blackjack-basic-strategy
k0s
blackjack-basic-strategy | k0s | |
---|---|---|
23 | 32 | |
26 | 2,775 | |
- | 5.3% | |
2.0 | 9.8 | |
about 1 year ago | 3 days ago | |
JavaScript | Go | |
MIT License | GNU General Public License v3.0 or later |
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.
blackjack-basic-strategy
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Show HN: Pip install inference, open source computer vision deployment
It’s an easy to use inference server for computer vision models.
The end result is a Docker container that serves a standardized API as a microservice that your application uses to get predictions from computer vision models (though there is also a native Python interface).
It’s backed by a bunch of component pieces:
* a server (so you don’t have to reimplement things like image processing & prediction visualization on every project)
* standardized APIs for computer vision tasks (so switching out the model weights and architecture can be done independently of your application code)
* model architecture implementations (which implement the tensor parsing glue between images & predictions) for supervised models that you've fine-tuned to perform custom tasks
* foundation model implementations (like CLIP & SAM) that tend to chain well with fine-tuned models
* reusable utils to make adding support for new models easier
* a model registry (so your code can be independent from your model weights & you don't have to re-build and re-deploy every time you want to iterate on your model weights)
* data management integrations (so you can collect more images of edge cases to improve your dataset & model the more it sees in the wild)
* ecosystem (there are tens of thousands of fine-tuned models shared by users that you can use off the shelf via Roboflow Universe[1])
Additionally, since it's focused specifically on computer vision, it has specific CV-focused features (like direct camera stream input) and makes some different tradeoffs than other more general ML solutions (namely, optimized for small-fast models that run at the edge & need support for running on many different devices like NVIDIA Jetsons and Raspberry Pis in addition to beefy cloud servers).
[1] https://universe.roboflow.com
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Open discussion and useful links people trying to do Object Detection
* Most of the time I find Roboflow extremely handy, I used it to merge datasets, augmentate, read tutorials and that kind of thing. Basically you just create your dataset with roboflow and focus on other aspects.
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TensorFlow Datasets (TFDS): a collection of ready-to-use datasets
For computer vision, there are 100k+ open source classification, object detection, and segmentation datasets available on Roboflow Universe: https://universe.roboflow.com
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Please suggest resources to learn how to work with pre-trained CV models
Solid website and app overall for learning more about computer vision, discovering datasets, and keeping up with advancements in the field: * https://roboflow.com/learn * https://universe.roboflow.com (datasets) | https://blog.roboflow.com/computer-vision-datasets-and-apis/ * https://blog.roboflow.com
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Suggestion for identification problem with shipping labels?
If you're lacking training images, you can also use [Roboflow Universe](https://universe.roboflow.com) to obtain them (over 100 million labeled images available)
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Ask HN: Who is hiring? (November 2022)
Roboflow | Multiple Roles | Full-time (Remote) | https://roboflow.com/careers
Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.
Over 100k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. And we now host the largest collection[2] of open source computer vision datasets and pre-trained models[3].
We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. (We especially love hiring past and future founders.)
We're hiring 3 full-stack engineers this quarter and we're also looking for an infrastructure engineer with Elasticsearch experience.
[1]: https://docs.roboflow.com
[2]: https://blog.roboflow.com/computer-vision-datasets-and-apis/
[3]: https://universe.roboflow.com
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When annotating an image, if a collection of an entity changes the nature of the entity, do you label them collectively or separately?
Based on what I do/use when I prepare models: A good framework for creating and improving this dataset faster is to use Roboflow Universe and search “flowers” and “bouquets of flowers” in the search bar (it’s like Google Images for CV Datasets). You can search images by subject, or metadata, and clone them directly into a free public workspace (they house up to 10k images without charge). * https://universe.roboflow.com/ * https://universe.roboflow.com/search?q=flowers * https://universe.roboflow.com/search?q=bouqets
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Need help on finding an area where machine learning is applicable on day-to-day life but not implemented already
Lots of ideas will come to mind if you look and search through open source datasets: https://universe.roboflow.com/
- Ask HN: Any good self-hosted image recognition software?
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SAAS for object detection?
Open source datasets: https://universe.roboflow.com/ Model training: https://docs.roboflow.com/train Model deployment: https://docs.roboflow.com/inference/hosted-api
k0s
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Seeking Guidance for Transitioning to Kubernetes and SRE/DevOps for traditional infrastructure team
I am myself studying it and going through the official documentation and toying with k8s flavors like kind, k3s and k0s.
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I was so excited to join this community
There's a whole community of hobbyists building Raspberry Pi clusters, porting things to work on various Arm processors, exploring and contributing to minimalist distros like k0s and microk8s, etc.
