Lightweight and modular MLOps library with the aim to make ML development more efficient targeted at small teams or individuals.
Cascade was built especially for individuals or small teams that are in need of MLOps, but don't have time or resources to integrate with platforms.
Included in Model Lifecycle section of Awesome MLOps list
pip install cascade-mlMore info on installation can be found in documentation
This section is divided into blocks based on what problem you can solve using Cascade. These are the simplest examples of what the library is capable of. See more in documentation.
Data processing pipelines need to be versioned and tracked as a part of model experiments.
To track changes and version everything about data Cascade has Datasets - special wrappers
that encapsulate operations on data.
from pprint import pprint
from cascade import data as cdd
from sklearn.datasets import load_digits
import numpy as np
X, y = load_digits(return_X_y=True)
pairs = [(x, y) for (x, y) in zip(X, y)]
ds = cdd.Wrapper(pairs)
ds = cdd.RandomSampler(ds)
train_ds, test_ds = cdd.split(ds)
train_ds = cdd.ApplyModifier(
train_ds,
lambda pair: pair + np.random.random() * 0.1 - 0.05
)
pprint(train_ds.get_meta())We see all the stages that we did in meta.
Click to see full pipeline metadata
[{"comments": [],
"description": null,
"len": 898,
"links": [],
"name": "cascade.data.apply_modifier.ApplyModifier",
"tags": [],
"type": "dataset"},
{"comments": [],
"description": null,
"len": 898,
"links": [],
"name": "cascade.data.range_sampler.RangeSampler",
"tags": [],
"type": "dataset"},
{"comments": [],
"description": null,
"len": 1797,
"links": [],
"name": "cascade.data.random_sampler.RandomSampler",
"tags": [],
"type": "dataset"},
{"comments": [],
"len": 1797,
"links": [],
"name": "cascade.data.dataset.Wrapper",
"obj_type": "<class 'list'>",
"tags": [],
"type": "dataset"}]See all datasets in zoo
See all use-cases in documentation
Cascade offers automatic pipeline versioning utilities
from cascade.data import Wrapper, Modifier, version
ds = Wrapper([0, 1, 2])
ds = Modifier(ds)
ver = version(ds, "version_log.yml")Will output 0.1 and for any change in the steps or any meta of the pipeline it will
bump the version. If you return to the previous version, then will return to previous one as well.
Not only data and pipelines changes over time. Models change more frequently and require special system to handle experiments and artifacts.
import random
from cascade import models as cdm
from cascade import data as cdd
model = cdm.Model()
model.add_metric('acc', random.random())
repo = cdm.ModelRepo('repos/use_case_repo')
line = repo.add_line('baseline')
line.save(model, only_meta=True)Repo is the collection of model lines and Line can be a bunch of experiments on one model type.
Click to see full model metadata
[{"comments": [],
"created_at": "2023-11-06T07:42:42.737248+00:00",
"description": null,
"links": [],
"metrics": [{"created_at": "2023-11-06T07:42:43.004261+00:00",
"name": "acc",
"value": 0.3730907820891789}],
"name": "cascade.models.model.Model",
"params": {},
"path": "/home/user/repos/use_case_repo/baseline/00001",
"saved_at": "2023-11-06T07:43:17.325593+00:00",
"slug": "cerulean_jaguarundi_of_trust",
"tags": [],
"type": "model"}]See all use-cases in documentation
Validation is an important part of pipelines. Simple asserts can do the thing, but
there are more useful validation tools.
Validators provide meaningful error messages and a way to perform many checks in one run over the dataset.
from cascade import meta as cme
from cascade import data as cdd
from sklearn.datasets import load_digits
import numpy as np
X, y = load_digits(return_X_y=True)
pairs = [(x, y) for (x, y) in zip(X, y)]
ds = cdd.Wrapper(pairs)
ds = cdd.RandomSampler(ds)
train_ds, test_ds = cdd.split(ds)
cme.PredicateValidator(
train_ds,
[
lambda pair: all(pair[0] < 20),
lambda pair: pair[1] in (i for i in range(10))
]
)See all use-cases in documentation
During experiments Cascade produces many metadata which can be analyzed later.
MetricViewer is the tool that allows to see the relationship between parameters and
metrics of all models in repository.
from cascade import meta as cme
from cascade import models as cdm
repo = cdm.ModelRepo('repos/use_case_repo')
# This runs web-server that relies on optional dependency
cme.MetricViewer(repo).serve()
HistoryViewer allows to see model's lineage, what parameters resulted in what metrics
from cascade import meta as cme
from cascade import models as cdm
repo = cdm.ModelRepo('repos/use_case_repo')
# This returns plotly figure
cme.HistoryViewer(repo).plot()
# This runs a server ans allows to see changes in real time (for example while models are trained)
cme.HistoryViewer(repo).serve()See all use-cases in documentation
Small and fast-prototyping AI-teams could use it as a tradeoff between total missingness of any MLOps practices and demanding enterprise solutions.
The key principles of Cascade are:
- Elegancy - ML code should be about ML with minimum meta-code
- Flexibility - to easily build prototypes and integrate existing projects with Cascade (don't pay for what you don't use)
- Reusability - code to be reused in similar projects with no effort
- Traceability - everything should have meta-data
Pull requests and issues are welcome! For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests and docs as appropriate.
This project uses Semantic Versioning - https://semver.org/
If you used the code in your research, please cite it with:
@software{ilia_moiseev_2023_8006995,
author = {Ilia Moiseev},
title = {Oxid15/cascade: Lightweight ML Engineering library},
month = jun,
year = 2023,
publisher = {Zenodo},
doi = {10.5281/zenodo.8006995},
url = {https://doi.org/10.5281/zenodo.8006995}
}