Backend-ai

Latest version: v1.3.0

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0.6.0

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**NEW**

- Add support for GPU-enabled kernels (using `nvidia-docker plugin`_).
The kernel images must be built upon nvidia-docker's base Ubuntu images and
have the label "io.sorna.nvidia.enabled" set ``yes``.

**CHANGES**

- Change the agent to add "lablup/" prefix when creating containers from
kernel image names, to ease setup and running using the public docker
repository. (e.g., "lablup/kernel-python3" instead of "kernel-python3")

- Change the prefix of kernel image labels from "com.lablup.sorna." to
"io.sorna." for simplicity.

- Increase the default idle timeout to 30 minutes for offline tutorial/workshops.

- Limit the CPU cores available in kernel containers.
It uses an optional "io.sorna.maxcores" label (default is 1 when not
specified) to determine the requested number of CPU cores in kernels, with a
hard limit of 4.

NOTE: You will still see the full count of CPU cores of the underlying
system when running ``os.cpu_count()``, ``multiprocessing.cpu_count()`` or
``os.sysconf("SC_NPROCESSORS_ONLN")`` because the limit is enforced by the CPU
affinity mask. To get the correct result, try
``len(os.sched_getaffinity(os.getpid()))``.

.. _nvidia-docker plugin: https://github.com/NVIDIA/nvidia-docker

0.5.1

------------------

**FIXES**

- Added a missing check for stale kernel sessions due to restarts of Sorna agents.
This bug has impacted public tutorial/workshops and demonstrations because the
manager does not recreate kernels at the right timing.

0.5.0

------------------

**NEW**

- First public release.



Changes
=======

0.2.5

----------------------

0.2.4

------------------

- Upgrade backend.ai-client to v1.3 series.

0.2.3

------------------

- Upgrade backend.ai-client to v1.2 series.

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