sagemaker-distribution
ML-Workspace
| sagemaker-distribution | ML-Workspace | |
|---|---|---|
| 1 | 7 | |
| 74 | 3,329 | |
| - | 0.6% | |
| 9.2 | 2.7 | |
| 5 days ago | 6 months ago | |
| Dockerfile | Jupyter Notebook | |
| 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
ML-Workspace
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[D] I recently quit my job to start a ML company. Would really appreciate feedback on what we're working on.
Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.
- ML-Workspace
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Coding for machine learning on Tab S8?
The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)
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Dynamically spin up VM (based on specific HTTPS request) and stop it once session is over?
It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)
- All-in-One Docker Based IDE for Data Science and ML
- Visual Studio Code now available as Web based editor for GitHub repos
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[P] Install or update CUDA, NVIDIA Drivers, Pytorch, Tensorflow, and CuDNN with a single command: Lambda Stack
I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.
What are some alternatives?
blender-docker-cli - :monkey_face: Blender in :whale: Docker
JupyterLab - JupyterLab computational environment.
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.
Gitpod - DEPRECATED since Gitpod 0.5.0; use https://github.com/gitpod-io/gitpod/tree/master/chart and https://github.com/gitpod-io/gitpod/tree/master/install/helm
oneAPI-samples - Samples for IntelĀ® oneAPI Toolkits
keytotext - Keywords to Sentences
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
cresset - Template repository to build PyTorch projects from source on any version of PyTorch/CUDA/cuDNN.
Code-Server - VS Code in the browser
sagemaker-python-sdk - A library for training and deploying machine learning models on Amazon SageMaker
cocalc-docker - DEPRECATED (was -- Docker setup for running CoCalc as downloadable software on your own computer)