AI Training - Monitor your cloud resources
Learn the resources provided by AI Training jobs
Learn the resources provided by AI Training jobs
Last updated 3rd May, 2021.
AI-Training provides users with access to several types of resources. Resource usage can be fetched through a dedicated UI on Grafana.
The URL to access the monitoring is built like this : https://monitoring.<REGION>
.training.ai.cloud.ovh.net/d/gpu?var-job=<JOB-ID>
It can be fetched in the CLI using this command:
ovhai job get <JOB-ID>
---
Name: ai-training-transformers-heuristic
ID: xxx
Created At: 2021-04-14 09:30:02.709449 UTC
Updated At: 2021-04-14 09:30:09.328392 UTC
User: user-xxx
Spec:
Image: ovhcom/ai-training-transformers:3.1.0
Command:
Env:
Default Port: 8080
Unsecure: false
Resources:
GPU: 1
GPU Model: Tesla-V100S
CPU: 13
Volumes:
Timeout: 0
Labels:
SSH Keys:
Status:
State: RUNNING
Queued At: 14-04-21 09:30
Started At: 14-04-21 09:30
Stopped At:
IP: 10.42.165.21
Duration: 2s
Job Url: https://xxx.job.console.preprod.training.ai.cloud.ovh.net
SSH Url:
Monitoring Url: https://monitoring.console.preprod.training.ai.cloud.ovh.net/d/gpu?var-job=xxx&from=1618392608000
Infos:
Data Sync:
Sync Progress:
History:
State Date
----- ----
QUEUED 2021-04-14 09:30:02.708117 UTC
INITIALIZING 2021-04-14 09:30:02.802995 UTC
PENDING 2021-04-14 09:30:02.824278 UTC
RUNNING 2021-04-14 09:30:09.327003 UTC
It is displayed in the line Monitoring Url.
You can also retrieve it using the UI by clicking on the Resource usage link in the job details.
We will now go through each panel to give a short description.
This panel displays the usage of each GPU allocated to your job.
This panel displays the usage and limit of memory for each GPU allocated to your Job
This panel displays the overall CPU usage of your job.
This panel displays the usage and limit of Memory allocated to your job.
This panel displays input and output traffic on your job.
This panel shows the usage and limit of ephemeral storage allocated to your job. Jobs can use ephemeral storage for data not within a synchronised container.
If your usage go beyond the limit of the ephemeral storage, your job will be evicted.
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