I had about 2TB of files on a cloud-based Linux host that I needed to backup to cloud storage. I had an Box Enterprise storage account with a 30PB limit on storage and a maximum file size of 150GB, so I decided to try to connect from Linux to Box and store all of the backup data in Box. You can check your own limits under Box “Account Settings”, bottom of the page:
The most difficult part of getting Box to work on a headless, cloud-based Linux host is getting authorization to work. Box wants to use OAuth2 web-based authentication, and I need to set up Box access on a remote host where I’m connecting via ssh and there is no web browser or desktop. The easiest way that I’ve found to do this is to generate an OAuth2 bearer token on my laptop that’s formatted using the JSON Web Token (JWT) format and then copy that to the Linux host.
I used rclone for the backup. I first installed rclone on the Ubuntu Linux host:
sudo apt-get install rclone
Then I installed rclone on my Mac laptop:
brew install rclone
I pulled up a terminal on the Mac and configured rclone so that my Box account was authorized:
rclone authorize box
This will cause a browser window to pop up and ask you to log into Box. Once you’ve logged in and authorized rclone to read and write files in your Box drive the command will finish up and spit out a bearer token:
$ rclone authorize box
2024/09/12 08:57:15 NOTICE: Config file "/Users/eruby/.config/rclone/rclone.conf" not found - using defaults
2024/09/12 08:57:15 NOTICE: If your browser doesn't open automatically go to the following link: http://127.0.0.1:53682/auth?state=qqf7swGNZ8pH4iJksvR3xA
2024/09/12 08:57:15 NOTICE: Log in and authorize rclone for access
2024/09/12 08:57:15 NOTICE: Waiting for code…
2024/09/12 08:57:45 NOTICE: Got code
Paste the following into your remote machine --->
{"access_token":"nOtaReaLacCessT0k3NsuCkas","token_type":"bearer","refresh_token":"nOtaReaLb3aR3rT0k3NKxRrg2J1rB7DKzKg6svazAlwAwHWKl","expiry":"2024-09-12T10:02:31.314087-07:00"}
<---End paste
The bearer token contains an access token, a refresh token, and an expiration date. The access token is good for an hour. After that it expires and application (rclone) will use the refresh token to get a new access token. This can keep happening until the user that generated the token (you) is no longer allowed to access Box or your password changes. If you change your password you’ll need to generate a new bearer token. The refresh token may expire before your password changes, depending on the security policy of the organization issuing the refresh token, so at some point you may need to regenerate the bearer token even if you don’t change your password.
Now you just have to paste the bearer token into the Linux host’s rclone config, so log into the Linux host and run rclone config. Here’s the entire interaction with the config command:
$ rclone config
2024/09/12 18:19:19 NOTICE: Config file "/home/eruby/.config/rclone/rclone.conf" not found - using defaults
No remotes found - make a new one
n) New remote
s) Set configuration password
q) Quit config
n/s/q> n
name> Box
Type of storage to configure.
Enter a string value. Press Enter for the default ("").
Choose a number from below, or type in your own value
1 / 1Fichier
\ "fichier"
2 / Alias for an existing remote
\ "alias"
3 / Amazon Drive
\ "amazon cloud drive"
4 / Amazon S3 Compliant Storage Provider (AWS, Alibaba, Ceph, Digital Ocean, Dreamhost, IBM COS, Minio, Tencent COS, etc)
\ "s3"
5 / Backblaze B2
\ "b2"
6 / Box
\ "box"
7 / Cache a remote
\ "cache"
8 / Citrix Sharefile
\ "sharefile"
9 / Dropbox
\ "dropbox"
10 / Encrypt/Decrypt a remote
\ "crypt"
11 / FTP Connection
\ "ftp"
12 / Google Cloud Storage (this is not Google Drive)
\ "google cloud storage"
13 / Google Drive
\ "drive"
14 / Google Photos
\ "google photos"
15 / Hubic
\ "hubic"
16 / In memory object storage system.
\ "memory"
17 / Jottacloud
\ "jottacloud"
18 / Koofr
\ "koofr"
19 / Local Disk
\ "local"
20 / Mail.ru Cloud
\ "mailru"
21 / Microsoft Azure Blob Storage
\ "azureblob"
22 / Microsoft OneDrive
\ "onedrive"
23 / OpenDrive
\ "opendrive"
24 / OpenStack Swift (Rackspace Cloud Files, Memset Memstore, OVH)
\ "swift"
25 / Pcloud
\ "pcloud"
26 / Put.io
\ "putio"
27 / SSH/SFTP Connection
\ "sftp"
28 / Sugarsync
\ "sugarsync"
29 / Transparently chunk/split large files
\ "chunker"
30 / Union merges the contents of several upstream fs
\ "union"
31 / Webdav
\ "webdav"
32 / Yandex Disk
\ "yandex"
33 / http Connection
\ "http"
34 / premiumize.me
\ "premiumizeme"
35 / seafile
\ "seafile"
Storage> 6
** See help for box backend at: https://rclone.org/box/ **
OAuth Client Id
Leave blank normally.
Enter a string value. Press Enter for the default ("").
client_id>
OAuth Client Secret
Leave blank normally.
Enter a string value. Press Enter for the default ("").
client_secret>
Box App config.json location
Leave blank normally.
Leading `~` will be expanded in the file name as will environment variables such as `${RCLONE_CONFIG_DIR}`.
Enter a string value. Press Enter for the default ("").
box_config_file>
Box App Primary Access Token
Leave blank normally.
Enter a string value. Press Enter for the default ("").
access_token>
Enter a string value. Press Enter for the default ("user").
Choose a number from below, or type in your own value
1 / Rclone should act on behalf of a user
\ "user"
2 / Rclone should act on behalf of a service account
\ "enterprise"
box_sub_type>
Edit advanced config? (y/n)
y) Yes
n) No (default)
y/n> n
Remote config
Use auto config?
* Say Y if not sure
* Say N if you are working on a remote or headless machine
y) Yes (default)
n) No
y/n> n
For this to work, you will need rclone available on a machine that has
a web browser available.
For more help and alternate methods see: https://rclone.org/remote_setup/
Execute the following on the machine with the web browser (same rclone
version recommended):
rclone authorize "box"
Then paste the result below:
result> {"access_token":"nOtaReaLacCessT0k3NsuCkas","token_type":"bearer","refresh_token":"nOtaReaLb3aR3rT0k3NKxRrg2J1rB7DKzKg6svazAlwAwHWKl","expiry":"2024-09-12T10:02:31.314087-07:00"}
--------------------
[Box]
token = {"access_token":"nOtaReaLacCessT0k3NsuCkas","token_type":"bearer","refresh_token":"nOtaReaLb3aR3rT0k3NKxRrg2J1rB7DKzKg6svazAlwAwHWKl","expiry":"2024-09-12T10:02:31.314087-07:00"}
--------------------
y) Yes this is OK (default)
e) Edit this remote
d) Delete this remote
y/e/d> y
Current remotes:
Name Type
==== ====
Box box
e) Edit existing remote
n) New remote
d) Delete remote
r) Rename remote
c) Copy remote
s) Set configuration password
q) Quit config
e/n/d/r/c/s/q> q
To explain:
I named the remote connection “Box”.
I made the type of remote connection “box” (choice 6 from the list of supported remote storage types),
I didn’t edit any options, just answered “n” when asked if I wanted to make edits.
When I got to the Use auto config? question I answered “n”.
I copied the entire bearer token I got from my Mac into the result> field on the Linux box. (This is the entire contents inside the curly braces “{ … }” that rclone tells you to “Paste the following into your remote machine”.)
At this point rclone should be configured, now I just have to copy the data into Box. All of the data in in the /testdata directory, so I ran:
There are symlinks in the directory, so --copy-links is needed.
If there are empty directories on the source, I want to copy them over, so --create-empty-src-dirs is needed.
