determined
bicep
determined | bicep | |
---|---|---|
10 | 74 | |
2,868 | 3,123 | |
2.5% | 0.8% | |
9.9 | 0.0 | |
4 days ago | 6 days ago | |
Go | Bicep | |
Apache License 2.0 | MIT License |
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.
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.
bicep
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The issue of recursive module calls in declarative infrastructure-as-code
I thought it was a good idea, but Bicep did not agree. I have submitted a proposal to the Bicep team for how this can be allowed. Vote for this issue if you agree!
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Rethinking Infrastructure as Code from Scratch
Bicep has limitations which makes it non-declarative even though it is marketed as declarative: https://learn.microsoft.com/en-us/azure/azure-resource-manag...
MSFT is trying to add features to make this better, but it is not in production yet: https://github.com/Azure/bicep/issues/10460
Additionally, Bicep does not support interacting with Azure Active Directory: https://github.com/Azure/bicep/issues/7724
So it really is not very useful. Terraform is better in almost every single conceivable way.
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Need an advice between Azure Bicep and Terraform.
Github: https://github.com/Azure/bicep/issues/9569
- Is Bicep built on top of ARM or not?
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Create your first Azure Bicep Template
Since its launch Bicep has become popular within the IT community. You can find blog posts, tweets, conference sessions, and plenty of interaction on the official Bicep GitHub space. Bicep became production ready at v0.3. It is supported by Microsoft Support Plans.
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How do you all start developing your arm template
Please give Azure Bicep a try. You get a really simple experience with all the benefits of using the platform native capabilities https://github.com/Azure/bicep
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DevOps ARM to Bicep Migration - Parameter Files
I was on the bicep call last month but that doesn't mean I didn't miss the announcement, it looks like they are getting close though - https://github.com/Azure/bicep/issues/8598
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Bicep Extension Finally Arrives in Visual Studio! Here's What You Need to Know
Bicep, the open source project used by Visual Studio Code to extend its capabilities, has finally arrived in Visual Studio, enabling users of Microsoft’s flagship IDE to use some of Bicep’s most popular features in the same program they have been using since they were introduced to it — in other words, Visual Studio itself.
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How to pass Bicep outputs between YAML steps
In addition, check the similar issue on GitHub.
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Bicep code design best practice - input very much appreciated!
There is an ongoing thread here https://github.com/Azure/bicep/issues/1853
What are some alternatives?
ColossalAI - Making large AI models cheaper, faster and more accessible
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
Dagger.jl - A framework for out-of-core and parallel execution
Pester - Pester is the ubiquitous test and mock framework for PowerShell.
aws-virtual-gpu-device-plugin - AWS virtual gpu device plugin provides capability to use smaller virtual gpus for your machine learning inference workloads
azure-cli - Azure Command-Line Interface
cfn-diagram - CLI tool to visualise CloudFormation/SAM/CDK stacks as visjs networks, draw.io or ascii-art diagrams.
azure-quickstart-templates - Azure Quickstart Templates
goofys - a high-performance, POSIX-ish Amazon S3 file system written in Go
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
alpa - Training and serving large-scale neural networks with auto parallelization.
infracost - Cloud cost estimates for Terraform in pull requests💰📉 Shift FinOps Left!