sagemaker-distribution
sagemaker-tensorflow-training-toolkit
| sagemaker-distribution | sagemaker-tensorflow-training-toolkit | |
|---|---|---|
| 1 | 1 | |
| 74 | 267 | |
| - | 0.0% | |
| 9.2 | 0.0 | |
| 5 days ago | about 1 year ago | |
| Dockerfile | Python | |
| Apache License 2.0 | Apache License 2.0 |
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sagemaker-distribution
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 ?
What are some alternatives?
blender-docker-cli - :monkey_face: Blender in :whale: Docker
editGAN_release
oneAPI-samples - Samples for Intel® oneAPI Toolkits
sagemaker-training-toolkit - Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
spotty - Training deep learning models on AWS and GCP instances
cresset - Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
sagemaker-python-sdk - A library for training and deploying machine learning models on Amazon SageMaker
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
libtensorflow_cc - Pre-built libtensorflow_cc.so and Docker Images for TensorFlow C++ API