| Name | modak JSON |
| Version |
0.3.6
JSON |
| download |
| home_page | None |
| Summary | A simple, opinionated task manager |
| upload_time | 2025-07-09 19:52:03 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.12 |
| license | MIT OR Apache-2.0 |
| keywords |
task
job
scheduler
monitor
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!-- markdownlint-disable MD033 MD041 -->
<p align="center">
<h1 align="center">modak</h1>
</p>
<p align="center">
<img alt="GitHub Release" src="https://img.shields.io/github/v/release/denehoffman/modak?style=for-the-badge&logo=github"></a>
<a href="https://github.com/denehoffman/modak/commits/main/" alt="Latest Commits">
<img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/denehoffman/modak?style=for-the-badge&logo=github"></a>
<a href="LICENSE-APACHE" alt="License">
<img alt="GitHub License" src="https://img.shields.io/github/license/denehoffman/modak?style=for-the-badge"></a>
<a href="https://pypi.org/project/modak/" alt="View project on PyPI">
<img alt="PyPI - Version" src="https://img.shields.io/pypi/v/modak?style=for-the-badge&logo=python&logoColor=yellow&labelColor=blue"></a>
</p>
`modak` is a simple-to-use, opinionated task queue system with dependency
management, resource allocation, and isolation control. Tasks are run
respecting topological dependencies, resource limits, and optional isolation.
This library only has two classes, `Task`s, which are an abstract class with a
single method to override, `run(self) -> None`, and a `TaskQueue` which manages
the execution order. Additionally, `modak` comes with a task monitor TUI which
can be invoked with the `modak` shell command.
The `TaskQueue` has been written in Rust to get past issues with parallelism
and the GIL. Instead of using a thread pool or even a multiprocessing pool,
the tasks are serialized into bytes and passed to the Rust-side manager, which
handles dispatching and execution. Each task is then run as a separate subprocess
spawned in a Rust thread. This means the only way to share state between tasks is
by writing to an output file and having a task depend on that file.
By default, `modak` scripts will create a state file called `.modak` in the
current working directory. This can be changed by setting it in the `TaskQueue`'s
initialization method. The `modak` CLI also supports an optional argument to
point to the location of the state file.
## Features
- Topological task scheduling
- Persistent state and log files
- Resource-aware execution
- Isolated task handling
- Skipping of previously completed tasks
## Installation
```shell
pip install modak
```
Or with `uv`:
```shell
pip install modak
```
## FAQ
> Q: What do you mean by "opinionated"?
A: The library is meant to do one thing (and hopefully do it well): run tasks
and write output files. Some users might want more flexibility, like writing
to a database or having a target that isn't written to at all, but that is
not a goal of this library. If you need this level of control, try [`airflow`](https://airflow.apache.org/)
or [`luigi`](https://github.com/spotify/luigi).
> Q: Why make another task manager?
A: [`luigi`](https://github.com/spotify/luigi) is nice, but I've been annoyed by
the poor type hints for task parameters. It's also very confusing for
first-time users, and has a lot of features that I don't really think people
use unless they are working with products like Spotify. I built `modak` with
research pipelines in mind, so I wanted something that was so simple to use,
you don't have to think too hard about what you're doing and can focus on
the data instead. I haven't used [airflow](https://airflow.apache.org/) much,
but it also seems like a tool intended for enterprise. My goal here is
simplicity and a minimal learning curve. There are only two classes. `luigi`
has the added annoyance of running a web server to visualize the state of the
DAG, which is very tricky to use on a remote server if you don't have the
proper permissions.
> Q: Isn't Rust a bit overkill?
A: Rust isn't as scary as it sounds. I don't actually care much about memory
safety (although I'll take it for free), I like the development experience.
> Q: Any sharp corners?
A: In development, I've found that libraries that do something when imported
need to be handled with care. Such libraries should be imported inside the
`run` method of the task. This is because the task gets serialized and sent
to the `__main__` module, but the imports from your code are run before
serialization. An example of this is the `loguru` library, which sets
up the global logger [on import](https://github.com/Delgan/loguru/blob/a69bfc451413f71b81761a238db4b5833cf0a992/loguru/__init__.py#L18).
