plotnine
pytorch-lightning
plotnine | pytorch-lightning | |
---|---|---|
36 | 19 | |
3,828 | 19,188 | |
- | - | |
9.6 | 9.9 | |
8 days ago | almost 2 years ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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plotnine
- FLaNK AI Weekly 18 March 2024
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A look at the Mojo language for bioinformatics
To your last point, have you tried plotnine? It's meant to be ggplot2 for python.
https://github.com/has2k1/plotnine
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Mastering Matplotlib: A Step-by-Step Tutorial for Beginners
plotnine - A grammar of graphics for Python based on ggplot2.
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Top 10 growing data visualization libraries in Python in 2023
Github: https://github.com/has2k1/plotnine
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Lets-Plot: An open-source plotting library by JetBrains
This seems quite similar to plotnine [0], which also provides a grammar of graphics interface for Python. That said, I love ggplot and I can't wait to use this in my research! I hope we can port/re-implement ggthemes, scientificplots [1], and other ggplot libraries for lets-plot.
0: https://plotnine.readthedocs.io/en/stable/
1: https://github.com/garrettj403/SciencePlots
- When would you use R instead of Python?
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[P] Easily make complex plots using ChatGPT [open source]
There is [plotnine](https://plotnine.readthedocs.io/en/stable/) which tries to implement ggplot in Python.
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Is R or Python an EASIER option for non-CS/SE grads?
You could use plotnine if you like the grammar of graphics concept: https://plotnine.readthedocs.io/en/stable/
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Every modeler is supposed to be a great Python programmer
> Python doesn’t yet have anything remotely close to ggplot for rapidly making exploratory graphics, for example.
Plug for plotnine (https://plotnine.readthedocs.io/en/stable/). I don't know R but use ggplot indirectly through this library for exploratory data analysis, and comparing the experience to any other python plotting library, I understand why R folks are usually so sad to be using Python.
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Why has nobody ported ggplot to Python?
They have, https://plotnine.readthedocs.io/en/stable/
pytorch-lightning
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Problem with pytorch lightning and optuna with multiple callbacks
def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None: # Trainer calls `on_validation_end` for sanity check. Therefore, it is necessary to avoid # calling `trial.report` multiple times at epoch 0. For more details, see # https://github.com/PyTorchLightning/pytorch-lightning/issues/1391. if trainer.sanity_checking: return
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Please comment on my planned research project structure
Under the hood, the ModelWrapper object will create a ML model based on the config (so far, an XGBoost model and a PyTorch Lightning model). Each of those will have a wrapper that conducts training and evaluation (since from my understanding of Lightning, Trainers are required to be outside of the class). In lack of a better name, I call these wrappers Fitters. For uniformity, I thought about adding a common interface IFitter, which is inherited by all model wrappers as outlined below.
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Watch out for the (PyTorch) Lightning
Join their Slack to ask the community questions and check out the GitHub here.
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[P] Composer: a new PyTorch library to train models ~2-4x faster with better algorithms
Pytorch lightning benchmarks against pytorch on every PR (benchmarks to make sure that it is mot slower.
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[D] What Repetitive Tasks Related to Machine Learning do You Hate Doing?
There is already a ton of momentum around automating ML workflows. I would suggest you contribute to a preexisting project like, for instance, PyTorch Lightning or fast.ai.
- PyTorch Lightening
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[D] Are you using PyTorch or TensorFlow going into 2022?
Is the problem the sheer number of options, or the fact that they are all together in one place? Would it be better if they were organized into the different trainer entrypoints (fit, validate, ...)? If that is the case, there was an RFC proposing this which you might find interesting, feel free to drop by and comment on the issue: https://github.com/PyTorchLightning/pytorch-lightning/issues/10444
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[D] Colab TPU low performance
I wanted to make a quick performance comparison between the GPU (Tesla K80) and TPU (v2-8) available in Google Colab with PyTorch. To do so quickly, I used an MNIST example from pytorch-lightning that trains a simple CNN.
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[D] How to avoid CPU bottlenecking in PyTorch - training slowed by augmentations and data loading?
We've noticed GPU 0 on our 3 GPU system is sometimes idle (which would explain performance differences). However its unclear to us why that may be. Similar to this issue
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[P] An introduction to PyKale https://github.com/pykale/pykale, a PyTorch library that provides a unified pipeline-based API for knowledge-aware multimodal learning and transfer learning on graphs, images, texts, and videos to accelerate interdisciplinary research. Welcome feedback/contribution!
If you want a good example for reference, take a look at Pytorch Lightning's readme (https://github.com/PyTorchLightning/pytorch-lightning) It answers the 3 questions of "what is this", "why should I care", and "how do i use it" almost instantly
What are some alternatives?
seaborn - Statistical data visualization in Python
mmdetection - OpenMMLab Detection Toolbox and Benchmark
matplotlib - matplotlib: plotting with Python
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Altair - Declarative statistical visualization library for Python
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
fastai - The fastai deep learning library
ggplot - ggplot port for python
composer - Supercharge Your Model Training
bokeh - Interactive Data Visualization in the browser, from Python
sparktorch - Train and run Pytorch models on Apache Spark.