determined VS cfn-diagram

17. Determined AI | Github | tutorial

Use Determined if you want a nice UI https://github.com/determined-ai/determined#readme

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.

- 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).

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.

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.

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/...

Take a look at Determined https://github.com/determined-ai/determined

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 do you think, is it worth writing a patch for cfn-diagrams to create these links automatically?

{ "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" } ], } ] }

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.