/testdata is the root directory that I’m copying over.
“Box” is the name I used for the remote connection.
backup-testdata is going to be the location of the data in my Box account.
Since this is going to take many hours to copy, I redirected all output to a log file and ran the process in the background. I can come back and check the log file later to see if everything worked.
Just to make sure everything is working I logged into Box with my laptop and I can see the new backup-testdata directory and it’s being populated with data, so everything appears to be working.
Hope you find this useful.
Thank you Adam Ru for the suggestion to try rclone.
I was invited to AI Field Day 4 in Santa Clara last week to present a couple of talks on running AI workloads on Intel AMX CPUs. This is a recording of the talk I did on setting up Tanzu Kubernetes for running workloads that use Intel AMX CPUs.
I was invited to AI Field Day 4 in Santa Clara last week to present a couple of talks on running AI workloads on Intel AMX CPUs. This is a recording of the talk I did on running LLMs.
I recently started using Google Spaces and wanted to have some automations send me messages when certain things happened on my systems, so I wrote a simple bash script to send a notification message.
If you’re the Space Manager of a Google Space you can create webhooks to send messages to that space. You can create a webhook for Google Spaces by going into Google Chat and selecting the Space you want to send messages to. Click on the Space name at the top of the screen then choose “Apps & integrations” from the drop-down menu. Then select “Webhooks” and create a webhook.
Each webhook has a name, an icon, and a URL. I wanted three types of messages: info, warning, and critical, so I created three webhooks named Info, Warning, and Critical, each with an appropriate icon.
Then I created the notify.sh script, copying the URLs for my space into an associative array. (Associative arrays were only introduced in Bash v4, so if you’re using an older version of bash this won’t work.)
#!/bin/bash
# List of webhooks from Google Spaces.
declare -A webhooks
webhooks=(
["info"]="https://chat.googleapis.com/v1/spaces/..."
["warning"]="https://chat.googleapis.com/v1/spaces/..."
["critical"]="https://chat.googleapis.com/v1/spaces/..."
)
# Use the first argument -- "info", "warning",
# or "critical" -- to pick a webhook URL.
GCHAT_WEBHOOK_URL="${webhooks[$1]}"
if [[ -z $GCHAT_WEBHOOK_URL ]]; then
# If the first argument wasn't "info", "warning",
# or "critical", use the "critical" webhook.
GCHAT_WEBHOOK_URL="${webhooks["critical"]}"
else
# Remove the first argument from the arg list.
shift
fi
# Everything after the first argument becomes the message.
msg="$*"
# Escape the message text to make it "safe".
escapedText=$(echo "$msg" | sed 's/\n/\\n/g' \
| sed 's/"/\\"/g' | sed "s/'/\\'/g" )
# Create a json object with the text.
json="{\"text\": \"$escapedText\"}"
# Use curl to send the JSON to Google.
curl -X POST -H 'Content-Type: application/json' \
-d "$json" "${GCHAT_WEBHOOK_URL}"
Now I can call the script like so:
notify.sh info "You should know about this."
notify.sh warning "There's a problem but it's not that bad."
notify.sh critical "There's a bad problem."
If I have another script that needs to get my attention I just have it call notify.sh.
Full disclosure: I used to work for a startup called Bitfusion, and that startup was bought by VMware, so I now work for VMware. At Bitfusion we developed a technology for accessing hardware accelerators, such as NVIDIA GPUs, remotely across networks using TCP/IP, Infiniband, and PVRDMA. Although I still do some work on the Bitfusion product at VMware, I spend most of my time these days seeing what I can do on the vSphere platform using the latest AI/ML accelerator hardware from NVIDIA, Intel, and AMD.
Although I work at VMware, this is my own personal blog, and any views, opinions, or mistakes I publish here are purely my own and are not official views or recommendations from VMware.
