Efficient Collaboration and Deployment with Keras and Trello
- Utilize Trello to organize and manage the development workflow of machine learning models built with Keras. This provides a transparent view for all stakeholders.
- Assign Trello cards to different phases in the ML pipeline such as data preprocessing, model building, testing, and deployment. Cards can be moved across lists to indicate progress for each task.
- Attach dataset descriptions, Keras model architecture diagrams, and performance metrics directly to Trello cards to ensure all team members have easy access to key information.
- Incorporate Trello’s calendar power-up to schedule key milestones and deadlines for model training, ensuring timely delivery of results.
- Integrate Trello with Jenkins to automate deployment processes. When a card is moved to a "Ready for Deployment" list, Jenkins can trigger scripts to deploy Keras models to production environments.
Sample Code for Keras Model Logging to Trello
import requests
from keras.callbacks import Callback
# Define Trello API credentials
api_key = 'your_trello_api_key'
token = 'your_trello_token'
board_id = 'your_trello_board_id'
list_id = 'your_trello_list_id'
# Custom callback to log Keras training progress to Trello
class TrelloLogger(Callback):
def on_epoch_end(self, epoch, logs=None):
card_name = f"Epoch #{epoch + 1} Results"
description = f"Loss: {logs['loss']}, Accuracy: {logs['accuracy']}"
self.create_trello_card(card_name, description)
def create_trello_card(self, card_name, description=''):
url = f"https://api.trello.com/1/cards"
query = {
'key': api_key,
'token': token,
'idList': list_id,
'name': card_name,
'desc': description
}
response = requests.post(url, params=query)
print(f"Trello card created: {response.json()}")
# Example usage: Set TrelloLogger as a callback during model training
model.fit(x_train, y_train, epochs=10, callbacks=[TrelloLogger()])
Advantages of Integration
- Promotes effective collaboration by centralizing all ML project resources on Trello, making it accessible to developers, project managers, and business stakeholders alike.
- Facilitates error tracking and resolution by maintaining a detailed log of experiments and outcomes directly within Trello.
- Streamlines deployment cycles through automation, leveraging Trello’s seamless integration with CI/CD tools like Jenkins for efficient model deployment.
- Ensures a smooth project workflow by visualizing all stages of ML development, thereby helping teams anticipate risks and manage workloads effectively.
