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https://github.com/AstraZeneca/runnable
Runnable
https://github.com/AstraZeneca/runnable
Last synced: 13 days ago
JSON representation
Runnable
- Host: GitHub
- URL: https://github.com/AstraZeneca/runnable
- Owner: AstraZeneca
- License: apache-2.0
- Created: 2022-01-18T16:43:54.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-01-07T21:58:21.000Z (24 days ago)
- Last Synced: 2025-01-07T22:37:02.101Z (24 days ago)
- Language: Python
- Homepage:
- Size: 9.98 MB
- Stars: 40
- Watchers: 2
- Forks: 4
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- top-pharma50 - **AstraZeneca/runnable** - 06-04 20:42:58 | (Ranked by starred repositories)
- top-pharma50 - **AstraZeneca/runnable** - 06-04 20:42:58 | (Ranked by starred repositories)
README
[Please check here for complete documentation](https://astrazeneca.github.io/runnable/)
## Example
The below data science flavored code is a well-known
[iris example from scikit-learn](https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html).
```python
"""
Example of Logistic regression using scikit-learn
https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html
"""
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
def load_data():
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
return X, Y
def model_fit(X: np.ndarray, Y: np.ndarray, C: float = 1e5):
logreg = LogisticRegression(C=C)
logreg.fit(X, Y)
return logreg
def generate_plots(X: np.ndarray, Y: np.ndarray, logreg: LogisticRegression):
_, ax = plt.subplots(figsize=(4, 3))
DecisionBoundaryDisplay.from_estimator(
logreg,
X,
cmap=plt.cm.Paired,
ax=ax,
response_method="predict",
plot_method="pcolormesh",
shading="auto",
xlabel="Sepal length",
ylabel="Sepal width",
eps=0.5,
)
# Plot also the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
plt.xticks(())
plt.yticks(())
plt.savefig("iris_logistic.png")
# TODO: What is the right value?
return 0.6
## Without any orchestration
def main():
X, Y = load_data()
logreg = model_fit(X, Y, C=1.0)
generate_plots(X, Y, logreg)
## With runnable orchestration
def runnable_pipeline():
# The below code can be anywhere
from runnable import Catalog, Pipeline, PythonTask, metric, pickled
# X, Y = load_data()
load_data_task = PythonTask(
function=load_data,
name="load_data",
returns=[pickled("X"), pickled("Y")], # (1)
)
# logreg = model_fit(X, Y, C=1.0)
model_fit_task = PythonTask(
function=model_fit,
name="model_fit",
returns=[pickled("logreg")],
)
# generate_plots(X, Y, logreg)
generate_plots_task = PythonTask(
function=generate_plots,
name="generate_plots",
terminate_with_success=True,
catalog=Catalog(put=["iris_logistic.png"]), # (2)
returns=[metric("score")],
)
pipeline = Pipeline(
steps=[load_data_task, model_fit_task, generate_plots_task],
) # (4)
pipeline.execute()
return pipeline
if __name__ == "__main__":
# main()
runnable_pipeline()
```
1. Return two serialized objects X and Y.
2. Store the file `iris_logistic.png` for future reference.
3. Define the sequence of tasks.
4. Define a pipeline with the tasks
The difference between native driver and runnable orchestration:
!!! tip inline end "Notebooks and Shell scripts"
You can execute notebooks and shell scripts too!!
They can be written just as you would want them, *plain old notebooks and scripts*.
```diff
- X, Y = load_data()
+load_data_task = PythonTask(
+ function=load_data,
+ name="load_data",
+ returns=[pickled("X"), pickled("Y")], (1)
+ )
-logreg = model_fit(X, Y, C=1.0)
+model_fit_task = PythonTask(
+ function=model_fit,
+ name="model_fit",
+ returns=[pickled("logreg")],
+ )
-generate_plots(X, Y, logreg)
+generate_plots_task = PythonTask(
+ function=generate_plots,
+ name="generate_plots",
+ terminate_with_success=True,
+ catalog=Catalog(put=["iris_logistic.png"]), (2)
+ )
+pipeline = Pipeline(
+ steps=[load_data_task, model_fit_task, generate_plots_task], (3)
```
---
- [x] ```Domain``` code remains completely independent of ```driver``` code.
- [x] The ```driver``` function has an equivalent and intuitive runnable expression
- [x] Reproducible by default, runnable stores metadata about code/data/config for every execution.
