sagemaker-distribution
sagemaker-python-sdk
| sagemaker-distribution | sagemaker-python-sdk | |
|---|---|---|
| 1 | 1 | |
| 74 | 2,045 | |
| - | 0.8% | |
| 9.2 | 9.7 | |
| 5 days ago | 5 days ago | |
| Dockerfile | 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.
sagemaker-distribution
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?
blender-docker-cli - :monkey_face: Blender in :whale: Docker
gluonts - Probabilistic time series modeling in Python
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.
stable-diffusion-docker - Run the official Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint.
oneAPI-samples - Samples for Intel® oneAPI Toolkits
robot - Functions and classes for gradient-based robot motion planning, written in Ivy.
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
cresset - Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
libtensorflow_cc - Pre-built libtensorflow_cc.so and Docker Images for TensorFlow C++ API
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.