Fast-Kubeflow
kserve
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Fast-Kubeflow | kserve | |
---|---|---|
7 | 3 | |
69 | 3,047 | |
- | 7.3% | |
3.6 | 9.4 | |
2 months ago | 4 days ago | |
Python | Python | |
- | Apache License 2.0 |
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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.
Fast-Kubeflow
kserve
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Show HN: Software for Remote GPU-over-IP
Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.
Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.
So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.
Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.
Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.
[0] - https://github.com/kserve/kserve
[1] - https://github.com/triton-inference-server/server
[2] - https://github.com/triton-inference-server/client
[3] - https://github.com/triton-inference-server/model_analyzer
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Run your first Kubeflow pipeline
Kubeflow has multiple components: central dashboard, Kubeflow Notebooks to manage Jupyter notebooks, Kubeflow Pipelines for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers, KF Serving for model serving (apparently superseded by KServe), Katib for hyperparameter tuning and model search, and training operators such as TFJob for training TF models on Kubernetes.
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[D] Serverless solutions for GPU inference (if there's such a thing)
If you can run on Kubernetes then KFServing is an open source solution that allows for GPU inference and is built upon Knative to allow scale to zero for GPU based inference. From release 0.5 it also has capabilities for multi-model serving as a alpha feature to allow multiple models to share the same server (and via NVIDIA Triton the same GPU).
What are some alternatives?
Fast-Docker - This repo covers containerization and Docker Environment: Docker File, Image, Container, Commands, Volumes, Networks, Swarm, Stack, Service, possible scenarios.
kubeflow - Machine Learning Toolkit for Kubernetes
Fast-Kubernetes - This repo covers Kubernetes with LABs: Kubectl, Pod, Deployment, Service, PV, PVC, Rollout, Multicontainer, Daemonset, Taint-Toleration, Job, Ingress, Kubeadm, Helm, etc.
aws-virtual-gpu-device-plugin - AWS virtual gpu device plugin provides capability to use smaller virtual gpus for your machine learning inference workloads
awesome-mlops - A curated list of references for MLOps
kind - Kubernetes IN Docker - local clusters for testing Kubernetes
kubeflow-learn
Python-Schema-Matching - A python tool using XGboost and sentence-transformers to perform schema matching task on tables.
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
Yatai - Model Deployment at Scale on Kubernetes 🦄️
MindsDB - The platform for customizing AI from enterprise data
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)