Set Up Your Environment
- Ensure that you have Python, PyTorch, and the Notion API client installed. You can install PyTorch using pip:
pip install torch
- Install the Notion API client:
pip install notion-client
Create a Notion Integration
- Go to Notion My Integrations and create a new integration.
- Take note of the secret token provided, as you'll need it to authenticate the API requests.
Set Up Your Notion Database
- Create a new database in Notion or use an existing one. Ensure it has the necessary columns to store the data you wish to log from PyTorch.
- Share the database with your newly created integration to provide access permissions.
Connect PyTorch and Notion in Your Script
- Import the necessary libraries in your Python script or Jupyter Notebook:
import torch
from notion_client import Client
- Initialize the Notion client with your secret token:
notion = Client(auth="your_secret_token")
Log Data to Notion
- Define a function to send PyTorch model data to your Notion database. This function will create a new page in the database:
def log_to_notion(database_id, title, data):
notion.pages.create(
parent={"database_id": database_id},
properties={
"title": [{"text": {"content": title}}],
"Other Data": {"rich_text": [{"text": {"content": str(data)}}]},
},
)
- Make sure to replace `"Other Data"` with your actual column name in the Notion database and adjust the data structure accordingly.
Integrate the Logger in Your Workflow
- After executing your PyTorch operations, call the logging function to store results in Notion:
# Example PyTorch computation
x = torch.tensor([1.0, 2.0, 3.0])
y = torch.tensor([4.0, 5.0, 6.0])
result = torch.add(x, y)
# Log the result to Notion
log_to_notion("your_database_id", "Computation Result", result)
- Ensure you replace `"your_database_id"` with the actual ID of your Notion database.
Expand Functionality
- Consider enhancing your script to log additional metrics, such as model performance or training duration, to provide more insight.
- Ensure your logging integrates smoothly with your PyTorch training process, calling the log function at key stages.
