arroyo
pytorch-image-models
arroyo | pytorch-image-models | |
---|---|---|
13 | 35 | |
3,389 | 30,295 | |
2.8% | 1.8% | |
9.5 | 9.4 | |
1 day ago | 8 days ago | |
Rust | Python | |
Apache License 2.0 | Apache License 2.0 |
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arroyo
- FLaNK AI Weekly 18 March 2024
- Arryo 0.8 released โ streaming SQL engine
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Query Engines: Push vs. Pull
Interesting - I looked into your code a bit. I found your window aggregation library [1]. You may be interested in looking into the Rust implementation of some of the research work I've been a part of [2].
In Flink, I believe the reason they need to implement their own backpressure system is that they multiplex TCP connections. That is, they have multiple logical streams flowing through a single TCP connection. If that's the case, you need to do some work to 1) detect which logical stream is the one that's blocking, and 2) don't block because other logical streams may be able to use the active TCP connection.
Thinking it through, I think what Flink's approach buys is not necessarily better performance, but better just a manageable number of connections. That is, imagine you have a process P1 with operators A, B and C. And then P2 has D, E, F. Now imagine that this is a shuffle, where A, B and C are fully connected to D, E and F. In my old system, you would have 9 TCP connections. In Flink, you will have 1.
[1] https://github.com/ArroyoSystems/arroyo/blob/master/arroyo-w...
- Arroyo
- Show HN: Arroyo โ Write SQL on streaming data
- Release v0.3.0 ยท ArroyoSystems/arroyo - Stream Processing Engine
- Arroyo 0.2 released - Rust stream processing engine, now on Kubernetes
- Distributed stream processing engine written in Rust
- ArroyoSystems/arroyo: Arroyo is a distributed stream processing engine written in Rust
- Arroyo, a new open-source SQL stream processing engine written in Rust
pytorch-image-models
- FLaNK AI Weekly 18 March 2024
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[D] Hugging face and Timm
I am a PyTorch user I work in CV, I usually use the PyTorch models. However, I see people use timm in research papers to train their models I don't understand what it is timm is it a new framework like PyTorch? Further, when I click https://pypi.org/project/timm/ homepage it takes me to hugging face GitHub https://github.com/huggingface/pytorch-image-models is there any connection between timm and hugging face many of my friends use hugging face but I also don't know about hugging face I use simple PyTorch and torchvision.models.
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FLaNK Stack Weekly for 07August2023
https://github.com/huggingface/pytorch-image-models https://huggingface.co/docs/timm/index
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[R] Nvidia RTX 4090 ML benchmarks. Under QEMU/KVM. Image + Transformers. FP16/FP32.
pytorch-image-models
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Inference on resent, cant work out the problem?
additionally, you might find the timm library handy for this sort of work.
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Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows
This is still being pursued. Ross Wightmann's timm[0,1] package (now on Hugging Face) has done a lot of this. There's also a V2 of ConvNext[2]. Ross does write about this a lot on Twitter fwiw. I should also mention that there are still many transformer based networks that still beat convs. So there probably won't be a resurgence in convs until someone can show that there's a really strong reason for them. They have some advantages but they also might not be flexible enough for the long range tasks in segmentation and detection. But maybe they are.
FAIR definitely did great work with ConvNext, and I do hope to see more. There always needs to be people pushing unpopular paradigms.
[0] https://github.com/huggingface/pytorch-image-models
[1] https://arxiv.org/abs/2110.00476
[2] https://arxiv.org/abs/2301.00808
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Problems with Learning Rate Finder in Pytorch Lightning
I am doing Binary classification with a pre-trained EfficientNet tf_efficientnet_l2. I froze all weights during training and replaced the classifier with a custom trainable one that looks like:
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PyTorch at the Edge: Deploying Over 964 TIMM Models on Android with TorchScript and Flutter
In this post, Iโm going to show you how you can pick from over 900+ SOTA models on TIMM, train them using best practices with Fastai, and deploy them on Android using Flutter.
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ImageNet Advise
The other thing is, try to find tricks to speed up your experiments (if not having done so already). The most obvious are mixed precision training, have your model train on a lower resolution input first and then increase the resolution later in the training, stochastic depth, and a bunch more stuffs. Look for implementations in https://github.com/rwightman/pytorch-image-models .
- Doubt about transformers
What are some alternatives?
bytewax - Python Stream Processing
yolov5 - YOLOv5 ๐ in PyTorch > ONNX > CoreML > TFLite
risingwave - SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.
mmdetection - OpenMMLab Detection Toolbox and Benchmark
Benthos - Fancy stream processing made operationally mundane [Moved to: https://github.com/redpanda-data/connect]
detectron2 - Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
cli - Railway CLI
mmcv - OpenMMLab Computer Vision Foundation
feldera - Feldera Continuous Analytics Platform
segmentation_models.pytorch - Semantic segmentation models with pretrained convolutional and transformer-based backbones. PyTorch.
timely-dataflow - A modular implementation of timely dataflow in Rust
yolact - A simple, fully convolutional model for real-time instance segmentation.