getting the list of all docker volumes docker volume ls
2. Passing that list to docker volume inspect
3. Sorting by ascending date

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Windows - Make conda available from Git-Bash

Source: https://discuss.codecademy.com/t/setting-up-conda-in-git-bash/534473

# Git-Bash

#Assuming miniconda got installed for all users here 'C:\ProgramData\miniconda3'
cd /c/ProgramData/miniconda3/etc/profile.d

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Exiftool

https://exiftool.org/exiftool_pod.html

Extract embedded thumbails from DNG files

exiftool -a -b -W %d%f_%t%-c.%s -preview:all <directory containing DNGs>

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Terminal Coloring in Docker container

This will look like this but username, @-symbol, hostname, workdir, and $-symbol differently colored.

🐳 username@hostname:/workdir$

Dockerfile

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Rebase instead of merge

Source: https://www.atlassian.com/de/git/tutorials/merging-vs-rebasing

git checkout feature
git checkout -b temporary-branch
git rebase -i main
# [Clean up the history]

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import logging
import torch
import torch.nn as nn
import torchvision.models as models
from torchvision.models import VGG16_Weights


class Vgg16PartiallyFrozenFeatures(nn.Module):
    def __init__(self, num_frozen_blocks=3):

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Reproducible Training Runs in PyTorch

For details check out: https://pytorch.org/docs/stable/notes/randomness.html

Seed points should be set at the beginning of an application.

import torch
import random 

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Useful (Python) Libraries

Library	Description	Links
JAX	High-performance array computing

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Retrieving disk usage information

Retrieving the disk space/usage

df -H

Retrieving file and folder sizes

Sorted by file/folder size

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TensorFlow - Best Practices

Randomization

Understanding Randomization in TF Datasets (including TFRecords) https://colab.research.google.com/github/christianmerkwirth/colabs/blob/master/Understanding_Randomization_in_TF_Datasets.ipynb

Transfer learning/fine-tuning

How to apply transfer learning and fine-tuning. https://www.tensorflow.org/guide/keras/transfer_learning