CLI for Job Monitoring

The csctl command-line interface (CLI) tool is preinstalled on the user node for efficient interaction with the Cerebras Cluster’s resource manager. This tool is pivotal for managing jobs within the cluster, which adheres to a first-come, first-served queuing system for resource allocation.

Key Features of csctl

Job Tracking: Inspect the state of submitted jobs, and cancel your own jobs if necessary.
Job Labelling: Apply labels to a job.
Queue Tracking: Review which jobs are queued and which jobs are running on the Cerebras cluster.
Get Configured Volumes: Get a list of configured volumes on the Cerebras cluster. These volumes can be used to stage code and training data.
Update the priority for a given job: Update the priority for a given job.
Log Export: Export Cerebras cluster logs of a given job to the user node. These logs can be useful when debugging a job failure and working with the Cerebras support team.
Worker SSD Cache: Query worker SSD cache usage.
Cluster Status: Query cluster status.

Usage Guidelines

Use the csctl tool directly from the terminal of your user node.

csctl –help

To get the help message:

csctl --help
Cerebras cluster command line tool.

Usage:
  csctl [command]

Available Commands:
  cancel             Cancel job
  check-volumes      Check volume validity on this usernode
  clear-worker-cache Clear the worker cache
  config             View csctl config files
  get                Get resources
  job                Job management commands
  label              Label resources
  log-export         Gather and download logs.
  stypes              Display resource types

Flags:
       --csconfig string    config file /opt/cerebras/config_v2 (default "/opt/cerebras/config_v2")
   -d, --debug int          higher debug values will display more fields in output objects
   -h, --help               help for csctl
   -n, --namespace string   configure csctl to talk to different user namespaces
   --version            version for csctl

Use "csctl [command] --help" for more information about a command.

Job Tracking

Each training job submitted to the Cerebras cluster launches two sequential jobs. First, a compilation job is launched. When compilation is completed, an execution job is launched. Each of these is identified by a jobID. The jobID for each job will be printed on the terminal after they start running on the Cerebras Wafer-Scale cluster.

Extracting the model from framework. This might take a few minutes.
WARNING:root:The following model params are unused: precision_opt_level, loss_scaling
2023-02-05 02:00:00,450 INFO:   Compiling the model. This may take a few minutes.
2023-02-05 02:00:00,635 INFO:   Initiating a new compile wsjob against the cluster server.
2023-02-05 02:00:00,761 INFO:   Compile job initiated
...
2023-02-05 02:02:00,899 INFO:   Ingress is ready.

2023-02-05 02:02:00,899 INFO:   Cluster mgmt job handle: {'job_id': 'wsjob-aaaaaaaaaa000000000', 'service_url': 'cluster-server.cerebras.local:443', 'service_authority': 'wsjob-aaaaaaaaaa000000000-coordinator-0.cluster-server.cerebras.local', 'compile_dir_absolute_path': '/cerebras/cached_compile/cs_0000000000111111'}

2023-02-05 02:02:00,901 INFO:   Creating a framework GRPC client: cluster-server.cerebras.local:443
2023-02-05 02:07:00,112 INFO:   Compile successfully written to cache directory: cs_000000000011111
2023-02-05 02:07:30,118 INFO:   Compile for training completed successfully!
2023-02-05 02:07:30,120 INFO:   Initiating a new execute wsjob against the cluster server.
2023-02-05 02:07:30,248 INFO:   Execute job initiated
...
2023-02-05 02:08:00,321 INFO:   Ingress is ready.

2023-02-05 02:08:00,321 INFO:   Cluster mgmt job handle: {'job_id': 'wsjob-bbbbbbbbbbb11111111', 'service_url': 'cluster-server.cerebras.local:443', 'service_authority': 'wsjob-bbbbbbbbbbb11111111-coordinator-0.cluster-server.cerebras.local', 'compile_artifact_dir': '/cerebras/cached_compile/cs_0000000000111111'}

...

