sagemaker-tensorflow-training-toolkit
sagemaker-python-sdk
| sagemaker-tensorflow-training-toolkit | sagemaker-python-sdk | |
|---|---|---|
| 1 | 1 | |
| 267 | 2,052 | |
| 0.0% | 1.2% | |
| 0.0 | 9.7 | |
| about 1 year ago | 4 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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sagemaker-tensorflow-training-toolkit
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Launch HN: Slai (YC W22) – Build ML models quickly and deploy them as apps
this is pretty cool! especially the opinionated structuring part.
now Sagemaker allows u to download ur running code and docker (https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangle...) . Also allows u to simulate local running - https://github.com/aws/sagemaker-tensorflow-training-toolkit
rather than anything else, this is basically just a way to calm worries about lock-in. Google ML resisted this for a long time, but even they had to finally do it - https://cloud.google.com/automl-tables/docs/model-export
are you planning something similar ?
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?
editGAN_release
gluonts - Probabilistic time series modeling in Python
sagemaker-distribution - A set of Docker images that include popular frameworks for machine learning, data science and visualization.
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
sagemaker-training-toolkit - Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
robot - Functions and classes for gradient-based robot motion planning, written in Ivy.
spotty - Training deep learning models on AWS and GCP instances
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
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.