From 6ae390f2912c6d0521a1d5860f8f2aeb4089ccee Mon Sep 17 00:00:00 2001 From: Caner Turkmen Date: Fri, 19 Apr 2024 10:13:26 +0200 Subject: [PATCH] add AGv1.1 announcement to README (#58) *Issue #, if available:* *Description of changes:* By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Abdul Fatir --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 7ca7890..c4d82a6 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ ## 🚀 News +- **19 Apr 2024**: 🚀 Chronos is now supported on [AutoGluon-TimeSeries](https://auto.gluon.ai/stable/tutorials/timeseries/index.html), the powerful AutoML package for time series forecasting which enables model ensembles, cloud deployments, and much more. Get started with the [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html). - **08 Apr 2024**: 🧪 Experimental [MLX inference support](https://github.com/amazon-science/chronos-forecasting/tree/mlx) added. If you have an Apple Silicon Mac, you can now obtain significantly faster forecasts from Chronos compared to CPU inference. This provides an alternative way to exploit the GPU on your Apple Silicon Macs together with the "mps" support in PyTorch. - **25 Mar 2024**: [v1.1.0 released](https://github.com/amazon-science/chronos-forecasting/releases/tag/v1.1.0) with inference optimizations and `pipeline.embed` to extract encoder embeddings from Chronos. - **13 Mar 2024**: Chronos [paper](https://arxiv.org/abs/2403.07815) and inference code released. @@ -63,6 +64,9 @@ To perform inference with Chronos models, install this package by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` +> [!TIP] +> The recommended way of using Chronos for production use cases is through [AutoGluon](https://auto.gluon.ai), which features ensembling with other statistical and machine learning models for time series forecasting as well as seamless deployments on AWS with SageMaker 🧠. Check out the AutoGluon Chronos [tutorial](https://auto.gluon.ai/stable/tutorials/timeseries/forecasting-chronos.html). + > [!NOTE] > We have added 🧪experimental support for [MLX](https://github.com/ml-explore/mlx) inference. If you have an Apple Silicon Mac, check out the [`mlx`](https://github.com/amazon-science/chronos-forecasting/tree/mlx) branch of this repository for instructions on how to install and use the MLX version of Chronos.