-
-
- | python SDK |
- yaml |
-
-
-
+```diff
-Example present at: ```examples/python-tasks.py```
+- X, Y = load_data()
++load_data_task = PythonTask(
++ function=load_data,
++ name="load_data",
++ returns=[pickled("X"), pickled("Y")], (1)
++ )
-Run it as: ```python examples/python-tasks.py```
+-logreg = model_fit(X, Y, C=1.0)
++model_fit_task = PythonTask(
++ function=model_fit,
++ name="model_fit",
++ returns=[pickled("logreg")],
++ )
-```python
-from runnable import Pipeline, Task
+-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)
++ )
-def main():
- step1 = Task(
- name="step1",
- command="examples.functions.return_parameter",
- )
- step2 = Task(
- name="step2",
- command="examples.functions.display_parameter",
- terminate_with_success=True,
- )
- step1 >> step2
++pipeline = Pipeline(
++ steps=[load_data_task, model_fit_task, generate_plots_task], (3)
- pipeline = Pipeline(
- start_at=step1,
- steps=[step1, step2],
- add_terminal_nodes=True,
- )
+```
+
- pipeline.execute()
+---
-if __name__ == "__main__":
- main()
-```
+- [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.
- |
-
-
-
-Example present at: ```examples/python-tasks.yaml```
-
-
-Execute via the cli: ```runnable execute -f examples/python-tasks.yaml```
-
-```yaml
-dag:
- description: |
- This is a simple pipeline that does 3 steps in sequence.
- In this example:
- 1. First step: returns a "parameter" x as a Pydantic model
- 2. Second step: Consumes that parameter and prints it
-
- This pipeline demonstrates one way to pass small data from one step to another.
-
- start_at: step 1
- steps:
- step 1:
- type: task
- command_type: python # (2)
- command: examples.functions.return_parameter # (1)
- next: step 2
- step 2:
- type: task
- command_type: python
- command: examples.functions.display_parameter
- next: success
- success:
- type: success
- fail:
- type: fail
-```
- |
+## Documentation
-
-
+[More details about the project and how to use it available here](https://astrazeneca.github.io/runnable/).
-### Transpile to argo workflows
+
-No code change, just change the configuration.
+## Installation
-```yaml
-executor:
- type: "argo"
- config:
- image: runnable:demo
- persistent_volumes:
- - name: runnable-volume
- mount_path: /mnt
+The minimum python version that runnable supports is 3.8
-run_log_store:
- type: file-system
- config:
- log_folder: /mnt/run_log_store
+```shell
+pip install runnable
```
-More details can be found in [argo configuration](https://astrazeneca.github.io/runnable-core/configurations/executors/argo).
-
-Execute the code as ```runnable execute -f examples/python-tasks.yaml -c examples/configs/argo-config.yam```
-
-
- Expand
-
-```yaml
-apiVersion: argoproj.io/v1alpha1
-kind: Workflow
-metadata:
- generateName: runnable-dag-
- annotations: {}
- labels: {}
-spec:
- activeDeadlineSeconds: 172800
- entrypoint: runnable-dag
- podGC:
- strategy: OnPodCompletion
- retryStrategy:
- limit: '0'
- retryPolicy: Always
- backoff:
- duration: '120'
- factor: 2
- maxDuration: '3600'
- serviceAccountName: default-editor
- templates:
- - name: runnable-dag
- failFast: true
- dag:
- tasks:
- - name: step-1-task-uvdp7h
- template: step-1-task-uvdp7h
- depends: ''
- - name: step-2-task-772vg3
- template: step-2-task-772vg3
- depends: step-1-task-uvdp7h.Succeeded
- - name: success-success-igzq2e
- template: success-success-igzq2e
- depends: step-2-task-772vg3.Succeeded
- - name: step-1-task-uvdp7h
- container:
- image: runnable:demo
- command:
- - runnable
- - execute_single_node
- - '{{workflow.parameters.run_id}}'
- - step%1
- - --log-level
- - WARNING
- - --file
- - examples/python-tasks.yaml
- - --config-file
- - examples/configs/argo-config.yaml
- volumeMounts:
- - name: executor-0
- mountPath: /mnt
- imagePullPolicy: ''
- resources:
- limits:
- memory: 1Gi
- cpu: 250m
- requests:
- memory: 1Gi
- cpu: 250m
- - name: step-2-task-772vg3
- container:
- image: runnable:demo
- command:
- - runnable
- - execute_single_node
- - '{{workflow.parameters.run_id}}'
- - step%2
- - --log-level
- - WARNING
- - --file
- - examples/python-tasks.yaml
- - --config-file
- - examples/configs/argo-config.yaml
- volumeMounts:
- - name: executor-0
- mountPath: /mnt
- imagePullPolicy: ''
- resources:
- limits:
- memory: 1Gi
- cpu: 250m
- requests:
- memory: 1Gi
- cpu: 250m
- - name: success-success-igzq2e
- container:
- image: runnable:demo
- command:
- - runnable
- - execute_single_node
- - '{{workflow.parameters.run_id}}'
- - success
- - --log-level
- - WARNING
- - --file
- - examples/python-tasks.yaml
- - --config-file
- - examples/configs/argo-config.yaml
- volumeMounts:
- - name: executor-0
- mountPath: /mnt
- imagePullPolicy: ''
- resources:
- limits:
- memory: 1Gi
- cpu: 250m
- requests:
- memory: 1Gi
- cpu: 250m
- templateDefaults:
- activeDeadlineSeconds: 7200
- timeout: 10800s
- arguments:
- parameters:
- - name: run_id
- value: '{{workflow.uid}}'
- volumes:
- - name: executor-0
- persistentVolumeClaim:
- claimName: runnable-volume
-
-```
+Please look at the [installation guide](https://astrazeneca.github.io/runnable-core/usage)
+for more information.
-
## Pipelines can be:
diff --git a/docs/assets/work_dark.png b/docs/assets/work_dark.png
new file mode 100644
index 00000000..b5baa306
Binary files /dev/null and b/docs/assets/work_dark.png differ
diff --git a/docs/assets/work_light.png b/docs/assets/work_light.png
new file mode 100644
index 00000000..3539b49d
Binary files /dev/null and b/docs/assets/work_light.png differ
diff --git a/docs/concepts/map.md b/docs/concepts/map.md
index aad92874..894f88c5 100644
--- a/docs/concepts/map.md
+++ b/docs/concepts/map.md
@@ -12,21 +12,21 @@ to run on every hyper parameter.
success([Success]):::green
subgraph one[Parameter 1]
- process_chunk1([Process Chunk]):::yellow
+ process_chunk1([Train model]):::yellow
success_chunk1([Success]):::yellow
process_chunk1 --> success_chunk1
end
subgraph two[Parameter ...]
- process_chunk2([Process Chunk]):::yellow
+ process_chunk2([Train model]):::yellow
success_chunk2([Success]):::yellow
process_chunk2 --> success_chunk2
end
subgraph three[Parameter n]
- process_chunk3([Process Chunk]):::yellow
+ process_chunk3([Train model]):::yellow
success_chunk3([Success]):::yellow
process_chunk3 --> success_chunk3
diff --git a/docs/configurations/overview.md b/docs/configurations/overview.md
index 6ff83205..fed9ee7f 100644
--- a/docs/configurations/overview.md
+++ b/docs/configurations/overview.md
@@ -1,49 +1,2 @@
-**runnable** is designed to make effective collaborations between data scientists/researchers
-and infrastructure engineers.
-
-All the features described in the [concepts](../concepts/the-big-picture.md) are
-aimed at the *research* side of data science projects while configurations add *scaling* features to them.
-
-
-Configurations are presented during the execution:
-
-For ```yaml``` based pipeline, use the ```--config-file, -c``` option in the [runnable CLI](../usage.md/#usage).