- [x] The pipeline is `runnable` in any environment.
## Documentation
[More details about the project and how to use it available here](https://astrazeneca.github.io/runnable/).
## Installation
The minimum python version that runnable supports is 3.8
```shell
pip install runnable
```
Please look at the [installation guide](https://astrazeneca.github.io/runnable-core/usage)
for more information.
## Pipelines can be:
### Linear
A simple linear pipeline with tasks either
[python functions](https://astrazeneca.github.io/runnable-core/concepts/task/#python_functions),
[notebooks](https://astrazeneca.github.io/runnable-core/concepts/task/#notebooks), or [shell scripts](https://astrazeneca.github.io/runnable-core/concepts/task/#shell)
[](https://mermaid.live/edit#pako:eNpl0bFuwyAQBuBXQVdZTqTESpxMDJ0ytkszhgwnOCcoNo4OaFVZfvcSx20tGSQ4fn0wHB3o1hBIyLJOWGeDFJ3Iq7r90lfkkA9HHfmTUpnX1hFyLvrHzDLl_qB4-1BOOZGGD3TfSikvTDSNFqdj2sT2vBTr9euQlXNWjqycsN2c7UZWFMUE7udwP0L3y6JenNKiyfvz8t8_b-gavT9QJYY0PcDtjeTLptrAChriBq1JzeoeWkG4UkMKZCoN8k2Bcn1yGEN7_HYaZOBIK4h3g4EOFi-MDcgKa59SMja0_P7s_vAJ_Q_YOH6o)
### [Parallel branches](https://astrazeneca.github.io/runnable-core/concepts/parallel)
Execute branches in parallel
[](https://mermaid.live/edit#pako:eNp9k01rwzAMhv-K8S4ZtJCzDzuMLmWwwkh2KMQ7eImShiZ2sB1KKf3vs52PpsWNT7LySHqlyBeciRwwwUUtTtmBSY2-YsopR8MpQUfAdCdBBekWNBpvv6-EkFICzGAtWcUTDW3wYy20M7lr5QGBK2j-anBAkH4M1z6grnjpy17xAiTwDII07jj6HK8-VnVZBspITnpjztyoVkLLJOy3Qfrdm6gQEu2370Io7WLORo84PbRoA_oOl9BBg4UHbHR58UkMWq_fxjrOnhLRx1nH0SgkjlBjh7ekxNKGc0NelDLknhePI8qf7MVNr_31nm1wwNTeM2Ao6pmf-3y3Mp7WlqA7twOnXfKs17zt-6azmim1gQL1A0NKS3EE8hKZE4Yezm3chIVFiFe4AdmwKjdv7mIjKNYHaIBiYsycySPFlF8NxzotkjPPMNGygxXu2pxp2FSslKzBpGC1Ml7IKy3krn_E7i1f_wEayTcn)
### [loops or map](https://astrazeneca.github.io/runnable-core/concepts/map)
Execute a pipeline over an iterable parameter.
[](https://mermaid.live/edit#pako:eNqVlF1rwjAUhv9KyG4qKNR-3AS2m8nuBgN3Z0Sy5tQG20SSdE7E_76kVVEr2CY3Ied9Tx6Sk3PAmeKACc5LtcsKpi36nlGZFbXciHwfLN79CuWiBLMcEULWGkBSaeosA2OCxbxdXMd89Get2bZASsLiSyuvQE2mJZXIjW27t2rOmQZ3Gp9rD6UjatWnwy7q6zPPukd50WTydmemEiS_QbQ79RwxGoQY9UaMuojRA8TCXexzyHgQZNwbMu5Cxl3IXNX6OWMyiDHpzZh0GZMHjOK3xz2mgxjT3oxplzG9MPp5_nVOhwJjteDwOg3HyFj3L1dCcvh7DUc-iftX18n6Waet1xX8cG908vpKHO6OW7cvkeHm5GR2b3drdvaSGTODHLW37mxabYC8fLgRhlfxpjNdwmEets-Dx7gCXTHBXQc8-D2KbQEVUEzckjO9oZjKo9Ox2qr5XmaYWF3DGNdbzizMBHOVVWGSs9K4XeDCKv3ZttSmsx7_AYa341E)
### [Arbitrary nesting](https://astrazeneca.github.io/runnable-core/concepts/nesting/)
Any nesting of parallel within map and so on.