pathml
Tools for computational pathology (by Dana-Farber-AIOS)
mpire
A Python package for easy multiprocessing, but faster than multiprocessing (by sybrenjansen)
pathml | mpire | |
---|---|---|
2 | 8 | |
364 | 1,912 | |
3.0% | 1.6% | |
8.0 | 7.5 | |
about 1 month ago | 8 days ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pathml
Posts with mentions or reviews of pathml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-17.
- Hilo Semanal de Consultas IT - Asesoría Técnica, Desarrollo Profesional y Aprendizaje
-
Dask – a flexible library for parallel computing in Python
We have been using dask to support our computational pathology workflows [1], where the images are so big that they cannot be loaded in memory, let alone analyzed (standard pathology whole slide images are ~1GB; some microscopy techniques generate images >1TB). We divide each image into a bunch of smaller tiles and process each tile independently. The dask.distributed scheduler lets us scale up by distributing the tile processing across a cluster.
Benefits of dask.distributed: easy to get up and running, and has support for spinning up clusters on lots of different computing platforms (local machines, HPC cluster, k8s, etc.)
One difficulty is optimizing performance - there are so many configuration details (job size, number of workers, worker resources, etc. etc.) that it's been hard to know what is best.
[1] https://github.com/Dana-Farber-AIOS/pathml
mpire
Posts with mentions or reviews of mpire.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-08-11.
- GitHub - sybrenjansen/mpire: A Python package for easy multiprocessing, but faster than multiprocessing
- Mpire: A Python package for easier and faster multiprocessing
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Which not so well known Python packages do you like to use on a regular basis and why?
mpire for multiprocessing.
-
How do you deal with parallelising parts of an ML pipeline especially on Python?
https://github.com/Slimmer-AI/mpire is a nice lib, with better performance than multiprocessing.
-
Dask – a flexible library for parallel computing in Python
Shout out to an alternative to Dask: MPIRE https://github.com/Slimmer-AI/mpire
- Multi-Threading in Python
-
I'd like to introduce MPIRE: MultiProcessing Is Really Easy
After several iterations of feedback and exposure to production environments, it is now the go-to multiprocessing library at Slimmer AI. Recently, we’ve made it publicly available on GitHub (https://github.com/Slimmer-AI/mpire).
What are some alternatives?
When comparing pathml and mpire you can also consider the following projects:
slideflow - Deep learning library for digital pathology, with both Tensorflow and PyTorch support.
Dask - Parallel computing with task scheduling