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Library to handle versioning and serialization of AI/ML models into Redis.
Store, version, and manage your ML models in Redis with ease. redis-model-store
provides a simple yet powerful interface for handling machine learning model artifacts in Redis.
# Using pip
pip install redis-model-store
# Or using poetry
poetry add redis-model-store
Here's a simple example using scikit-learn:
from redis import Redis
from redis_model_store import ModelStore
from sklearn.ensemble import RandomForestClassifier
# Connect to Redis and initialize store
redis = Redis(host="localhost", port=6379)
store = ModelStore(redis)
# Train your model
model = RandomForestClassifier()
model.fit(X_train, y_train)
# Save model with version tracking
version = store.save_model(
model,
name="my-classifier",
description="Random forest trained on dataset v1"
)
# List available models
models = store.list_models()
print(f"Available models: {models}")
# Load latest version
model = store.load_model("my-classifier")
# Load specific version
model = store.load_model("my-classifier", version=version)
# View all versions
versions = store.get_all_versions("my-classifier")
for v in versions:
print(f"Version: {v.version}, Created: {v.created_at}")
We welcome contributions! Here's how to get started:
git clone https://github.com/redis-applied-ai/redis-model-store.git
cd redis-model-store
curl -sSL https://install.python-poetry.org | python3 -
poetry install --all-extras
poetry run format
poetry run check-mypy
poetry run test
poetry run test-verbose
git checkout -b feat/your-feature-name
Make your changes and ensure:
docs/
directory.Push changes and open a PR
For more usage examples check out tbhis Example Notebook.
FAQs
Library to handle versioning and serialization of AI/ML models into Redis.
We found that redis-model-store demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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