determined VS leapp

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.

Summary:

Do you find yourself overwhelmed with work, requests, or complaints and in need of assistance to alleviate the pressure, enhance communication, facilitate organization, prioritize tasks, and foster greater trust and transparency?

Alternatively, I can work as a full stack developer.

AWS Community builder, AWS User group Leader, public speaker (https://www.youtube.com/watch?v=qdu58NAQfU0&t=271s)

Or perhaps you need both? =)

I have 4+ years of experience as a product manager and 8 in product development (before pm: agile coach, UX designer, and developer).

I've been the co-founder of the open-core company behind the OSS project Leapp (https://github.com/Noovolari/leapp)

Please feel free to reach out.

You can switch to saml2aws using the browser method instead of the Okta method and it will continue to work after the upgrade. There is also a really neat GUI tool to manage your session tokens that also works. https://www.leapp.cloud

Take a try to Leapp: https://github.com/Noovolari/leapp

Hello everyone, I'm the maintainer of an open-source DeveloperTool (https://github.com/Noovolari/leapp)

With a heuristic of 7000 users daily, I started feeling the need to have more information on how Users are using the project to improve it.

Is it the right thing to do to create a better Developer Experience and gain feedback for the end users?

On a side:

- For all your employees I can advice you Leapp as open-source project (https://github.com/Noovolari/leapp). It solve mayor of the problem listed here:

I am looking to manage Leapp (https://www.leapp.cloud/) from the StreamDeck. Leapp allows you to manage and switch between different Cloud Accounts (AWS, Azure, etc). Leapp has a command line interface which I could automate with a StreamDeck plugin. Unfortunately it looks like the only official SDK is the sandboxed JavaScript one. This means I cannot automate command line tools with it.

If you’re wondering if there is a tool that allows you to stop thinking about AWS credentials and where to store them in the right way, give a look at Leapp! It takes the responsibility of storing long-term credentials in the system vault, generating/refreshing short-term credentials, and placing them in the right place for the clients to use them.

Still, there is an elementary problem that we need to address, and it’s more on the operational side of things. Once we secured and implemented a tremendous multi-account strategy, how do people access AWS accounts? It turns out there is a fantastic open-source tool that lets you handle that with no effort, and its name is Leapp.

When you have to configure access to multiple AWS accounts using the Assume Role access pattern, it becomes difficult to get rid of all the Named Profiles configuration data and relationships. When you’ve to deal with a complex access scenario, tools like Leapp (https://www.leapp.cloud) come to the rescue! Leapp avoids you to specify relationships between Named Profiles in the config file, as the access methods are stored in the tool-specific configuration file.

Leapp Github repository