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:
Newer release available (0.3.30)

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-0.3.29.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-0.3.29-py3-none-any.whl (33.5 kB view details)

Uploaded Python 3

File details

Details for the file hideandseek-0.3.29.tar.gz.

File metadata

  • Download URL: hideandseek-0.3.29.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-0.3.29.tar.gz
Algorithm Hash digest
SHA256 c8dabd4072a6524ce69f15017459490945376672698b7c54e643b3cc779373e9
MD5 dc910198c4825956c6560ac01910b4e9
BLAKE2b-256 8303dda3f4501df189f8c678ccad583a3169f89c39d138e61d11ef16098b20f3

See more details on using hashes here.

File details

Details for the file hideandseek-0.3.29-py3-none-any.whl.

File metadata

  • Download URL: hideandseek-0.3.29-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-0.3.29-py3-none-any.whl
Algorithm Hash digest
SHA256 f2d29655717c82edfa98478737631b31796c272081038ee1ca257f6a067245bb
MD5 a903cd5637854abe8f57d4909d76d94c
BLAKE2b-256 4a7aa104dbfd039d098c7ea8bda5cfc0330c8acb4367fa249323b17b7ff3a3bc

See more details on using hashes here.

Release history Release notifications | RSS feed

0.3.30

2 files

This release

0.3.29 This release

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