daskol/ttpy repository overview

⁠Quick reference

• Maintained by: the TTPy developers⁠

• Where to get help: the TTPy issues⁠

⁠Simple Tags

• 1.2.0⁠, latest⁠

⁠Quick reference (cont.)

• Where to file issues: https://github.com/oseledets/ttpy/issues⁠

• Supported architectures: (more info⁠) i386⁠

• Published image artifact details: repo-info repo's root directory⁠ (history⁠) (image metadata, transfer size, etc)

⁠What is TTPy?

TTPy is an implementation of the Tensor Train (TT) Toolbox in Python. It contains several important packages for working with the TT-format in Python. It is able to do TT-interpolation, solve linear systems, eigenproblems, solve dynamical problems. Several computational routines are done in Fortran (which can be used separatedly), and are wrapped with the f2py tool.

⁠How to use this image

⁠Create a Dockerfile in your Python app project

FROM daskol/ttpy

COPY requirements.txt ./

RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD [ "python", "./your-numerical-problem.py" ]

You can then build and run the Docker image as follows.

$ docker build -t my-numerical-solver.
$ docker run -it --rm --name my-running-solver my-numerical-solver

⁠Run a single Python script

For many simple, single file projects, you may find it inconvenient to write a complete Dockerfile. In such cases, you can run a Python script by using the Python Docker image directly.

$ docker run -it --rm --name my-running-solver -v "$PWD":/workspace daskol/ttpy your-numerical-solver.py

⁠License

View license information for TTPy⁠.

As with all Docker images, these likely also contain other software which may be under other licenses (such as Bash, etc from the base distribution, along with any direct or indirect dependencies of the primary software being contained).

As for any pre-built image usage, it is the image user's responsibility to ensure that any use of this image complies with any relevant licenses for all software contained within.