Various fixes and updates
- Dump node listing on unusable - Update Gluster to 4.1 on Ubuntu/Debian/RemoteFS - Update Python to 3.6.6 in Windows Docker images - Update dependencies - Minor doc updates - Fix appveyor sha256 artifact upload
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@ -226,7 +226,7 @@ deploy:
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description: 'Batch Shipyard release'
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auth_token:
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secure: +f4N6Qsv3HvJyii0Bs+8qBx3YS7+7FJUWbFSiAdEIUDubFQnNkJgFnBw0Ew2SLkv
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artifact: /.*\.exe/,/.*\.exe.sha256/
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artifact: /.*\.exe.*/
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draft: true
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prerelease: true
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force_update: true
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@ -45,4 +45,4 @@ monitoring:
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port: 9090
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scrape_interval: 10s
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grafana:
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additional_dashboards: null
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additional_dashboards: {}
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@ -686,6 +686,8 @@ def _block_for_nodes_ready(
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suppress_confirm=True)
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unusable_delete = True
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else:
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# list nodes to dump exact error
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list_nodes(batch_client, config, pool_id=pool_id, nodes=nodes)
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raise RuntimeError(
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('Unusable nodes detected in pool {}. You can delete '
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'unusable nodes with "pool nodes del --all-unusable" '
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@ -59,7 +59,7 @@ from .version import __version__
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logger = logging.getLogger(__name__)
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util.setup_logger(logger)
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# global defines
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_BLOBXFER_VERSION = '1.3.0'
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_BLOBXFER_VERSION = '1.3.1'
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_MEGABYTE = 1048576
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_MAX_READ_BLOCKSIZE_BYTES = 4194304
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_FILE_SPLIT_PREFIX = '_shipyard-'
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@ -1,7 +1,7 @@
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# Dockerfile for Azure/batch-shipyard Cargo (Windows)
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# Adapted from: https://github.com/StefanScherer/dockerfiles-windows/blob/master/python/Dockerfile
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FROM python:3.6.5-windowsservercore-ltsc2016
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FROM python:3.6.6-windowsservercore-ltsc2016
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MAINTAINER Fred Park <https://github.com/Azure/batch-shipyard>
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ENV chocolateyUseWindowsCompression false
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@ -30,8 +30,8 @@ COPY --from=0 /batch-shipyard/cargo /batch-shipyard
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SHELL ["powershell", "-Command", "$ErrorActionPreference = 'Stop'; $ProgressPreference = 'SilentlyContinue';"]
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ENV PYTHON_VERSION 3.6.5
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ENV PYTHON_PIP_VERSION 9.0.3
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ENV PYTHON_VERSION 3.6.6
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ENV PYTHON_PIP_VERSION 10.0.1
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RUN $env:PATH = 'C:\Python;C:\Python\Scripts;{0}' -f $env:PATH ; \
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Set-ItemProperty -Path 'HKLM:\SYSTEM\CurrentControlSet\Control\Session Manager\Environment\' -Name Path -Value $env:PATH ; \
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@ -1,7 +1,7 @@
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# Dockerfile for Azure/batch-shipyard CLI (Windows)
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# Adapted from: https://github.com/StefanScherer/dockerfiles-windows/blob/master/python/Dockerfile
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FROM python:3.6.5-windowsservercore-ltsc2016
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FROM python:3.6.6-windowsservercore-ltsc2016
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MAINTAINER Fred Park <https://github.com/Azure/batch-shipyard>
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ENV chocolateyUseWindowsCompression false
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@ -34,8 +34,8 @@ COPY --from=0 /batch-shipyard /batch-shipyard
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SHELL ["powershell", "-Command", "$ErrorActionPreference = 'Stop'; $ProgressPreference = 'SilentlyContinue';"]
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ENV PYTHON_VERSION 3.6.5
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ENV PYTHON_PIP_VERSION 9.0.3
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ENV PYTHON_VERSION 3.6.6
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ENV PYTHON_PIP_VERSION 10.0.1
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RUN $env:PATH = 'C:\Python;C:\Python\Scripts;{0}' -f $env:PATH ; \
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Set-ItemProperty -Path 'HKLM:\SYSTEM\CurrentControlSet\Control\Session Manager\Environment\' -Name Path -Value $env:PATH ; \
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@ -448,7 +448,13 @@ to both compute node A and B. However, if `job-1`:`task-3` is then run on
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compute node A after `job-1`:`task-1`, then the `input_data` is not
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transferred again. This object currently supports `azure_batch` and
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`azure_storage` as members.