- Blog: KWOK: Kubernetes WithOut Kubelet
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KWOK : mettre en place un cluster de milliers de nœuds en quelques secondes …
root@localhost:~# curl -sSLf https://get.k0s.sh | sudo sh Downloading k0s from URL: https://github.com/k0sproject/k0s/releases/download/v1.25.4+k0s.0/k0s-v1.25.4+k0s.0-amd64 k0s is now executable in /usr/local/bin root@localhost:~# k0s install controller --single root@localhost:~# k0s start root@localhost:~# k0s status Version: v1.25.4+k0s.0 Process ID: 1064 Role: controller Workloads: true SingleNode: true Kube-api probing successful: true Kube-api probing last error: root@localhost:~# k0s kubectl cluster-info Kubernetes control plane is running at https://localhost:6443 CoreDNS is running at https://localhost:6443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy To further debug and diagnose cluster problems, use 'kubectl cluster-info dump'. 443/TCP 97s root@localhost:~# k0s kubectl get nodes -o wide NAME STATUS ROLES AGE VERSION INTERNAL-IP EXTERNAL-IP OS-IMAGE KERNEL-VERSION CONTAINER-RUNTIME localhost Ready control-plane 100s v1.25.4+k0s 172.105.131.23 Ubuntu 22.04.1 LTS 5.15.0-47-generic containerd://1.6.9 root@localhost:~# curl -LO https://storage.googleapis.com/kubernetes-release/release/v1.25.4/bin/linux/amd64/kubectl && chmod +x kubectl && mv kubectl /usr/bin/ % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 42.9M 100 42.9M 0 0 75.2M 0 --:--:-- --:--:-- --:--:-- 75.3M root@localhost:~# k0s kubeconfig admin > ~/.kube/config root@localhost:~# type kubectl kubectl is hashed (/usr/bin/kubectl) root@localhost:~# kubectl get po,svc -A NAMESPACE NAME READY STATUS RESTARTS AGE kube-system pod/kube-proxy-clxh7 1/1 Running 0 3m56s kube-system pod/kube-router-88x25 1/1 Running 0 3m56s kube-system pod/coredns-5d5b5b96f9-4xzsl 1/1 Running 0 4m3s kube-system pod/metrics-server-69d9d66ff8-fxrt7 1/1 Running 0 4m2s NAMESPACE NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE default service/kubernetes ClusterIP 10.96.0.1 443/TCP 4m20s kube-system service/kube-dns ClusterIP 10.96.0.10 53/UDP,53/TCP,9153/TCP 4m8s kube-system service/metrics-server ClusterIP 10.98.18.100 443/TCP 4m2s
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vcluster as a Service
I use k0s btw ,and it is fantastic.
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Any Kubernetes provider you could recommend me?
Here is link number 1 - Previous text "k0s"
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Some thoughts on cert-manager moving from Bazel to Make
So for example, in my own personal infra repos and for projects I do, Make orchestrates Pulumi, dnscontrol (Holy shit is that tool underrated), ansible, k0s/k0sctl (I run that distro), and all the kubernetes stuff.
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Is the Synology NAS able to run a Kubernetes Cluster ?
I wasn’t able to run Kubernetes in NAS last time I tried it. https://github.com/k0sproject/k0s/issues/1184. As for public access you don’t want to do it for security reasons and instead rely on vpn. Tailscale and ZeroTier are easy to setup.
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Kubernetes at Home With K3s
I prefer k0s, https://k0sproject.io/ .
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Cloudflare Uses HashiCorp Nomad
actually that is not really true - i strongly urge you to try out http://k3s.io/ or https://k0sproject.io/
these are full-fledged, certified k8s distributions that run on raspberry pi as well as all the way in production.
https://www.youtube.com/results?search_query=raspberry+pi+k3...
What are some alternatives?
uxp-photoshop-plugin-samples - UXP Plugin samples for Photoshop 22 and higher.
k3s - Lightweight Kubernetes
wallet - The official repository for the Valora mobile cryptocurrency wallet.
k3d - Little helper to run CNCF's k3s in Docker
process-google-dataset - Process Google Dataset is a tool to download and process images for neural networks from a Google Image Search using a Chrome extension and a simple Python code.
microk8s - MicroK8s is a small, fast, single-package Kubernetes for datacenters and the edge.
rollup-react-example - An example React application using Rollup with ES modules, dynamic imports, Service Workers, and Flow.
kind - Kubernetes IN Docker - local clusters for testing Kubernetes
edenai-javascript - The best AI engines in one API: vision, text, speech, translation, OCR, machine learning, etc. SDK and examples for JavaScript developers.
Gravitational Teleport - The easiest, and most secure way to access and protect all of your infrastructure.
Speed-Coding-Games-in-JavaScript - Games Repository from Speed Coding channel
istio - Connect, secure, control, and observe services.