Submit a Run:ai Job for execution.
- Flags of type stringArray mean that you can add multiple values. You can either separate values with a comma or add the flag twice.
All examples assume a Run:ai Project has been setup using
runai config project <project-name>.
Start an interactive Job:
(see: build Quickstart).
(see: build with ports Quickstart).
Start a Training Job
(see: training Quickstart).
Use GPU Fractions
(see: GPU fractions Quickstart).
Submit a Job without a name (automatically generates a name)
Submit a job using the system autogenerated name to an external URL:
Submit a job without a name to a system generated a URL :
Submit a Job without a name with a pre-defined prefix and an incremental index suffix
Mark this Job as interactive.
--completions < int >¶
Number of successful pods required for this job to be completed. Used with HPO.
--parallelism < int >¶
Number of pods to run in parallel at any given time. Used with HPO.
Interactive preemptible jobs can be scheduled above guaranteed quota but may be reclaimed at any time.
The timeframe after which a completed or failed job is automatically deleted. Configured in seconds, minutes, or hours (for example 5s, 2m, or 3h). If set to 0, the job will be deleted immediately after completing or failing.
Naming and Shortcuts¶
The prefix to use to automatically generate a Job name with an incremental index. When a Job name is omitted Run:ai will generate a Job name. The optional
--job-name-prefix flagcreates Job names with the provided prefix.
The name of the Job.
Load default values from a workload.
Add linux capabilities to the container.
-a | --annotation
Set annotations variables in the container.
Default is false. If set to true, wait for the Pod to start running. When the pod starts running, attach to the Pod. The flag is equivalent to the command runai attach.
The --attach flag also sets
Overrides the image's entry point with the command supplied after '--'. When not using the
--commandflag, the entry point will not be overrided and the string after
--will be appended as arguments to the entry point command.
--command -- run.sh 1 54will start the docker and run
run.sh 1 54
-- script.py 10000will augment
script.py 10000to the entry point command (e.g.
Create a temporary home directory for the user in the container. Data saved in this directory will not be saved when the container exits. For more information see non root containers.
<stringArray> | --environment
Define environment variables to be set in the container. To set multiple values add the flag multiple times (
-e BATCH_SIZE=50 -e LEARNING_RATE=0.2).
<string> | -i
Image to use when creating the container for this Job
Pulling policy of the image when starting a container. Options are:
Always(default): force image pulling to check whether local image already exists. If the image already exists locally and has the same digest, then the image will not be downloaded.
IfNotPresent: the image is pulled only if it is not already present locally.
Never: the image is assumed to exist locally. No attempt is made to pull the image.
For more information see Kubernetes documentation.
-l | --label
Set labels variables in the container.
If possible, all pods of this job will be scheduled onto nodes that have a label with this key and identical values.
Enforce scheduling pods of this job onto nodes that have a label with this key and identical values.
Keep stdin open for the container(s) in the pod, even if nothing is attached.is attached.
-t | --tty¶
Allocate a pseudo-TTY.
Starts the container with the specified directory as the current directory.
CPU units to allocate for the Job (0.5, 1, .etc). The Job will receive at least this amount of CPU. Note that the Job will not be scheduled unless the system can guarantee this amount of CPUs to the Job.
Limitations on the number of CPUs consumed by the Job (for example 0.5, 1). The system guarantees that this Job will not be able to consume more than this amount of CPUs.
Request access to extended resource, syntax
<resource-name> = < resource_quantity >
-g | --gpu
GPU units to allocate for the Job (0.5, 1).
GPU memory to allocate for this Job (1G, 20M, .etc). The Job will receive this amount of memory. Note that the Job will not be scheduled unless the system can guarantee this amount of GPU memory to the Job.
CPU memory to allocate for this Job (1G, 20M, .etc). The Job will receive at least this amount of memory. Note that the Job will not be scheduled unless the system can guarantee this amount of memory to the Job.
CPU memory to allocate for this Job (1G, 20M, .etc). The system guarantees that this Job will not be able to consume more than this amount of memory. The Job will receive an error when trying to allocate more memory than this limit.
MIG profile to allocate for the job (1g.5gb, 2g.10gb, 3g.20gb, 4g.20gb, 7g.40gb)
The number of times the Job will be retried before failing. The default is 6. This flag will only work with training workloads (when the
--interactiveflag is not specified).
Clone a git repository into the container running the Job. The parameter should follow the syntax:
Mount a large /dev/shm device.
Enable HostToContainer mount propagation for all container volumes
Use this flag to specify a default NFS host for --volume flag. Alternatively, you can specify NFS host for each volume individually (see --volume for details).
Mount a persistent volume claim into a container.
This option is being deprecated from version 2.10 and above. To mount existing or newly created Persistent Volume Claim (PVC), use the parameters
The 2 syntax types of this command are mutually exclusive. You can either use the first or second form, but not a mixture of both.
Storage_Class_Name is a storage class name that can be obtained by running
kubectl get storageclasses.storage.k8s.io. This parameter may be omitted if there is a single storage class in the system, or you are using the default storage class.