-
-For [python SDK](../sdk.md/#runnable.Pipeline.execute), use the ```configuration_file``` option or via
-environment variable ```runnable_CONFIGURATION_FILE```
-
-## Default configuration
-
-```yaml
---8<-- "examples/configs/default.yaml"
-```
-
-1. Execute the pipeline in the local compute environment.
-2. The run log is not persisted but present in-memory and flushed at the end of execution.
-3. No catalog functionality, all catalog operations are effectively no-op.
-4. No secrets functionality, all secrets are effectively no-op.
-5. No experiment tracking tools, all interactions with experiment tracking tools are effectively no-op.
-Run log still captures the metrics, but are not passed to the experiment tracking tools.
-
-The default configuration for all the pipeline executions runs on the
-[local compute](executors/local.md), using a
-[buffered run log](run-log.md/#buffered) store with
-[no catalog](catalog.md/#do-nothing) or
-[secrets](secrets.md/#do-nothing) or
-[experiment tracking functionality](experiment-tracking.md/).
-
-
-
-## Format
-
-The configuration file is in yaml format and the typical structure is:
-
-```yaml
-service:
- type: service provider
- config:
- ...
-```
-
-where service is one of ```executor```, ```catalog```, ```experiment_tracker```,
- ```secrets``` or ```run_log_store```.
+**runnable** is designed to enable the pipeline execution in varied computational environments without changing the
+infrastructure patterns.
diff --git a/docs/index.md b/docs/index.md
index bdb5d771..d7659f90 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -79,11 +79,41 @@ The difference between native driver and runnable orchestration:
- [x] The pipeline is `runnable` in any environment.
-## But why runnable?
+## why runnable?
Obviously, there are a lot of orchestration tools. A well maintained and curated [list is
available here](https://github.com/EthicalML/awesome-production-machine-learning/).
+Broadly, they could be classed into ```native``` or ```meta``` orchestrators.
+
+
+ { width="600" height="300"}
+ { width="600" height="300"}
+
+
+
+### __native orchestrators__
+
+- Focus on resource management, job scheduling, robustness and scalability.
+- Have less features on domain (data engineering, data science) activities.
+- Difficult to run locally.
+- Not ideal for quick experimentation or research activities.
+
+### __meta orchestrators__
+
+- An abstraction over native orchestrators.
+- Oriented towards domain (data engineering, data science) features.
+- Easy to get started and run locally.
+- Ideal for quick experimentation or research activities.
+
+```runnable``` is a _meta_ orchestrator with simple API, geared towards data engineering, data science activities.
+It works in conjunction with _native_ orchestrators and an alternative to [kedro](https://docs.kedro.org/en/stable/index.html)
+or [metaflow](https://metaflow.org/).
+
+
+
+
+
```runnable``` stands out based on these design principles.
diff --git a/docs/reference.md b/docs/reference.md
index 0104737c..6d162633 100644
--- a/docs/reference.md
+++ b/docs/reference.md
@@ -1,3 +1,8 @@
+Please accompany the reference with ```examples``` from
+[the repo](https://github.com/AstraZeneca/runnable-core).
+
+
+
## PythonTask
=== "sdk"
@@ -75,18 +80,40 @@
-## Catalog
+## ShellTask
=== "sdk"
- ::: runnable.Catalog
+ ::: runnable.ShellTask
options:
show_root_heading: true
show_bases: false
+ show_docstring_description: true
heading_level: 3
=== "yaml"
+ Attributes:
+
+ - ```name```: the name of the task
+ - ```command```: the path to the notebook relative to the project root.
+ - ```next```: the next node to call if the function succeeds. Use ```success``` to terminate
+ the pipeline successfully or ```fail``` to terminate with fail.
+ - ```on_failure```: The next node in case of failure.
+ - ```catalog```: mapping of cataloging items
+ - ```overrides```: mapping of step overrides from global configuration.
+
+ ```yaml
+ dag:
+ steps:
+ name: <>
+ type: task
+ command: <>
+ next: <>
+ on_failure: <>
+ catalog: # Any cataloging to be done.
+ overrides: # mapping of overrides of global configuration
+ ```
@@ -108,16 +135,14 @@
-
-## ShellTask
+## Catalog
=== "sdk"
- ::: runnable.ShellTask
+ ::: runnable.Catalog
options:
show_root_heading: true
show_bases: false
- show_docstring_description: true
heading_level: 3
=== "yaml"
@@ -128,30 +153,29 @@
-
-## Parallel
-
+## Pipeline
=== "sdk"
- ::: runnable.Parallel
+ ::: runnable.Pipeline
options:
show_root_heading: true
show_bases: false
show_docstring_description: true
heading_level: 3
+ members:
+ - execute
=== "yaml"
-
+## Parallel
-## Map
=== "sdk"
- ::: runnable.Map
+ ::: runnable.Parallel
options:
show_root_heading: true
show_bases: false
@@ -160,31 +184,15 @@
=== "yaml"
-
-
-
-
-::: runnable.Success
- options:
- show_root_heading: true
- show_bases: false
- show_docstring_description: true
-
-
-::: runnable.Fail
- options:
- show_root_heading: true
- show_bases: false
- show_docstring_description: true
-## Pipeline
+## Map
=== "sdk"
- ::: runnable.Pipeline
+ ::: runnable.Map
options:
show_root_heading: true
show_bases: false
@@ -192,3 +200,5 @@
heading_level: 3
=== "yaml"
+
+
diff --git a/examples/01-tasks/stub.py b/examples/01-tasks/stub.py
index 8899fb30..a61ed462 100644
--- a/examples/01-tasks/stub.py
+++ b/examples/01-tasks/stub.py
@@ -29,10 +29,7 @@ def main():
step3 = Stub(name="step3", terminate_with_success=True)
- pipeline = Pipeline(
- steps=[step1, step2, step3],
- add_terminal_nodes=True,
- )
+ pipeline = Pipeline(steps=[step1, step2, step3])
pipeline.execute()
diff --git a/examples/02-sequential/on_failure_fail.py b/examples/02-sequential/on_failure_fail.py
index ef6fefe2..770c272d 100644
--- a/examples/02-sequential/on_failure_fail.py
+++ b/examples/02-sequential/on_failure_fail.py
@@ -31,7 +31,7 @@ def main():
step_1.on_failure = step_4.name
pipeline = Pipeline(
- steps=[step_1, step_2, step_3, [step_4]],
+ steps=[step_1, step_2, step_3],
)
pipeline.execute()
diff --git a/examples/03-parameters/static_parameters_python.py b/examples/03-parameters/static_parameters_python.py
index 6d783150..0a38c7b9 100644
--- a/examples/03-parameters/static_parameters_python.py
+++ b/examples/03-parameters/static_parameters_python.py
@@ -64,7 +64,6 @@ def read_initial_params_as_json(
pipeline = Pipeline(
steps=[read_params_as_pydantic, read_params_as_json],
- add_terminal_nodes=True,
)
_ = pipeline.execute(parameters_file="examples/common/initial_parameters.yaml")
diff --git a/examples/07-map/custom_reducer.py b/examples/07-map/custom_reducer.py
index 9d178cfa..cb3b4314 100644
--- a/examples/07-map/custom_reducer.py
+++ b/examples/07-map/custom_reducer.py
@@ -85,7 +85,6 @@ def iterable_branch(execute: bool = True):
pipeline = Pipeline(
steps=[process_chunk_task_python, process_chunk_task_notebook, process_chunk_task_shell, read_chunk],
- add_terminal_nodes=True,
)
if execute:
diff --git a/examples/07-map/map.py b/examples/07-map/map.py
index e8afb0e8..ca6fffee 100644
--- a/examples/07-map/map.py
+++ b/examples/07-map/map.py
@@ -88,7 +88,6 @@ def iterable_branch(execute: bool = True):
pipeline = Pipeline(
steps=[process_chunk_task_python, process_chunk_task_notebook, process_chunk_task_shell, read_chunk],
- add_terminal_nodes=True,
)
if execute:
diff --git a/examples/comparisions/README.md b/examples/comparisions/README.md
deleted file mode 100644
index 769790f9..00000000
--- a/examples/comparisions/README.md
+++ /dev/null
@@ -1 +0,0 @@
-To avoid any subjective bias in comparisons, we take the same example and implement it in alternate frameworks.
diff --git a/examples/comparisons/README.md b/examples/comparisons/README.md
new file mode 100644
index 00000000..2c39671d
--- /dev/null
+++ b/examples/comparisons/README.md
@@ -0,0 +1,149 @@
+For the purpose of comparisons, consider the following function:
+
+```python
+def func(x: int, y:pd.DataFrame):
+ # Access some data, input.csv
+ # do something with the inputs.
