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Tilebox Workflows
Tilebox Workflows, or the Tilebox workflow orchestrator is a parallel processing engine that allows an intuitive creation of dynamic tasks that can be parallelized out of the box and executed across compute environments or on-premise as well as in auto-scaling clusters in public clouds.
Quickstart
Install using pip:
pip install tilebox-workflows
For interactive job progress in Jupyter notebooks, install tilebox-workflows[notebook].
Without this extra, jobs use their plain-text representation.
For S3 client type information, install boto3-stubs[s3] in your development environment.
Create a task:
from tilebox.workflows import Task
class MyFirstTask(Task):
def execute(self):
print("Hello World from my first Tilebox task!")
Submit a job
from tilebox.workflows import Client
# create your API key at https://console.tilebox.com
client = Client(token="YOUR_TILEBOX_API_KEY")
jobs = client.jobs()
jobs.submit("my-very-first-job", MyFirstTask())
And run it:
runner = client.runner(tasks=[MyFirstTask])
runner.run_all()
Concurrent worker execution
A worker runtime can execute multiple tasks concurrently in one Python process. Each execution receives a newly
deserialized task instance and its own ExecutionContext, including task-local subtask and progress state. The
RunnerContext, configured JobCache, and any class or module state are process-level resources shared by those
executions.
Custom runner contexts, caches, and shared task state must therefore support concurrent access from multiple threads. Asynchronous task executions may also run on different event loops. Configure and register these resources before the worker starts; do not mutate runner configuration while tasks are executing. Compound cache operations are not atomic unless the cache implementation explicitly provides that guarantee.
Concurrency in one runtime avoids repeated process initialization and allows overlapping I/O or native code that releases Python's GIL. CPU-bound Python code still needs multiple runtime processes for parallel execution.
Reading automation objects
Storage-event tasks can read objects from Amazon S3, Google Cloud Storage, Azure Blob Storage, or the local filesystem:
content = self.trigger.storage.read(self.trigger.location)
The method returns bytes. Cloud reads use the credentials configured for the runner.
On macOS, GCS or Azure CLI authentication can emit gRPC fork diagnostics even when a read succeeds. These messages alone do not indicate a failed read; do not downgrade gRPC to hide them.
Documentation
Check out the Tilebox Workflows documentation for more information.
License
Distributed under the MIT License (The MIT License).
2 maintainers
Metadata
2 maintainers
Release files for tilebox-workflows 0.63.0
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| tilebox_workflows-0.63.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 272.1 kB
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