onepanel
polyaxon
Our great sponsors
onepanel | polyaxon | |
---|---|---|
4 | 9 | |
696 | 3,476 | |
0.0% | 0.7% | |
0.0 | 8.8 | |
about 1 year ago | 6 days ago | |
Go | Python | |
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.
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 β Cloud-native deep learning platform
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[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
polyaxon
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Any MLOps platform you use?
If you're not concerned about self-hosting, WandB is one of the more fully featured training monitoring tools (I've used it in the past without any issues but the lack of data and training privacy and lack of self-hosting possibilities makes it a hard no for anything that isn't scholastic). Polyaxon is an alternative but rewriting all your variable logging to conform to their requirements makes it very difficult to switch to it in the middle of a project so you have to commit to it from the get-go.
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[D] Kubernetes for ML - how are y'all doing it?
We use Polyaxon and itβs pretty good
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Polyaxon.
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Does anyone have experience with polyaxon?
I just came across https://github.com/polyaxon/polyaxon because mlflow gives me a hard time and costs my company money by the day because it is not working as expected.
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[D] Productionalizing machine learning pipelines for small teams
For running experiments, http://polyaxon.com/ is a really good free open-source package that has lots of nice integrations so you can quickly run experiments in k8s but it might be overkill in some cases.
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Top 5 tools to get started with MLOps !
Polyaxon : https://polyaxon.com
- Open source alternative to AWS Sagemaker, Google AI Platform, and Azure ML
What are some alternatives?
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
MLflow - Open source platform for the machine learning lifecycle
fake-news - Building a fake news detector from initial ideation to model deployment
kubeflow - Machine Learning Toolkit for Kubernetes
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
netron - Visualizer for neural network, deep learning and machine learning models
dvc - π¦ ML Experiments and Data Management with Git
MetaSpore - A unified end-to-end machine intelligence platform
neptune-client - π The MLOps stack component for experiment tracking
skyhookml - SkyhookML is an easy-to-use web platform for computer vision.
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]