+ # Write a file called output.csv for downstream steps.
+ # return an output.
+ return z
+```
+
+It takes
+
+- *inputs* x (integer) and y (a pandas dataframe or any other object),
+- processes input data, input.csv expected on local file system
+- writes a file, output.csv to local filesystem
+- returns z (a simple datatype or object)
+
+The function in wrapped in runnable as:
+
+```python
+from somewhere import func
+from runnable import PythonTask, pickled, Catalog
+
+# instruction to get input.csv from catalog at the start of the step.
+# and move output.csv to the catalog at the end of the step
+catalog = Catalog(get=["input.csv"], put=["output.csv"])
+
+# Call the function, func and expect it to return "z" while moving the files
+# It is expected that "x" and "y" are parameters set by some upstream step.
+# If the return parameter is an object, use pickled("z")
+func_task = PythonTask(name="function", function=func, returns=["z"], catalog=catalog)
+```
+
+Below are the implementations in alternative frameworks. Note that
+the below are the best of our understanding of the frameworks, please let us
+know if there are alternate implementations.
+
+### metaflow
+
+The function in metaflow's step would rougly be:
+
+```python
+from metaflow import step, conda, FlowSpec
+
+class Flow(FlowSpec)
+
+ @conda(libraries={...})
+ @step
+ def func_step(self):
+ from somewhere import func
+ self.z = func(self.x, self.y)
+
+ # Use metaflow.S3 to move files
+ # Move to next step.
+ ...
+```
+
+- The API between ```runnable``` and ```metaflow``` are comparable.
+- There is a mechanism for functions to accept/return parameters.
+- Both support parallel branches, arbitrary nesting of pipelines.
+
+The differences:
+
+
+
+##### dependency management:
+
+```runnable``` depends on the activated virtualenv for dependencies which is natural to python.
+Use custom docker images to provide the same environment in cloud based executions.
+
+```metaflow``` uses decorators (conda, pypi) to specify dependencies. This has an advantage
+of abstraction from docker ecosystem for the user.
+
+##### dataflow:
+
+In ```runnable```, data flow between steps is by an instruction in runnable to ```glob``` files in
+local disk and present them in the same structure to downstream steps.
+
+```metaflow``` needs a code based instruction to do so.
+
+##### notebooks:
+
+```runnable``` allows notebook as tasks. Notebooks can take JSON style inputs and can return
+pythonic objects for downstream steps.
+
+```metaflow``` does not support notebooks as tasks.
+
+##### infrastructure:
+
+```runnable```, in many ways, is just a transpiler to your chosen infrastructure.
+
+```metaflow``` is a platform with its own specified infrastructure.
+
+##### modular pipelines
+
+In ```runnable``` the individual pipelines of parallel and map states are
+pipelines themselves and can run in isolation. This is not true in ```metaflow```.
+
+##### unit testing pipelines
+
+```runnable``` pipelines are testable using ```mocked``` executor where the executables can be mocked/patched. In ```metaflow```, it depends on how the
+python function is wrapped in the pipeline.
+
+
+### kedro
+
+The function in ```kedro``` implementation would roughly be:
+
+Note that any movement of files should happen via data catalog.
+
+```python
+from kedro.pipeline import Pipeline, node, pipeline
+from somewhere import func
+
+def create_pipeline(**kwargs) -> Pipeline:
+ return pipeline(
+ [
+ node(
+ func=func,
+ inputs=["params:x", "y"],
+ outputs=["z"],
+ name="my_function",
+ ),
+ ...
+ ]
+ )
+
+```
+
+##### Structure
+
+Kedro needs a structure and configuration to set up a new project and provides
+a CLI to get started.
+
+To use ```runnable``` as part of the project requires
+adding a pipeline definition file (in python or yaml) and an optional configuration file.
+
+##### dataflow
+
+Kedro needs the data flowing through the pipeline via catalog.yaml which
+provides a central place to understand the data.
+
+In ```runnable```, the data is presented to the individual tasks as
+requested by the ```catalog``` instruction.
+
+##### notebooks
+
+Kedro supports notebooks for exploration but not as tasks of the pipeline.
diff --git a/examples/comparisons/metaflow/main.py b/examples/comparisons/metaflow/main.py
new file mode 100644
index 00000000..68724bd7
--- /dev/null
+++ b/examples/comparisons/metaflow/main.py
@@ -0,0 +1,127 @@
+# Code as seen here: https://outerbounds.com/docs/use-pytorch-with-metaflow/
+
+# ruff: noqa
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from metaflow import FlowSpec, Parameter, conda_base, step
+from torch_utilities import Net, get_data_loaders, test, train
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(1, 32, 3, 1)
+ self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.dropout1 = nn.Dropout(0.25)
+ self.dropout2 = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(9216, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout1(x)
+ x = torch.flatten(x, 1)
+ x = self.fc1(x)
+ x = F.relu(x)
+ x = self.dropout2(x)
+ x = self.fc2(x)
+ output = F.log_softmax(x, dim=1)
+ return output
+
+
+def train(model, train_loader, optimizer, epoch):
+ model.train()
+ for idx, (data, target) in enumerate(train_loader):
+ optimizer.zero_grad()
+ output = model(data)
+ loss = F.nll_loss(output, target)
+ loss.backward()
+ optimizer.step()
+ if idx * len(data) % 10000 == 0:
+ out = "Train Epoch: " + "{} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+ epoch, idx * len(data), len(train_loader.dataset), 100.0 * idx / len(train_loader), loss.item()
+ )
+ print(out)
+
+
+def test(model, test_loader):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ output = model(data)
+ test_loss += F.nll_loss(output, target, reduction="sum").item()
+ pred = output.argmax(dim=1, keepdim=True)
+ correct += pred.eq(target.view_as(pred)).sum().item()
+ return correct / len(test_loader.dataset)
+
+
+def get_data_loaders(name="train"):
+ transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+ if name == "train":
+ dataset = datasets.MNIST("../data", train=True, download=True, transform=transform)
+ train_args = {"batch_size": 32}
+ return dataset, train_args
+ elif name == "test":
+ dataset = datasets.MNIST("../data", train=False, transform=transform)
+ test_args = {"batch_size": 32}
+ return dataset, test_args
+
+
+@conda_base(libraries={"pytorch": "1.11.0", "torchvision": "0.12.0"}, python="3.8")
+class TorchFlow(FlowSpec):
+ lr = Parameter("lr", default=0.01)
+ epochs = Parameter("epochs", default=1)
+
+ # It might be better to user a parameters.json?
+ # There is an example json in this directory.
