This is a backend, see CutSolverFrontend for a human usable version.
This API can be used to solve the common problem of finding the perfect placement of cuts for specified lengths. It seems like no other free service tackles this specific problem in an easy-to-use format, so this is my attempt.
You are very welcome to share how you use this tool!
This Solver is using integers exclusively, as there is no need for arbitrary precision (yet). Feel free to shift your numbers a few decimals if you need fractions. It has no concept of units, so you can use whatever you want.
Nerd talk: This is the 2D "Cutting Stock Problem", which is NP-hard. It can be reduced to the Bin-Packing-Problem ( BPP). No efficient algorithm exists to calculate a perfect solution in an acceptable timeframe, therefore brute force (perfect solution) is used for small jobs (<13 entries) and FFD (fast solution) für larger ones. Don't be surprised if you get different results, many combinations have equal trimmings and are therefore seen as equally good.
Feel free to run manually, but the easiest (and advised) way to deploy this is by using Docker and pulling an up-to-date image.
Send POST-Requests to [localhost]/solve to get your results, see /docs for further information.
You don't need to check out this repository and build your own image, I am pushing prebuild ones to Docker Hub.
Download and start this container by using the provided docker-compose file or
with docker run [--rm -it] -p80:80 modischfabrications/cutsolver:latest.
Note: Replace latest with a version number if you depend on this interface, I can guarantee you that the interface
will change randomly. It's not like I know what I'm doing, expect a learning curve.
Both linux/amd64 and linux/arm/v7 are currently supported, more will be build whenever I get around to it, message
me if
you need another architecture.
If it can run Docker it will probably be able to run CutSolver. 1 vCPU with 500MB RAM should be fine for small workloads.
Runtimes strongly depend on the single-core performance of your CPU.
You can expect 10 entries to be solved after ~20s with bruteforceand <0.1s with FFD for generic desktops, slower on
weaker machines.
Multiple cores won't speed up job time, but will enable efficient solving of parallel jobs.
Feel free to contact me or make a pull-request if you want to participate.
Install pre-commit with pre-commit install && pre-commit install -t pre-push.
You might need to replace #!/bin/sh with #!/usr/bin/env sh in the resulting *.legacy file on Windows.
All obvious errors should be checked and or fixed by pre-commit, execute pre-commit run --all-files --hook-stage push
to run
manually.
Change version number in main.py:version for newer releases, git tags will be created automatically.
Remember to test your changes using pytest [--durations=5].
- Build and start this image using
docker-compose up - wait a while for dependencies to build... (1000s)
- Hope that everything works
docker run --rm -it -p 8000:80 $(docker build -q .) is also useful to start a temporary container for testing.
Docker Hub Images should be updated automatically, but feel free to build yourself should everything else fail.
Adding "[skip ci]" to the commit message will prevent any ci builds should the need arise.
Thankfully, local builds are easy with the modern buildx workflow.
Installation of a multibuilder (once):
docker buildx create --name multibuilder --use
docker buildx inspect multibuilder --bootstrap
Build and push the new multi-arch image with the following steps (add version, e.g. v0.3.7):
docker login -u modischfabrications
docker buildx build --platform linux/amd64,linux/arm/v7,linux/arm64 \
-t modischfabrications/cutsolver:<VERSION> \
-t modischfabrications/cutsolver:latest --push .
Wait a while for every dependency to build (~1000s) and all layers to be pushed (~400s). Feel free to drink some water and be bored, that's healthy from time to time.
Check Docker Hub to see results.
Everything should be handled by Docker and/or pipenv.
This project uses: