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The IBM AIU hardware and software is designed to accelerate inference of Deep Neural Networks (DNNs). The design supports a technique pioneered by IBM called approximate computing, which leverages lower precision computation and a purpose-built architecture, resulting in highly energy-efficient gains for AI workloads. The simple layout is designed to streamline AI workflows by sending data directly from one compute engine to the next.

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Refer to the example below, and be sure to replace <your_user> and <your_password> with the credentials we provide you <NetID> with your NetID.

oc login -u <your_user> -p <your_password> --server=<NetID> --server=https://api.ua-aiu.its.albany.edu:6443

It will proceed to prompt you to enter the LDAP password, which is the same password you use to log into the cluster. If the login is successful, the following steps will be displayed:

The server uses a certificate signed by an unknown authority.
You can bypass the certificate check, but any data you send to the server could be intercepted by others.
Use insecure connections? (y/n): y

WARNING: Using insecure TLS client config. Setting this option is not supported!

Authentication required for https://api.ua-aiu.its.albany.edu:6443.albany.edu:6443 (openshift)
Username: <Your NetID>
Password: %!(EXTRA []interface {}=[])
Login successful.

Then, switch to the project that we will also provide you.

oc project <your_project>

Once connected, navigate to the IBM AIU Files directory within your lab directory. This directory will have been placed there by RTS during your onboarding. Please refer to the following example, and be sure to replace <your_lab> with your own the lab name given to you.

cd /network/rit/lab/<your_lab>/IBM-AIUFiles-FilesNetID

Deploying a Pod

To test deploying a single AIU pod, start by creating a 1aiu.yaml file using the following example YAML. Replace <your pod name> with the desired name for your pod.

apiVersion: v1
kind: Pod
metadata:
  name: <your pod name>
  labels:
    app: <your pod name>
spec:
  securityContext:
    runAsUser: 56551
    runAsGroup: 972
    fsGroup: 3052
  containers:
  - name: c1
    imagePullPolicy: Always
    image: icr.io/ibmaiu/release_2024_08/e2e_stable
    command: ["/usr/bin/pause"] ## starts the pod
    workingDir: /tmp/
    resources: ##starting the variable
      requests:
        ibm.com/aiu_pf: 1
      limits:
        ibm.com/aiu_pf: 1
    env:
    - name: HOME
      value: /tmp
    - name: HF_HOME
      value: /tmp/.cache
    - name: FLEX_COMPUTE
      value: "SENTIENT"
    - name: FLEX_DEVICE
      value: "VFIO"
    - name: dev-shm
      mountPath: /dev/shm
    volumeMounts:
    - name: modeldata
      mountPath: /datasets
  volumes:
  - name: modeldata
    persistentVolumeClaim:
      claimName: modelstore
      readOnly: true

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Defaulted container "c1" out of: c1, aiu-monitor
[56551@jun-pod ~]$

Then, you can run the following command to access your IBM folder. You can also map your IBM folder to your computer using the link.

cd /datasets

Inference Using PyTorch Models

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