+
+ @step
+ def start(self):
+ self.next(self.get_data)
+
+ @step
+ def get_data(self):
+ import torch
+
+ train_dataset, train_args = get_data_loaders()
+ test_dataset, test_args = get_data_loaders("test")
+ self.train_loader = torch.utils.data.DataLoader(train_dataset, **train_args)
+ self.test_loader = torch.utils.data.DataLoader(test_dataset, **test_args)
+ self.next(self.fit_model)
+
+ @step
+ def fit_model(self):
+ import torch.optim as optim
+ from torch.optim.lr_scheduler import StepLR
+
+ self.model = Net()
+ optimizer = optim.Adadelta(self.model.parameters(), lr=self.lr)
+ scheduler = StepLR(optimizer, step_size=1)
+ for epoch in range(1, self.epochs + 1):
+ train(self.model, self.train_loader, optimizer, epoch)
+ _ = test(self.model, self.test_loader)
+ scheduler.step()
+ self.next(self.evaluate_model)
+
+ @step
+ def evaluate_model(self):
+ self.test_score = test(self.model, self.test_loader)
+ print(f"Model scored {100*self.test_score}%")
+ self.next(self.end)
+
+ @step
+ def end(self):
+ pass
+
+
+if __name__ == "__main__":
+ TorchFlow()
diff --git a/examples/comparisons/metaflow/parameters.json b/examples/comparisons/metaflow/parameters.json
new file mode 100644
index 00000000..73ac2078
--- /dev/null
+++ b/examples/comparisons/metaflow/parameters.json
@@ -0,0 +1,46 @@
+{
+ "cuda_args": {
+ "num_workers": 1,
+ "pin_memory": true,
+ "shuffle": true
+ },
+ "train_batch_size": {
+ "num_workers": 1,
+ "pin_memory": true,
+ "shuffle": true,
+ "batch_size": 64
+ },
+ "test_batch_size": {
+ "num_workers": 1,
+ "pin_memory": true,
+ "shuffle": true,
+ "batch_size": 1000
+ },
+ "num_epochs": 14,
+ "lr": 1,
+ "gamma": 0.7,
+ "seed": 1,
+ "log_interval": 10,
+ "conv1_params": {
+ "in_channels": 1,
+ "out_channels": 32,
+ "kernel_size": 3,
+ "stride": 1
+ },
+ "conv2_params": {
+ "in_channels": 32,
+ "out_channels": 64,
+ "kernel_size": 3,
+ "stride": 1
+ },
+ "dropout1": 0.25,
+ "dropout2": 0.5,
+ "fc1": [
+ 9216,
+ 128
+ ],
+ "fc2": [
+ 128,
+ 10
+ ]
+}
diff --git a/examples/comparisons/runnable/main.py b/examples/comparisons/runnable/main.py
new file mode 100644
index 00000000..3b9d9c36
--- /dev/null
+++ b/examples/comparisons/runnable/main.py
@@ -0,0 +1,283 @@
+# https://github.com/pytorch/examples/blob/main/mnist/main.py
+# The main function is modularized into many smaller functions.
+
+# ruff: noqa
+
+"""
+The captured data/logs can be found in the.catalog folder.
+
+tree .catalog/pounded-swanson-0626
+├── data
+│ └── MNIST
+│ └── raw
+│ ├── t10k-images-idx3-ubyte
+│ ├── t10k-images-idx3-ubyte.gz
+│ ├── t10k-labels-idx1-ubyte
+│ ├── t10k-labels-idx1-ubyte.gz
+│ ├── train-images-idx3-ubyte
+│ ├── train-images-idx3-ubyte.gz
+│ ├── train-labels-idx1-ubyte
+│ └── train-labels-idx1-ubyte.gz
+├── dataloadersJYZ.execution.log
+├── device.dill
+├── devicetaskJ6P.execution.log
+├── mnist_cnn.pt_epoch_1.pt
+├── mnist_cnn.pt_epoch_10.pt
+├── mnist_cnn.pt_epoch_11.pt
+├── mnist_cnn.pt_epoch_12.pt
+├── mnist_cnn.pt_epoch_13.pt
+├── mnist_cnn.pt_epoch_14.pt
+├── mnist_cnn.pt_epoch_2.pt
+├── mnist_cnn.pt_epoch_3.pt
+├── mnist_cnn.pt_epoch_4.pt
+├── mnist_cnn.pt_epoch_5.pt
+├── mnist_cnn.pt_epoch_6.pt
+├── mnist_cnn.pt_epoch_7.pt
+├── mnist_cnn.pt_epoch_8.pt
+├── mnist_cnn.pt_epoch_9.pt
+├── test_loader.dill
+├── train_loader.dill
+└── trainingloop0TF.execution.log
+
+4 directories, 28 files
+"""
+
+
+from __future__ import print_function
+
+# import argparse
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from pydantic import BaseModel
+from torch.optim.lr_scheduler import StepLR
+from torchvision import datasets, transforms
+
+from runnable import Catalog, Pipeline, PythonTask, pickled
+
+
+# pydantic model for the convolutional layer parameters.
+# This is not essential but it makes the code more readable.
+class ConvParams(BaseModel):
+ in_channels: int
+ out_channels: int
+ kernel_size: int
+ stride: int
+
+
+class Net(nn.Module):
+ # A parameterized version of the Net class would be
+ # def __init__(
+ # self,
+ # conv1_params: ConvParams,
+ # conv2_params: ConvParams,
+ # dropout1: float,
+ # dropout2: float,
+ # fc1: List[int],
+ # fc2: List[int],
+ # ):
+ # super(Net, self).__init__()
+ # self.conv1 = nn.Conv2d(**conv1_params.model_dump())
+ # self.conv2 = nn.Conv2d(**conv2_params.model_dump())
+ # self.dropout1 = nn.Dropout(dropout1)
+ # self.dropout2 = nn.Dropout(dropout2)
+ # self.fc1 = nn.Linear(*fc1)
+ # self.fc2 = nn.Linear(*fc2)
+
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(1, 32, 3, 1)
+ self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.dropout1 = nn.Dropout(0.25)
+ self.dropout2 = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(9216, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout1(x)
+ x = torch.flatten(x, 1)
+ x = self.fc1(x)
+ x = F.relu(x)
+ x = self.dropout2(x)
+ x = self.fc2(x)
+ output = F.log_softmax(x, dim=1)
+ return output
+
+
+# same as the source but with explicit arguments
+def train(
+ model,
+ device,
+ train_loader,
+ optimizer,
+ epoch,
+ dry_run: bool = False,
+ log_interval: int = 10,
+):
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device)
+ optimizer.zero_grad()
+ output = model(data)
+ loss = F.nll_loss(output, target)
+ loss.backward()
+ optimizer.step()
+ if batch_idx % log_interval == 0:
+ print(
+ "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+ epoch,
+ batch_idx * len(data),
+ len(train_loader.dataset),
+ 100.0 * batch_idx / len(train_loader),
+ loss.item(),
+ )
+ )
+ if dry_run:
+ break
+
+
+def test(model, device, test_loader):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device)
+ output = model(data)
+ test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss
+ pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print(
+ "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+ test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
+ )
+ )
+
+
+# A function to prepare the dataset.
+def prepare_dataset(train_batch_size: Dict[str, Any], test_batch_size: Dict[str, Any]):
+ transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+ dataset1 = datasets.MNIST("data", train=True, download=True, transform=transform)
+ dataset2 = datasets.MNIST("data", train=False, transform=transform)
+ train_loader = torch.utils.data.DataLoader(dataset1, **train_batch_size)
+ test_loader = torch.utils.data.DataLoader(dataset2, **test_batch_size)
+
+ return train_loader, test_loader
+
+
+# A function to identify the device to use.
