hydra
duckdb
hydra | duckdb | |
---|---|---|
14 | 52 | |
8,229 | 16,749 | |
1.6% | 4.5% | |
6.3 | 10.0 | |
22 days ago | 6 days ago | |
Python | C++ | |
MIT License | MIT License |
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hydra
- Hydra – a Framework for configuring complex applications
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Show HN: Hydra - Open-Source Columnar Postgres
Nice tool, only unfortunate name, consider changing it. Already very well know security tool named hydra https://github.com/vanhauser-thc/thc-hydra been around since 2001. Then facebook went ahead and named their config tool hydra https://github.com/facebookresearch/hydra on top of it. Like we get it, hydra popular mythology but we could use more original naming for tools
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Show HN: Hydra 1.0 – open-source column-oriented Postgres
This looks really impressive, and I'm excited to see how it performs on our data!
P.S., I think the name conflicts with Hydra, the configuration management library: https://hydra.cc/
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Best practice for saving logits/activation values of model in PyTorch Lightning
I've been trying to learn PyTorch Lightning and Hydra in order to use/create my own custom deep learning template (e.g. like this) as it would greatly help with my research workflow. A lot of the work I do requires me to analyse metrics based on the logits/activations of the model.
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[D] Alternatives to fb Hydra?
However, hydra seems to have several limitations that are really annoying and are making me reconsider my choice. Most problematic is the inability to group parameters together in a multirun. Hydra only supports trying all combinations of parameters, as described in https://github.com/facebookresearch/hydra/issues/1258, which does not seem to be a priority for hydra. Furthermore, hydras optuna optimizer implementation does not allow for early pruning of bad runs, which while not a deal breaker is definitely a nice to have feature.
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Show HN: Lightweight YAML Config CLI for Deep Learning Projects
Do you hate the fact that they don't let you return the config file: https://github.com/facebookresearch/hydra/issues/407
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Config management for deep learning
I kind of built this due to frustrations with Hydra. Hydra is an end to end framework, it locks you into a certain DL project format, it decides logging, model saving and a whole host of things. For example Hydra can do the same config file overwriting that I allow but you have to store the config file with the name config.yaml inside a specific folder. On top of that hydra doesn’t let you return the config file from the main function so you have to put all the major logic in the main function itself (link), the authors claim this is by design. I can find Hydra useful for a mature less experimental project. But in my robotics and ML research, I like being able to write code where I want and integrating it how I want, especially when debugging for which I think this package is useful. TLDR; If you just want the config file functionality use my package, if you want a complete DL project manager use Hydra. While hydra implements this config file functionality, it also adds a lot of restrictions to project structure that you might not like.
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The YAML Document from Hell
For managing configs of ML experiments (where each experiment can override a base config, and "variant" configs can further override the experiment config, etc), Hydra + Yaml + OmegaConf is really nice.
https://hydra.cc/
I admit I don't fully understand all the advanced options in Hydra, but the basic usage is already very useful. A nice guide is here:
https://florianwilhelm.info/2022/01/configuration_via_yaml_a...
- Hydra - namestitev in osnovna uporaba
- Hydra - namestitevt in osnovna uporaba
duckdb
- 🪄 DuckDB sql hack : get things SORTED w/ constraint CHECK
- DuckDB: Move to push-based execution model (2021)
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DuckDB performance improvements with the latest release
I'm not sure if the fix is reassuring or not: https://github.com/duckdb/duckdb/pull/9411/files
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Building a Distributed Data Warehouse Without Data Lakes
It's an interesting question!
The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.
It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.
[1] https://github.com/bacalhau-project/bacalhau
[2] https://github.com/duckdb/duckdb
- DuckDB 0.9.0
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Push or Pull, is this a question?
[4] Switch to Push-Based Execution Model by Mytherin · Pull Request #2393 · duckdb/duckdb (github.com)
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Show HN: Hydra 1.0 – open-source column-oriented Postgres
it depends on your query obviously.
In general, I did very deep benchmarking of pg, clickhouse and duckdb, and I sure didn't make stupid mistakes like this: https://news.ycombinator.com/item?id=36990831
My dataset has 50B rows and 2tb of data, and I think columnar dbs are very overhiped and I chose pg because:
- pg performance is acceptable, maybe 2-3x times slower than clickhouse and duckdb on some queries if pg is configured correctly and run on compressed storage
- clickhouse and duckdb start falling apart very fast because they specialized on very narrow type of queries: https://github.com/ClickHouse/ClickHouse/issues/47520 https://github.com/ClickHouse/ClickHouse/issues/47521 https://github.com/duckdb/duckdb/discussions/6696
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🦆 Effortless Data Quality w/duckdb on GitHub ♾️
This action installs duckdb with the version provided in input.
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Using SQL inside Python pipelines with Duckdb, Glaredb (and others?)
Duckdb: https://github.com/duckdb/duckdb - seems pretty popular, been keeping an eye on this for close to a year now.
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CSV or Parquet File Format
The Parquet-Go library is very complex, not yet success to use it. So I ask whether DuckDB can provide API https://github.com/duckdb/duckdb/issues/7776
What are some alternatives?
dynaconf - Configuration Management for Python ⚙
ClickHouse - ClickHouse® is a free analytics DBMS for big data
ConfigParser
sqlite-worker - A simple, and persistent, SQLite database for Web and Workers.
python-dotenv - Reads key-value pairs from a .env file and can set them as environment variables. It helps in developing applications following the 12-factor principles.
datasette - An open source multi-tool for exploring and publishing data
python-decouple - Strict separation of config from code.
octosql - OctoSQL is a query tool that allows you to join, analyse and transform data from multiple databases and file formats using SQL.
django-environ - Django-environ allows you to utilize 12factor inspired environment variables to configure your Django application.
metabase-clickhouse-driver - ClickHouse database driver for the Metabase business intelligence front-end
classyconf - Declarative and extensible library for configuration & code separation
datafusion - Apache DataFusion SQL Query Engine