Deploy on-premise¶

Warning

Valohai agents can be installed on a on-premises machines running Linux, preferably Ubuntu 20.04.

The Compute and Data Layer of Valohai can be deployed to your on-premise environment. This enables you to:

Use your own on-premises machines to run machine learning jobs.
Use your own cloud storage for storing training artefacts, like trained models, preprocessed datasets, visualizations, etc.
Mount local data to your on-premises workers.
Access databases and date warehouses directly from the workers, which are inside your network.

Valohai doesn’t have direct access to the on-premises machine that executes the machine learning jobs. Instead it communicates with a separate static virtual machine in your on-premise environment that’s responsible for storing the job queue, job states, and short-term logs.

Installing the Valohai worker¶

The Valohai agent (Peon) is responsible for fetching new jobs, writing logs, and updating the job states for Valohai.

You’ll need to have Python 3.8+ installed on the machines by default. The peon-bringup (bup) will install other dependencies, like docker and if needed nvidia-docker.

Warning

Before running the template you’ll need the following information from Valohai:

name the queue name that this on-premises machine will use.
queue-address will be assigned for the queue in your subscription.
redis-password that your queue uses. This is usually stored in your cloud providers Secret Manager.
url download URL for the Valohai worker agent.

sudo su
apt-get update -y && apt-get install -y python3 python3-distutils

TEMPDIR=$(mktemp -d)
pushd $TEMPDIR

export NAME=<queue-name>
export QUEUE_ADDRESS=<queue-address>
export PASSWORD=<redis-password>
export URL=<bup-url>

curl $URL --output bup.pex
chmod u+x bup.pex
env "CLOUD=none" "ALLOW_MOUNTS=true" "INSTALLATION_TYPE=private-worker" "REDIS_URL=rediss://:$PASSWORD@$QUEUE_ADDRESS:63790"  "QUEUES=$NAME" ./bup.pex

popd

Frequently Asked Questions¶

Question	Answer
Can I run multiple jobs in parallel on the same on-premise machine?	Yes. You can add `SINGLE_GPU_PER_PEON=true` in the peon configuration file (`/etc/peon.config`) to Valohai to run multiple jobs in parallel. Each job will have access to one GPU and will take up as much memory/CPU as it needs.
Can I define per execution how many GPUs I want to use?	No. The `SINGLE_GPU_PER_PEON` inside `/etc/peon.config` defines if Valohai will always use all the GPUs for a job, or run one job per GPU.
I have just one GPU on my machine. Can I run multiple jobs on the same GPU?	Yes. You’ll need to udpate your the peon service file. Rename `/etc/systemd/system/peon.service` to `/etc/systemd/system/peon@.service` Run `systemctl daemon-reload` read the new service file Enable multiple peons: `systemctl enable --now peon@1` `systemctl enable --now peon@2` `systemctl enable --now peon@3`

Question

Answer

Can I run multiple jobs in parallel on the same on-premise machine?

Yes. You can add SINGLE_GPU_PER_PEON=true in the peon configuration file (/etc/peon.config) to Valohai to run multiple jobs in parallel. Each job will have access to one GPU and will take up as much memory/CPU as it needs.

Can I define per execution how many GPUs I want to use?

No. The SINGLE_GPU_PER_PEON inside /etc/peon.config defines if Valohai will always use all the GPUs for a job, or run one job per GPU.

I have just one GPU on my machine. Can I run multiple jobs on the same GPU?

Yes. You’ll need to udpate your the peon service file.

Rename /etc/systemd/system/peon.service to /etc/systemd/system/peon@.service
Run systemctl daemon-reload read the new service file
Enable multiple peons:
- systemctl enable --now peon@1
- systemctl enable --now peon@2
- systemctl enable --now peon@3