The jobID is also recorded in a file run_meta.json inside the <model_dir>/cerebras_logs folder. All jobIDs that use the same model directory <model_dir> are appended in the run_meta.json . run_meta.json contains two sections: compile_jobs and execute_jobs. Once a training job is submitted and before compilation is done, the compile job will be recorded under compile_jobs. For this example you will see:

{
     "compile_jobs": [
        {

                    "id": "wsjob-aaaaaaaaaa000000000",

                    "log_path": "/cerebras/workdir/wsjob-aaaaaaaaaa000000000",
                    "start_time": "2023-02-05T02:00:00Z",
        },
     ]
}

After the compilation job has been completed and the training job is scheduled, then the compile job will report additional log information and the jobID of the training job will be recorded under execute_jobs. To correlate between compilation job and training job, you can correlate between the available time of the compilation job and the start time of the training job. For this example, you will see

{
    "compile_jobs": [
        {

            "id": "wsjob-aaaaaaaaaa000000000",

            "log_path": "/cerebras/workdir/wsjob-aaaaaaaaaa000000000",
            "start_time": "2023-02-05T02:00:00Z",
            "cache_compile": {
                "location": "/cerebras/cached_compile/cs_0000000000111111",

                "available_time": "2023-02-05T02:02:00Z"

            }
        }
    ],
    "execute_jobs": [
        {

            "id": "wsjob-bbbbbbbbbbb11111111",

            "log_path": "/cerebras/workdir/wsjob-bbbbbbbbbbb11111111",

            "start_time": "2023-02-05T02:02:00Z"

        }
    ]
}

Using the jobID, you can query information about status of a job in the system using

csctl [-d int] get job <jobID> [-o json|yaml]

where:

Flag	Default	Description
-o	table	Output Format: table, json, yaml
-d, -debug	0	Debug level. Choosing a higher level of debug prints more fields in the output objects. Only applicable to json or yaml output format.

csctl -d0 get job wsjob-000000000000 -oyaml
meta:
  createTime: "2022-12-07T05:10:16Z"
  labels:
    label: customed_label
    user: user1
  name: wsjob-000000000000
  type: job
spec:
  user:
    gid: "1001"
    uid: "1000"
  volumeMounts:
  - mountPath: /data
    name: data-volume-000000
    subPath: ""
  - mountPath: /dev/shm
    name: dev-shm
    subPath: ""
status:
  phase: SUCCEEDED
  systems:
  - systemCS2_1

Compilation and execution jobs are queued and executed sequentially in the Cerebras cluster. This means that the compilation job is completed before the execution job is scheduled. Compilation jobs do not require CS-X resources, but it requires some resources on the server nodes. In 1.8, we allow only one concurrent compilation running in the cluster. Execution jobs require CS-X resources, they will be queued up until sufficient CS-X resources are available. Compilation and execution jobs have different jobID.

Job Termination

You can terminate any compilation or execution job before completion by providing the jobID. More details on jobID in Job Tracking. To cancel a job, you can use

csctl cancel job <jobID>

Terminating a job releases all resources and sets the job to a cancelled state. An example output to cancel a job is

csctl cancel job wsjob-000000000000
Job cancelled success

In 1.8, this command might cause the client logs to print

cerebras.appliance.errors.ApplianceUnknownError: Received unexpected gRPC error (StatusCode.UNKNOWN) : 'Stream removed' while monitoring Coordinator for Runtime server errors

This is expected.

Job Labelling

You can add labels to your jobs, to help categorize your jobs better. There are 2 ways to add labels to your jobs. One way is to use the flag --job_labels when you submit your training job. You can use a list of equal-sign-separated key value pairs served as job labels. For example, to assign a job label to training a GPT-2 model using PyTorch, you would use:

python run.py \
   CSX \
   --job_labels framework=pytorch model=GPT2 \
   --params params.yaml \
   --num_csx=1 \
   --model_dir=model_dir \
   --mode train \
   --mount_dirs <paths to data> \
   --python_paths <paths to modelzoo and other python code if used> \

