semvec/pypi-packages huggingface dataset:
fs-task-queue 0.1.9
Filesystem based task queue
License: BSD 3-Clause License
Requires Python: >=3.7

# Filesystem Task Queue

A task queue using the filesystem as the message queue. This project
was motivated by the use case where it is hard or near impossible to
run a persistent service like redis, rabbitmq, or database. If you are
able to run a persistent service you should prefer that approach. The
initial motivation for this package was a way to submit tasks to an
HPC cluster and process the tasks in HPC worker nodes without a
running service on the login node.

This project uses [filelock](https://pypi.org/project/filelock/). With
this library it is safe to say that if the underlying filesystem obeys
[flock](https://linux.die.net/man/2/flock) calls then each task is
guaranteed to be executed once. If any of the workers are on a
non-conforming filesystems at least once execution is guaranteed.

Keep in mind that NFS v2 and v3 have an external file lock system via
`rpc.lockd` which is not enabled everywhere since it is an external
service. The current NFS v4 has built in support for file locks but
the problem is that many HPC centers still use v3. Otherwise it is
safe these days to assume your filesystem supports locks. 

Keep in mind that task state is managed on the filesystem. So do not
use this if you have an enormous amount of tasks. Think of possibly
chunking them or using plugins like
`file_queue.plugins.dask.DaskWorker` to send tasks to dask (then
breaking it into many small tasks). Each task state modifications
results in 2-4 IOPS on the filesystem.

# Install

 - `pip install fs-task-queue`

# API

Creating a queue is as simple as supplying a directory where the queue
will reside.

```python
from fs_task_queue import Queue

queue = Queue("path/to/queue")
```

Submitting jobs and monitoring over SSH is also supported via the same
interface. Workers currently cannot connect over SSH.

```python
from fs_task_queue.plugins import SSHQueue

queue = SSHQueue("ssh://<username>:<password>@<hostname>:<port>/<path>")
```

Next we can submit/enqueue jobs to the queue.

```python
import operator

job = queue.enqueue(operator.add, 1, 2)
```

You can immediately try and fetch the result of the job or get its
status.

```python
print(job.get_status())
print(job.result)
```

You can wait on the job to finish

```python
result = job.wait()
```

# Worker

Starting a worker is as simple as giving a filesystem directory where
the queue will reside.

```shell
fs-task-queue-worker --path ./path/to/queue
```

A `dask` worker is supported via `fs_task_queue.plugin.dask.DaskWorker`
for sending jobs to the dask cluster instead of executing locally.

A worker runs a continuous loop gathering tasks in the task queue. The
worker creates a file `path/to/queue/workers/<worker-id>` where it
will:
 - continuously touch the file every 30 seconds
 - check that the file exists and if not stop the worker

# License

[BSD-3](./LICENSE)