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Streamlining dev team communication using Google Colab, Slack, GitHub, and Trello

Streamlining Dev Team Communication:

Enhance Development Team Collaboration with

Google Colab

Slack

GitHub

Trello

Student Lab Learning Workbook

Introduction:

In this lab, you'll learn how to coordinate your development team's communication by synchronizing

Google Colab Notebook with

Slack,

GitHub

Trello.

You'll also explore how to manage Git Issues from Trello cards for efficient software engineering project management.

Project Development Assets Setup: Exercise 1: Setting Up the Environment

1.1 Create accounts (if you don't have them already): - Google account (for Google Colab) - Slack - GitHub - Trello

1.2 Join or create a Slack workspace for your team. (Project Requirement: Set up your Team’s own Slack workspace at Slack.com)

1.3 Create a new GitHub repository for your project. (Project Requirement)

1.4 Set up a Trello board for your project. {Keep using your Assignment TRELLO board}

Exercise 2: Integrating Google Colab with Slack

2.1 Open a new Google Colab notebook.

2.2 Install the Slack SDK for Python: !pip install slackclient

Include in your Project Video: A training presentation on how to do all the stuff you are doing:

Setting up the messaging workflow across Slack, GCN, GITHUB, HFS

2.3 Set up Slack integration: import os from slack import WebClient from slack.errors import SlackApiError

slack_token = os.environ["SLACK_API_TOKEN"] client = WebClient(token=slack_token)

try: response = client.chat_postMessage( channel="#your-channel-name", text="Hello from Google Colab!" ) print("Message sent successfully!") except SlackApiError as e: print(f"Error sending message: {e}") ```

2.4 Run the code and verify that the message appears in your Slack channel.

At the end of the Project, you can point PowerBI to GIT REPO: and generate beautiful graphical reports.

Exercise 3: Managing Git Issues with Trello

3.1 Install the Trello Power-Up for GitHub Issues on your Trello board.

3.2 Connect your GitHub repository to the Trello board.

3.3 Create a new card on your Trello board.

3.4 Use the GitHub Issues Power-Up to create a new issue directly from the Trello card: - Click on the card - Click on the GitHub Issues Power-Up - Select "Create Issue" - Fill in the issue details - Click "Create Issue"

3.5 Verify that the issue appears in your GitHub repository.

Exercise 4: Automating Workflow with GitHub Actions (= CI CD Process)

4.1 In your GitHub repository, create a new workflow file: `.github/workflows/trello-github-sync.yml`

4.2 Add the following content to the file: ```yaml name: Trello GitHub Sync

on: issues: types: [opened, closed, reopened]

jobs: sync: runs-on: ubuntu-latest steps: - name: Sync GitHub Issue to Trello uses: jessicazu/trello-github-actions@v1.0 with: trello-api-key: ${{ secrets.TRELLO_API_KEY }} trello-auth-token: ${{ secrets.TRELLO_AUTH_TOKEN }} trello-board-id: your-board-id trello-list-id: your-list-id github-repo: ${{ github.repository }} ```

4.3 Set up the necessary secrets in your GitHub repository settings: - TRELLO_API_KEY - TRELLO_AUTH_TOKEN

4.4 Create a new issue in your GitHub repository and verify that a corresponding card is created in Trello.

Exercise 5: Integrating Google Colab with GitHub

5.1 In your Google Colab notebook, add the following code to clone your GitHub repository: ```python !git clone https://github.com/your-username/your-repo.git %cd your-repo ```

5.2 Make changes to a file in the repository using Colab.

5.3 Commit and push changes to GitHub: ```python !git config --global user.email "you@example.com" !git config --global user.name "Your Name" !git add . !git commit -m "Update from Google Colab" !git push ```

5.4 Verify that the changes appear in your GitHub repository.

Exercise 6: Creating a Project Dashboard

6.1 In your Trello board, create lists for: - To Do - In Progress - Review - Done

6.2 Add cards for different tasks in your project.

6.3 Use labels to categorize tasks (e.g., bug, feature, documentation).

6.4 Assign team members to cards.

6.5 Set due dates for tasks.

Exercise 7: Communication Workflow

7.1 In Slack, create channels for: - #general - #dev-updates - #code-reviews

7.2 Set up Slack notifications for: - New GitHub issues - Pull requests - Trello card movements

7.3 Create a daily standup routine: (Daily Agile Stand Up meeting connection):

This is the connection between AGILE and TRELLO:

- Each team member posts their daily update in the #dev-updates channel - Include: What was done yesterday, what will be done today, any blockers

Exercise 8: Code Review Process

8.1 Create a pull request in GitHub for a recent change.

8.2 Use the GitHub Integration in Slack to notify the team about the new pull request.

8.3 Assign reviewers to the pull request.

8.4 Conduct a code review, leaving comments in GitHub.

8.5 Once approved, merge the pull request and update the corresponding Trello card.

Conclusion: By completing these exercises, you've set up an integrated workflow using Google Colab, Slack, GitHub, and Trello.

