YARN-7223. Document GPU isolation feature. Contributed by Wangda Tan.
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Sunil G
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<!---
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. See accompanying LICENSE file.
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-->
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# Using GPU On YARN
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# Prerequisites
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- As of now, only Nvidia GPUs are supported by YARN
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- YARN node managers have to be pre-installed with Nvidia drivers.
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- When Docker is used as container runtime context, nvidia-docker 1.0 needs to be installed (Current supported version in YARN for nvidia-docker).
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# Configs
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## GPU scheduling
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In `resource-types.xml`
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Add following properties
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```
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<configuration>
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<property>
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<name>yarn.resource-types</name>
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<value>yarn.io/gpu</value>
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</property>
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</configuration>
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```
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In `yarn-site.xml`
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`DominantResourceCalculator` MUST be configured to enable GPU scheduling/isolation.
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For `Capacity Scheduler`, use following property to configure `DominantResourceCalculator` (In `capacity-scheduler.xml`):
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| Property | Default value |
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| --- | --- |
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| yarn.scheduler.capacity.resource-calculator | org.apache.hadoop.yarn.util.resource.DominantResourceCalculator |
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## GPU Isolation
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### In `yarn-site.xml`
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```
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<property>
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<name>yarn.nodemanager.resource-plugins</name>
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<value>yarn.io/gpu</value>
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</property>
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```
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This is to enable GPU isolation module on NodeManager side.
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By default, YARN will automatically detect and config GPUs when above config is set. Following configs need to be set in `yarn-site.xml` only if admin has specialized requirements.
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**1) Allowed GPU Devices**
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| Property | Default value |
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| --- | --- |
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| yarn.nodemanager.resource-plugins.gpu.allowed-gpu-devices | auto |
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Specify GPU devices which can be managed by YARN NodeManager (split by comma).
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Number of GPU devices will be reported to RM to make scheduling decisions.
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Set to auto (default) let YARN automatically discover GPU resource from
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system.
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Manually specify GPU devices if auto detect GPU device failed or admin
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only want subset of GPU devices managed by YARN. GPU device is identified
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by their minor device number and index. A common approach to get minor
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device number of GPUs is using `nvidia-smi -q` and search `Minor Number`
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output.
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When minor numbers are specified manually, admin needs to include indice of GPUs
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as well, format is `index:minor_number[,index:minor_number...]`. An example
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of manual specification is `0:0,1:1,2:2,3:4"`to allow YARN NodeManager to
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manage GPU devices with indices `0/1/2/3` and minor number `0/1/2/4`.
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numbers .
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**2) Executable to discover GPUs**
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| Property | value |
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| --- | --- |
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| yarn.nodemanager.resource-plugins.gpu.path-to-discovery-executables | /absolute/path/to/nvidia-smi |
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When `yarn.nodemanager.resource.gpu.allowed-gpu-devices=auto` specified,
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YARN NodeManager needs to run GPU discovery binary (now only support
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`nvidia-smi`) to get GPU-related information.
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When value is empty (default), YARN NodeManager will try to locate
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discovery executable itself.
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An example of the config value is: `/usr/local/bin/nvidia-smi`
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**3) Docker Plugin Related Configs**
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Following configs can be customized when user needs to run GPU applications inside Docker container. They're not required if admin follows default installation/configuration of `nvidia-docker`.
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| Property | Default value |
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| --- | --- |
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| yarn.nodemanager.resource-plugins.gpu.docker-plugin | nvidia-docker-v1 |
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Specify docker command plugin for GPU. By default uses Nvidia docker V1.0.
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| Property | Default value |
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| --- | --- |
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| yarn.nodemanager.resource-plugins.gpu.docker-plugin.nvidia-docker-v1.endpoint | http://localhost:3476/v1.0/docker/cli |
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Specify end point of `nvidia-docker-plugin`. Please find documentation: https://github.com/NVIDIA/nvidia-docker/wiki For more details.
