mldash-sdk 0.1.0

Export trained ML models to ML-Dash

mldash

Export trained ML models from notebooks to ML-Dash.

Install

pip install mldash

Quick Start

import mldash

# One-time login (saved to ~/.config/mldash/credentials)
mldash.login()

# Set active project for this notebook
mldash.init(project="churn-analysis")

# Train your model
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Export — just model + name, everything else is resolved
mldash.export(model=model, name="Churn Predictor")

Authentication

mldash.login() — One-time setup

Run once per machine. You'll be prompted for your ML-Dash URL and API key:

>>> mldash.login()
ML-Dash URL: http://localhost:8000
Get your API key from: http://localhost:8000/developers/keys
API Key: ········
Authenticated with http://localhost:8000
Credentials saved to ~/.config/mldash/credentials

Credentials are stored locally at ~/.config/mldash/credentials (chmod 600) and reused automatically.

You can also pass values directly (useful for CI or scripting):

mldash.login(url="http://localhost:8000", api_key="mlda_xxx")

mldash.logout()

mldash.logout()  # removes stored credentials

mldash.whoami()

mldash.whoami()
# URL:     http://localhost:8000
# API Key: mlda_abcde...
# Project: churn-analysis

Alternative auth methods

These work without calling login():

Environment variables:

export MLDASH_URL=http://localhost:8000
export MLDASH_API_KEY=mlda_your_key_here

Google Colab Secrets:

Add MLDASH_URL and MLDASH_API_KEY to your Colab Secrets (key icon in sidebar).

Project Association

mldash.init() — Set active project

mldash.init(project="churn-analysis")

# All exports now go to "churn-analysis"
mldash.export(model=model1, name="Logistic v1")
mldash.export(model=model2, name="XGBoost v1")

The project is saved to credentials, so it persists across sessions. Override per-export with project_id=:

mldash.export(model=model, name="Experiment", project_id="other-project")

Usage

Export with artifacts

mldash.export(
    model=model,
    name="Sales Predictor",
    scaler=scaler,              # StandardScaler, MinMaxScaler, etc.
    encoder=encoder,            # OneHotEncoder, LabelEncoder, etc.
    label_map={"0": "no", "1": "yes"},
    features={"age": "numeric", "dept": "categorical", "review": "text"},
    target={"churn": "categorical"},
)

Save to local ZIP (offline)

mldash.save(
    model=model,
    name="My Model",
    path="./my_model.zip",
)
# Upload the ZIP manually via the ML-Dash web UI

Auto-detection

The package auto-detects:

• task_type — classification or regression (from sklearn mixins)
• framework — sklearn, xgboost, lightgbm, etc. (from module path)
• hyperparameters — via model.get_params()
• model_class — e.g., RandomForestClassifier
• n_features — from model.n_features_in_
• class_labels — from model.classes_

Typical Notebook Workflow

# Cell 1: Setup (run once)
import mldash
mldash.login()
mldash.init(project="mas-648-final")

# Cell 2-N: Train models...
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Cell N+1: Export
mldash.export(model=model, name="RF 100 trees")

Errors

from mldash import MLDashValidationError, MLDashAuthError, MLDashUploadError

try:
    mldash.export(model=model, name="Test")
except MLDashValidationError as e:
    print(f"Invalid input: {e}")
except MLDashAuthError as e:
    print(f"Auth failed: {e}")
except MLDashUploadError as e:
    print(f"Upload failed: {e}")