When launching a Domino execution, users specify a Hardware Tier that determines the resources available for the execution and the node type on which it must run.
At a Kubernetes level, hardware tiers specify the CPU, Memory, and GPU for the pods that will host Domino executions. Use the following properties to configure the resource requests and limits for execution pods.
- Cores Requested
The number of requested CPUs.
- Cores Limit
The maximum number of CPUs. Domino recommends that this is the same as the request.
Unless the option Allow executions to exceed request when unused CPU is available is selected, Domino will automatically apply a CPU limit equal to the request.
- Memory Requested (GiB)
The amount of requested memory.
- Memory Limit (GiB)
The maximum amount of memory. Domino recommends that this is the same as the request.
Unless the option Allow executions memory limit to exceed request is selected, Domino will automatically apply a memory limit equal to the request.Warning
Allowing the memory limit to exceed the request must be used with caution since it can make executions more likely to be evicted under memory pressure. You can find more information about how memory requests and limits influence Kubernetes eviction decisions here.
- Number of GPUs
The number of requested GPUs.
The request values, CPU Cores, Memory, and GPUs, are thresholds used to determine whether a node has capacity to host an execution pod. These requested resources are effectively reserved for the pod. The limit values control the amount of resources a pod can use above and beyond the amount requested. If there’s additional headroom on the node, the pod can use resources up to this limit.
However, if resources are in contention, and a pod is using resources beyond those it requested, and thereby causing excess demand on a node, the offending pod might be evicted from the node by Kubernetes and the associated Domino Run is terminated. For this reason, Domino strongly recommends setting the requests and limits to the same values.
Your Hardware Tier’s CPU, memory, and GPU requests must not exceed the available resources of the machines in the target node pool after accounting for overhead. Otherwise, an execution using such Hardware Tier will never start. If you need more resources than are available on existing nodes, you might have to add a new node pool with different specifications. This might mean adding individual nodes to a static cluster, or configuring new auto-scaling components that provision new nodes with the required specifications and labels.
Hardware Tiers also control the underlying machine type on which a Domino execution will run.
Nodes that have the same value for the
Kubernetes node label form a Node Pool. Executions with a matching value in the
Node Pool field will then run on these nodes.
As an example, in the previous screenshot, the
large-k8s Hardware Tier is
configured to use the
default node pool.
The following diagram shows a cluster configured with two node pools for
Domino, one named
default and one named
default-gpu. You can make
additional node pools available to Domino by labeling them with the same
dominodatalab.com/node-pool=<node-pool-name>. The arrows in
this diagram represent Domino requesting that a node with a given label
be assigned to an execution. Kubernetes will then assign the execution
to a node in the specified pool that has sufficient resources.
By default, Domino creates a node pool with the label
dominodatalab.com/node-pool=default and all compute nodes Domino
creates in cloud environments are assumed to be in this pool. In cloud environments with automatic node scaling, you will configure
scaling components like AWS Auto Scaling Groups or Azure Scale Sets with
these labels to create elastic node pools.
There are several advanced Hardware Tier settings that provide you even more control.
When you must limit the deployment wide capacity accessible through a given Hardware Tier, but creating a dedicated node pool for this Hardware Tire is not practical, a Domino administrator can use the Maximum Simultaneous Executions setting. When the setting is used, Domino ensures that no more than the specified number of executions can use the selected Hardware Tier at the same time. Additional executions beyond the limit will be queued.
On cloud deployments enabled for autoscaling, new nodes will be provisioned in response to capacity requests. This mechanism minimizes cost, but provisioning a new node can take several minutes causing users to wait while this happens. The situation can be particularly painful in the mornings when many users first log onto a system that has scaled down overnight.
You can address this problem by overprovisioning several "warm" slots for popular Hardware Tiers. Domino will automatically pre-provision any nodes that might be necessary to accommodate the specified number of overprovisioned executions using this Hardware Tier. This will minimize the chance that a user must to wait for a new node to spin up. To keep costs under control, you can apply overprovisioning on a scheduled basis for periods when many new users are expected.