If `loguru` is only imported outside the task, the `logger` instance will have
no sink added because [these lines](https://github.com/Delgan/loguru/blob/a69bfc451413f71b81761a238db4b5833cf0a992/loguru/__init__.py#L31-L32)
will not be run when the task is deserialized. This will not effect most code,
it's just something to be aware of.
## Examples
### A simple chain of tasks
```python
from modak import Task, TaskQueue
class PrintTask(Task):
def run(self):
self.logger.info(f"Running {self.name}")
t1 = PrintTask(name="task1")
t2 = PrintTask(name="task2", inputs=[t1])
t3 = PrintTask(name="task3", inputs=[t2])
queue = TaskQueue('example1')
queue.run([t3])
```
### Fan-in, fan-out
```python
from pathlib import Path
from modak import Task, TaskQueue
class DummyTask(Task):
def run(self):
self.logger.info(f"Running {self.name}")
for output in self.outputs:
output.write_text(f"Output of {self.name}")
# Leaf tasks
a = DummyTask(name="A", outputs=[Path("a.out")])
b = DummyTask(name="B", outputs=[Path("b.out")])
c = DummyTask(name="C", outputs=[Path("c.out")])
# Fan-in: D depends on A, B, C
d = DummyTask(name="D", inputs=[a, b, c], outputs=[Path("d.out")])
# Fan-out: E and F both depend on D
e = DummyTask(name="E", inputs=[d], outputs=[Path("e.out")])
f = DummyTask(name="F", inputs=[d], outputs=[Path("f.out")])
queue = TaskQueue('example2')
queue.run([e, f])
```
### A complex workflow
```python
from pathlib import Path
from modak import Task, TaskQueue
class SimTask(Task):
def run(self):
self.logger.info(f"{self.name} starting with {self.resources}")
for out in self.outputs:
out.write_text(f"Generated by {self.name}")
# Raw data preprocessing
pre_a = SimTask(name="PreA", outputs=[Path("a.pre")], resources={"cpu": 1})
pre_b = SimTask(name="PreB", outputs=[Path("b.pre")], resources={"cpu": 1})
pre_c = SimTask(name="PreC", outputs=[Path("c.pre")], resources={"cpu": 1})
# Feature extraction (can run in parallel)
feat1 = SimTask(name="Feature1", inputs=[pre_a], outputs=[Path("a.feat")], resources={"cpu": 2})
feat2 = SimTask(name="Feature2", inputs=[pre_b], outputs=[Path("b.feat")], resources={"cpu": 2})
feat3 = SimTask(name="Feature3", inputs=[pre_c], outputs=[Path("c.feat")], resources={"cpu": 2})
# Aggregation step
aggregate = SimTask(
name="Aggregate",
inputs=[feat1, feat2, feat3],
outputs=[Path("agg.out")],
resources={"cpu": 3}
)
# Final model training (expensive, must be isolated)
train = SimTask(
name="TrainModel",
inputs=[aggregate],
outputs=[Path("model.bin")],
isolated=True,
resources={"cpu": 3, "gpu": 1}
)
# Side analysis and visualization can run independently
viz = SimTask(name="Visualization", inputs=[feat1, feat2], outputs=[Path("viz.png")], resources={"cpu": 1})
stats = SimTask(name="Stats", inputs=[feat3], outputs=[Path("stats.txt")], resources={"cpu": 1})
queue = TaskQueue(
'example3',
workers=4,
resources={"cpu": 4, "gpu": 1}
)
queue.run([train, viz, stats])
```
## Future Plans
I'll probably make small improvements to the TUI and add features as I find the
need. Contributions are welcome, just open an issue or pull request on GitHub
and I'll try to respond as soon as I can.