The other way to add labels to your jobs is through csctl command

csctl label job wsjob-000000000000 framework=pytorch

You can use this command to remove a label from your job

csctl label job wsjob-000000000000 framework=pytorch

Queue Tracking

To obtain a full list of running and queued jobs on the Cerebras cluster, you can use

csctl get jobs

By default, this command produces a table including:

Field	Description
Name	jobID identification
Age	Time since job submission
Duration	How long the job ran
Phase	One of QUEUED, RUNNING, SUCCEDED, FAILED, CANCELLED
Systems	CS-X systems used in this job
User	User that starts this job
Labels	Customized labels by user
Dashboard	Grafana dashboard link for this job

For example:

csctl get jobs
NAME                AGE  DURATION  PHASE      SYSTEMS                   USER  LABELS             DASHBOARD
wsjob-000000000001  18h  20s       RUNNING    systemCS2_1, systemCS2_2  user2 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000001
wsjob-000000000002   1h  6m25s     QUEUED                               user2 model=neox,team=ml https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000002
wsjob-000000000003  10m  2m01s     QUEUED                               user1 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000003

Directly executing the command prints out a long list of current and past jobs. You can use -l options to return jobs that match with the given set of labels as

csctl get jobs -l model=neox,team=ml
NAME                AGE  DURATION  PHASE      SYSTEMS                   USER  LABELS             DASHBOARD
wsjob-000000000002   1h  6m25s     QUEUED                               user2 model=neox,team=ml https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000002

If you only want to see your own jobs, use the -m option.

csctl get jobs -m
NAME                AGE  DURATION  PHASE      SYSTEMS                   USER  LABELS             DASHBOARD
wsjob-000000000003  10m  2m01s     QUEUED                               user1 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000003

To also include completed and failed jobs, use the -a option. csctl get jobs -a

csctl get jobs -a
NAME                AGE  DURATION  PHASE      SYSTEMS                   USER  LABELS             DASHBOARD
wsjob-000000000000  43h  6m27s     SUCCEEDED  systemCS2_1               user1 model=gpt3xl       https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000000
wsjob-000000000001  18h  20s       RUNNING    systemCS2_1, systemCS2_2  user2 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000001
wsjob-000000000002   1h  6m25s     QUEUED                               user2 model=neox,team=ml https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000002
wsjob-000000000003  10m  2m01s     QUEUED                               user1 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000003

These filter options can be combined. For example, to see your complete job history:

csctl get jobs -a -m
NAME                AGE  DURATION  PHASE      SYSTEMS                   USER  LABELS             DASHBOARD
wsjob-000000000000  43h  6m27s     SUCCEEDED  systemCS2_1               user1 model=gpt3xl       https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000000
wsjob-000000000003  10m  2m01s     QUEUED                               user1 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000003

You also can use grep to extract relevant information of what jobs are queued versus running and how many systems are occupied. When you grep 'RUNNING', you see a list of jobs that are currently running on the cluster. For example, as shown below, there is one job running:

csctl get jobs | grep 'RUNNING'
wsjob-000000000001  18h  20s       RUNNING    systemCS2_1, systemCS2_2  user2 model=gpt3-tiny    https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000001

When you grep 'QUEUED', you see a list of jobs that are currently queued and waiting for system availability to start training. For example, at the same time of the above running job, there is another job currently queued, as shown below:

csctl get jobs | grep 'QUEUED'
wsjob-000000000002   1h  6m25s     QUEUED                               user2 model=neox,team=ml https://grafana.cerebras.local/d/WebHNShVz/wsjob-dashboard?orgId=1&var-wsjob=wsjob-000000000002

Get Configured Volumes

After installing Cerebras cluster, the system admin will configure a few volumes to be used in your jobs to access code and training data. To get a list of mounted volumes on the Cerebras cluster, you can use

csctl get volume

For example:

csctl get volume
NAME                  TYPE  CONTAINERPATH  SERVER       SERVERPATH  READONLY
training-data-volume  nfs   /ml            10.10.10.10  /ml         false

Update the Priority for a Job

To update the priority for a given job, use the following command:

csctl job set-priority <wsjob_id> <bucket_name | priority_value>

For example:

$ csctl job set-priority wsjob-xxxxxx p2

This updates the job’s priority to P2. Confirmation is displayed:

job-operator/wsjob-jv58f5mb95kwpe5hwuujrk priority successfully updated.