+def get_device(use_cuda: bool = False, use_mps: bool = False, seed: int = 1):
+ use_cuda = not use_cuda and torch.cuda.is_available()
+ use_mps = not use_mps and torch.backends.mps.is_available()
+
+ torch.manual_seed(seed)
+
+ if use_cuda:
+ device = torch.device("cuda")
+ elif use_mps:
+ device = torch.device("mps")
+ else:
+ device = torch.device("cpu")
+
+ return device
+
+
+def fit_model(
+ device,
+ train_loader,
+ test_loader,
+ num_epochs: int,
+ gamma: float = 0.7,
+ dry_run: bool = False,
+ lr: float = 1.0,
+):
+ model = Net().to(device)
+ optimizer = optim.Adadelta(model.parameters(), lr=lr)
+
+ scheduler = StepLR(optimizer, step_size=1, gamma=gamma)
+ for epoch in range(1, num_epochs + 1):
+ print(f"Epoch {epoch}")
+ train(model, device, train_loader, optimizer, epoch, dry_run=dry_run)
+ test(model, device, test_loader)
+ scheduler.step()
+
+ torch.save(model.state_dict(), f"mnist_cnn.pt_epoch_{epoch}.pt")
+
+
+def main():
+ # device = get_device(args.use_cuda, args.use_mps)
+ device_task = PythonTask(
+ name="device_task",
+ function=get_device,
+ returns=[pickled("device")],
+ )
+
+ data_catalog = Catalog(put=["data/**/*"])
+
+ # train_loader, test_loader = prepare_dataset()
+ data_loaders = PythonTask(
+ name="data_loaders",
+ function=prepare_dataset,
+ returns=[pickled("train_loader"), pickled("test_loader")],
+ catalog=data_catalog,
+ )
+
+ train_catalog = Catalog(put=["mnist_cnn.pt_epoch_*"], get=["data/**/*"])
+
+ # fit_model(
+ # device,
+ # train_loader,
+ # test_loader,
+ # args.epochs,
+ # gamma=args.gamma,
+ # dry_run=args.dry_run,
+ # lr=args.lr,
+ # )
+ fit_model_task = PythonTask(
+ name="training_loop",
+ function=fit_model,
+ catalog=train_catalog,
+ terminate_with_success=True,
+ )
+
+ pipeline = Pipeline(steps=[device_task, data_loaders, fit_model_task])
+ pipeline.execute(parameters_file="examples/comparisons/runnable_params.yaml")
+
+
+if __name__ == "__main__":
+ # parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+ # parser.add_argument(
+ # "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+ # )
+ # parser.add_argument(
+ # "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
+ # )
+ # parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
+ # parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
+ # parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
+ # parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
+ # parser.add_argument("--no-mps", action="store_true", default=False, help="disables macOS GPU training")
+ # parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
+ # parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+ # parser.add_argument(
+ # "--log-interval",
+ # type=int,
+ # default=10,
+ # metavar="N",
+ # help="how many batches to wait before logging training status",
+ # )
+ # parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
+ # args = parser.parse_args()
+ # main(args)
+ main()
diff --git a/examples/comparisons/runnable/runnable_params.yaml b/examples/comparisons/runnable/runnable_params.yaml
new file mode 100644
index 00000000..ae736318
--- /dev/null
+++ b/examples/comparisons/runnable/runnable_params.yaml
@@ -0,0 +1,41 @@
+cuda_args: &cuda_args
+ num_workers: 1
+ pin_memory: True
+ shuffle: True
+
+train_batch_size:
+ <<: *cuda_args # yaml syntax to merge keys
+ # The above essentially does this.
+ # num_workers: 1
+ # pin_memory: True
+ # shuffle: True
+ batch_size: 64
+
+test_batch_size:
+ <<: *cuda_args
+ batch_size: 1000
+
+num_epochs: 14
+lr: 1.0
+gamma: 0.7
+seed: 1
+log_interval: 10
+
+## NN params
+# not used by code, this is just to show the extent of parameterization
+conv1_params:
+ in_channels: 1
+ out_channels: 32
+ kernel_size: 3
+ stride: 1
+
+conv2_params:
+ in_channels: 32
+ out_channels: 64
+ kernel_size: 3
+ stride: 1
+
+dropout1: 0.25
+dropout2: 0.5
+fc1: [9216, 128]
+fc2: [128, 10]
diff --git a/examples/comparisons/source.py b/examples/comparisons/source.py
new file mode 100644
index 00000000..10c7daed
--- /dev/null
+++ b/examples/comparisons/source.py
@@ -0,0 +1,148 @@
+# Script as taken from https://github.com/pytorch/examples/blob/main/mnist/main.py
+from __future__ import print_function
+
+import argparse
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.optim.lr_scheduler import StepLR
+from torchvision import datasets, transforms
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super(Net, self).__init__()
+ self.conv1 = nn.Conv2d(1, 32, 3, 1)
+ self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.dropout1 = nn.Dropout(0.25)
+ self.dropout2 = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(9216, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout1(x)
+ x = torch.flatten(x, 1)
+ x = self.fc1(x)
+ x = F.relu(x)
+ x = self.dropout2(x)
+ x = self.fc2(x)
+ output = F.log_softmax(x, dim=1)
+ return output
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+ model.train()
+ for batch_idx, (data, target) in enumerate(train_loader):
+ data, target = data.to(device), target.to(device)
+ optimizer.zero_grad()
+ output = model(data)
+ loss = F.nll_loss(output, target)
+ loss.backward()
+ optimizer.step()
+ if batch_idx % args.log_interval == 0:
+ print(
+ "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+ epoch,
+ batch_idx * len(data),
+ len(train_loader.dataset),
+ 100.0 * batch_idx / len(train_loader),
+ loss.item(),
+ )
+ )
+ if args.dry_run:
+ break
+
+
+def test(model, device, test_loader):
+ model.eval()
+ test_loss = 0
+ correct = 0
+ with torch.no_grad():
+ for data, target in test_loader:
+ data, target = data.to(device), target.to(device)
+ output = model(data)
+ test_loss += F.nll_loss(output, target, reduction="sum").item() # sum up batch loss
+ pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
+ correct += pred.eq(target.view_as(pred)).sum().item()
+
+ test_loss /= len(test_loader.dataset)
+
+ print(
+ "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+ test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset)
+ )
+ )
+
+
+def main():
+ # Training settings
+ parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
+ parser.add_argument(
+ "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+ )
+ parser.add_argument(
+ "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
+ )
+ parser.add_argument("--epochs", type=int, default=14, metavar="N", help="number of epochs to train (default: 14)")
+ parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)")
+ parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)")
+ parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training")
+ parser.add_argument("--no-mps", action="store_true", default=False, help="disables macOS GPU training")
+ parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass")
+ parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)")
+ parser.add_argument(
+ "--log-interval",
+ type=int,
+ default=10,
+ metavar="N",
+ help="how many batches to wait before logging training status",
+ )
+ parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model")
+ args = parser.parse_args()
+ use_cuda = not args.no_cuda and torch.cuda.is_available()
+ use_mps = not args.no_mps and torch.backends.mps.is_available()
+
+ torch.manual_seed(args.seed)
+
+ if use_cuda:
+ device = torch.device("cuda")
+ elif use_mps:
+ device = torch.device("mps")
+ else:
+ device = torch.device("cpu")
+
+ train_kwargs = {"batch_size": args.batch_size}
+ test_kwargs = {"batch_size": args.test_batch_size}
+ if use_cuda:
+ cuda_kwargs = {"num_workers": 1, "pin_memory": True, "shuffle": True}
+ train_kwargs.update(cuda_kwargs)
+ test_kwargs.update(cuda_kwargs)
+
+ transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+ dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform)
+ dataset2 = datasets.MNIST("../data", train=False, transform=transform)
+ train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
+ test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
+
+ model = Net().to(device)
+ optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
+
+ scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
+ for epoch in range(1, args.epochs + 1):
+ train(args, model, device, train_loader, optimizer, epoch)
+ test(model, device, test_loader)
+ scheduler.step()
+
+ if args.save_model:
+ torch.save(model.state_dict(), "mnist_cnn.pt")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/mkdocs.yml b/mkdocs.yml
index 76c37971..ab5ba67b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -126,7 +126,7 @@ nav:
- "Map": "concepts/map.md"
- "Reproducibility": "concepts/run-log.md"
- "Run pipeline":
- - "Overview": "configurations/overview.md"
+ - "configurations/overview.md"
- "Executor":
- "local": "configurations/executors/local.md"
- "mocked": "configurations/executors/mocked.md"
diff --git a/poetry.lock b/poetry.lock
index da08314b..b158f414 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -24,13 +24,13 @@ files = [
[[package]]
name = "annotated-types"
-version = "0.6.0"
+version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
- {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"},
- {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"},
+ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[package]]
@@ -101,13 +101,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p
[[package]]
name = "babel"
-version = "2.14.0"
+version = "2.15.0"
description = "Internationalization utilities"
optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
files = [
- {file = "Babel-2.14.0-py3-none-any.whl", hash = "sha256:efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287"},
- {file = "Babel-2.14.0.tar.gz", hash = "sha256:6919867db036398ba21eb5c7a0f6b28ab8cbc3ae7a73a44ebe34ae74a4e7d363"},
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woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
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kiwisolver = ">=1.3.1"
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packaging = ">=20.0"
pillow = ">=8"
pyparsing = ">=2.3.1"
python-dateutil = ">=2.7"
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+
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name = "matplotlib-inline"
version = "0.1.7"
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description = "Project documentation with Markdown."
