guildai
pytorch-lightning
Our great sponsors
guildai | pytorch-lightning | |
---|---|---|
16 | 19 | |
856 | 19,188 | |
0.6% | - | |
8.8 | 9.9 | |
9 months ago | almost 2 years ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
guildai
-
guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
-
[D] Who here are convinced that they have a really good setup that keeps track of their ML experiments?
Experiment tracking in DvC is implemented using git to store snapshots of a project and related artifacts. You might take a look at Guild AI's support for DvC, which is tightly integrated with DvC stages. You can run any of the stages defined for a project and you get a properly isolated run (each run is a project copy to ensure that you're not corrupting the run if you modify files while it's running - as well as properly supporting concurrent runs). Once you have runs in Guild, you can use any number of tools to study, compare, export, etc.
-
[D] Deploying SOTA models into my own projects
I built an experiment tracking tool (Guild AI) that focuses on code/model reuse and so this question is dear to my heart :) Best of luck!
-
[P] I reviewed 50+ open-source MLOps tools. Here’s the result
I'm not aware of experiment tracking in Jupyter notebooks themselves. Guild AI is able to run notebooks as experiments however.
-
[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Guild AI so take this for the biased opinion that it is.
-
[N] Experiment tracking with DvC and Guild AI
I'm the author of Guild AI (open source experiment tracking). For some time now Guild users have asked for DvC support. This is now available as a pre-release.
-
[D] Why doesn’t your team use an experiment tracking tool?
Guild AI now has support for running DvC stages as experiments. DvC uses git under the covers to manage project state for each experiment, along with the experiment results. Guild doesn't touch your git repo and instead copies your project source to a new run directory. This ensures that you have a correct record of your experiment without churning your project state.
-
Data Science toolset summary from 2021
Guild.ai - https://guild.ai/
- [D] How do you ensure reproducibility?
-
[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.
pytorch-lightning
-
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
-
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.
-
Watch out for the (PyTorch) Lightning
Join their Slack to ask the community questions and check out the GitHub here.
-
[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.
-
[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
-
[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
-
[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.
-
[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
-
[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?
MLflow - Open source platform for the machine learning lifecycle
mmdetection - OpenMMLab Detection Toolbox and Benchmark
aim - Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
dvc - 🦉 ML Experiments and Data Management with Git
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
fastai - The fastai deep learning library
wandb - 🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
composer - Supercharge Your Model Training
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
sparktorch - Train and run Pytorch models on Apache Spark.