Size is the volume size you want to allocate. See Kubernetes documentation for how to specify volume sizes
Container_Mount_Path. A path internal to the container where the storage will be mounted
Pvc_Name. The name of a pre-existing Persistent Volume Claim to mount into the container
--pvc :3Gi:/tmp/john:ro- Allocate
3GBfrom the default Storage class. Mount it to
--pvc my-storage:3Gi:/tmp/john:ro- Allocate
my-storagestorage class. Mount it to /tmp/john as read-only
--pvc :3Gi:/tmp/john- Allocate
3GBfrom the default storage class. Mount it to
--pvc my-pvc:/tmp/john- Use a Persistent Volume Claim named
my-pvc. Mount it to
--pvc my-pvc-2:/tmp/john:ro- Use a Persistent Volume Claim named
my-pvc-2. Mount it to
Mount a persistent volume. You must include a
- claim name—The name of the persistent colume claim. Can be obtained by running
kubectl get storageclasses.storage.k8s.io
- path—the path internal to the container where the storage will be mounted
Use the format:
Mount a persistent volume claim (PVC). If the PVC does not exist, it will be created based on the parameters entered. If a PVC exists, it will be used with its defined attributes and the parameters in the command will be ignored.
- claim name—The name of the persistent colume claim.
- storage class—A storage class name that can be obtained by running
kubectl get storageclasses.storage.k8s.io.
storageclassmay be omitted if there is a single storage class in the system, or you are using the default storage class.
- size—The volume size you want to allocate for the PVC when creating it. See Kubernetes documentation to specify volume sizes.
- accessmode—The description of thr desired volume capabilities for the PVC.
- ro—Mount the PVC with read-only access.
- ephemeral—The PVC will be created as volatile temporary storage which is only present during the running lifetime of the job.
Use the format:
storageclass= <storageclass>,size= <size>, path= <path>, ro, accessmode-rwm
Mount an S3 compatible storage into the container running the job. The parameter should follow the syntax:
All the fields, except url=URL, are mandatory. Default for url is
-v | --volume 'Source:Container_Mount_Path:[ro]:[nfs-host]'¶
Volumes to mount into the container.
Mount /root/data to local path /raid/public/john/data for read-only access.
Mount /root/data to NFS path /public/data on NFS server nfs.example.com for read-write access.
Use the host's ipc namespace. Controls whether the pod containers can share the host IPC namespace. IPC (POSIX/SysV IPC) namespace provides separation of named shared memory segments, semaphores, and message queues. Shared memory segments are used to accelerate inter-process communication at memory speed, rather than through pipes or the network stack.
For further information see docker run reference documentation.
Use the host's network stack inside the container. For further information see docker run referencedocumentation.
-s | --service-type
External access type to jobs. Options are:
nodeport- add one or more ports using
external-url- add one port and an optional custom URL using
runai submit test-jup -p team-a -i gcr.io/run-ai-demo/jupyter-tensorboard --service-type external-url --port 8888
runai submit test-np -p team-a -i ubuntu --service-type nodeport --port 30000:7070
This flag supports more than one
service-type. Multiple service types are supported in CSV style using multiple instances of the same option and commas to separate the values for them.
runai submit test-np -p team-a -i ubuntu --service-type nodeport,port=30000:7070 --service-type external-url,port=30001
runai submit test-np -p team-a -i ubuntu --service-type nodeport,port=30000:7070,port=9090 --service-type external-url,port=8080,custom-url=https://my.domain.com/url
Expose ports from the Job container. You can use a port number (for example 9090) or use the numbers of
hostport:containerport(for example, 30000:7070).
An optional argument that specifies a custom URL when using the
external-urlservice type. If not provided, the system will generate a URL automatically.
Allow the job to gain additional privileges after start.
Run in the context of the current user running the Run:ai command rather than the root user. While the default container user is root (same as in Docker), this command allows you to submit a Job running under your Linux user. This would manifest itself in access to operating system resources, in the owner of new folders created under shared directories, etc. Alternatively, if your cluster is connected to Run:ai via SAML, you can map the container to use the Linux UID/GID which is stored in the organization's directory. For more information see non root containers.
Instructs the scheduler to run this workload using specific set of nodes which are part of a Node Pool. You can specify one or more node pools to form a prioritized list of node pools that the scheduler will use to find one node pool that can provide the workload's specification. To use this feature your Administrator will need to label nodes as explained here: Limit a Workload to a Specific Node Group or use existing node labels, then create a node-pool and assign the label to the node-pool. This flag can be used in conjunction with node-type and Project-based affinity. In this case, the flag is used to refine the list of allowable node groups set from a node-pool. For more information see: Working with Projects.
Allows defining specific Nodes (machines) or a group of Nodes on which the workload will run. To use this feature your Administrator will need to label nodes as explained here: Limit a Workload to a Specific Node Group.
Specify one or more toleration criteria, to ensure that the workload is not scheduled onto an inappropriate node. This is done by matching the workload tolerations to the taints defined for each node. For further details see Kubernetes Taints and Tolerations Guide.
The format of the string:
Set the logging level. One of: debug | info | warn | error (default "info")
--project | -p (string)¶
Specify the Project to which the command applies. Run:ai Projects are used by the scheduler to calculate resource eligibility. By default, commands apply to the default Project. To change the default Project use
runai config project <project-name>.
--help | -h¶
Show help text.
The command will attempt to submit a Job. You can follow up on the Job by running
runai list jobs or
runai describe job <job-name>.
Note that the submit call may use a policy to provide defaults to any of the above flags.