Runai tensorflow submit
runai tensorflow submit¶
submit tf training
Examples¶
# Submit a tf training job
runai training tf submit <name> -p <project_name> -i runai.jfrog.io/demo/quickstart-demo
Options¶
--allow-privilege-escalation Allow the job to gain additional privileges after starting
--annotation stringArray Set of annotations to populate into the container running the workspace
--attach If true, wait for the pod to start running, and then attach to the pod as if 'runai attach' was called. Attach makes tty and stdin true by default. Defaults to false
--auto-deletion-time-after-completion duration The length of time (like 5s, 2m, or 3h, higher than zero) after which a completed job is automatically deleted (default 0s)
--backoff-limit int The number of times the job will be retried before failing
--capability stringArray The POSIX capabilities to add when running containers. Defaults to the default set of capabilities granted by the container runtime.
-c, --command If true, override the image's entrypoint with the command supplied after '--'
--configmap-map-volume stringArray Mount ConfigMap as a volume. Use the fhe format name=CONFIGMAP_NAME,path=PATH
--cpu-core-limit float CPU core limit (e.g. 0.5, 1)
--cpu-core-request float CPU core request (e.g. 0.5, 1)
--cpu-memory-limit string CPU memory limit to allocate for the job (e.g. 1G, 500M)
--cpu-memory-request string CPU memory to allocate for the job (e.g. 1G, 500M)
--create-home-dir Create a temporary home directory. Defaults to true when --run-as-user is set, false otherwise
-e, --environment stringArray Set environment variables in the container
--existing-pvc stringArray Mount an existing persistent volume. Use the format: claimname=CLAIM_NAME,path=PATH
--extended-resource stringArray Request access to an extended resource. Use the format: resource_name=quantity
--external-url stringArray Expose URL from the job container. Use the format: container=9443,url=https://external.runai.com,authusers=user1,authgroups=group1
--git-sync stringArray Specifies git repositories to mount into the container. Use the format: name=NAME,repository=REPO,path=PATH,secret=SECRET,rev=REVISION
-g, --gpu-devices-request int32 GPU units to allocate for the job (e.g. 1, 2)
--gpu-memory-limit string GPU memory limit to allocate for the job (e.g. 1G, 500M)
--gpu-memory-request string GPU memory to allocate for the job (e.g. 1G, 500M)
--gpu-portion-limit float GPU portion limit, must be no less than the gpu-memory-request (between 0 and 1, e.g. 0.5, 0.2)
--gpu-portion-request float GPU portion request (between 0 and 1, e.g. 0.5, 0.2)
--gpu-request-type string GPU request type (portion|memory|migProfile)
-h, --help help for submit
--host-ipc Whether to enable host IPC. (Default: false)
--host-network Whether to enable host networking. (Default: false)
--host-path stringArray Volumes to mount into the container. Use the format: path=PATH,mount=MOUNT,mount-propagation=None|HostToContainer,readwrite
-i, --image string The image for the workload
--image-pull-policy string Set image pull policy. One of: Always, IfNotPresent, Never. Defaults to Always (default "Always")
--label stringArray Set of labels to populate into the container running the workspace
--large-shm Request large /dev/shm device to mount
--master-args Arguments to pass to the master pod container command. If used together with --master-command, overrides the image's entrypoint of the master pod container with the given command
--master-environment stringArray Set master environment variables in the container
--master-extended-resource stringArray Request access to an extended resource. Use the format: resource_name=quantity
--master-gpu-devices-request int32 GPU units to allocate for the job (e.g. 1, 2)
--master-gpu-portion-limit float GPU portion limit, must be no less than the gpu-memory-request (between 0 and 1, e.g. 0.5, 0.2)
--master-gpu-portion-request float GPU portion request (between 0 and 1, e.g. 0.5, 0.2)
--master-no-pvcs Do not mount any persistent volumes in the master pod
--max-replicas int32 Maximum number of replicas for an elastic PyTorch job
--mig-profile string [Deprecated] MIG profile to allocate for the job (1g.5gb, 2g.10gb, 3g.20gb, 4g.20gb, 7g.40gb)
--min-replicas int32 Minimum number of replicas for an elastic PyTorch job
--name-prefix string Set defined prefix for the workload name and add index as a suffix
--new-pvc stringArray Mount a persistent volume, create it if it does not exist. Use the format: claimname=CLAIM_NAME,storageclass=STORAGE_CLASS,size=SIZE,path=PATH,accessmode-rwo,accessmode-rom,accessmode-rwm,ro,ephemeral
--nfs stringArray NFS storage details. Use the format: path=PATH,server=SERVER,mountpath=MOUNT_PATH,readwrite
--no-master Do not create a separate pod for the master
--node-pools stringArray List of node pools to use for scheduling the job, ordered by priority
--node-type string Enforce node type affinity by setting a node-type label
--pod-running-timeout duration Pod check for running state timeout.
--port stringArray Expose ports from the job container. Use the format: service-type=NodePort,container=80,external=8080
--preferred-pod-topology-key string If possible, all pods of this job will be scheduled onto nodes that have a label with this key and identical values
-p, --project string Specify the project to which the command applies. By default, commands apply to the default project. To change the default project use ‘runai config project <project name>’
--required-pod-topology-key string Enforce scheduling pods of this job onto nodes that have a label with this key and identical values
--run-as-gid int The group ID the container will run with
--run-as-uid int The user ID the container will run with
--run-as-user takes the uid, gid, and supplementary groups fields from the token, if all the fields do not exist, uses the local running terminal user credentials. if any of the fields exist take only the existing fields
--s3 stringArray s3 storage details. Use the format: name=NAME,bucket=BUCKET,path=PATH,accesskey=ACCESS_KEY,url=URL
--seccomp-profile string Indicates which kind of seccomp profile will be applied to the container, options: RuntimeDefault|Unconfined|Localhost
--stdin Keep stdin open on the container(s) in the pod, even if nothing is attached
--supplemental-groups ints Comma seperated list of groups (IDs) that the user running the container belongs to
--toleration stringArray Toleration details. Use the format: operator=Equal|Exists,key=KEY,[value=VALUE],[effect=NoSchedule|NoExecute|PreferNoSchedule],[seconds=SECONDS]
-t, --tty Allocate a TTY for the container
--user-group-source string Indicate the way to determine the user and group ids of the container, options: fromTheImage|fromIdpToken|fromIdpToken
--wait-for-submit duration Waiting duration for the workload to be created in the cluster. Defaults to 1 minute (1m)
--workers int32 the number of workers that will be allocated for running the workload
--working-dir string Set the container's working directory
Options inherited from parent commands¶
--config-file string config file name; can be set by environment variable RUNAI_CLI_CONFIG_FILE (default "config.json")
--config-path string config path; can be set by environment variable RUNAI_CLI_CONFIG_PATH
-d, --debug enable debug mode
-q, --quiet enable quiet mode, suppress all output except error messages
--verbose enable verbose mode
SEE ALSO¶
- runai tensorflow - alias for tensorflow management