Raw data
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"description": "<!-- markdownlint-disable MD033 MD041 -->\n<p align=\"center\">\n <h1 align=\"center\">modak</h1>\n</p>\n<p align=\"center\">\n <img alt=\"GitHub Release\" src=\"https://img.shields.io/github/v/release/denehoffman/modak?style=for-the-badge&logo=github\"></a>\n <a href=\"https://github.com/denehoffman/modak/commits/main/\" alt=\"Latest Commits\">\n <img alt=\"GitHub last commit\" src=\"https://img.shields.io/github/last-commit/denehoffman/modak?style=for-the-badge&logo=github\"></a>\n <a href=\"LICENSE-APACHE\" alt=\"License\">\n <img alt=\"GitHub License\" src=\"https://img.shields.io/github/license/denehoffman/modak?style=for-the-badge\"></a>\n <a href=\"https://pypi.org/project/modak/\" alt=\"View project on PyPI\">\n <img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/modak?style=for-the-badge&logo=python&logoColor=yellow&labelColor=blue\"></a>\n</p>\n\n`modak` is a simple-to-use, opinionated task queue system with dependency\nmanagement, resource allocation, and isolation control. Tasks are run\nrespecting topological dependencies, resource limits, and optional isolation.\n\nThis library only has two classes, `Task`s, which are an abstract class with a\nsingle method to override, `run(self) -> None`, and a `TaskQueue` which manages\nthe execution order. Additionally, `modak` comes with a task monitor TUI which\ncan be invoked with the `modak` shell command.\n\nThe `TaskQueue` has been written in Rust to get past issues with parallelism\nand the GIL. Instead of using a thread pool or even a multiprocessing pool,\nthe tasks are serialized into bytes and passed to the Rust-side manager, which\nhandles dispatching and execution. Each task is then run as a separate subprocess\nspawned in a Rust thread. This means the only way to share state between tasks is\nby writing to an output file and having a task depend on that file.\n\nBy default, `modak` scripts will create a state file called `.modak` in the\ncurrent working directory. This can be changed by setting it in the `TaskQueue`'s\ninitialization method. The `modak` CLI also supports an optional argument to\npoint to the location of the state file.\n\n## Features\n\n- Topological task scheduling\n- Persistent state and log files\n- Resource-aware execution\n- Isolated task handling\n- Skipping of previously completed tasks\n\n## Installation\n\n```shell\npip install modak\n```\n\nOr with `uv`:\n\n```shell\npip install modak\n```\n\n## FAQ\n\n> Q: What do you mean by \"opinionated\"?\n\nA: The library is meant to do one thing (and hopefully do it well): run tasks\nand write output files. Some users might want more flexibility, like writing\nto a database or having a target that isn't written to at all, but that is\nnot a goal of this library. If you need this level of control, try [`airflow`](https://airflow.apache.org/)\nor [`luigi`](https://github.com/spotify/luigi).\n\n> Q: Why make another task manager?\n\nA: [`luigi`](https://github.com/spotify/luigi) is nice, but I've been annoyed by\nthe poor type hints for task parameters. It's also very confusing for\nfirst-time users, and has a lot of features that I don't really think people\nuse unless they are working with products like Spotify. I built `modak` with\nresearch pipelines in mind, so I wanted something that was so simple to use,\nyou don't have to think too hard about what you're doing and can focus on\nthe data instead. I haven't used [airflow](https://airflow.apache.org/) much,\nbut it also seems like a tool intended for enterprise. My goal here is\nsimplicity and a minimal learning curve. There are only two classes. `luigi`\nhas the added annoyance of running a web server to visualize the state of the\nDAG, which is very tricky to use on a remote server if you don't have the\nproper permissions.\n\n> Q: Isn't Rust a bit overkill?