fret 0.3.1

Framework for Reproducible ExperimenTs.

fret

Framework for Reproducible ExperimenTs. Read on for a quick guide. Full documentation here.

Installation

From pip:

pip install fret

From source: clone the repository and then run: python setup.py install.

Tutorial

Basic Usage

Create a file named app.py with content:

import fret

@fret.command
def run(ws):
    model = ws.build()
    print(model)

@fret.configurable
class Model:
    def __init__(self, x=3, y=4):
        ...

Then under the same directory, you can run:

$ fret config Model
[ws/_default] configured "main" as "Model" with: x=3, y=4
$ fret run
Model(x=3, y=4)
$ fret config Model -x 5 -y 10
[ws/_default] configured "main" as "Model" with: x=5, y=10
$ fret run
Model(x=5, y=10)

Using Workspace

You can specify different configuration in different workspace:

$ fret -w ws/model1 config Model
[ws/model1] configured "main" as "Model" with: x=3, y=4
$ fret -w ws/model2 config Model -x 5 -y 10
[ws/model2] configured "main" as "Model" with: x=5, y=10
$ fret -w ws/model1 run
Model(x=3, y=4)
$ fret -w ws/model2 run
Model(x=5, y=10)

Save/Load

import fret

@fret.command
def train(ws):
    model = ws.build()
    model.train()
    ws.save(model, 'trained')

@fret.command
def test(ws):
    model = ws.load('ws/best/snapshot/main.trained.pt')
    print(model.weight)

@fret.configurable(states=['weight'])
class Model:
    def __init__(self):
        self.weight = 0
    def train(self):
        self.weight = 23

$ fret -w ws/best config Model
[ws/_default] configured "main" as "Model"
$ fret -w ws/best train
$ fret test
23

An Advanced Workflow

In app.py:

import time
import fret

@fret.configurable(states=['value'])
class Model:
    def __init__(self):
        self.value = 0

@fret.command
def resumable(ws):
    model = ws.build()
    with ws.run('exp-1') as run:
        run.register(model)
        cnt = run.acc()
        for e in fret.nonbreak(run.range(5)):
            # with `nonbreak`, the program always finish this loop before exit
            model.value += e
            time.sleep(0.5)
            cnt += 1
            print('current epoch: %d, sum: %d, cnt: %d' %
                  (e, model.value, cnt))

Then you can stop and restart this program anytime, with consistent results:

$ fret resumable
current epoch: 0, sum: 0, cnt: 1
current epoch: 1, sum: 1, cnt: 2
^CW SIGINT received. Delaying KeyboardInterrupt.
current epoch: 2, sum: 3, cnt: 3
Traceback (most recent call last):
    ...
KeyboardInterrupt
W cancelled by user
$ fret resumable
current epoch: 3, sum: 6, cnt: 4
current epoch: 4, sum: 10, cnt: 5

Dynamic commands

You can specify commands inside configurables, and run them depending on current workspace setup:

import fret

@fret.configurable
class App1:
    @fret.command
    def check(self):
        print('running check from App1')

@fret.configurable
class App2:
    @fret.command
    def check(self, msg):
        print('running check from App2 with message: ' + msg)

Then run:

$ fret config App1
[ws/_default] configured "main" as "App1"
$ fret check
running check from App1
$ fret config App2
[ws/_default] configured "main" as "App2"
$ fret check -m hello
running check from App2 with message: hello

Submodule

@fret.configurable
class A:
    def __init__(self, foo):
        ...

@fret.configurable(submodules=['sub'], build_subs=False)
class B:
    def __init__(self, sub, bar=3):
        self.sub = sub(foo='bar')   # call sub to build submodule

$ fret config sub A
[ws/_default] configured "sub" as "A"
$ fret config B
[ws/_default] configured "main" as "B" with: sub='sub', bar=3
$ fret run
B(sub=A(), bar=3)

Inheritance

@fret.configurable
class A:
    def __init__(self, foo='bar', sth=3):
        ...

@fret.configurable
class B(A):
    def __init__(self, bar=3, **others):
        super().__init__(**others)
        ...

$ fret config B -foo baz -bar 0
[ws/_default] configured "main" as "B" with: bar=0, foo='baz', sth=3
$ fret run
B(bar=0, foo='baz', sth=3)

Internals

>>> config = fret.Configuration({'foo': 'bar'})
>>> config
foo='bar'