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pytorch-forecasting
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| DeepSpeed | pytorch-forecasting | |
|---|---|---|
| 41 | 8 | |
| 25,088 | 2,849 | |
| 61.0% | - | |
| 9.6 | 7.9 | |
| 2 days ago | about 2 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
DeepSpeed
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Using --deepspeed requires lots of manual tweaking
Filed a discussion item on the deepspeed project: https://github.com/microsoft/DeepSpeed/discussions/3531
Solution: I don't know; this is where I am stuck. https://github.com/microsoft/DeepSpeed/issues/1037 suggests that I just need to 'apt install libaio-dev', but I've done that and it doesn't help.
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Whether the ML computation engineering expertise will be valuable, is the question.
There could be some spectrum of this expertise. For instance, https://github.com/NVIDIA/FasterTransformer, https://github.com/microsoft/DeepSpeed
- FLiPN-FLaNK Stack Weekly for 17 April 2023
- DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-Like Models
- DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-Like Models
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12-Apr-2023 AI Summary
DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales (https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)
- Microsoft DeepSpeed
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Apple: Transformer architecture optimized for Apple Silicon
I'm following this closely, together with other efforts like GPTQ Quantization and Microsoft's DeepSpeed, all of which are bringing down the hardware requirements of these advanced AI models.
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Facebook LLAMA is being openly distributed via torrents
- https://github.com/microsoft/DeepSpeed
Anything that could bring this to a 10GB 3080 or 24GB 3090 without 60s/it per token?
pytorch-forecasting
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LSTM/CNN architectures for time series forecasting[Discussion]
Pytorch-forecasting
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[P] Beware of false (FB-)Prophets: Introducing the fastest implementation of auto ARIMA [ever].
To name a few: https://github.com/jdb78/pytorch-forecasting, https://github.com/unit8co/darts, https://github.com/Nixtla/neuralforecast
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A python library for easy manipulation and forecasting of time series.
Darts is a pretty nice one. I've recently been using pytorch-forecasting for larger models like the Temporal Fusion Transformer. https://github.com/jdb78/pytorch-forecasting
What are some alternatives?
ColossalAI - Making large AI models cheaper, faster and more accessible
fairscale - PyTorch extensions for high performance and large scale training.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
TensorRT - NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.
Megatron-LM - Ongoing research training transformer models at scale
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
llama - Inference code for LLaMA models
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
Lime-For-Time - Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification