Configure and train PyTorch models with a lot of the details already or partially implemented.
DISCLAIMER: This repo is used for my research. New versions are not necessarily backwards compatible. The API is subject to change at a moment's notice. If you happen to use it in your research or work, make sure in your requirements.txt to pin the version or reference the specific commit you used so you don't suffer unwanted surprises.
Install
pip install --upgrade simplepytorch
Try an example
Download and extract DRIVE dataset to ./data/DRIVE
$ ls data/DRIVE
test test.zip training training.zip
Run an experiment, and give it a name.
$ python examples/simple_example.py --experiment_id test_experiment_1 --epochs 10
$ ls results/test_experiment_1
checkpoints log perf.csv
Look at training curves (note: demo only runs for 2 epochs)
simplepytorch_plot test_experiment --mode1-subplots
Run demo, with (customizable) guarantees that code completes exactly once and distributes jobs across GPUs. (note: uses Redis database to temporarily monitor current jobs).
redis-server # must be installed to use the default example
./bin/example_experiments.sh
Use hyperparameter optimization with Ray Tune library.
pip install -U "simplepytorch[ray]"
python examples/hyperparam_opt.py --experiment_id test_hyperband_1
Datasets:
The library provides PyTorch Dataset implementations for different datasets.
To use the pre-defined dataset classes, you must download the data and unzip it yourself. Consult Dataset class docstring for usage details.
import simplepytorch.datasets as D
dset = D.RITE(use_train_set=True)
dset[0]
For example, some downloaded datasets I use have the following structure:
$ ls data/{arsn_qualdr,eyepacs,messidor,IDRiD_segmentation,RITE}
data/IDRiD_segmentation:
'1. Original Images' '2. All Segmentation Groundtruths' CC-BY-4.0.txt LICENSE.txt
data/RITE:
AV_groundTruth.zip introduction.txt read_me.txt test training
data/arsn_qualdr:
README.md annotations annotations.zip imgs1 imgs1.zip imgs2 imgs2.zip
data/eyepacs:
README.md test test.zip.003 test.zip.006 train.zip.001 train.zip.004
sample.zip test.zip.001 test.zip.004 test.zip.007 train.zip.002 train.zip.005
sampleSubmission.csv.zip test.zip.002 test.zip.005 train train.zip.003 trainLabels.csv.zip
data/messidor:
Annotation_Base11.csv Annotation_Base21.csv Annotation_Base31.csv Base11 Base21 Base31
Annotation_Base12.csv Annotation_Base22.csv Annotation_Base32.csv Base12 Base22 Base32
Annotation_Base13.csv Annotation_Base23.csv Annotation_Base33.csv Base13 Base23 Base33
Annotation_Base14.csv Annotation_Base24.csv Annotation_Base34.csv Base14 Base24 Base34
1 maintainer
Metadata
1 maintainer
Release files for simplepytorch 1.5.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| simplepytorch-1.5.3.tar.gz | 63.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| simplepytorch-1.5.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 136.7 kB
Release files / simplepytorch-1.5.3.tar.gz
| Download URL | simplepytorch-1.5.3.tar.gz |
|---|---|
| Size | 63.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
70eaade266b5338d2c40596f593f9d9cea9bf8cdae8aa014662302961338a35e
|
|
BLAKE2b-256 checksum How to use checksums |
d77e8f435f1ef19c23c4d9c782a32abb8dee369311943cc6466e898b66c8bfd1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Release files / simplepytorch-1.5.3-py3-none-any.whl
| Download URL | simplepytorch-1.5.3-py3-none-any.whl |
|---|---|
| Size | 73.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
47f5a888d7948a57607639d77cd23ae290f5c00d23552b81cef05e12db46e1a4
|
|
BLAKE2b-256 checksum How to use checksums |
08b8249f9410c7d2a96e1ce0e540bd9767eefe0b003713859dd28a03f27f6e98
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.5
|
Security for simplepytorch 1.5.3
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
