arena
ML-Workspace
| arena | ML-Workspace | |
|---|---|---|
| 1 | 7 | |
| 709 | 3,329 | |
| 1.8% | 0.6% | |
| 8.3 | 2.7 | |
| 5 days ago | 6 months ago | |
| Go | 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.
arena
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Volcano vs Yunikorn vs Knative
tldr; you should start with KubeFlow 99% of the time. The respective job scheduling workflows (including volano) can be managed with Kubeflow Arena. Vulcano is ok, but I personally prefer Nvidia's Merlin + Triton inference on top of ONNX and MS ONNX Runtime. I do like to train with GPU's on Merlin in GKE (TabularNV and HugeCTR's tbe), and run TFKeras ReLu models on CPU's with OpenVino on AWS EKS, to optimize costs a bit. I do use Kubeflow on top of TektonCD for OpenShift, while some folks do prefer Argo Workflows and Apache Airflow, in the end - it's all DAG pipelines, so doesn't really matter.
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?
determined - Determined is an open-source machine learning platform that simplifies distributed training, hyperparameter tuning, experiment tracking, and resource management. Works with PyTorch and TensorFlow.
JupyterLab - JupyterLab computational environment.
kube-batch - A batch scheduler of kubernetes for high performance workload, e.g. AI/ML, BigData, HPC
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
cromwell - Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
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.