Skip to main content

Data driven development based deep learning deepleaps(pytorch)

Project description

deep-leaps is a deep learning training framework written based on DDD (data driven development). It has the following features.

  • Training/testing procedure abstracted by graph

    the training & testing procedure is based on graphs. then, you can add or remove nodes in the graph at runtime. For example, if you want to visualize the model in the middle of training, you can add a visualization command at runtime. this does not affect training.

  • Instruction modification at runtime If you want to modify the data or force the learning rate to be adjusted, you can modify the instruction using ipc at runtime.

  • Code modification at runtime deep-leaps allows code modification at runtime. you can command the python file recompile command at runtime using ipc.


pip install deep-leaps

make workspace

Run the following python command in the project root.

from import App


|    |--0001.jpg
|    |--0002.jpg
|    |--0000.jpg
|    |--output
|        |
|    |--configs
|        |--dataloader
|            |--exampleDataLoader.yaml
|        |--model
|            |--SimpleLayerModel.yaml
|        |--dataset
|            |--Example.yaml
|        |--command
|            |--default_runnable_command.yaml
|        |--trainer
|            |--ExampleContainer.yaml
|        |--default.yaml
|    |--dataloader
|        |
|        |
|        |
|    |--model
|        |
|    |--trainer
|        |
|    |--ipc
|        |


Unlike the existing framework, deep-leaps is executed depending on the command. the basic training process can be applied as follows. please check the example project for more details.


    command: 'TrainerContainerLoaderCommand'
    args: 'resource/configs/trainer/ExampleContainer.yaml'
    finish: True

    command: 'ModuleLoadClass'
    required: ['MODEL', 'OPTIMIZER']
    base_path: '$base'
    finish: True
        _reload: False
        use_hook: True
        file_name: $


  ipc_host: ''
  ipc_port: 1568

You can specify ipc_host and ipc_port through configuration. Using this address, you can communicate with the training network model. the communication example is as follows.

import socket
import threading
from struct import pack

host = ''
port = 1568
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect((host, port))

    while True:
        data = input(':')
        length = pack('>Q', len(data))
        ack = client_socket.recv(1)
        print('send ack ok')
except Exception as e:

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

deep-leaps-0.0.14.tar.gz (25.5 kB view hashes)

Uploaded source

Built Distribution

deep_leaps-0.0.14-py3-none-any.whl (76.4 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page