sagemaker-tensorflow-training-toolkit
sagemaker-distribution
sagemaker-tensorflow-training-toolkit | sagemaker-distribution | |
---|---|---|
1 | 1 | |
267 | 75 | |
0.0% | - | |
0.0 | 9.2 | |
about 1 year ago | 6 days ago | |
Python | Dockerfile | |
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-distribution
What are some alternatives?
editGAN_release
blender-docker-cli - :monkey_face: Blender in :whale: Docker
sagemaker-training-toolkit - Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
oneAPI-samples - Samples for Intel® oneAPI Toolkits
spotty - Training deep learning models on AWS and GCP instances
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
sagemaker-python-sdk - A library for training and deploying machine learning models on Amazon SageMaker
cresset - Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
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