book
ML-Workspace
| book | ML-Workspace | |
|---|---|---|
| 2 | 7 | |
| 183 | 3,329 | |
| 0.0% | 0.6% | |
| 2.7 | 2.7 | |
| 12 months ago | 6 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | Apache License 2.0 |
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book
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[P] Using Sparsity & Clustering to compress your models: Efficient Deep Learning Book
We now have a new chapter focusing on sparsity and clustering, two advanced compression techniques that you can use to reduce the footprint of your model (size, latency, etc.) while retaining your model accuracy. You can read the chapter here, and go through the accompanying codelabs here.
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[P] Efficient Deep Learning Book
The goal is to introduce these ideas in a single place, without having to parse many papers, try to get a working code sample, and then spend time debugging. With the accompanying codelabs, we hope that our readers can make their models 4-20x smaller, faster, and better in quality.
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?
DeepLearningExamples - State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
JupyterLab - JupyterLab computational environment.
rikai - Parquet-based ML data format optimized for working with unstructured data
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
keytotext - Keywords to Sentences
self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
Code-Server - VS Code in the browser
cocalc-docker - DEPRECATED (was -- Docker setup for running CoCalc as downloadable software on your own computer)
pycaret - An open-source, low-code machine learning library in Python
RStudio Server - RStudio is an integrated development environment (IDE) for R
Vue Storefront - Alokai is a Frontend as a Service solution that simplifies composable commerce. It connects all the technologies needed to build and deploy fast & scalable ecommerce frontends. It guides merchants to deliver exceptional customer experiences quickly and easily.
vscode-neovim - Vim mode for VSCode, powered by Neovim