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docs: aws_batch/kfp fix dead links + change user agent to avoid 403 #746

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Jul 26, 2023
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docs: aws_batch/kfp fix dead links + change user agent to avoid 403 #746

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2 changes: 2 additions & 0 deletions docs/source/conf.py
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Expand Up @@ -46,6 +46,8 @@
#
needs_sphinx = "1.6"

user_agent = "Mozilla/5.0 (X11; Linux x86_64; rv:25.0) Gecko/20100101 Firefox/25.0 github.com/pytorch/torchx"

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
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2 changes: 1 addition & 1 deletion torchx/pipelines/kfp/adapter.py
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Expand Up @@ -97,7 +97,7 @@ def component_from_app(
component_from_app takes in a TorchX component/AppDef and returns a KFP
ContainerOp factory. This is equivalent to the
`kfp.components.load_component_from_*
<https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.components.html#kfp.components.load_component_from_text>`_
<https://kubeflow-pipelines.readthedocs.io/en/1.8.22/source/kfp.components.html#kfp.components.load_component_from_text>`_
methods.

Args:
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2 changes: 1 addition & 1 deletion torchx/schedulers/aws_batch_scheduler.py
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Expand Up @@ -378,7 +378,7 @@ class AWSBatchScheduler(DockerWorkspaceMixin, Scheduler[AWSBatchOpts]):
See :py:func:`torchx.specs.parse_mounts` for more info.

For other filesystems such as FSx you can mount them onto the host and bind
mount them into your job: https://aws.amazon.com/premiumsupport/knowledge-center/batch-fsx-lustre-file-system-mount/
mount them into your job: https://repost.aws/knowledge-center/batch-fsx-lustre-file-system-mount

For Elastic Fabric Adapter (EFA) you'll need to use a device mount to mount
them into the container: https://docs.aws.amazon.com/batch/latest/userguide/efa.html
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