determined VS PostHog

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.

It would be a shame not to mention PostHog as the telemetry provider we are using, since it turned out to be extremely useful. Because it is hard to find people who will talk with you about your product, gathering statistics gave us a much greater insight into our users.

6- PostHog

Hi HN, PostHog employee here. I'm working on our Web Analytics product, which is currently in beta. It's fun to see us mentioned here :)

I should mention that we have a ton of SDKs (see https://posthog.com/docs/libraries) for back end frameworks and languages, so if you wanted to use PostHog without any client-side JS you could send pageviews and other events manually, but for the vast majority of people it makes more sense to use our JS snippet.

Hijacking this comment to share the roadmap for web analytics https://github.com/PostHog/posthog/issues/18547. It's very much in the launch-early-and-be-embarassed phase, but I would love to hear any feedback or suggestions that people have, particularly if you're already a PostHog user.

how's this different than https://posthog.com/ ?

PostHog - Open Source Alternative to Mixpanel

Don't insert the logs/events/analytics into your Application DB. Usually, you send those to specialist datastores (OLAP etc) that process such high volume of data. You can use something like clickhouse [0] for example or use 3rd party SAAS solutions like posthog [1] etc that are built on top of clickhouse

[0] https://clickhouse.com

[1] https://posthog.com

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

PostHog | Remote (US/Europe timezones) | Full stack engineer, technical ex-founder, tech lead | https://posthog.com

PostHog is the only open-source Product OS, combining product analytics, session recordings, feature flags, cdp and a data warehouse in one.

We have a culture of written async communication (see our handbook [0]), lots of individual responsibility and an opportunity to make a huge impact. Being fully remote means we're able to create a team that is truly diverse. We're based all over the world, and the team includes former YC founders, CTOs turned developers and recent grads.

To apply see https://posthog.com/careers or email us [email protected]

[0] https://posthog.com/handbook/

There seems to be posthog.com analytics and AB or feature flag functionality that is blocked by adblockers. Probably that?