cfn-diagram
determined
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cfn-diagram | determined | |
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6 | 10 | |
60 | 2,861 | |
- | 3.8% | |
5.1 | 9.9 | |
about 1 month ago | 3 days ago | |
JavaScript | Go | |
- | 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.
cfn-diagram
- AWS Architecture Diagram tool recommendations
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Create AWS Architectures diagram-based
What do you think, is it worth writing a patch for cfn-diagrams to create these links automatically?
- Looking for experience & recommendations of visualisation tools
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One week at globaldatanet as AWS Cloud Engineer - David Edition
{ "week": [ { "Id": "16", "AWS-Services": [ "AWS CloudFormation", "AWS CloudTrail", "AWS Config", "Amazon CloudFront", "AWS Cost Explorer", "AWS Lambda", "AWS Key Management Service", "Amazon Athena" ], "Languages": [ "Python", "nodejs", ], "Tools": [ { "name": "ORGTOOL", "github": "https://github.com/daknhh/aws-orgtool" }, { "name": "cfn-python-lint", "github": "https://github.com/aws-cloudformation/cfn-python-lint" }, { "name": "cfn-diagram", "github": "https://github.com/mhlabs/cfn-diagram" }, { "name": "Taskfile", "github": "https://github.com/Wildhoney/Taskfile" } ], } ] }
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AWS open source news and updates No.41
cfn-diagram how many times have you wished you could take your CloudFormation YAML or JSON and then visualise it? When this open source tool provides a CLI as well as integration into tools like VSCode that help visualise CloudFormation templates as draw.io diagrams. Very nice Lars Jacobsson and the MatHem tech team. There is also the experimental cfn-diagram-ci which you can have a look at as well.
determined
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Open Source Advent Fun Wraps Up!
17. Determined AI | Github | tutorial
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ML Experiments Management with Git
Use Determined if you want a nice UI https://github.com/determined-ai/determined#readme
- Determined: Deep Learning Training Platform
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Queueing/Resource Management Solutions for Self Hosted Workstation?
I looked up and found [Determined Platform](determined.ai), tho it looks a very young project that I don't know if it's reliable enough.
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Ask HN: Who is hiring? (June 2022)
- Developer Support Engineer (~1/3 client facing, triaging feature requests and bug reports, etc; 2/3 debugging/troubleshooting)
We are developing enterprise grade artificial intelligence products/services for AI engineering teams and fortune 500 companies and need more software devs to fill the increasing demand.
Find out more at https://determined.ai/. If AI piques your curiosity or you want to interface with highly skilled engineers in the community, apply within (search "determined ai" at careers.hpe.com and drop me a message at asnell AT hpe PERIOD com).
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How to train large deep learning models as a startup
Check out Determined https://github.com/determined-ai/determined to help manage this kind of work at scale: Determined leverages Horovod under the hood, automatically manages cloud resources and can get you up on spot instances, T4's, etc. and will work on your local cluster as well. Gives you additional features like experiment management, scheduling, profiling, model registry, advanced hyperparameter tuning, etc.
Full disclosure: I'm a founder of the project.
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[D] managing compute for long running ML training jobs
These are some of the problems we are trying to solve with the Determined training platform. Determined can be run with or without k8s - the k8s version inherits some of the scheduling problems of k8s, but the non-k8s version uses a custom gang scheduler designed for large scale ML training. Determined offers a priority scheduler that allows smaller jobs to run while being able to schedule a large distributed job whenever you need, by setting a higher priority.
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Cerebras’ New Monster AI Chip Adds 1.4T Transistors
Ah I see - I think we're pretty much on the same page in terms of timetables. Although if you include TPU, I think it's fair to say that custom accelerators are already a moderate success.
Updated my profile. I've been working on DL training platforms and distributed training benchmarking for a bit so I've gotten a nice view into the GPU/TPU battle.
Shameless plug: you should check out the open-source training platform we are building, Determined[1]. One of the goals is to take our hard-earned expertise on training infrastructure and build a tool where people don't need to have that infrastructure expertise. We don't support TPUs, partially because a lack of demand/TPU availability, and partially because our PyTorch TPU experiments were so unimpressive.
[1] GH: https://github.com/determined-ai/determined, Slack: https://join.slack.com/t/determined-community/shared_invite/...
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[D] Software stack to replicate Azure ML / Google Auto ML on premise
Take a look at Determined https://github.com/determined-ai/determined
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AWS open source news and updates No.41
determined is an open-source deep learning training platform that makes building models fast and easy. This project provides a CloudFormation template to bootstrap you into AWS and then has a number of tutorials covering how to manage your data, train and then deploy inference endpoints. If you are looking to explore more open source machine learning projects, then check this one out.
What are some alternatives?
cdk-appsync-project - Projen managed AppSync Transformer project
ColossalAI - Making large AI models cheaper, faster and more accessible
aws-sdk-js-v3 - Modularized AWS SDK for JavaScript.
Dagger.jl - A framework for out-of-core and parallel execution
cdk-dia - Automated diagrams of CDK provisioned infrastructure
aws-virtual-gpu-device-plugin - AWS virtual gpu device plugin provides capability to use smaller virtual gpus for your machine learning inference workloads
Taskfile - :package: Yet another attempt at a simple task runner for npm with parallelisation support using bash commands via YAML.
goofys - a high-performance, POSIX-ish Amazon S3 file system written in Go
booster - Software development framework specialized in building highly scalable microservices with CQRS and Event-Sourcing. It uses the semantics of the code to build a fully working GraphQL API that supports real-time subscriptions.
alpa - Training and serving large-scale neural networks with auto parallelization.
cfn-python-lint - CloudFormation Linter
Prefect - The easiest way to build, run, and monitor data pipelines at scale.