Metadata-Version: 2.1
Name: torchember
Version: 0.2.7
Summary: Tracking and Visualize after the burning PyTorch
Home-page: https://github.com/raynardj/torchember
Author: raynardj
Author-email: email@example.com
License: Apache Software License 2.0
Description: # Torch Ember
        > Tracking and visualize after the burning pytorch
        
        
        ## This framework tracks the pytorch model:
        
        * On ```nn.Module``` level
        * Down to the metrics/ features of all tensors, includes
            * inputs/outputs of each module
            * weight/grad tensors
        * By **minimal** extra coding
        
        ![WebUI](nbs/001.png)
        
        ## Other lovely features
        * Customizable metrics, with easy decorator syntax
        * Split the tracking log in the way you like, just ```mark(k=v,k1=v2...)```
        * You can easily switch on/off the tracking:
            * Even cost of computation is tiny, torchember don't have to calculate metric for every iteration
            * Hence, you can track eg. only the last steps, only each 200 steps .etc
        
        ## Installation
        ```pip install torchember```
        
        ## Fast Tutorial
        
        * **30 seconds** [tutorial](https://github.com/raynardj/torchember/blob/master/nb_test/test_cnn.ipynb)
        
        * Full [documentations](https://raynardj.github.io/torchember/)
        
        ### Step1, Track your model
        
        Place you torch ember tracker on your model
        
        ```python
        from torchember.core import torchEmber
        te = torchEmber(model)
        ```
        
        The above can track input and output of every module,The following can track status of every module
        
        ```python
        for i in range(1000):
            ...
            loss.backward()
            optimizer.step()
            
            te.log_model()
        
        ```
        
        Train your model as usual
        
        ### Step2, Check the analysis on the WebUI
        
        Run the service from terminal
        ```shell
        $ torchember
        ```
        The default port will be 8080
        
        Or assign a port
        ```shell
        $ torchember --port=4200
        ```
        
        Visit your analysis at ```http://[host]:[port]```
        
Keywords: pytorch statistics tracker torchember
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
