datasciencecoursera
ML-Workspace
| datasciencecoursera | ML-Workspace | |
|---|---|---|
| 44 | 7 | |
| 2,196 | 3,324 | |
| - | 0.4% | |
| 0.0 | 2.7 | |
| about 1 year ago | 6 months ago | |
| HTML | Jupyter Notebook | |
| - | Apache License 2.0 |
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datasciencecoursera
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?
random-dose-of-knowledge - Using the latest Software Engineering practices to create a modern and simple app.
JupyterLab - JupyterLab computational environment.
data-science-interviews - Data science interview questions and answers
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
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
keytotext - Keywords to Sentences
Data-science-best-resources - Carefully curated resource links for data science in one place
self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
Code-Server - VS Code in the browser
lme4cens - Simple Mixed Effect Models and Censoring
cocalc-docker - DEPRECATED (was -- Docker setup for running CoCalc as downloadable software on your own computer)