Everyone wants the latest, greatest GPUs for AI/ML training and inference workloads. As I’m sure most of you know, GPUs are just specialized matrix processors. They can quickly perform mathematical operations — in parallel — on matrices of numbers. Although GPUs were originally designed for graphics, it turns out that being able to do matrix math is extremely useful for AI/ML.
Unfortunately, every GPU vendor on the planet seems to be having about a one year order backlog when it comes to shipping datacenter-class GPUs. If you’re having a hard time buying GPUs, one thing you can do to increase the performance of your AI/ML workloads is to let the CPU’s AMX instructions do some of that AI/ML work, lessening the need for expensive and hard-to-procure GPUs.
Advanced Matrix Extensions (AMX) are a new set of instructions available on x86 CPUs. These instructions are designed to work on matrices to accelerate artificial intelligence and machine learning -related workloads. These instructions are beginning to blur the lines between CPUs and GPUs when it comes to machine learning applications.
When I started hearing that Intel Sapphire Rapids CPUs were embedding matrix operations in the CPU’s instruction set I started wondering what can I do with those instructions using AI/ML tools?
“We can do good inference on Skylake, we added instructions in Cooper Lake, Ice Lake, and Cascade Lake. But AMX is a big leap, including for training.”
— Bob Valentine, the processor architect for Sapphire Rapids
.
As you replace older hosts with Sapphire Rapids -based hosts you not only get performance improvements for traditional computing, you also get AMX capabilities for AI/ML workloads. You can execute diverse AI & non-AI multi-tenant workloads side by side in a virtualized environment. You have the flexibility to repurpose the IT infrastructure for AI and non-AI use cases as demand changes without additional capex. The ubiquity of Intel Xeon & vSphere in on-Prem and cloud environments, combined with an optimized AI software stack, allows you to quickly scale the compute in hybrid environments. You can run your entire end to end AI pipeline — data prep, training, optimization, inference – using CPUs with built-in AI acceleration.
Does this really work? What kind of workloads can I run?
Here’s a demo I did using an llm-foundry LLM with a 7B parameter model from HuggingFace. The code is installed in a container and the model is loaded in a Kubernetes volume. I first start the LLM in a Tanzu cluster on an Ice Lake CPU -based system with no GPUs. As you can see it takes a while just to load the model into memory, then when it starts it’s pretty jerky and slow.
I start the same exact container on Tanzu cluster running on a Sapphire Rapids CPU -based system with no GPUs. The hardware is roughly equivalent (both are using what would be considered mid-range servers at the time they were purchased), the VMs are equivalent in memory and vCPUs, but the Sapphire Rapids system runs much faster than the previous generation Ice Lake system.
LLM running on Sapphire Rapids with AMX
In addition to the above side-by-side comparison of an LLM running on Ice Lake vs Sapphire Rapids, we also fine-tuned an LLM using just Sapphire Rapids CPUs. Starting with an off-the-shelf LLAMA2-7B model, we fine-tuned it with a dataset “Finance-Alpaca” of about 17,500 queries. We used cnvrg.io to manage the AI pipeline and Pytorch distributed fine-tuning. It took about 3.5 hours to complete on a 4 VM Tanzu cluster with Sapphire Rapids Xeon 4 hardware.
Once the model was fine-tuned with financial data we ran 3 chatbots on a single host. Now that the model was fine-tuned we could ask it questions such as “What is IRR?”, “What in NPV?”, “What is the difference between IRR and NPV?” and get correct and detailed answers back from the LLM.
3 Finance Chatbots running on Sapphire Rapids with AMX
We just took an off-the-shelf LLM, fine-tuned it with financial services information in about 3.5 hours, and now we have a chatbot that can answer basic questions about finance and financial terms. No GPUs were used to do any of this.
You may not want to run every ML workload you have on just CPUs, but there are a lot of them that you can run on just CPUs. Workloads will run even faster with GPUs, but you may not want to pay for GPUs for every workload you run if the speed of a CPU is good enough.
vSphere Requirements for using AMX
If you want to try this in your vSphere environment this is what you’ll need:
Hardware with Sapphire Rapids CPUs.
