determined VS Metabase

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.

Remote Code Execution via H2

We've used it for about a year - Blazer is okay if you need a quick SQL query console, but we found it lacking as an actual business intelligence tool. The support for graphs and dashboards is limited, for graphs it requires you to structure the query in an exact way as you can see in the Blazer readme.

After some research on available alternatives that don't break the bank, we decided to deploy a self-hosted instance of Metabase[0]. This took only a few minutes to set up using their Docker image[1] and it has much better graphing capabilities and you can easily put a custom layout together for dashboards. Upgrading is similarly easy (just redeploy). Also easy to configure: data sources, hiding or changing the data type of a column, G Suite sign-in for our domain. Highly recommend it if you need anything more than Blazer's table output.

[0]: https://github.com/metabase/metabase

I've never used Tableau, but heard a lot of hate about it. However, in my previous role, we were big fans of Metabase (https://metabase.com). You can also self-host it, which was a huge win for us.

It seems folks want a working example. Here's one in prod:

Metabase is a BI tool, backend written mostly in Clojure. Like basically all BI tools they have this intermediate representation language thing so you write the same thing in "MBQL (metabase query language)" and it theoretically becomes same query in like, Postgres and Mongo and whatever. End user does not usually write MBQL, it's a service for the frontend querybuilding UI thing and lots of other frontend UI stuff mainly in usage.

Whole processing from MBQL -> your SQL or whatever is done via a buncha big-ass transducers. Metabase is not materially faster than other BI tools (because all the other BI tools do something vaguely similar in their langs) but it's pretty comparable speed and the whole thing was materially written by like 5 peeps

https://github.com/metabase/metabase/blob/master/src/metabas...

(nb: I used to work for Metabase but currently do not. but open core is open core)

They haven't released the source, and the compiled versions are non-trivial to diff (e.g. there are nondeterministic numbers from the clojure compiler that seem to have changed from one to the other, and .clj files have been removed from the jar).

The old version has `hash=1bb88f5`, which is a public commit: https://github.com/metabase/metabase/commit/1bb88f5

We are unsure about the right license to use, so this is a great feedback. We had a MIT license one week ago that we know that we cannot hold on long term and we felt we were lying to the community by keeping an MIT license and changing it in one year.

By using AGPL, we feel it's the right level of restriction. It's the license used by Metabase for example (https://github.com/metabase/metabase) that many companies use internally.

The solution really depends on what sort of problems you are trying to solve and who your customers are.

There are a fair few low-code solutions out there for reporting and data visualisation that are great for finance and marketing teams for example. e.g. https://metabase.com/ , https://evidence.dev/

For multipurpose SMB workflows and organisational processes, I have used n8n in the recent past and found it was quite good and incredibly easy to maintain. https://n8n.io/engineering-resources/

For enterprise processes I'd go with Camunda (solely based on recommendations and not first hand experience). Although only parts of their platform are OSS https://github.com/camunda

Bear in mind that some of these are not suitable if you want to build something that competes with them while taking their OSS code. But are perfectly fine otherwise.

Anyway, I have a collection of scripts that merge ActivityWatch data from all my machines and WakaTime exports to a PostgreSQL database which I then query with a project called Metabase. If you're curious, the scripts are in a repository called sqrt-data. I've been playing with this for ~4-5 years already I think.

Metabase | https://metabase.com | REMOTE | Full-time | Backend, Frontend, Full Stack, and DevOps engineers

Metabase is open source analytics software that lets anyone in your company rummage around in the databases you have. It connects to a number of databases / data warehouses (BigQuery, Redshift, Snowflake, Postgres, MySQL, etc).