To do this, use the Overprovisioning pods and Overprovisioning schedule Hardware Tier settings.
By default, Domino will request GPU resources of type
This works well for most NVIDIA GPU enabled devices, but when your
deployment is backed by different GPUs devices (for example, NVIDIA MIG GPUs,
AMD GPUs, AWS vGPUs, Xilinx FPGA) you must use a different name
for the GPU resources.
To do so, select Use custom GPU resource name and specify the appropriate GPU resource name corresponding to the name of the GPU devices being discovered and reported by Kubernetes.
For example, with an NVIDIA A100 GPU configured in MIG Mixed
Mode, you can use resources like
You can allow hardware tiers to exceed the default limit of 64MB for shared memory. This is especially beneficial for applications that can use shared memory.
To enable this, select the Allow executions to exceed the default
shared memory limit Hardware Tier setting. Selecting this option overrides the
/dev/shm (shared memory) limit, and any shared memory consumption will count toward the overall memory limit of the hardware tier. Consider and incorporate the size of
/dev/shm in any memory usage calculations for a hardware tier with
this option enabled.
The amount of compute power required for your Domino cluster will fluctuate over time as users start and stop executions. Domino relies on Kubernetes to find space for each execution on existing compute resources. In cloud autoscaling environments, if there’s not enough CPU or memory to satisfy a given execution request, the Kubernetes cluster autoscaler will start new compute nodes to fulfill that increased demand. In environments with static nodes, or in cloud environments where you have reached the autoscaling limit, the execution request will be queued until resources are available.
Autoscaling Kubernetes clusters will shut nodes down when they are idle for more than a configurable duration. This reduces your costs by ensuring that nodes are used efficiently, and terminated when not needed.
Cloud autoscaling resources have properties like the minimum and maximum number of nodes they can create. You must set the node maximum to whatever you are comfortable with given the size of your team and expected volume of workloads. All else equal, it is better to have a higher limit than a lower one, as compute node cost is are cheap to start up and shut down, while your users' time is very valuable. If the cluster cannot scale up any further, your users' executions will wait in a queue until the cluster can service their request.
To prevent a single user from monopolizing a Domino deployment, an administrator can set a limit on the number of simultaneous executions that a user can have running concurrently. Once the number of simultaneously running executions is reached for a given user, any additional executions will be queued. This includes executions for Domino workspaces, jobs, web applications, as well as any executions that make up an on-demand distributed compute cluster. For example, in the case of an on-demand Spark cluster an execution slot will be consumed for each Spark executor and for the master.
See important settings for details.
To prevent too many queued executions from overwhelming a Domino deployment, an administrator can set a global or per-user limit on the number of execution slots that can be queued. Once either limit is reached, additional executions submitted will be rejected and fail. This includes executions for Domino workspaces, jobs, and web applications.
See important settings for details.
From the top menu bar in the admin UI, click Infrastructure. You will
see both Platform and Compute nodes in this interface. Click the name of
a node to get a complete description, including all applied labels,
available resources, and currently hosted pods. This is the full
kubectl describe for the node. Non-Platform nodes in this interface
with a value in the Node Pool column are compute nodes that can be
used for Domino executions by configuring a Hardware Tier to use the
From the menu bar in the admin application, click Executions. This interface
lists active Domino execution pods and shows the type of workload, the
Hardware Tier used, the originating user and project, and the status for
each pod. There are also links to view a full
kubectl describe output
for the pod and the node, and an option to download the deployment
lifecycle log for the pod generated by Kubernetes and the Domino
Each Spark node, including master and worker nodes, launched as part of an on-demand Spark cluster will be displayed as a separate row in the executions interface, with complete information available on the originating project and user, as well as the hardware tier.
The following settings in the
common namespace of the Domino central
configuration affect compute grid behavior.
Value: Maximum number of executions each user might have running concurrently. If a user tries to run more than this, the excess executions will queue until existing executions finish. Default is 25.
Value: Number of seconds an execution pod that cannot be assigned due to execution quota limitations will wait for resources to become available before timing out. Default is 24 * 60 * 60 (24 hours).