Guest VMs running Linux kernel 5.16 or later. Kernel 5.19 or later recommended.
Guest VMs using HW version 20 (ESXI 8.0u1, vCenter 8.0u1).
If you’re running Kubernetes, your worker nodes will also need to run Linux kernel 5.16 or later.
Support for AMX was added to the Linux 5.16 kernel, so if you want to use AMX you’ll need to use 5.16 or a later kernel. In my tests for guest VMs I tried Ubuntu 22.04 images with the 5.19 kernel and images using 6.2 kernels, both of which worked fine. Although Ubuntu 22.04 ships with a 5.15 kernel, the 6.2 kernel is available using the hardware enablement (HWE) kernel package that comes with 22.04. The HWE kernel can be installed with apt:
Which capabilities of the underlying hardware are virtualized in vSphere is determined by the hardware version (HW version) of the guest VM. The AMX instructions are virtualized in HW version 20, so if you want to access AMX instructions in vSphere you need to be using HW version 20 on your VMs.
To find out what HW version a VM is using, in vCenter go to the VM, click the Updates tab, and click the CHECK STATUS button.
HW version 20 is supported on ESXI 8.0u1. To run ESXI 8.0u1 you’ll need vCenter 8.0u1. If you’re still running vCenter 7 and you want to try this technology out I suggest that you upgrade to vCenter 8 as soon as you can, then start upgrading ESXI hosts to ESXI 8.
Once you have a Linux VM with a 5.19 kernel (or later) running HW version 20, any AI/ML framework that you run on that VM will have access to the hardware’s AMX instructions. If you run Docker on the VM any AI/ML containers that you run will be running on a the VM’s kernel and will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.
Tanzu Requirements for using AMX
The kernel requirement also applies to Tanzu worker nodes. Whatever kernel is installed on your worker nodes is the kernel that your Kubernetes pods use. To use AMX your Tanzu worker nodes need to be running kernel 5.16 or later.
Tanzu comes with a set of pre-built, automatically-updated node images called Tanzu Kubernetes Releases (TKRs). Each image is an OVA file that deploys a Kubernetes control node or a worker node. A node is just a Linux VM with a specific version of Kubernetes installed on it and a specific Linux kernel.
When you upgrade a Tanzu Kubernetes cluster, say from Kubernetes 1.23 to 1.24, the Tanzu Supervisor Cluster will create a new VM from 1.24 TKR image, wait for it to join the cluster, then it will evacuate, shut down, and delete one of your 1.23 nodes. The Supervisor Cluster repeats this over and over, first replacing your cluster’s control nodes, then replacing the cluster’s worker nodes, until all of the nodes in the cluster are running Kubernetes 1.24.
Note: Kubernetes should only be upgraded from one minor release to the next minor release. If you have a cluster running Kubernetes 1.20 and you want to upgrade to 1.24, you have to first upgrade to 1.21, then 1.22, then 1.23, and finally to 1.24. Skipping a minor version is not recommended and may break your cluster.
VMware publishes two different TKR images for each version of Kubernetes, one based on PhotonOS and one based on Ubuntu.
At this time VMware has not yet published a TKR with a 5.19 (or later) kernel. If you want to start using Sapphire Rapids AMX instructions and you want to use Tanzu Kubernetes, you have two choices:
Wait for the official TKR from VMware with a 5.19 (or later) kernel.
Build your own TKR using the Bring Your Own Image (BYOI) process.
To build an image, follow the instructions on the Github page vSphere Tanzu Kubernetes Grid Image Builder. The process is fairly straightforward. The steps I followed were:
I edited the packer-variables/vsphere.j2 file so it contained information about my vSphere environment. I also created a folder called “BYOI” under my cluster in vCenter and specified that folder in the config, so any “work in progress” images or VMs generated by the BYOI tool would be created in one place.
