ultimate-utils 0.5.4

Brando's ultimate utils for science, machine learning and AI

Ultimate-utils

Ulitmate-utils (or uutils) is collection of useful code that Brando has collected through the years that has been useful accross his projects. Mainly for machine learning and programming languages tasks.

Installing Ultimate-utils

Standard pip install [Recommended]

If you are going to use a gpu the do this first before continuing (or check the offical website: https://pytorch.org/get-started/locally/):

pip3 install torch==1.9.1+cu111 torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

Otherwise, just doing the follwoing should work.

pip install ultimate-utils

If that worked, then you should be able to import is as follows:

import uutils

note the import statement is shorter than the library name (ultimate-utils vs uutils).

Manual installation [for Development]

To use uutils first get the code from this repo (e.g. fork it on github):

git clone [e-mail]:brando90/ultimate-utils.git

Then install it in development mode in your python env with python >=3.9 (read modules_in_python.md to learn about python envs). E.g. create your env with conda:

conda create -n uutils_env python=3.9
conda activate uutils_env

Then install uutils in edibable mode and all it's depedencies with pip in the currently activated conda environment:

pip install -e ~/ultimate-utils/ultimate-utils-proj-src

No error should show up from pip. To test the installation uutils do:

python -c "import uutils; uutils.hello()"
python -c "import uutils; uutils.torch_uu.hello()"

it should print something like the following:

hello from uutils __init__.py in:
<module 'uutils' from '/Users/brando/ultimate-utils/ultimate-utils-proj-src/uutils/__init__.py'>


hello from torch_uu __init__.py in:
<module 'uutils.torch_uu' from '/Users/brando/ultimate-utils/ultimate-utils-proj-src/uutils/torch_uu/__init__.py'>

To test (any) pytorch do:

python -c "import uutils; uutils.torch_uu.gpu_test_torch_any_device()"

output:

(meta_learning_a100) [miranda9@hal-dgx diversity-for-predictive-success-of-meta-learning]$ python -c "import uutils; uutils.torch_uu.gpu_test()"
device name: A100-SXM4-40GB
Success, no Cuda errors means it worked see:
out=tensor([[ 0.5877],
        [-3.0269]], device='cuda:0')
(meta_learning_a100) [miranda9@hal-dgx diversity-for-predictive-success-of-meta-learning]$ python -c "import uutils; uutils.torch_uu.gpu_test_torch_any_device()"
device name: A100-SXM4-40GB
Success, torch works with whatever device is shown in the output tensor:
out=tensor([[-1.9061],
        [ 1.3525]], device='cuda:0')

GPU TEST: To test if pytorch works with gpu do (it should fail if no gpus are available):

python -c "import uutils; uutils.torch_uu.gpu_test()"

output should be something like this:

(meta_learning_a100) [miranda9@hal-dgx diversity-for-predictive-success-of-meta-learning]$ python -c "import uutils; uutils.torch_uu.gpu_test()"
device name: A100-SXM4-40GB
Success, no Cuda errors means it worked see:
out=tensor([[ 0.5877],
        [-3.0269]], device='cuda:0')

[Adavanced] If using pygraphviz functions

If you plan to use the functions that depend on pygraphviz you will likely need to install graphviz first. On mac, brew install graphviz. On Ubuntu, sudo apt install graphviz.

Then install pygraphviz with

pip install pygraphviz

If the previous steps didn't work you can also try installing using conda (which seems to install both pygraphviz and graphviz`):

conda install -y -c conda-forge pygraphviz

to see details on that approach see the following stack overflow link question: https://stackoverflow.com/questions/67509980/how-does-one-install-pygraphviz-on-a-hpc-cluster-without-errors-even-when-graphv

To test if pygraphviz works do:

python -c "import pygraphviz"

Nothing should return if successful.

Contributing

Feel free to push code with pull request. Please include at least 1 self-contained test (that works) before pushing.

How modules are imported in a python project

Read the modules_in_python.md to have an idea of the above development/editable installation commands.

Executing tensorboard experiment logs from remote

• visualize the remote logs using pycharm and my code (TODO: have the download be automatic...perhaps not needed)

1. Download the code from the cluster using pycharm remote
2. Then copy paste the remote path (from pycharm, browse remote)
3. Using the copied path run tbb path2log e.g. tbbb /home/miranda9/data/logs/logs_Mar06_11-15-02_jobid_0_pid_3657/tb

to have tbbb work as the command add to your .zshrc (or .bashrc):

alias tbb="sh ${HOME}/ultimate-utils/run_tb.sh"

then the command tbb path2log should work.

ref: see files

• https://github.com/brando90/ultimate-utils/blob/master/run_tb.sh
• https://github.com/brando90/ultimate-utils/blob/master/ultimate-utils-proj-src/execute_tensorboard.py

Pushing to pypi

See: ~/ultimate-utils/tutorials_for_myself/pushing_to_pypi/README.md

Citation

If you use this implementation consider citing us:

@software{brando2021ultimateutils,
    author={Brando Miranda},
    title={Ultimate Utils - the Ultimate Utils library for Machine Learning and Artificial Intelligence},
    url={https://github.com/brando90/ultimate-utils},
    year={2021}
}

A permanent link lives here: https://www.ideals.illinois.edu/handle/2142/112797