Release notes¶

Release v0.2¶

Use Tornado for worker-scheduler communication. Communication between scheduler and workers is now using tornado instead of dask to be more lightweight and reliable. Furthermore, a worker client is persistent across blocks, allowing it to request and receive multiple blocks to process on. This change is heavily motivated by the long queuing delay of lsf/slurm and bring-up delay of tensorflow. Furthermore, the user now has an API for acquiring and releasing block, allowing them to write their own Python module implementation of workers. By :user:`Tri Nguyen <trivoldus28>`, :user:`Jan Junke <funkey>`
Introduce Task and Parameter, and daisy.distribute() to execute Task chain.
Changed SharedGraphProvider and SharedSubGraph APIs, and added many new features including ability to have directed and undirected graphs. For the mongo backend, also added the ability to store node/edge features in separate collections, and filter by node/edge feature values.

Task sub-ROI request. A sub-region of a task’s available total_roi can be restricted/requested explicitly using the daisy.distribute() interface.

Example:
```
task_spec = {'task': mytask, 'request': [daisy.Roi((3,), (2,))]}
daisy.distribute([task_spec])
```
The request will be expanded to align with write_roi.
Multiple Task targets. A single daisy.distribute() can execute multiple target tasks simultaneous.

Example:
```
task_spec0 = {'task': mytask0}
task_spec1 = {'task': mytask1}
daisy.distribute([task_spec0, task_spec1])
```
Tasks’ dependencies (shared or not) will be processed correctly.
Periodic status report. Daisy gives a status report of running/finished tasks, blocks running/finished status, and an ETA based on the completion rate of the last 2 minutes.
Z-order block-wise scheduling.

Drop support for Python 3.4.x and 3.5.x. We have moved to using Python’s asyncio capability as the sole backend for Tornado. Python 3.4.x does not have asyncio. While Python 3.5.x does have asyncio built-in, its implementation is buggy.