Make sure you put the correct values for your vSphere environment in the packer-variables/vsphere.j2 file. The first time I tried this I was using another group’s environment to build a TKR, I used the wrong network name, and I spent about 2 hours trying to figure out why the image was erroring out.
I ran make list-versions to get a list of the available versions:
$ make list-versions
Kubernetes Version | Supported OS
v1.24.9+vmware.1 | [photon-3,ubuntu-2004-efi]
v1.25.7+vmware.3-fips.1 | [photon-3,ubuntu-2004-efi]
I am going to use v1.24.9+vmware.1, so I ran this to download the artifacts:
$ make run-artifacts-container KUBERNETES_VERSION=v1.24.9+vmware.1
Using default port for artifacts container 8081
Error: No such container: v1.24.9---vmware.1-artifacts-server
Unable to find image 'projects.registry.vmware.com/tkg/tkg-vsphere-linux-resource-bundle:v1.24.9_vmware.1-tkg.1' locally
v1.24.9_vmware.1-tkg.1: Pulling from tkg/tkg-vsphere-linux-resource-bundle
2731d8df91a4: Pull complete
73c864854baf: Pull complete
08eb7dea6abf: Pull complete
52654f918c81: Pull complete
da27b4bff06e: Pull complete
797512e2c717: Pull complete
0a994466e4a6: Pull complete
31d1a74dbc07: Pull complete
b3444fea81b1: Pull complete
193c65bff1b1: Pull complete
Digest: sha256:9dcec246657fa7cf5ece1feab6164e200c9bc82b359471bbdec197d028b8e577
Status: Downloaded newer image for projects.registry.vmware.com/tkg/tkg-vsphere-linux-resource-bundle:v1.24.9_vmware.1-tkg.1
26a10c7dea32e04b07e6de760982253b5044ab5a06d1330fef52c5463f19e26c
Customize the TKR OVA Image
The last step is to build the TKR OVA file, but before I build it I want to add two customizations. I need to need to use VM Hardware version (aka “VMX version”) 20 for the OVA, and I need to make sure that we build an Ubuntu OVA with a kernel >= 5.16.
I edit the packer-variables/default-args.j2 file and change the vmx_version:
"vmx_version": "20",
Install a Kernel >= 5.16 on the Image
Earlier when I ran make list-versions I noticed that the v1.24.9+vmware.1 Kubernetes version supports Ubuntu 20.04. However, the only way to get a packaged kernel >= 5.16 installed is to install the Ubuntu 22.04 linux-image-generic-hwe-22.04 package, and vsphere-tanzu-kubernetes-grid-image-builder does not currently have a base image for 22.04.
Since I need 22.04, and 20.04 is the only version available, I’m going to force Packer to do a release upgrade before generating the OVA. To do that I’m going to install the jammy-updates repo from 22.04. When I do that, the vSphere Tanzu Kubernetes Grid Image Builder will cause Packer to upgrade the image to Ubuntu 22.04 and I can then install the Ubuntu 22.04 linux-image-generic-hwe-22.04 package.
I create a file ansible/files/repos/ubuntu.list which contains the lines:
deb http://us.archive.ubuntu.com/ubuntu/ jammy-updates main restricted
deb http://security.ubuntu.com/ubuntu jammy-security main restricted
deb http://us.archive.ubuntu.com/ubuntu/ jammy main restricted
I create the file packer-variables/repos.j2 which contains:
Doing all of that will add the jammy-updates repo to the TKR image. Now to add the kernel package I go back to the same packer-variables/default-args.j2 file we were editing earlier, I look for the extra_debs line and add the HWE kernel package for Ubuntu 22.04, linux-image-generic-hwe-22.04:
Now that I’ve made those changes I can build the TKR OVA.
