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description= Create reservations of Compute Engine VM instances for a single project.;
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st request post https compute googleapis com compute v1 projects project_id zones zone reservations name reservation_name specificreservation count number_of_vms sourceinstancetemplate instance_template_url replace the following project_id the id of the project where you want to reserve resources and where the instance template exists zone the zone in which to reserve resources reservation_name the name of the reservation to create number_of_vms the number of vms to reserve instance_template_url the url of an existing instance template specify one of the following values for a regional instance template projects project_id regions region instancetemplates instance_template_name for a global instance template instance_template_name for example to create a reservation for ten vms in zone us central1 a by specifying a global instance template make the following post request post https compute googleapis com compute v1 projects example project zones us central1 a reservations name my reservation specificreservation count 10 sourceinstancetemplate example instance template optionally you can do one or more of the following to specify that only vms that specifically target this reservation can consume it include the specificreservationrequired field in the request body and set the field to true post https compute googleapis com compute v1 projects project_id zones zone reservations name reservation_name specificreservation count number_of_vms sourceinstancetemplate instance_template_url specificreservationrequired true to let custom training jobs or prediction jobs in vertex ai consume a reservation of gpu vms include the servicesharetype field and set it to allow_all post https compute googleapis com compute v1 projects project_id zones zone reservations name reservation_name reservationsharingpolicy servicesharetype allow_all specificreservation count number_of_vms sourceinstancetemplate instance_template_url to enable compute engine to automatically delete the reservation select one of the following methods preview the deleteattime and deleteafterduration fields this feature is subject to the pre ga offerings terms in the general service terms section of the service specific terms pre ga features are available as is and might have limited support for more information see the launch stage descriptions to delete the reservation at a specific date and time make a post request to the beta reservations insert method in the request body include the deleteattime field post https compute googleapis com compute beta projects project_id zones zone reservations deleteattime delete_at_time name reservation_name specificreservation count number_of_vms sourceinstancetemplate instance_template_url replace delete_at_time with a date and time formatted as an rfc 3339 timestamp which must be as follows yyyy mm dd t hh mm ss offset replace the following yyyy mm dd a date formatted as a 4 digit year 2 digit month and a 2 digit day of the month separated by hyphens hh mm ss a time formatted as a 2 digit hour using a 24 hour time 2 digit minutes and 2 digit seconds separated by colons offset the time zone formatted as an offset of coordinated universal time utc for example to use the pacific standard time pst specify 08 00 alternatively to use no offset specify z to delete the reservation after a specific duration make a post request to the beta reservations insert method in the request body include the deleteafterduration field post https compute googleapis com compute beta projects project_id zones zone reservations deleteafterduration seconds delete_after_duration name reservation_name specificreservation count number_of_vms sourceinstancetemplate instance_template_url replace delete_after_duration with a duration in seconds for example specify 86400 for 86 400 seconds 1 day specify an existing vm you can only create a reservation based on an existing vm in the same zone as the vm after creating the reservation you can consume it by creating vms with properties that match the reference vm you can do this by doing one of the following create and use an instance template as follows create an instance template based on the reference vm without overriding the reference vm s properties create vms using the newly created template by doing one or both of the following create a single vm create a zonal managed instance group mig create a vm based on the reference vm while ensuring that the properties of the new vm and the reference vm exactly match to create a single project reservation that uses the properties of an existing vm do the following in the google cloud console go to the reservations page go to reservations click add_box create reservation the create a reservation page appears in the name field enter a name for your reservation in the region and zone lists select where you want to reserve resources optional to let custom training jobs or prediction jobs in vertex ai consume a reservation of gpu vms in the google cloud services section select share reservation in the use with vm instance section select one of the following options to allow matching vms to automatically consume this reservation select use reservation automatically to consume this reservation s resources only when you create matching vms that specifically target the reservation by name select select specific reservation in the resource details section do the following in the number of vm instances field enter the number of vms that you want to reserve to specify vm configurations by using an instance template select use existing vm and then in the list that appears select the vm optional to specify a compact placement policy for a reservation that meets the requirements in the group placement policy section click the select or create a group placement policy list and then do one of the following to create a compact placement policy to specify in this reservation do the following important by using this method you can only create a compact placement policy with an unspecified maximum distance value if you want to specify a new compact placement policy with a maximum distance value of 3 or 2 then you must create a compact placement policy separately and select the existing compact placement policy instead click create group placement policy the create a group placement policy pane appears in the policy name field enter a name for your policy click create creating the compact placement policy might take a few seconds to complete otherwise select an existing compact placement policy in the auto delete section you can enable the auto delete option to let compute engine automatically delete the reservation at a specific date and time automatically deleting reservations can be useful to avoid unnecessary charges when you stop consuming the reservation to create the reservation click create the reservations page appears creating the reservation can take up to a minute to complete to verify when compute engine finishes creating it view your reservations specify properties directly to create a single project reservation by specifying properties directly select one of the following options console in the google cloud console go to the reservations page go to reservations click add_box create reservation the create a reservation page appears in the name field enter a name for your reservation in the region and zone lists select where you want to reserve