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**4) CGroups mount**
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GPU isolation uses CGroup [devices controller](https://www.kernel.org/doc/Documentation/cgroup-v1/devices.txt) to do per-GPU device isolation. Following configs should be added to `yarn-site.xml` to automatically mount CGroup sub devices, otherwise admin has to manually create devices subfolder in order to use this feature.
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| Property | Default value |
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| --- | --- |
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| yarn.nodemanager.linux-container-executor.cgroups.mount | true |
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### In `container-executor.cfg`
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In general, following config needs to be added to `container-executor.cfg`
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```
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[gpu]
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module.enabled=true
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```
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When user needs to run GPU applications under non-Docker environment:
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```
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[cgroups]
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# This should be same as yarn.nodemanager.linux-container-executor.cgroups.mount-path inside yarn-site.xml
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root=/sys/fs/cgroup
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# This should be same as yarn.nodemanager.linux-container-executor.cgroups.hierarchy inside yarn-site.xml
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yarn-hierarchy=yarn
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```
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When user needs to run GPU applications under Docker environment:
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**1) Add GPU related devices to docker section:**
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Values separated by comma, you can get this by running `ls /dev/nvidia*`
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```
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[docker]
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docker.allowed.devices=/dev/nvidiactl,/dev/nvidia-uvm,/dev/nvidia-uvm-tools,/dev/nvidia1,/dev/nvidia0
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```
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**2) Add `nvidia-docker` to volume-driver whitelist.**
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```
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[docker]
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...
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docker.allowed.volume-drivers
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```
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**3) Add `nvidia_driver_<version>` to readonly mounts whitelist.**
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```
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[docker]
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...
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docker.allowed.ro-mounts=nvidia_driver_375.66
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```
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# Use it
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## Distributed-shell + GPU
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Distributed shell currently support specify additional resource types other than memory and vcores.
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### Distributed-shell + GPU without Docker
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Run distributed shell without using docker container (Asks 2 tasks, each task has 3GB memory, 1 vcore, 2 GPU device resource):
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```
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yarn jar <path/to/hadoop-yarn-applications-distributedshell.jar> \
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-jar <path/to/hadoop-yarn-applications-distributedshell.jar> \
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-shell_command /usr/local/nvidia/bin/nvidia-smi \
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-container_resources memory-mb=3072,vcores=1,yarn.io/gpu=2 \
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-num_containers 2
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```
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You should be able to see output like
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```
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Tue Dec 5 22:21:47 2017
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+-----------------------------------------------------------------------------+
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| NVIDIA-SMI 375.66 Driver Version: 375.66 |
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|-------------------------------+----------------------+----------------------+
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| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
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| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
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|===============================+======================+======================|
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| 0 Tesla P100-PCIE... Off | 0000:04:00.0 Off | 0 |
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| N/A 30C P0 24W / 250W | 0MiB / 12193MiB | 0% Default |
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+-------------------------------+----------------------+----------------------+
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| 1 Tesla P100-PCIE... Off | 0000:82:00.0 Off | 0 |
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| N/A 34C P0 25W / 250W | 0MiB / 12193MiB | 0% Default |
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+-------------------------------+----------------------+----------------------+
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+-----------------------------------------------------------------------------+
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| Processes: GPU Memory |
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| GPU PID Type Process name Usage |
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|=============================================================================|
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| No running processes found |
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+-----------------------------------------------------------------------------+
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```
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For launched container task.
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### Distributed-shell + GPU with Docker
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You can also run distributed shell with Docker container. `YARN_CONTAINER_RUNTIME_TYPE`/`YARN_CONTAINER_RUNTIME_DOCKER_IMAGE` must be specified to use docker container.
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```
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yarn jar <path/to/hadoop-yarn-applications-distributedshell.jar> \
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-jar <path/to/hadoop-yarn-applications-distributedshell.jar> \
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-shell_env YARN_CONTAINER_RUNTIME_TYPE=docker \
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-shell_env YARN_CONTAINER_RUNTIME_DOCKER_IMAGE=<docker-image-name> \
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-shell_command nvidia-smi \
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-container_resources memory-mb=3072,vcores=1,yarn.io/gpu=2 \
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-num_containers 2
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```
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