Agent on Slurm/PBS#
As an alternative to using the HPC Launcher, you may instead utilize the Determined agent. In this usage model, the system administrator creates a custom resource pool for each Determined user. You then start a Determined agent on one or more compute nodes of the cluster using Slurm or PBS commands to provide the resources for your resource pool. As work is submitted to this resource pool, it is distributed to the set of available agents. If your Slurm/PBS job is terminated (for example due to a time limit) before your Determined work is completed, your Determined work remains in your resource pool until additional agents are started. You may add additional resources to your resource pool by starting additional agents on your cluster. If your Determined work is complete before any time limits are hit on the Slurm/PBS job providing resources, you terminate the agent jobs manually using Slurm/PBS commands.
The primary advantages of this model are:
You have dedicated access to the compute resources provided by the agents you start for the duration of your HPC job. This can provide more predictable throughput as it avoids contention in a highly utilized cluster.
Your Determined experiments are seen by the workload manager as a single large job, rather than many smaller jobs. In some HPC environments, larger jobs are given preference in workload manager scheduling.
If you have jobs of different sizes sharing the same set of resources, you reduce the potential for fragmentation where larger jobs may be delayed in running because the free resources are distributed across many nodes.
It eliminates the need for user impersonation, which the HPC Launcher uses to submit jobs to the Slurm or PBS workload manager on your behalf, using a sudo configuration.
There are several disadvantages to this model as well:
You must interact with Slurm or PBS directly to submit and terminate jobs. Using the HPC launcher provides a more seamless user experience that focuses solely on interacting with Determined commands and interfaces.
Overall system utilization will likely be less. Direct human control over resource allocation and release introduces inefficiency. If you fail to keep sufficient work queued up in your resource pool or fail to terminate the Determined agents when you are through, you prevent other users from accessing those resources.
Install the Determined Master and Agent#
Before users can make use of Determined agents, a system administrator must provide the following:
The system administrator installs the on-premise Determined master component, as described in Install Determined Using Linux Packages, and the Determined agent on all nodes of the cluster, but does not enable or start the
The system administrator creates a custom resource pool in the resource_pools configuration for each Determined user in the
master.yaml. A fragment for creating custom resource pools for
user2using the default settings is as follows:
resource_pools: - pool_name: user1 - pool_name: user2
It is recommended that RBAC be used to limit access to the intended user of each of these resource pools.
Create a per-user Agent Configuration#
This step may be completed either by the system administrator or the intended user. In a
cluster-wide shared directory (examples in this section use
$HOME), create an
file. Below is a minimal example using a resource pool named for the user (
singularity as the container runtime platform. If configured using variables such as
agent.yaml could be shared by all users.
master_host: master.mycluster.com master_port: 8090 resource_pool: $USER container_runtime: singularity
There are several other settings commonly configured in the agent.yaml which are listed in the table below. For the full list of options, see Agent Configuration Reference.
To avoid multiple image downloads, configure an image cache as per Configuring an Apptainer/Singularity Image Cache Directory
Secure the communications between the master and agent using
TLS. Configure the sections of the
Start Per-User Agents to Provide Compute Resources#
The user may then start one or more agents to provide resources to their resource pool using the agent.yaml configured above.
In the command examples below, it is assumed that the agent.yaml for a given user is provided in $HOME`. Paths may need to be updated depending on your local configuration.
On Slurm, you can allocate resources with the
sbatch commands with the desired
resource configuration options.
srun --gpus=8 /usr/bin/determined-agent --config-file $HOME/agent.yaml
sbatch -N4 --gpus-per-node=tesla:4 --wrap="srun /usr/bin/determined-agent --config-file $HOME/agent.yaml"
On PBS, you can launch the agent on multiple nodes with the qsub command.
qsub -l select=2:ngpus=4 -- /opt/pbs/bin/pbsdsh -- /usr/bin/determined-agent --config-file $HOME/agent.yaml
You can add incremental resources to your resource pool, by submitting an additional job and starting additional agents.
Launch Jobs and Experiments on the Resource Pool#
You can then submit experiments or other tasks to the agents you have started by selecting the
proper resource pool. The resource pool to be used can be specified on the command line or via the
experiment config using the
det command run --config resources.resource_pool=$USER hostname
Release the Cluster Resources#
When your jobs and experiments have been completed, be sure to release the resources by canceling your Slurm/PBS job.