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https://github.com/iterative/mlem
🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞
https://github.com/iterative/mlem
cli data-science deployment developer-tools git machine-learning mlem model-registry python
Last synced: about 2 months ago
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🐶 A tool to package, serve, and deploy any ML model on any platform. Archived to be resurrected one day🤞
- Host: GitHub
- URL: https://github.com/iterative/mlem
- Owner: iterative
- License: apache-2.0
- Archived: true
- Created: 2021-09-30T08:07:34.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-13T18:27:00.000Z (about 1 year ago)
- Last Synced: 2024-07-23T20:09:52.438Z (about 2 months ago)
- Topics: cli, data-science, deployment, developer-tools, git, machine-learning, mlem, model-registry, python
- Language: Python
- Homepage: https://mlem.ai
- Size: 1.3 MB
- Stars: 719
- Watchers: 25
- Forks: 44
- Open Issues: 131
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Awesome Lists containing this project
- awesome-llmops - MLEM - square) | (Large Scale Deployment / Model Management)
- awesome-mlops - MLEM - Version and deploy your ML models following GitOps principles. (Model Serving)
README
[](https://github.com/iterative/mlem/actions/workflows/check-test-release.yml)
[](https://codecov.io/gh/iterative/mlem)
[](https://pypi.org/project/mlem)
[](https://github.com/iterative/mlem/blob/master/LICENSE)
MLEM helps you package and deploy machine learning models.
It saves ML models in a standard format that can be used in a variety of production scenarios such as real-time REST serving or batch processing.
- **Run your ML models anywhere:**
Wrap models as a Python package or Docker Image, or deploy them to Heroku, SageMaker or Kubernetes (more platforms coming soon).
Switch between platforms transparently, with a single command.
- **Model metadata into YAML automatically:**
Automatically include Python requirements and input data needs into a human-readable, deployment-ready format.
Use the same metafile on any ML framework.
- **Stick to your training workflow:**
MLEM doesn't ask you to rewrite model training code.
Add just two lines around your Python code: one to import the library and one to save the model.
- **Developer-first experience:**
Use the CLI when you feel like DevOps, or the API if you feel like a developer.
## Why is MLEM special?
The main reason to use MLEM instead of other tools is to adopt a **GitOps approach** to manage model lifecycles.
- **Git as a single source of truth:**
MLEM writes model metadata to a plain text file that can be versioned in Git along with code.
This enables GitFlow and other software engineering best practices.
- **Unify model and software deployment:**
Release models using the same processes used for software updates (branching, pull requests, etc.).
- **Reuse existing Git infrastructure:**
Use familiar hosting like Github or Gitlab for model management, instead of having separate services.
- **UNIX philosophy:**
MLEM is a modular tool that solves one problem very well.
It integrates well into a larger toolset from Iterative.ai, such as [DVC](https://dvc.org/) and [CML](https://cml.dev/).
## Usage
This a quick walkthrough showcasing deployment functionality of MLEM.
Please read [Get Started guide](https://mlem.ai/doc/get-started) for a full version.
### Installation
MLEM requires Python 3.
```console
$ python -m pip install mlem
```
> To install the pre-release version:
>
> ```console
> $ python -m pip install git+https://github.com/iterative/mlem
> ```
### Saving the model
```python
# train.py
from mlem.api import save
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
def main():
data, y = load_iris(return_X_y=True, as_frame=True)
rf = RandomForestClassifier(
n_jobs=2,
random_state=42,
)
rf.fit(data, y)
save(
rf,
"models/rf",
sample_data=data,
)
if __name__ == "__main__":
main()
```
### Codification
Check out what we have:
```shell
$ ls models/
rf
rf.mlem
$ cat rf.mlem
```
Click to show `cat` output
```yaml
artifacts:
data:
hash: ea4f1bf769414fdacc2075ef9de73be5
size: 163651
uri: rf
model_type:
methods:
predict:
args:
- name: data
type_:
columns:
- sepal length (cm)
- sepal width (cm)
- petal length (cm)
- petal width (cm)
dtypes:
- float64
- float64
- float64
- float64
index_cols: []
type: dataframe
name: predict
returns:
dtype: int64
shape:
- null
type: ndarray
predict_proba:
args:
- name: data
type_:
columns:
- sepal length (cm)
- sepal width (cm)
- petal length (cm)
- petal width (cm)
dtypes:
- float64
- float64
- float64
- float64
index_cols: []
type: dataframe
name: predict_proba
returns:
dtype: float64
shape:
- null
- 3
type: ndarray
type: sklearn
object_type: model
requirements:
- module: sklearn
version: 1.0.2
- module: pandas
version: 1.4.1
- module: numpy
version: 1.22.3
```
### Deploying the model
If you want to follow this Quick Start, you'll need to sign up on https://heroku.com,
create an API_KEY and populate `HEROKU_API_KEY` env var (or run `heroku login` in command line).
Besides, you'll need to run `heroku container:login`. This will log you in to Heroku
container registry.
Now we can [deploy the model with `mlem deploy`](https://mlem.ai/doc/get-started/deploying)
(you need to use different `app_name`, since it's going to be published on https://herokuapp.com):
```shell
$ mlem deployment run heroku app.mlem \
--model models/rf \
--app_name example-mlem-get-started-app
⏳️ Loading model from models/rf.mlem
⏳️ Loading deployment from app.mlem
🛠 Creating docker image for heroku
🛠 Building MLEM wheel file...
💼 Adding model files...
🛠 Generating dockerfile...
💼 Adding sources...
💼 Generating requirements file...
🛠 Building docker image registry.heroku.com/example-mlem-get-started-app/web...
✅ Built docker image registry.heroku.com/example-mlem-get-started-app/web
🔼 Pushing image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
✅ Pushed image registry.heroku.com/example-mlem-get-started-app/web to registry.heroku.com
🛠 Releasing app example-mlem-get-started-app formation
✅ Service example-mlem-get-started-app is up. You can check it out at https://example-mlem-get-started-app.herokuapp.com/
```
## Contributing
Contributions are welcome! Please see our [Contributing Guide](https://mlem.ai/doc/contributing/core)
for more details.
Thanks to all our contributors!
## Copyright
This project is distributed under the Apache license version 2.0 (see the LICENSE file in the project root).
By submitting a pull request to this project, you agree to license your contribution under the Apache license version 2.0 to this project.