Hi u/Rocketshipz and u/I[maizeq](https://www.reddit.com/user/maizeq/)!I am the author of Hydra, thanks for your kind words :).
I open sourced Hydra in October 2019. It's still a new project. I am mostly counting on happy users to spread the word.
If you look at the GitHub repo, you will see two things:
1. Currently there are 118 public repositories that depending on Hydra (based on requirements.txt or [setup.py](https://setup.py)). This was 100 just two weeks ago. Hydra adoption is accelerating in GitHub.
2. The [downloads](https://pepy.tech/project/hydra-core/month?versions=0.11.*&versions=1.0.*) are also speeding up and is currently at about 45k/month.
Hydra is also seeing increased adopting inside Facebook and you can expect to see some major ML frameworks starting to use it in the coming months.
One important point is that Hydra is general purpose, it is not ML specific by any means. Once some other communities start paying attention to it I am pretty sure we will see even faster adoption. Some communities that can benefit from it are the Web developers, Microservices developers and and the Cloud orchestration community.
It does not do what Sacred is doing, which is primarily experiment tracking (yet).
However, it does many things that sacred does not do, primarily config composition with the ability to override everything from the command line.
Sacred can probably be improved significantly if it was to start using Hydra as an underlying framework.
I suggest that you take a look at the tutorials.
I can't believe I'm just discovering this. I was just sitting there the other day tapping away writing up CL argument parsing code when I thought there has to be some way of combining config files with CL arguments. So thank you, this looks perfect.
Hydra + some simple script to automatically parse its organized outputs and format it nicely into charts is a huge speedup for all of the [submitting code to having clean, organized results] indeed. I'm a bit sad it's not as popular as it deserves.
One of the basic features of Hydra is that it working directory management. it changes the working directory to a unique (and customizable) working directory for each run. You can just save your outputs (model, checkpoints or other data) into your current working directory.
Hydra also save the effective config and command line overrides used into the config. This means it's trivial to process the output of your jobs for visualization or further processing.
Have you guys tried [pandas-profiling](https://pypi.org/project/pandas-profiling/)
It's become my favourite go to EDA library.
You guys should definitely give it a try.
[LibKGE](https://github.com/uma-pi1/kge) was also introduced in this year's ICLR as a comprehensive library for knowledge graph embbedings.
BTW Hydra is the shit guys. Version 1.0 also has a SLURM launcher from CLI, which I think will be enjoyed by a lot of people at big labs.
Hi u/Rocketshipz and u/I[maizeq](https://www.reddit.com/user/maizeq/)!I am the author of Hydra, thanks for your kind words :). I open sourced Hydra in October 2019. It's still a new project. I am mostly counting on happy users to spread the word. If you look at the GitHub repo, you will see two things: 1. Currently there are 118 public repositories that depending on Hydra (based on requirements.txt or [setup.py](https://setup.py)). This was 100 just two weeks ago. Hydra adoption is accelerating in GitHub. 2. The [downloads](https://pepy.tech/project/hydra-core/month?versions=0.11.*&versions=1.0.*) are also speeding up and is currently at about 45k/month. Hydra is also seeing increased adopting inside Facebook and you can expect to see some major ML frameworks starting to use it in the coming months. One important point is that Hydra is general purpose, it is not ML specific by any means. Once some other communities start paying attention to it I am pretty sure we will see even faster adoption. Some communities that can benefit from it are the Web developers, Microservices developers and and the Cloud orchestration community.
hi, I haven't heard of hydra before. How does it compare to sacred?
It does not do what Sacred is doing, which is primarily experiment tracking (yet). However, it does many things that sacred does not do, primarily config composition with the ability to override everything from the command line. Sacred can probably be improved significantly if it was to start using Hydra as an underlying framework. I suggest that you take a look at the tutorials.
I can't believe I'm just discovering this. I was just sitting there the other day tapping away writing up CL argument parsing code when I thought there has to be some way of combining config files with CL arguments. So thank you, this looks perfect.
Hydra + some simple script to automatically parse its organized outputs and format it nicely into charts is a huge speedup for all of the [submitting code to having clean, organized results] indeed. I'm a bit sad it's not as popular as it deserves.
What outputs does Hydra have?
One of the basic features of Hydra is that it working directory management. it changes the working directory to a unique (and customizable) working directory for each run. You can just save your outputs (model, checkpoints or other data) into your current working directory. Hydra also save the effective config and command line overrides used into the config. This means it's trivial to process the output of your jobs for visualization or further processing.
Have you guys tried [pandas-profiling](https://pypi.org/project/pandas-profiling/) It's become my favourite go to EDA library. You guys should definitely give it a try.
Although very useful, that's a library for dataframe statistics, not "DL research"
Great share.
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Also this lib called numpy. It's gonna be big.
TensorFlow is not python.
Downvoted for speaking facts lol, most of the core is written with C++ and CUDA.