singtown_ai 1.9.3

SingTown AI Python Client

SingTown AI Python SDK

This SDK is designed to interact with SingTown AI Cloud Service or SingTown AI Standalone(self-hosted).

Installation

pip install singtown_ai

Usage

Login Configuration

• SingTown AI Cloud Service: The host is "https://ai.singtown.com".
• SingTown AI Standalone (self-hosted): The host is something like "http://127.0.0.1:8000".

You can obtain the token and task_id from Project -> Information.

Environment Variables:

export SINGTOWN_AI_HOST="https://ai.singtown.com"  # Or the cloud service URL
export SINGTOWN_AI_TOKEN="your token"            # Your token
export SINGTOWN_AI_TASK_ID="your id"             # Your task ID

Alternatively, set them directly in code:

from singtown_ai import SingTownAiClient
client = SingTownAiClient(
  host="https://ai.singtown.com",  # Or the cloud service URL
  token="your token",            # Your token
  task_id="your id"              # Your task ID
)

Basic Usage

import time
from singtown_ai import SingTownAiClient
client = SingTownAiClient()
print(client.task)
client.export_class_folder(export_path) # or client.export_yolo(export_path)

metrics = []
for i in range(10):
    print("Train:", i)
    metrics.append({"epoch": i, "accuracy": i * 10})
    client.update_metrics(metrics)
    time.sleep(1)

client.upload_results_zip(uploadfile)

Update Metrics

metrics = [
    {"epoch": 0, "accuracy": 0.8, "loss": 0.2},
    {"epoch": 1, "accuracy": 0.9, "loss": 0.1},
]
client = SingTownAiClient()
client.update_metrics(metrics)

• The field names in metrics are not restricted, and they will appear on the Metrics page in SingTown AI.

Watching metrics.csv

from singtown_ai import file_watcher

client = SingTownAiClient()

@file_watcher("path/to/metrics.csv", interval=3)
def file_on_change(content: str):
    import csv
    from io import StringIO

    metrics = list(csv.DictReader(StringIO(content)))
    if not metrics:
        return
    client.update_metrics(metrics)

• Every 1 seconds, the SDK will parse the metrics.csv and upload metrics.

Logging

client = SingTownAiClient()
client.log("line")

Logging sys.stdout and stderror

from singtown_ai import stdout_watcher

client = SingTownAiClient()

@stdout_watcher(interval=1)
def on_stdout_write(content: str):
    client.log(content, end="")

• Every 1 seconds, the SDK will upload messages to logging.

Uploading Result Files

client = SingTownAiClient()
client.upload_results_zip("your.zip")

• This method uploads a .zip result file.

Mock

• mock_task.json

{
  "project": {
    "labels": ["cat", "dog"],
    "type": "CLASSIFICATION"
  },
  "device": "openmv-cam-h7-plus",
  "model_name": "mobilenet_v2_0.35_128",
  "freeze_backbone": true,
  "batch_size": 16,
  "epochs": 1,
  "learning_rate": 0.001,
  "early_stopping": 3,
  "export_width": 128,
  "export_height": 128
}

• mock_dataset.json

[
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/cat.0.jpg",
    "subset": "TRAIN",
    "classification": "cat"
  },
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/cat.1.jpg",
    "subset": "VALID",
    "classification": "cat"
  },
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/cat.2.jpg",
    "subset": "TEST",
    "classification": "cat"
  },
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/dog.0.jpg",
    "subset": "TRAIN",
    "classification": "dog"
  },
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/dog.1.jpg",
    "subset": "VALID",
    "classification": "dog"
  },
  {
    "url": "https://github.com/singtown-ai/singtown-ai-datasets/raw/main/images/dog.2.jpg",
    "subset": "TEST",
    "classification": "dog"
  }
]

• The JSON keys may use either snake_case or camelCase.

Environment Variables:

export SINGTOWN_AI_MOCK_TASK_PATH="mock_task.json"
export SINGTOWN_AI_MOCK_DATASET_PATH="mock_dataset.json"

Alternatively, set them directly in code:

client = SingTownAiClient(
    mock_task_path="mock_task.json",
    mock_dataset_path="mock_dataset.json",
)

• Set mock_data, Will mock demo task and dataset, this is useful for debugging.