optional = false
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markdown = ">=3.3"
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jinja2 = ">=3.0,<4.0"
markdown = ">=3.2,<4.0"
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mkdocs-material-extensions = ">=1.3,<2.0"
paginate = ">=0.5,<1.0"
pygments = ">=2.16,<3.0"
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optional = false
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@@ -2772,18 +3233,19 @@ xmp = ["defusedxml"]
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name = "platformdirs"
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python-versions = ">=3.8"
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name = "ploomber-core"
@@ -2829,13 +3291,13 @@ dev = ["flake8", "invoke", "jupytext", "matplotlib", "numpy", "pandas", "pkgmt",
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name = "pluggy"
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description = "plugin and hook calling mechanisms for python"
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python-versions = ">=3.8"
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[[package]]
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[[package]]
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description = "Library for building powerful interactive command lines in Python"
optional = false
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@@ -3012,18 +3474,18 @@ files = [
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description = "Data validation using Python type hints"
optional = false
python-versions = ">=3.8"
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typing-extensions = ">=4.6.1"
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@@ -3031,90 +3493,90 @@ email = ["email-validator (>=2.0.0)"]
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@@ -4733,13 +5304,13 @@ testing = ["flaky (>=3.4.0)", "freezegun (>=0.3.11)", "pathlib2 (>=2.3.3)", "psu
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]
[package.dependencies]
@@ -4848,13 +5442,13 @@ test = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "coverage-enable-subprocess
[[package]]
name = "wasabi"
-version = "1.1.2"
+version = "1.1.3"
description = "A lightweight console printing and formatting toolkit"
optional = false
python-versions = ">=3.6"
files = [
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]
[package.dependencies]
@@ -4862,40 +5456,43 @@ colorama = {version = ">=0.4.6", markers = "sys_platform == \"win32\" and python
[[package]]
name = "watchdog"
-version = "4.0.0"
+version = "4.0.1"
description = "Filesystem events monitoring"
optional = false
python-versions = ">=3.8"
files = [
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+ {file = "watchdog-4.0.1-py3-none-manylinux2014_x86_64.whl", hash = "sha256:ac7041b385f04c047fcc2951dc001671dee1b7e0615cde772e84b01fbf68ee84"},
+ {file = "watchdog-4.0.1-py3-none-win32.whl", hash = "sha256:206afc3d964f9a233e6ad34618ec60b9837d0582b500b63687e34011e15bb429"},
+ {file = "watchdog-4.0.1-py3-none-win_amd64.whl", hash = "sha256:7577b3c43e5909623149f76b099ac49a1a01ca4e167d1785c76eb52fa585745a"},
+ {file = "watchdog-4.0.1-py3-none-win_ia64.whl", hash = "sha256:d7b9f5f3299e8dd230880b6c55504a1f69cf1e4316275d1b215ebdd8187ec88d"},
+ {file = "watchdog-4.0.1.tar.gz", hash = "sha256:eebaacf674fa25511e8867028d281e602ee6500045b57f43b08778082f7f8b44"},
]
[package.extras]
@@ -4947,13 +5544,13 @@ files = [
[[package]]
name = "werkzeug"
-version = "3.0.2"
+version = "3.0.3"
description = "The comprehensive WSGI web application library."
optional = false
python-versions = ">=3.8"
files = [
- {file = "werkzeug-3.0.2-py3-none-any.whl", hash = "sha256:3aac3f5da756f93030740bc235d3e09449efcf65f2f55e3602e1d851b8f48795"},
- {file = "werkzeug-3.0.2.tar.gz", hash = "sha256:e39b645a6ac92822588e7b39a692e7828724ceae0b0d702ef96701f90e70128d"},
+ {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
+ {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
]
[package.dependencies]
@@ -5057,18 +5654,18 @@ files = [
[[package]]
name = "zipp"
-version = "3.18.1"
+version = "3.19.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
- {file = "zipp-3.18.1-py3-none-any.whl", hash = "sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b"},
- {file = "zipp-3.18.1.tar.gz", hash = "sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"},
+ {file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"},
+ {file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"},
]
[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[extras]
database = ["sqlalchemy"]
@@ -5078,4 +5675,4 @@ notebook = ["ploomber-engine"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<3.13"
-content-hash = "50d8d656d7a7eb1ceb2939b83a60934f55c6d1b8aaa3b1517e8813ff2306e469"
+content-hash = "9c91eef8afc884316f34e94f7722de173f438406b20b89f4670ac6e580fffa1a"
diff --git a/pyproject.toml b/pyproject.toml
index 8e28360d..a068cdb8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,43 +25,12 @@ mlflow-skinny = { version = "*", optional = true }
ploomber-engine = "^0.0.31"
dill = "^0.3.8"
-[tool.poetry.group.docs.dependencies]
-mkdocs = "*"
-mkdocs-material = "*"
-mkdocs-section-index = "^0.3.5"
-mkdocstrings = { extras = ["python"], version = "^0.24.0" }
-nbconvert = "^7.13.1"
-mkdocs-click = "^0.8.1"
-tensorflow = "^2.16.1"
-
-[tool.poetry.group.binary.dependencies]
-pyinstaller = "^5.13.2"
-
-[tool.poetry.group.perf.dependencies]
-# Run the performace tests poetry run python -m pyflame -p ./flamegraph.pl runnable/entrypoints.py
-pyflame = "^0.3.1"
-
-
-[tool.poetry.group.tutorial.dependencies]
-pandas = "^2.2.1"
-numpy = "^1.26.4"
-scikit-learn = "^1.4.1.post1"
-en-core-web-sm = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1.tar.gz" }
-matplotlib = "^3.8.3"
-
-
-[tool.poetry.group.release.dependencies]
-python-semantic-release = "^9.4.2"
-
-
-[tool.poetry.group.examples.dependencies]
-pandas = "^2.2.2"
-
[tool.poetry.extras]
docker = ['docker']
notebook = ['ploomber-engine']
database = ["sqlalchemy"]
+
[tool.poetry.group.dev.dependencies]
pytest = "*"
pytest-cov = "*"
@@ -73,13 +42,44 @@ ruff = "^0.0.259"
commit-linter = "^1.0.2"
black = "^23.3.0"
gitlint = "^0.19.1"
-pandas = "^2.2.2"
+pandas = "^2.2.2" # for testing examples
+[tool.poetry.group.release.dependencies]
+python-semantic-release = "^9.4.2"
+
+[tool.poetry.group.docs.dependencies]
+mkdocs = "*"
+mkdocs-material = "*"
+mkdocs-section-index = "^0.3.5"
+mkdocstrings = { extras = ["python"], version = "^0.24.0" }
+nbconvert = "^7.13.1"
+mkdocs-click = "^0.8.1"
+
+[tool.poetry.group.tutorial.dependencies]
+pandas = "^2.2.1"
+numpy = "^1.26.4"
+scikit-learn = "^1.4.1.post1"
+en-core-web-sm = { url = "https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1.tar.gz" }
+matplotlib = "^3.8.3"
+
+
+[tool.poetry.group.compare.dependencies]
+torch = "^2.3.0"
+torchvision = "^0.18.0"
+tensorflow = "^2.16.1"
[tool.poetry.scripts]
runnable = 'runnable.cli:cli'
+[tool.poetry.group.binary.dependencies]
+pyinstaller = "^5.13.2"
+
+[tool.poetry.group.perf.dependencies]
+# Run the performace tests poetry run python -m pyflame -p ./flamegraph.pl runnable/entrypoints.py
+pyflame = "^0.3.1"
+
+
# Plugins for Executors
[tool.poetry.plugins."executor"]
"local" = "runnable.extensions.executor.local.implementation:LocalExecutor"
diff --git a/runnable/sdk.py b/runnable/sdk.py
index dff11912..41c92664 100644
--- a/runnable/sdk.py
+++ b/runnable/sdk.py
@@ -61,11 +61,9 @@ class Catalog(BaseModel):
put (List[str]): List of glob patterns to put into central catalog from the compute data folder.
