skyhookml
onepanel
| skyhookml | onepanel | |
|---|---|---|
| 1 | 4 | |
| 8 | 696 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| almost 3 years ago | about 1 year ago | |
| Go | Go | |
| MIT License | 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.
skyhookml
-
SkyhookML: Easy-to-use Web Platform for Computer Vision
But if you have a chance to try it, I'd love to get some feedback on the system! SkyhookML requires a Linux machine with NVIDIA GPU, and the easiest way to deploy it is via an all-in-one Docker container.
onepanel
- Onepanel - open source machine learning IDE that you can deploy in any cloud or on-premises
- Onepanel - open source alternative to AWS SageMaker you can run on any cloud or on-premises
- Onepanel – Cloud-native deep learning platform
-
[P] Onepanel - latest open source release now includes browser accessible deep learning desktop, hyperparameter tuning and Python DSL for defining parallel data processing or training pipelines.
GitHub repository: https://github.com/onepanelio/onepanel Documentation: https://docs.onepanel.ai
What are some alternatives?
caire - Content aware image resize library
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
fake-news - Building a fake news detector from initial ideation to model deployment
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
netron - Visualizer for neural network, deep learning and machine learning models
MetaSpore - A unified end-to-end machine intelligence platform
kubernetes-operator-roiergasias - 'Roiergasias' kubernetes operator is meant to address a fundamental requirement of any data science / machine learning project running their pipelines on Kubernetes - which is to quickly provision a declarative data pipeline (on demand) for their various project needs using simple kubectl commands. Basically, implementing the concept of No Ops. The fundamental principle is to utilise best of docker, kubernetes and programming language features to run a workflow with minimal workflow definition syntax. It is a Go based workflow running on command line or Kubernetes with the help of a custom operator for a quick and automated data pipeline for your machine learning projects (a flavor of MLOps).
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
openmodelz - One-click machine learning deployment (LLM, text-to-image and so on) at scale on any cluster (GCP, AWS, Lambda labs, your home lab, or even a single machine).