Metadata-Version: 2.1
Name: experiment_utils
Version: 0.0.3
Summary: Helpers utils for manage and track experiments.
Home-page: https://github.com/ayasyrev/experiment_utils
Author: Andrei Yasyrev
Author-email: a.yasyrev@gmail.com
License: Apache Software License 2.0
Description: # Experiment_utils
        > Helper utils for track and manage Dl experimets with pytorch.
        
        
        Very early stage - just draft for my utils.
        
        ## Install
        
        `pip install experiment-utils`
        
        `Editeble install: 
        git clone https://github.com/ayasyrev/experiment-utils  
        cd experiment-utils  
        pip install -e .  `
        
        ## How to use
        
        Import Experiment:
        
        ```python
        from experiment_utils.experiment import *
        ```
        
        After import you has p (stands for Parameters) and e (Experiment) objects.
        
        Name the experiment, later it will be used in logs.
        
        ```python
        p.exp_name = 'test1'
        p.exp_name
        ```
        
        
        
        
            'test1'
        
        
        
        ```python
        e.p.exp_name
        ```
        
        
        
        
            'test1'
        
        
        
        Load learner
        
        ```python
        e.get_learner()
        ```
        
        ```python
        e.p.model
        ```
        
        
        
        
            functools.partial(<function resnet18 at 0x7fc8d4dd08c0>, num_classes=10)
        
        
        
        ```python
        e.learn.model.fc
        ```
        
        
        
        
            Linear(in_features=512, out_features=10, bias=True)
        
        
        
        Short notation for learn - l
        
        ```python
        e.l.model.conv1
        ```
        
        
        
        
            Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        
        
        
        Now we can easy change some parameter anr start train with pipeline what yuo neg.
        
        `from experiment_utils.utils import train_fc, plot`
        
        `p.pipeline = [train_fc, plot]`
        
        `p.lr = 0.001`
        
        `p.epochs = 10`
        
        `e(repeat_times=2)`
        
Keywords: DL pytorch experiment
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
