hideandseek

Highly modularized deep learning training library.

Why use hideandseek?

  • Only code the experiment specific parts of ANN experiment (dataset, nn.Module definition, criterion, forward pass)

  • Define every ANN experiment parameters in keyword argument, for easy experimental control

  • Takes care of every other logistics (logging, device type matching, amp, random batching control)

  • Easy training & saving deep learning models along with other modules (ex: preprocessing modules) required in inference

  • Run multiple deep learning experiments in parallel on multiples GPUs (powered by hydra, and python multiprocessing)

  • Design and analyze experiments scientifically by modifying variables (powered by hydra)

  • Modularized machine learning pipeline allows using the same script for all types of experiments

  • The same training code can be run in privacy preserving setting by minimal modifications

Look at Simple/train.py for simple run cases

Single run:

python train.py lr=1e-3 batch_Size=32 random_seed=0

Multirun with batch of experiments (Hyperparameter sweep):

python train.py -m lr=1e-3,1e-2 batch_size=32,64 "random_seed=range(0,5)" \
hydra/launcher=joblib hydra.launcher.n_jobs=8
# Runs total of 2*2*5=40 batch of experiments, with 8 processes at a time. Experiment results are stored in hydra.sweep.dir which can be overridden.

To do

  • Draw figures to explain hideandseek
  • .py based tutorial
  • `.ipynb' based tutorial
  • GUI for generating experiment scripts when conducting variable sweeps

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for jsyoo61 from gravatar.com jsyoo61

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Jaesung Yoo

License

MIT License

Requires

Python >=3.6

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hideandseek-0.3.30.tar.gz (23.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hideandseek-0.3.30-py3-none-any.whl (33.5 kB view details)

Uploaded Python 3

File details

Details for the file hideandseek-0.3.30.tar.gz.

File metadata

  • Download URL: hideandseek-0.3.30.tar.gz
  • Upload date:
  • Size: 23.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for hideandseek-0.3.30.tar.gz
Algorithm Hash digest
SHA256 3be101aee07b5ea670d35e891025e0a476445e2fa0b057badd59e0445e509769
MD5 c1229e59b33d573fd726961764d08dc1
BLAKE2b-256 bfb50a017c1273e6e019bfe975710c67ccac24416d0fc953c38f846e1dfa3097

See more details on using hashes here.

File details

Details for the file hideandseek-0.3.30-py3-none-any.whl.

File metadata

  • Download URL: hideandseek-0.3.30-py3-none-any.whl
  • Upload date:
  • Size: 33.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for hideandseek-0.3.30-py3-none-any.whl
Algorithm Hash digest
SHA256 1bcbc3de035e0ea6a44909e962cc5482eec25d42eb991a593791479ee3634dab
MD5 728071af639a4fd225b09167cff1d1fc
BLAKE2b-256 b2310b7ea92404e304666952d50602b55d84a5d80e37b92d10356a258871f779

See more details on using hashes here.

Release history Release notifications | RSS feed

This release

0.3.30 This release

2 files

0.3.29

2 files

0.3.28

2 files

0.3.27

2 files

0.3.26

2 files

0.3.25

2 files

0.3.24

2 files

0.3.23

2 files

0.3.22

2 files

0.3.21

2 files

0.3.20

2 files

0.3.19

2 files

0.3.18

2 files

0.3.17

2 files

0.3.16

2 files

0.3.15

2 files

0.3.13

2 files

0.3.12

2 files

0.3.11

2 files

0.3.10

2 files

0.3.9

2 files

0.3.8

2 files

0.3.7

2 files

0.3.6

2 files

0.3.5

2 files

0.3.4

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.4

2 files

0.2.3

2 files

0.2.2

2 files

0.2.1

2 files

0.2.0

2 files

0.1.29

2 files

0.1.28

2 files

0.1.27

2 files

0.1.26

2 files

0.1.25

2 files

0.1.24

2 files

0.1.23

2 files

0.1.22

2 files

0.1.21

2 files

0.1.20

2 files

0.1.19

2 files

0.1.18

2 files

0.1.17

2 files

0.1.16

2 files

0.1.15

2 files

0.1.14

2 files

0.1.13

2 files

0.1.12

2 files

0.1.11

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

0.1.7

2 files

0.1.6

2 files

0.1.5

2 files

0.1.4

2 files

0.1.3

2 files

0.1.2.0

3 files

0.1.1.13

2 files

0.1.1.12

2 files

0.1.1.11

2 files

0.1.1.10

2 files

0.1.1.9

2 files

0.1.1.8

2 files

0.1.1.7

2 files

0.1.1.6

2 files

0.1.1.5

2 files

0.1.1.4

2 files

0.1.1.3

2 files

0.1.1.2

2 files

0.1.1.1

2 files

0.1.1

2 files

0.1.0

2 files

0.0.7.2

2 files

0.0.7.1

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page