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A deep learning experiment tool to help you develop and manage your project

Project description


### Installation
* Install Hyperopt(Don't install by pip. The Hyperopt version in pip is too old. Install it from source.)
- git clone https://github.com/hyperopt/hyperopt.git
- python setup.py install
- sudo apt-get install mongodb-server
* install torchnet
### Designs
A deep-learning project usually consists of data loading, model building, model training/testing phases.
Also we need some auxiliary functions such as autosave/load, visualization, auto hyperparameter optimization and some debug tools, etc. to help us.

#### Optimizing hyperparameters
* Available libraries: Hyperopt, HPOlib2, neupy



# torchnet_Venus
The base framework for deep learning based on pytorch, torchnet, etc.


## TODO
[TODO Issue](https://github.com/ShanghaiTechVENUS/torchnet_Venus/issues/1)
## Credits

Primarily referenced tnt of pytorch: [Torchnet @pytorch](https://github.com/pytorch/tnt)

Many thanks to [@pytorch](https://github.com/pytorch).


# Code Structure: a lib that helps us to do some debugs, tune parameters, visualize, config
Wish the lib to be a wrapper, but users can also use the modules separately.

* debug utilities

## Recently
* base
* initializer
* visual
* hyperopt: tune hyper parameters
* optimizer: multistep learner
* config
* autosave, autoload: If unexpected interruption or active keyboard interruption happens to the program, then will save the checkpoint and parameters automatically.
* utils
- seed initialization
- weight initialization


## Long-term goal
* profiler
* common dataloader
* search structure
* tnt such as engine, meter
* autoselect idle graphic card


# Class graph
config

Engine: Tune network parameters
- autosave
- train
- test
- load_test
- load_train

StructureSearcher
Hyperopt -> Engine -> config
-> visual


DataLoader

Should be a template or a library?
Flexibility should be the first.
Utilities follows.


Project details


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