Log Export

To download Cerebras cluster logs of a given job to the user node, you can use

csctl log-export <jobID> [-b]

with optional flags:

Flag	Default Value	Description
-b, –binaries	False	Include binary debugging artifacts
-h, –help		Informative message for log-export

For example:

csctl log-export wsjob-example-0
Gathering log data within cluster...
Starting a fresh download of log archive.
Downloaded 0.55 MB.
Logs archive: ./wsjob-example-0.zip

Cerebras cluster logs can be useful when debugging a job failure and working with Cerebras support.

Worker SSD Cache

To speed up the process of large amount of input data, we allow the users to stage their data in the worker nodes’ local SSD cache. This cache is shared among different users.

Get Worker Cache Usage

Use this command to obtain the current worker cache usage on each worker node:

csctl get worker-cache
NODE       DISK USAGE
worker-01  57.86%
worker-02  50.84%
worker-03  49.47%
worker-04  63.56%
worker-05  63.56%
worker-06  63.71%
worker-07  63.22%
worker-09  65.80%

Clear Worker Cache

If the cache is full, use the clear command to delete the contents of all caches on all nodes.

csctl get worker-cache
Worker caches cleared successfully

Cluster Status

You can check the status and system load of all CS-X systems and all cluster nodes by running

csctl get cluster

In this table, note that the CPU and MEM columns are only relevant for nodes, and system-in-use is only relevant for CS-X systems. The CPU percentage is scaled so that 100% indicates that all CPU cores are fully utilized. For example:

csctl get cluster
NAME               TYPE             CPU     MEM     SYSTEM-IN-USE  JOBID                         JOBLABELS     STATE  NOTES
systemf103         system           n/a     n/a     InUse          wsjob-jcvs23zpsxopvu9ymd2e5u  wsjob-label=  ok
systemf116         system           n/a     n/a     InUse          wsjob-jcvs23zpsxopvu9ymd2e5u  wsjob-label=  ok
cs-swx001-sx-sr18  broadcastreduce  22.17%  14.20%  n/a            n/a                                         ok
cs-wse002-mg-sr01  management       3.23%   9.45%   n/a            n/a                                         ok
cs-wse005-mx-sr04  memory           13.00%  12.93%  n/a            n/a                                         ok

You can filter the output with the following options:

Flag	Description
-e, -error-only	Only show nodes/systems in an error state
-n, -node-only	Only show nodes, omit the system list
-s, -system-only	Only show CS-X systems, omit the node list

Conclusion

By leveraging csctl, users can effectively manage their jobs and resources on the Cerebras Cluster, ensuring optimal use of available computational assets.

Getting Started

Concepts

Model Zoo

CS Torch

Cluster Monitoring

Fundamentals

Support

CLI for Job Monitoring

Key Features of csctl

Usage Guidelines

csctl –help

Job Tracking

Job Termination

Job Labelling

Queue Tracking

Get Configured Volumes

Update the Priority for a Job

Log Export

Worker SSD Cache

Get Worker Cache Usage

Clear Worker Cache

Cluster Status

Conclusion

Getting Started

Concepts

Model Zoo

CS Torch

Cluster Monitoring

Fundamentals

Support

​Key Features of csctl

​Usage Guidelines

​csctl –help

​Job Tracking

​Job Termination

​Job Labelling

​Queue Tracking

​Get Configured Volumes

​Update the Priority for a Job

​Log Export

​Worker SSD Cache

​Get Worker Cache Usage

​Clear Worker Cache

​Cluster Status

​Conclusion

Key Features of csctl

Usage Guidelines

csctl –help

Job Tracking

Job Termination

Job Labelling

Queue Tracking

Get Configured Volumes

Update the Priority for a Job

Log Export

Worker SSD Cache

Get Worker Cache Usage

Clear Worker Cache

Cluster Status

Conclusion