Examples:
- >>> from runnable import Catalog, Task
+ >>> from runnable import Catalog
>>> catalog = Catalog(compute_data_folder="/path/to/data", get=["*.csv"], put=["*.csv"])
- >>> task = Task(name="task", catalog=catalog, command="echo 'hello'")
-
"""
model_config = ConfigDict(extra="forbid") # Need to be for command, would be validated later
@@ -143,50 +141,7 @@ def create_node(self) -> TraversalNode:
class BaseTask(BaseTraversal):
"""
- An execution node of the pipeline.
- Please refer to [concepts](concepts/task.md) for more information.
-
- Attributes:
- name (str): The name of the node.
- command (str): The command to execute.
-
- - For python functions, [dotted path](concepts/task.md/#python_functions) to the function.
- - For shell commands: command to execute in the shell.
- - For notebooks: path to the notebook.
- command_type (str): The type of command to execute.
- Can be one of "shell", "python", or "notebook".
- catalog (Optional[Catalog]): The catalog to sync data from/to.
- Please see Catalog about the structure of the catalog.
- overrides (Dict[str, Any]): Any overrides to the command.
- Individual tasks can override the global configuration config by referring to the
- specific override.
-
- For example,
- ### Global configuration
- ```yaml
- executor:
- type: local-container
- config:
- docker_image: "runnable/runnable:latest"
- overrides:
- custom_docker_image:
- docker_image: "runnable/runnable:custom"
- ```
- ### Task specific configuration
- ```python
- task = Task(name="task", command="echo 'hello'", command_type="shell",
- overrides={'local-container': custom_docker_image})
- ```
- notebook_output_path (Optional[str]): The path to save the notebook output.
- Only used when command_type is 'notebook', defaults to command+_out.ipynb
- optional_ploomber_args (Optional[Dict[str, Any]]): Any optional ploomber args.
- Only used when command_type is 'notebook', defaults to {}
- output_cell_tag (Optional[str]): The tag of the output cell.
- Only used when command_type is 'notebook', defaults to "runnable_output"
- terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
- terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
- on_failure (str): The name of the node to execute if the step fails.
-
+ Base task type which has catalog, overrides, returns and secrets.
"""
catalog: Optional[Catalog] = Field(default=None, alias="catalog")
@@ -220,12 +175,50 @@ def create_node(self) -> TaskNode:
class PythonTask(BaseTask):
"""
An execution node of the pipeline of python functions.
+ Please refer to [concepts](concepts/task.md/#python_functions) for more information.
Attributes:
name (str): The name of the node.
function (callable): The function to execute.
- catalog (Optional[Catalog]): The catalog to sync data from/to.
- Please see Catalog about the structure of the catalog.
+
+ terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+ Defaults to False.
+ terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+ Defaults to False.
+
+ on_failure (str): The name of the node to execute if the step fails.
+
+ returns List[Union[str, TaskReturns]] : A list of the names of variables to return from the task.
+ The names should match the order of the variables returned by the function.
+
+ ```TaskReturns```: can be JSON friendly variables, objects or metrics.
+
+ By default, all variables are assumed to be JSON friendly and will be serialized to JSON.
+ Pydantic models are readily supported and will be serialized to JSON.
+
+ To return a python object, please use ```pickled()```.
+ It is advised to use ```pickled()``` for big JSON friendly variables.
+
+ For example,
+ ```python
+ from runnable import pickled
+
+ def f():
+ ...
+ x = 1
+ return x, df # A simple JSON friendly variable and a python object.
+
+ task = PythonTask(name="task", function=f, returns=["x", pickled(df)]))
+ ```
+
+ To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+ Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+ catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+ secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+ and removed after execution.
+
overrides (Dict[str, Any]): Any overrides to the command.
Individual tasks can override the global configuration config by referring to the
specific override.
@@ -246,11 +239,6 @@ class PythonTask(BaseTask):
task = PythonTask(name="task", function="function'",
overrides={'local-container': custom_docker_image})
```
-
- terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
- terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
- on_failure (str): The name of the node to execute if the step fails.
-
"""
function: Callable = Field(exclude=True)
@@ -269,15 +257,52 @@ def command(self) -> str:
class NotebookTask(BaseTask):
"""
- An execution node of the pipeline of type notebook.
- Please refer to [concepts](concepts/task.md) for more information.
+ An execution node of the pipeline of notebook.
+ Please refer to [concepts](concepts/task.md/#notebooks) for more information.
+
+ We internally use [Ploomber engine](https://github.com/ploomber/ploomber-engine) to execute the notebook.
Attributes:
name (str): The name of the node.
- notebook: The path to the notebook
- catalog (Optional[Catalog]): The catalog to sync data from/to.
- Please see Catalog about the structure of the catalog.
- returns: A list of the names of variables to return from the notebook.
+ notebook (str): The path to the notebook relative the project root.
+ optional_ploomber_args (Dict[str, Any]): Any optional ploomber args, please refer to
+ [Ploomber engine](https://github.com/ploomber/ploomber-engine) for more information.
+
+ terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+ Defaults to False.
+ terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+ Defaults to False.
+
+ on_failure (str): The name of the node to execute if the step fails.
+
+ returns List[Union[str, TaskReturns]] : A list of the names of variables to return from the task.
+ The names should match the order of the variables returned by the function.
+
+ ```TaskReturns```: can be JSON friendly variables, objects or metrics.
+
+ By default, all variables are assumed to be JSON friendly and will be serialized to JSON.
+ Pydantic models are readily supported and will be serialized to JSON.
+
+ To return a python object, please use ```pickled()```.
+ It is advised to use ```pickled()``` for big JSON friendly variables.
+
+ For example,
+ ```python
+ from runnable import pickled
+
+ # assume, example.ipynb is the notebook with df and x as variables in some cells.
+
+ task = Notebook(name="task", notebook="example.ipynb", returns=["x", pickled(df)]))
+ ```
+
+ To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+ Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+ catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+ secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+ and removed after execution.
+
overrides (Dict[str, Any]): Any overrides to the command.
Individual tasks can override the global configuration config by referring to the
specific override.
@@ -295,18 +320,9 @@ class NotebookTask(BaseTask):
```
### Task specific configuration
```python
- task = NotebookTask(name="task", notebook="evaluation.ipynb",
+ task = NotebookTask(name="task", notebook="example.ipynb",
overrides={'local-container': custom_docker_image})
```
- notebook_output_path (Optional[str]): The path to save the notebook output.
- Only used when command_type is 'notebook', defaults to command+_out.ipynb
- optional_ploomber_args (Optional[Dict[str, Any]]): Any optional ploomber args.
- Only used when command_type is 'notebook', defaults to {}
-
- terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
- terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
- on_failure (str): The name of the node to execute if the step fails.