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* `azure_batch` contains the following members:
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* `azure_batch` will transfer data from a compute node that has run the
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specified task. Note that there is no implied dependency. It is
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recommended to specify a `depends_on` in order to ensure that the
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specified task runs before this one (note that `depends_on` requires
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that the upstream task must exist within the same job). Additionally,
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the compute node which ran the task must not have been deleted or
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resized out of the pool.
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* (required) `job_id` the job id of the task
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* (required) `task_id` the id of the task to fetch files from
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* (optional) `include` is an array of include filters
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@ -669,7 +675,13 @@ ingressed for this specific task. This object currently supports
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`azure_batch` and `azure_storage` as members. Note for multi-instance
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tasks, transfer of `input_data` is only applied to the task running the
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application command.
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* `azure_batch` contains the following members:
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* `azure_batch` will transfer data from a compute node that has run the
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specified task. Note that there is no implied dependency. It is
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recommended to specify a `depends_on` in order to ensure that the
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specified task runs before this one (note that `depends_on` requires
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that the upstream task must exist within the same job). Additionally,
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the compute node which ran the task must not have been deleted or
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resized out of the pool.
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* (required) `job_id` the job id of the task
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* (required) `task_id` the id of the task to fetch files from
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* (optional) `include` is an array of include filters
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@ -46,7 +46,7 @@ monitoring:
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port: 9090
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scrape_interval: 10s
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grafana:
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additional_dashboards: null
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additional_dashboards: {}
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```
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The `monitoring` property has the following members:
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@ -113,7 +113,8 @@ resource monitor.
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be exclusive to the resource monitor and cannot be shared with other
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resources, including Batch compute nodes. Batch compute nodes and storage
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clusters can co-exist on the same virtual network, but should be in
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separate subnets.
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separate subnets. It's recommended that the monitor VM be in a separate
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subnet as well.
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* (required) `name` is the subnet name.
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* (required) `address_prefix` is the subnet address prefix to use for
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allocation of the resource monitor virtual machine to.
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@ -126,7 +127,7 @@ to the resource monitoring virtual machine.
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* (optional) `grafana` rule allows grafana HTTPS (443) server port to be
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exposed to the specified address prefix. Multiple address prefixes
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can be specified.
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* (optional) `prometheus` rule allows the Prometheus server pot to be
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* (optional) `prometheus` rule allows the Prometheus server port to be
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exposed to the specified address prefix. Multiple address prefixes
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can be specified.
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* (optional) `custom_inbound_rules` are custom inbound rules for other
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@ -278,10 +278,12 @@ shipyard monitor add --poolid mybatchpool
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```
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After the monitor is added, you can point your web browser at the
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monitoring resource FQDN emitted above. You can remove individual
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resources to monitor with the command `shipyard monitor remove`.
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Once you have no need for your monitoring resource, you can either suspend
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it or remove it altogether.
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monitoring resource FQDN emitted above. Note that there will be a delay
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between `monitor add` and the resource showing up in Grafana.
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You can remove individual resources to monitor with the command
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`monitor remove`. Once you have no need for your monitoring resource, you
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can either suspend it or destroy it entirely.
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```shell
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# remove the prior Batch pool monitor
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@ -1,7 +1,7 @@
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adal==1.0.2
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azure-batch==4.1.3
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azure-cosmosdb-table==1.0.3
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azure-keyvault==1.0.0b1
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azure-keyvault==1.0.0
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azure-mgmt-authorization==0.50.0
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azure-mgmt-batch==5.0.1
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azure-mgmt-compute==3.1.0rc3
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@ -11,7 +11,7 @@ azure-mgmt-storage==2.0.0rc3
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azure-storage-blob==1.3.0
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azure-storage-common==1.3.0
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azure-storage-file==1.3.0
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blobxfer==1.3.0
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blobxfer==1.3.1
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click==6.7
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future==0.16.0
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futures==3.2.0; python_version < '3'
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@ -10,7 +10,7 @@ DOCKER_CE_VERSION_DEBIAN=18.03.1
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DOCKER_CE_VERSION_CENTOS=18.03.1
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DOCKER_CE_VERSION_SLES=17.09.1
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NVIDIA_DOCKER_VERSION=2.0.3
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GLUSTER_VERSION_DEBIAN=4.0
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GLUSTER_VERSION_DEBIAN=4.1
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GLUSTER_VERSION_CENTOS=40
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# consts
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@ -6,7 +6,7 @@ set -o pipefail
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export DEBIAN_FRONTEND=noninteractive
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# constants
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GLUSTER_VERSION=4.0
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GLUSTER_VERSION=4.1
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gluster_brick_mountpath=/gluster/brick
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gluster_brick_location=$gluster_brick_mountpath/brick0
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ipaddress=$(ip addr list eth0 | grep "inet " | cut -d' ' -f6 | cut -d/ -f1)
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