shardingsphere
pytorch-lightning
shardingsphere | pytorch-lightning | |
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23 | 19 | |
19,475 | 19,188 | |
0.5% | - | |
10.0 | 9.9 | |
2 days ago | almost 2 years ago | |
Java | 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.
shardingsphere
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Managing Data Residency - the demo
Opposite to what the documentation tells, the full prefix is jdbc:shardingsphere:absolutepath. I've opened a PR to fix the documentation.
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ShardingSphere 5.3.0 is released: new features and improvements
🔗 Release Notes
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ElasticJob 3.0.2 is released including failover optimization, scheduling stability, and Java 19 compatibility
ElasticJob, one of the sub-projects of the Apache ShardingSphere community, is a distributed scheduling solution oriented towards Internet applications and massive tasks.
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ShardingSphere 5.2.1 is released — Here are the highlights
Following 1.5 months in development, Apache ShardingSphere 5.2.1 is released. Our community merged 614 PRs from teams and individuals around the world. The resulting 5.2.1 release has been optimized in terms of features, performance, testing, documentation, examples, etc.
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Apache ShardingSphere 5.2.0 is Released!
🔗 Update Logs
- Apache ShardingSphere Enterprise User Case — Energy Monster
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DistSQL Applications: Building a Dynamic Distributed Database
GitHub Issues
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ShardingSphere-Proxy Front-End Protocol Troubleshooting Guide and Examples
[8] https://github.com/apache/shardingsphere/pull/17914 [9] https://github.com/apache/shardingsphere/blob/2c9936497214b8a654cb56d43583f62cd7a6b76b/shardingsphere-proxy/shardingsphere-proxy-frontend/shardingsphere-proxy-frontend-core/src/main/java/org/apache/shardingsphere/proxy/frontend/netty/ServerHandlerInitializer.java [10] https://shardingsphere.apache.org/document/current/cn/downloads/
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ShardingSphere-JDBC Driver Released: A JDBC Driver That Requires No Code Modifications
Relevant Links: GitHub issue
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Cloud native deployment for a high-performance data gateway + new API driver: Apache ShardingSphere 5.1.2 is released
Release Notes
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?
MySQL - MySQL Server, the world's most popular open source database, and MySQL Cluster, a real-time, open source transactional database.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
scalardb - Universal transaction manager
pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
postgrest - REST API for any Postgres database
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
awesome - 😎 Awesome lists about all kinds of interesting topics
fastai - The fastai deep learning library
eladmin - eladmin jpa 版本:项目基于 Spring Boot 2.6.4、 Jpa、 Spring Security、Redis、Vue的前后端分离的后台管理系统,项目采用分模块开发方式, 权限控制采用 RBAC,支持数据字典与数据权限管理,支持一键生成前后端代码,支持动态路由
composer - Supercharge Your Model Training
ObjectiveSql - Writing SQL using Java syntax
sparktorch - Train and run Pytorch models on Apache Spark.