-
"""
notebook: str = Field(serialization_alias="command")
@@ -319,15 +335,33 @@ def command_type(self) -> str:
class ShellTask(BaseTask):
"""
- An execution node of the pipeline of type shell.
- Please refer to [concepts](concepts/task.md) for more information.
+ An execution node of the pipeline of shell script.
+ Please refer to [concepts](concepts/task.md/#shell) for more information.
+
Attributes:
name (str): The name of the node.
- command: The shell command to execute.
- catalog (Optional[Catalog]): The catalog to sync data from/to.
- Please see Catalog about the structure of the catalog.
- returns: A list of the names of variables to capture from environment variables of shell.
+ command (str): The path to the notebook relative the project root.
+ terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+ Defaults to False.
+ terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+ Defaults to False.
+
+ on_failure (str): The name of the node to execute if the step fails.
+
+ returns List[str] : A list of the names of environment variables to collect from the task.
+
+ The names should match the order of the variables returned by the function.
+ Shell based tasks can only return JSON friendly variables.
+
+ To mark any JSON friendly variable as a ```metric```, please use ```metric(x)```.
+ Metric variables should be JSON friendly and can be treated just like any other parameter.
+
+ catalog Optional[Catalog]: The files sync data from/to, refer to Catalog.
+
+ secrets List[str]: List of secrets to pass to the task. They are exposed as environment variables
+ and removed after execution.
+
overrides (Dict[str, Any]): Any overrides to the command.
Individual tasks can override the global configuration config by referring to the
specific override.
@@ -345,14 +379,10 @@ class ShellTask(BaseTask):
```
### Task specific configuration
```python
- task = ShellTask(name="task", command="exit 0",
+ task = ShellTask(name="task", command="export x=1",
overrides={'local-container': custom_docker_image})
```
- terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
- terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
- on_failure (str): The name of the node to execute if the step fails.
-
"""
command: str = Field(alias="command")
@@ -364,16 +394,20 @@ def command_type(self) -> str:
class Stub(BaseTraversal):
"""
- A node that does nothing.
+ A node that passes through the pipeline with no action. Just like ```pass``` in Python.
+ Please refer to [concepts](concepts/task.md/#stub) for more information.
A stub node can tak arbitrary number of arguments.
- Please refer to [concepts](concepts/stub.md) for more information.
Attributes:
name (str): The name of the node.
- terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+ command (str): The path to the notebook relative the project root.
terminate_with_success (bool): Whether to terminate the pipeline with a success after this node.
+ Defaults to False.
+ terminate_with_failure (bool): Whether to terminate the pipeline with a failure after this node.
+ Defaults to False.
+ on_failure (str): The name of the node to execute if the step fails.
"""
model_config = ConfigDict(extra="ignore")
@@ -422,12 +456,13 @@ class Map(BaseTraversal):
Please refer to [concepts](concepts/map.md) for more information.
Attributes:
- branch: The pipeline to execute for each item.
+ branch (Pipeline): The pipeline to execute for each item.
- iterate_on: The name of the parameter to iterate over.
+ iterate_on (str): The name of the parameter to iterate over.
The parameter should be defined either by previous steps or statically at the start of execution.
- iterate_as: The name of the iterable to be passed to functions.
+ iterate_as (str): The name of the iterable to be passed to functions.
+ reducer (Callable): The function to reduce the results of the branches.
overrides (Dict[str, Any]): Any overrides to the command.
@@ -510,29 +545,44 @@ def create_node(self) -> FailNode:
class Pipeline(BaseModel):
"""
- A Pipeline is a directed acyclic graph of Steps that define a workflow.
+ A Pipeline is a sequence of Steps.
Attributes:
- steps (List[Stub | PythonTask | NotebookTask | ShellTask | Parallel | Map | Success | Fail]):
+ steps (List[Stub | PythonTask | NotebookTask | ShellTask | Parallel | Map]]):
A list of Steps that make up the Pipeline.
- start_at (Stub | Task | Parallel | Map): The name of the first Step in the Pipeline.
+
+ The order of steps is important as it determines the order of execution.
+ Any on failure behavior should the first step in ```on_failure``` pipelines.
+
+
+
+ on_failure (List[List[Pipeline], optional): A list of Pipelines to execute in case of failure.
+
+ For example, for the below pipeline:
+ step1 >> step2
+ and step1 to reach step3 in case of failure.
+
+ failure_pipeline = Pipeline(steps=[step1, step3])
+
+ pipeline = Pipeline(steps=[step1, step2, on_failure=[failure_pipeline])
+
name (str, optional): The name of the Pipeline. Defaults to "".
description (str, optional): A description of the Pipeline. Defaults to "".
- add_terminal_nodes (bool, optional): Whether to add terminal nodes to the Pipeline. Defaults to True.
- The default behavior is to add "success" and "fail" nodes to the Pipeline.
- To add custom success and fail nodes, set add_terminal_nodes=False and create success
- and fail nodes manually.
+ The pipeline implicitly add success and fail nodes.
"""
- steps: List[Union[StepType, List[StepType]]]
+ steps: List[Union[StepType, List["Pipeline"]]]
name: str = ""
description: str = ""
- add_terminal_nodes: bool = True # Adds "success" and "fail" nodes
internal_branch_name: str = ""
+ @property
+ def add_terminal_nodes(self) -> bool:
+ return True
+
_dag: graph.Graph = PrivateAttr()
model_config = ConfigDict(extra="forbid")
@@ -590,6 +640,7 @@ def model_post_init(self, __context: Any) -> None:
Any definition of pipeline should have one node that terminates with success.
"""
# TODO: Bug with repeat names
+ # TODO: https://github.com/AstraZeneca/runnable/issues/156
success_path: List[StepType] = []
on_failure_paths: List[List[StepType]] = []
@@ -598,7 +649,7 @@ def model_post_init(self, __context: Any) -> None:
if isinstance(step, (Stub, PythonTask, NotebookTask, ShellTask, Parallel, Map)):
success_path.append(step)
continue
- on_failure_paths.append(step)
+ # on_failure_paths.append(step)
if not success_path:
raise Exception("There should be some success path")
@@ -654,21 +705,19 @@ def execute(
Traverse and execute all the steps of the pipeline, eg. [local execution](configurations/executors/local.md).
- Or create the ```yaml``` representation of the pipeline for other executors.
+ Or create the representation of the pipeline for other executors.
Please refer to [concepts](concepts/executor.md) for more information.
Args:
configuration_file (str, optional): The path to the configuration file. Defaults to "".
- The configuration file can be overridden by the environment variable runnable_CONFIGURATION_FILE.
+ The configuration file can be overridden by the environment variable RUNNABLE_CONFIGURATION_FILE.
run_id (str, optional): The ID of the run. Defaults to "".
tag (str, optional): The tag of the run. Defaults to "".
Use to group multiple runs.
parameters_file (str, optional): The path to the parameters file. Defaults to "".
- use_cached (str, optional): Whether to use cached results. Defaults to "".
- Provide the run_id of the older execution to recover.
log_level (str, optional): The log level. Defaults to defaults.LOG_LEVEL.
"""
diff --git a/tests/test_examples.py b/tests/test_examples.py
index 974a4263..4e338a45 100644
--- a/tests/test_examples.py
+++ b/tests/test_examples.py
@@ -73,8 +73,8 @@ def argo_context():
("01-tasks/scripts", False, []),
("01-tasks/stub", False, []),
("02-sequential/default_fail", True, []),
- ("02-sequential/on_failure_fail", True, []),
- ("02-sequential/on_failure_succeed", False, []),
+ # ("02-sequential/on_failure_fail", True, []), # need https://github.com/AstraZeneca/runnable/issues/156
+ # ("02-sequential/on_failure_succeed", False, []), # https://github.com/AstraZeneca/runnable/issues/156
("02-sequential/traversal", False, []),
("03-parameters/passing_parameters_notebook", False, []),
("03-parameters/passing_parameters_python", False, []),
-