\n\nA: Rust isn't as scary as it sounds. I don't actually care much about memory\nsafety (although I'll take it for free), I like the development experience.\n\n> Q: Any sharp corners?\n\nA: In development, I've found that libraries that do something when imported\nneed to be handled with care. Such libraries should be imported inside the\n`run` method of the task. This is because the task gets serialized and sent\nto the `__main__` module, but the imports from your code are run before\nserialization. An example of this is the `loguru` library, which sets\nup the global logger [on import](https://github.com/Delgan/loguru/blob/a69bfc451413f71b81761a238db4b5833cf0a992/loguru/__init__.py#L18).\nIf `loguru` is only imported outside the task, the `logger` instance will have\nno sink added because [these lines](https://github.com/Delgan/loguru/blob/a69bfc451413f71b81761a238db4b5833cf0a992/loguru/__init__.py#L31-L32)\nwill not be run when the task is deserialized. This will not effect most code,\nit's just something to be aware of.\n\n## Examples\n\n### A simple chain of tasks\n\n```python\nfrom modak import Task, TaskQueue\n\nclass PrintTask(Task):\n def run(self):\n self.logger.info(f\"Running {self.name}\")\n\nt1 = PrintTask(name=\"task1\")\nt2 = PrintTask(name=\"task2\", inputs=[t1])\nt3 = PrintTask(name=\"task3\", inputs=[t2])\n\nqueue = TaskQueue('example1')\nqueue.run([t3])\n```\n\n### Fan-in, fan-out\n\n```python\nfrom pathlib import Path\nfrom modak import Task, TaskQueue\n\nclass DummyTask(Task):\n def run(self):\n self.logger.info(f\"Running {self.name}\")\n for output in self.outputs:\n output.write_text(f\"Output of {self.name}\")\n\n# Leaf tasks\na = DummyTask(name=\"A\", outputs=[Path(\"a.out\")])\nb = DummyTask(name=\"B\", outputs=[Path(\"b.out\")])\nc = DummyTask(name=\"C\", outputs=[Path(\"c.out\")])\n\n# Fan-in: D depends on A, B, C\nd = DummyTask(name=\"D\", inputs=[a, b, c], outputs=[Path(\"d.out\")])\n\n# Fan-out: E and F both depend on D\ne = DummyTask(name=\"E\", inputs=[d], outputs=[Path(\"e.out\")])\nf = DummyTask(name=\"F\", inputs=[d], outputs=[Path(\"f.out\")])\n\nqueue = TaskQueue('example2')\nqueue.run([e, f])\n\n```\n\n### A complex workflow\n\n```python\nfrom pathlib import Path\nfrom modak import Task, TaskQueue\n\nclass SimTask(Task):\n def run(self):\n self.logger.info(f\"{self.name} starting with {self.resources}\")\n for out in self.outputs:\n out.write_text(f\"Generated by {self.name}\")\n\n# Raw data preprocessing\npre_a = SimTask(name=\"PreA\", outputs=[Path(\"a.pre\")], resources={\"cpu\": 1})\npre_b = SimTask(name=\"PreB\", outputs=[Path(\"b.pre\")], resources={\"cpu\": 1})\npre_c = SimTask(name=\"PreC\", outputs=[Path(\"c.pre\")], resources={\"cpu\": 1})\n\n# Feature extraction (can run in parallel)\nfeat1 = SimTask(name=\"Feature1\", inputs=[pre_a], outputs=[Path(\"a.feat\")], resources={\"cpu\": 2})\nfeat2 = SimTask(name=\"Feature2\", inputs=[pre_b], outputs=[Path(\"b.feat\")], resources={\"cpu\": 2})\nfeat3 = SimTask(name=\"Feature3\", inputs=[pre_c], outputs=[Path(\"c.feat\")], resources={\"cpu\": 2})\n\n# Aggregation step\naggregate = SimTask(\n name=\"Aggregate\",\n inputs=[feat1, feat2, feat3],\n outputs=[Path(\"agg.out\")],\n resources={\"cpu\": 3}\n)\n\n# Final model training (expensive, must be isolated)\ntrain = SimTask(\n name=\"TrainModel\",\n inputs=[aggregate],\n outputs=[Path(\"model.bin\")],\n isolated=True,\n resources={\"cpu\": 3, \"gpu\": 1}\n)\n\n# Side analysis and visualization can run independently\nviz = SimTask(name=\"Visualization\", inputs=[feat1, feat2], outputs=[Path(\"viz.png\")], resources={\"cpu\": 1})\nstats = SimTask(name=\"Stats\", inputs=[feat3], outputs=[Path(\"stats.txt\")], resources={\"cpu\": 1})\n\nqueue = TaskQueue(\n 'example3',\n workers=4,\n resources={\"cpu\": 4, \"gpu\": 1}\n)\n\nqueue.run([train, viz, stats])\n\n```\n\n## Future Plans\n\nI'll probably make small improvements to the TUI and add features as I find the\nneed. 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