hydra
python-dotenv
hydra | python-dotenv | |
---|---|---|
14 | 27 | |
8,229 | 7,129 | |
1.6% | - | |
6.3 | 5.7 | |
21 days ago | 5 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" 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
python-dotenv
- What are the best ways to prevent writing secrets in the code.
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Chat with GPT-4 Web App in only 80 lines of Python
I personally just use .env to keep api keys
- I create a library for managing configurations as mappings, supports .env by default
- Error - UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
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Elegantly Handle Environment Variables in Python with Pydantic
similar to dotenv, I like this object oriented approach though.
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pdm-dotenv: Simplify Your Project's Environment Variable Management
Are you working on a Python project that uses pdm for dependency management and dotenv for local environment variable and secrets management? Do you find it frustrating when CLI tools like pgcli don't automatically pick up your .env file, forcing you to resort to npm install -g dotenv-cli? I've got a more convenient solution for you!
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Don't know how to work with .gitignore, first time actively working on a public repo
Similarly, I use python-dotenv which reads environment variables, or variables set in a .env file. Then in your settings.py file you can do things like:
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Writing unit tests, constants, and source control (GIT) question!?
Locally you may want to use a .env file that is also in .gitignore and python-dotenv to auto activate them. Also having an .env.template is also a good idea to help others working on the project know what they need to set in order for things to work.
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Need help running Django at a local machine after deploying it
But there's no web UI to set them for your local dev version. But there are various Python modules that will read environment variables from a file named .env. I like https://github.com/theskumar/python-dotenv myself. So try using that - create a .env file for your local dev site and use dotenv instead of os.getenv() to read the environment variables.
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Build Simple CLI-Based Voice Assistant with PyAudio, Speech Recognition, pyttsx3 and SerpApi
python-dotenv
What are some alternatives?
dynaconf - Configuration Management for Python ⚙
python-decouple - Strict separation of config from code.
ConfigParser
django-environ - Django-environ allows you to utilize 12factor inspired environment variables to configure your Django application.
django-dotenv - Loads environment variables from .env
classyconf - Declarative and extensible library for configuration & code separation
parse_it - A python library for parsing multiple types of config files, envvars & command line arguments that takes the headache out of setting app configurations.
environs - simplified environment variable parsing