The Python Research Framework.

Research code has some of the fastest shifting requirements of any type of code. It's nearly impossible to plan ahead of time the proper abstractions, because it is exceedingly likely that in the course of the project what you originally thought was your main focus suddenly no longer is. Further, research code (especially in ML) often involves big and complicated pipelines, typically involving many different machines, which are either run by hand or using shell scripts that are far more complicated than any shell script ever should be.

Therefore, the objective of pyfra is to make it as fast and low-friction as possible to write research code involving complex pipelines over many machines. This entails making it as easy as possible to implement a research idea in reality, at the cost of fine-grained control and the long-term maintainability of the system. In other words, pyfra expects that code will either be rapidly obsoleted by newer code, or rewritten using some other framework once it is no longer a research project and requirements have settled down.

Pyfra is in its very early stages of development. The interface may change rapidly and without warning.

Current features:

• Spin up an internal webserver complete with a permissions system using only a few lines of code
• Extremely elegant shell integration—run commands on any server seamlessly. All the best parts of bash and python combined
• (Coming soon) Tools for painless functional programming in python
• (Coming soon) Automated remote environment setup, so you never have to worry about provisioning machines by hand again
• (Coming soon) High level API for experiment management/scheduling and resource provisioning

from pyfra import *

loc = Remote()
rem = Remote("user@example.com")
nas = Remote("user@example2.com")

@page("Run experiment", dropdowns={'server': ['local', 'remote']})
def run_experiment(server: str, config_file: str, some_numerical_value: int, some_checkbox: bool):
    r = loc if server == 'local' else rem

    r.sh("git clone https://github.com/EleutherAI/gpt-neox")
    
    # rsync as a function can do local-local, local-remote, and remote-remote
    rsync(config_file, r.file("gpt-neox/configs/my-config.yml"))
    rsync(nas.file('some_data_file'), r.file('gpt-neox/data/whatever'))
    
    return r.sh('cd gpt-neox; python3 main.py')

@page("Write example file and copy")
def example():
    rem.fwrite("testing.txt", "hello world")
    
    # tlocal files can be specified as just a string
    rsync(rem.file('testing123.txt'), 'test1.txt')
    rsync(rem.file('testing123.txt'), loc.file('test2.txt'))

    loc.sh('cat test1.txt')
    
    assert fread('test1.txt') == fread('test2.txt')
    
    # fread, fwrite, etc can take a `rem.file` instead of a string filename.
    # you can also use all *read and *write functions directly on the remote too.
    assert fread('test1.txt') == fread(rem.file('testing123.txt'))
    assert fread('test1.txt') == rem.fread('testing123.txt')

    # ls as a function returns a list of files (with absolute paths) on the selected remote.
    # the returned value is displayed on the webpage.
    return '\n'.join(rem.ls('/'))

@page("List files in some directory")
def list_files(directory):
    return sh(f"ls -la {directory | quote}")


# start internal webserver
webserver()

pip3 install git+https://github.com/EleutherAI/pyfra/

The version of PyPI is not up to date, do not use it.