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loadams authored Nov 25, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/release.yml
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jobs:
deploy:
runs-on: ubuntu-24.04
runs-on: ubuntu-22.04
environment: release-env

steps:
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199 changes: 199 additions & 0 deletions blogs/deepspeed-domino/README.md

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2 changes: 1 addition & 1 deletion docs/code-docs/source/index.rst
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Expand Up @@ -71,7 +71,7 @@ Optimizers
optimizers

Learning Rate Schedulers
--------------------
------------------------
.. toctree::
:maxdepth: 2

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2 changes: 1 addition & 1 deletion docs/code-docs/source/inference-engine.rst
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Inference API
============
=============

:func:`deepspeed.init_inference` returns an *inference engine*
of type :class:`InferenceEngine`.
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2 changes: 1 addition & 1 deletion docs/code-docs/source/initialize.rst
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Expand Up @@ -38,7 +38,7 @@ Example usage:
.. autofunction:: deepspeed.initialize

Distributed Initialization
-----------------------
--------------------------
Optional distributed backend initialization separate from ``deepspeed.initialize()``. Useful in scenarios where the user wants to use torch distributed calls before calling ``deepspeed.initialize()``, such as when using model parallelism, pipeline parallelism, or certain data loader scenarios.

.. autofunction:: deepspeed.init_distributed
2 changes: 1 addition & 1 deletion docs/code-docs/source/moe.rst
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Mixture of Experts (MoE)
====================
========================

Layer specification
--------------------
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2 changes: 1 addition & 1 deletion docs/code-docs/source/schedulers.rst
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Learning Rate Schedulers
===================
=================================

DeepSpeed offers implementations of ``LRRangeTest``, ``OneCycle``, ``WarmupLR``, ``WarmupDecayLR``, ``WarmupCosineLR`` learning rate schedulers. When using a DeepSpeed's learning rate scheduler (specified in the `ds_config.json` file), DeepSpeed calls the `step()` method of the scheduler at every training step (when `model_engine.step()` is executed). When not using a DeepSpeed's learning rate scheduler:
* if the schedule is supposed to execute at every training step, then the user can pass the scheduler to `deepspeed.initialize` when initializing the DeepSpeed engine and let DeepSpeed manage it for update or save/restore.
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1 change: 1 addition & 0 deletions docs/code-docs/source/zero3.rst
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Expand Up @@ -310,6 +310,7 @@ DeepSpeed can automatically detect the following external parameter scenarios:


.. `Module.apply <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=module+apply#torch.nn.Module.apply>`_
Overriding Module.apply
===============================
A convenient mechanism for customizing model initialization is `Module.apply <https://pytorch.org/docs/stable/generated/torch.nn.Module.html?highlight=module+apply#torch.nn.Module.apply>`_.
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2 changes: 1 addition & 1 deletion version.txt
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0.15.5
0.16.1

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