This streamlined process will enhance your team's communication and collaboration, leading to more efficient project delivery.

Reflection Questions: Project Research Questions:

1. How does this integrated workflow improve your team's productivity? 2. What challenges did you face during the setup process? 3. Can you think of any additional integrations that could further enhance your team's workflow? 4. How might this workflow need to be adjusted for larger teams or more complex projects?

Remember to continually refine your workflow based on your team's needs and feedback.

Now, let’s fact Hugging Face Spaces into this workflow:

Now what about factoring HuggingFace Spaces into our Study.

The advantage of HuggingFace Spaces is that it is a Model Server for the students' models. Let’s include a full CI CD with slack, git issues and actions, TRELLO to front end the project management, and ci cd workflow to promote the new model to HuggingFace Spaces

Streamlining AI Development and Deployment:

Integrating Google Colab, Slack, GitHub, Trello, and HuggingFace Spaces

Student Lab Learning Workbook

Introduction:

This lab will guide you through setting up an end-to-end workflow for AI model development, collaboration, and deployment. You'll use Google Colab for development, GitHub for version control, Slack for communication, Trello for project management, and HuggingFace Spaces for model serving.

Exercise 1: Setting Up the Environment

1.1 Create accounts (if you don't have them already):

Google account (for Google Colab)

Slack

GitHub

Trello

HuggingFace

1.2 Join or create a Slack workspace for your team.

1.3 Create a new GitHub repository for your AI project.

1ификр4 Set up a Trello board for your project.

1.5 Create a new HuggingFace Space for your project.

Exercise 2: Developing an AI Model in Google Colab

2.1 Open a new Google Colab notebook.

2.2 Develop a simple AI model (e.g., a text classifier using transformers):

python

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!pip install transformers datasets

from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments

from datasets import load_dataset

# Load a dataset

dataset = load_dataset("imdb", split="train")

# Load tokenizer and model

tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")

model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")

# Tokenize the dataset

def tokenize_function(examples):

return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Set up training arguments

training_args = TrainingArguments(

output_dir="./results",

num_train_epochs=3,

per_device_train_batch_size=16,

per_device_eval_batch_size=64,

warmup_steps=500,

weight_decay=0.01,

logging_dir="./logs",

)

# Create Trainer instance

trainer = Trainer(

model=model,

args=training_args,

train_dataset=tokenized_datasets,

)

# Train the model

trainer.train()

# Save the model

model.save_pretrained("./my_model")

tokenizer.save_pretrained("./my_model")

2.3 Test your model to ensure it's working correctly.

Exercise 3: Integrating with GitHub

3.1 Initialize a git repository in your Colab notebook:

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!git init

!git config --global user.email "you@example.com"

!git config --global user.name "Your Name"

3.2 Add your files and commit:

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!git add .

!git commit -m "Initial commit: Add AI model"

3.3 Connect to your GitHub repository:

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!git remote add origin https://github.com/your-username/your-repo.git

!git push -u origin master

Exercise 4: Setting Up GitHub Actions for CI/CD

4.1 Create a .github/workflows/ci-cd.yml file in your repository:

yaml

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name: CI/CD

on:

push:

branches: [ master ]

pull_request:

branches: [ master ]

jobs:

test:

runs-on: ubuntu-latest

steps:

- uses: actions/checkout@v2

- name: Set up Python

uses: actions/setup-python@v2

with:

python-version: '3.x'

- name: Install dependencies

run: |

python -m pip install --upgrade pip

pip install transformers datasets torch

- name: Run tests

run: python -m unittest discover tests

deploy:

needs: test

runs-on: ubuntu-latest

steps:

- uses: actions/checkout@v2

- name: Deploy to HuggingFace Spaces

env:

HF_TOKEN: ${{ secrets.HF_TOKEN }}

run: |

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