resources optional to let custom training jobs or prediction jobs in vertex ai consume a reservation of gpu vms in the google cloud services section select share reservation in the use with vm instance section select one of the following options to allow matching vms to automatically consume this reservation select use reservation automatically to consume this reservation s resources only when you create matching vms that specifically target the reservation by name select select specific reservation in the resource details section do the following in the number of vm instances field enter the number of vms that you want to reserve specify the machine series and type that you want to reserve optional to specify a compact placement policy for a reservation that meets the reservation requirements in the group placement policy section click the select or create a group placement policy list and then do one of the following to create a compact placement policy to specify in this reservation do the following important by using this method you can only create a compact placement policy with an unspecified maximum distance value if you want to specify a new compact placement policy with a maximum distance value of 3 or 2 then you must create a compact placement policy separately and select the existing compact placement policy instead click create group placement policy the create a group placement policy pane appears in the policy name field enter a name for your policy click create creating the compact placement policy might take a few seconds to complete otherwise select an existing compact placement policy in the auto delete section you can enable the auto delete option to let compute engine automatically delete the reservation at a specific date and time automatically deleting reservations can be useful to avoid unnecessary charges when you stop consuming the reservation to create the reservation click create the reservations page appears creating the reservation can take up to a minute to complete to verify when compute engine finishes creating it view your reservations gcloud to create a single project reservation use the gcloud compute reservations create command to create a single project reservation by specifying properties directly and without including any optional flags run the following command gcloud compute reservations create reservation_name machine type machine_type vm count number_of_vms zone zone replace the following reservation_name the name of the reservation to create machine_type a machine type to use for each vm number_of_vms the number of vms to reserve zone the zone in which to reserve resources for example to create a reservation in zone us central1 a for ten vms that each use an n2 predefined machine type with 4 vcpus run the following command gcloud compute reservations create my reservation machine type n2 standard 4 vm count 10 zone us central1 a optionally you can do one or more of the following to attach gpus to your reserved n1 vms include the accelerator flag gcloud compute reservations create reservation_name accelerator count number_of_accelerators type accelerator_type machine type machine_type vm count number_of_vms zone zone replace the following number_of_accelerators the number of gpus to add per reserved vm accelerator_type a supported gpu model for n1 vms ensure that your chosen gpu model is available in the zone where you want to reserve resources otherwise creating the reservation fails to attach local ssd disks to a machine type that doesn t include local ssd disks by default include the local ssd flag you can only attach local ssd disks of 375 gb gcloud compute reservations create reservation_name local ssd count number_of_local_ssd_disks size 375 interface interface_type machine type machine_type vm count number_of_vms zone zone replace the following number_of_local_ssd_disks the number of local ssd disks to attach interface_type the type of disk interface that you want each local ssd disk to use and that your specified machine type supports specify one of the following values for the nvme disk interface nvme for the scsi disk interface scsi to have the reserved vms use a specific minimum cpu platform instead of the zone s default cpu platform include the min cpu platform flag gcloud compute reservations create reservation_name machine type machine_type min cpu platform min_cpu_platform vm count number_of_vms zone zone replace min_cpu_platform with a minimum cpu platform to make sure that a cpu platform is available in the zone where you re reserving resources view the available cpu platforms by zone to specify that only vms that specifically target this reservation can consume it include the require specific reservation flag gcloud compute reservations create reservation_name machine type machine_type require specific reservation vm count number_of_vms zone zone to specify a compact placement policy for lower network latency among vms include the resource policies policy flag gcloud compute reservations create reservation_name machine type machine_type resource policies policy compact_placement_policy_name require specific reservation vm count number_of_vms zone zone replace compact_placement_policy_name with the name of an existing compact placement policy additionally to avoid errors when creating a single project reservation that specifies a compact placement policy make sure to specify the following a supported machine type and maximum number of vms for compact placement policies a zone within the region where the compact placement policy is located the require specific reservation flag this indicates that only vms that specifically target the reservation can consume it to let custom training jobs or prediction jobs in vertex ai consume a reservation of gpu vms include the reservation sharing policy flag set to allow_all gcloud compute reservations create reservation_name machine type machine_type reservation sharing policy allow_all vm count number_of_vms zone zone to enable compute engine to automatically delete the reservation select one of the following methods preview the delete at time and delete after duration flags this feature is subject to the pre ga offerings terms in the general service terms section of the service specific terms pre ga features are available as is and might have limited support for more information see the launch stage descriptions to delete the reservation at a specific date and time use the gcloud beta compute reservations create command with the delete at time flag gcloud beta compute reservations create reservation_name delete at time delete_at_time machine type machine_type vm count number_of_vms zone zone replace delete_at_time with a date and time formatted as an rfc 3339 timestamp which must be as follows yyyy mm dd t hh mm ss offset replace the following yyyy mm dd a date formatted as a 4 digit year 2 digit month and a 2 digit day of the month separated by hyphens hh mm ss a time formatted as a 2 digit hour using a 24 hour time 2 digit minutes and 2 digit seconds separated by colons offset the time zone formatted as an offset of coordinated universal time utc for example to use the pacific standard time pst specify 08 00 alternatively to use no offset specify z to delete the reservation after a specific duration use the gcloud beta compute reservations create command with the delete after duration flag gcloud beta compute reservations create reservation_name delete after duration delete_after_duration machine type machine_type vm count numbe...
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