gluonts
sagemaker-python-sdk
| gluonts | sagemaker-python-sdk | |
|---|---|---|
| 4 | 1 | |
| 4,308 | 2,045 | |
| 2.3% | 0.7% | |
| 8.7 | 9.7 | |
| 2 days ago | 2 days 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.
gluonts
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Show HN: Auto Wiki v2 – Turn your codebase into a Wiki now with diagrams
https://github.com/awslabs/gluonts is a great candidate for a sample wiki. It is an OSS lib, not great documentation, very hard to RTFM (unlike, say, sklearn which already has a great wiki), doubtful that awslabs would pay to produce.
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gluonts VS darts - a user suggested alternative
2 projects | 13 Apr 2023
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[Q] `py.typed` and `.typesafe`
I was looking at [`gluonts`](https://github.com/awslabs/gluonts/tree/dev/src/gluonts/core) source code and I found a `py.typed` file. That is something I always put in my type-annotated modules: it's literally an empty file which denotes that the module is marked for "internal or external use in type checking" [mypy docs](https://mypy.readthedocs.io/en/stable/installed_packages.html?highlight=py.typed#creating-pep-561-compatible-packages). However, I never saw before the `.typesafe` file. What does it denote? Does it have to be used alongside a `py.typed`?
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Cash-flow forecasting
-GluonTS
sagemaker-python-sdk
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AWS Sagemaker: can you utilize Asynchronous Inference with a Pipeline Model?
Not sure why they didn't include that in the SDK. You could create an issue: https://github.com/aws/sagemaker-python-sdk/issues
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
pytorch-forecasting - Time series forecasting with PyTorch
robot - Functions and classes for gradient-based robot motion planning, written in Ivy.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
sagemaker-distribution - A set of Docker images that include popular frameworks for machine learning, data science and visualization.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
sagemaker-tensorflow-training-toolkit - Toolkit for running TensorFlow training scripts on SageMaker. Dockerfiles used for building SageMaker TensorFlow Containers are at https://github.com/aws/deep-learning-containers.
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
time-series-transformers-review - A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.