Build the Image
The main Github README page says I can run make build-node-image to build the OVA, but I want to use a specific version of Kubernetes and I want to use Ubuntu 20.04, so I assume I need to pass some extra parameters to make. Typing make help gives me all of the information I need to construct the right build command:
IP=[my VM's IP address, where the artifact container is running]
make build-node-image \
OS_TARGET=ubuntu-2004-efi \
KUBERNETES_VERSION=v1.24.9+vmware.1 \
TKR_SUFFIX=spr \
HOST_IP=$IP \
IMAGE_ARTIFACTS_PATH=${HOME}/image
This takes a while to run and will create and configure a VM on your vSphere cluster that will be used to create the TKR OVA image. If you want to watch the build, run the docker logs command that make build-node-image spits out:
When the process is done you should have an image file named ${HOME}/image/ovas/ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova
Add the Image to a local Content Library
In order for Tanzu to be able to use the image it has to be added to a local content library. If you don’t have a local content library create one by going to vSphere Client > Content Libraries > Create.
Once you’ve created the library click the library name to pull it up on the screen and click Actions > Import Item. Upload the ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova file.
Associate the Content Library with the Cluster Namespace
Go to vSphere Client > Workload Management > “your cluster namespace”, then click MANAGE CONTENT LIBRARIES on the VM Service tile. Make sure that the local library, and any other libraries used by your Cluster Namespace, are checked.
Deploy Your Own Image
To create a Kubernetes cluster you create a YAML file and run kubectl on in. The following YAML file builds a cluster based on the ubuntu-2004-amd64-v1.24.9---vmware.1-spr.ova TKR image, which is based on Ubuntu 20.04 and contains Kubernetes 1.24.9 and a Linux HWE kernel (currently kernel 6.2).
For a stable, easily-upgradable cluster I recommend a minimum of 3 control plane nodes and 3 worker nodes.
The metadata section’s annotations line must be present to use an Ubuntu TKR as the base image.
The TKR reference just refers to the first part of the TKR’s file name. You can see the TKR file names by looking in the vCenter Content Library you set up for Tanzu. To get a list of valid reference names: kubectl config use-context $my-tanzu-kubernetes-cluster-namespace kubectl get tanzukubernetesreleases Only the names that have READY=True and COMPATIBLE=True can be used to deploy a cluster.
In order to allocate a separate, larger volume for storing docker images on the worker nodes I added a volumes section. I have a storage class defined named vsan-default-storage-policy and the volumes section will allocate a 160GiB volume using the disk specified by vsan-default-storage-policy and mount it on the worker node using the path /var/lib/containerd, which is where container images are stored. Change vsan-default-storage-policy to the name of a storage policy defined for your tanzu-kubernetes-cluster-namespace if you want this to work on your system.
Since images are downloaded as needed, the containerd volume will be destroyed when a worker node is destroyed. It will be destroyed and recreated (empty) when a worker node is upgraded.
I recommend deploying a fresh cluster using this YAML file just so you can try it out and see how it works. Once you’ve deployed a new cluster any AI/ML containers that you run will be running on a 6.2 kernel and will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.
Upgrading an existing Tanzu Kubernetes cluster to the new TKR image
To upgrade an existing Tanzu Kubernetes 1.23 cluster to 1.24 using the new TKR image:
Modify the existing 1.23 cluster’s YAML file to refer to the v1.24.9---vmware.1-spr TKR image.
Make sure that the YAML file has the annotations line so the Supervisor will deploy an Ubuntu-based TKR.
This will pull up a system editor (vim on my system) containing the cluster’s freshly-generated current YAML file. Make the changes and save the file. Any changes you make will be applied immediately when you save the file.
Check the deployed cluster VMs
You can ssh into a cluster’s VMs and check the kernel version running and verify that you can see the amx flags for the CPUs, indicating that the extra instructions are accessible. In vCenter find one of the cluster’s VMs and get the IP address. To get the ssh password:
With these instructions you should now be able to create VMs and Kubernetes clusters that can access Sapphire Rapids AMX instructions. Any AI/ML framework that you run will have access to the hardware’s AMX instructions. If the version of the tools that you’re using were compiled to use AMX, they’ll now run faster using the matrix math capabilities of the Sapphire Rapids CPU — no GPUs necessary.
Hope you find this useful.
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