SecureAnyCloud
Deployment guide

AIStore

Overview

AIStore (AIS) is an open source object storage system from NVIDIA, built for the read-heavy, high-throughput access patterns of AI and machine learning training. It stores objects in buckets on local disks and exposes them two ways at once. You can use its own cluster-aware API and the ais command line client, or an S3-compatible REST API that unmodified S3 tools and SDKs, such as the AWS CLI and boto3, can point at directly.

This SecureAnyCloud image runs AIStore 1.4 on Ubuntu 24.04 on Azure as a complete single-node cluster. AIStore normally scales out across many storage nodes. This appliance runs the smallest useful shape of the same software, one gateway and one storage target, on one VM, managed by systemd. No Kubernetes, second VM, external coordination service or cloud account is required.

That makes it a practical home for training datasets, shards, model checkpoints and other large artifacts, without rewriting the code that reads them. Everything in this guide works against local disk.

AIStore has no web interface. It is a storage system: objects in, objects out. You work with it through the ais client, the AWS CLI, boto3 or plain HTTP.

Security by design. AIStore in its default upstream form is unauthenticated, and an open S3-compatible object store on a reachable address exposes your data. This image inverts that default:

  • Authentication is on in the shipped configuration. The cluster runs with AIStore's own AuthN token service enabled from its first start. Every gateway path, native and S3 alike, requires a valid token and is refused with HTTP 401 or 403 without one.
  • Nothing is baked into the image. The image contains no signing key, no password and no cluster identity. All three services cannot start until first boot has generated your VM's own signing key and administrator password. No unauthenticated object store is ever listening, and no secret is shared between customers.
  • The only unauthenticated path is the liveness probe. GET /v1/health returns HTTP 200 and an uptime header, and nothing else. It discloses no version, no configuration, no cluster map and no data, so an Azure Load Balancer or Application Gateway health probe works without a credential.
  • Object data lives on its own volume. A dedicated 32 GB data disk is attached and mounted at /ais/disk0. It is the storage target's only mountpath, so objects never consume the operating system disk.

What you get:

  • AIStore 1.4.9 built from the pinned upstream release tag, running under systemd as ais-proxy.service (the gateway) and ais-target.service (the storage node)
  • AIStore AuthN, the token service that issues and validates the JSON Web Tokens the cluster requires, running as ais-authn.service
  • The ais command line client, built from the same source revision as the cluster so client and server versions always agree
  • The S3-compatible API on the gateway, with upstream's reverse proxy mode enabled so a stock S3 client talks to one endpoint and one port
  • The AWS CLI v2 and boto3, already installed so you can exercise the S3 API as soon as you connect
  • ais-env and aistore-selfcheck, two bundled commands. One sets up an authenticated shell in a single line, and the other verifies the whole appliance end to end
  • A per-VM signing key and administrator password, generated on first boot and written to a root-only credentials file
  • Unattended security upgrades, left enabled so the appliance keeps receiving patches

Prerequisites

You need:

  • An active Azure subscription, an SSH public key, and a VNet with a subnet in the target region
  • A subscription to this listing on Azure Marketplace
  • A network security group (NSG) that allows TCP 22 for administration and TCP 8080 for the AIStore gateway, which carries both the native API and the S3-compatible API

Restrict 8080 to the subnet your training nodes or applications run in rather than opening it to the internet. TCP 8081 is the storage target. Only clients that use the native API from another machine need it, and the S3 API never does.

The intra-cluster ports 9080, 10080, 9081 and 10081 are bound to the VM's own network address. You never need to open them.

Recommended VM size: Standard_B2s with 2 vCPU and 4 GB RAM suits evaluation and light dataset serving. For real training workloads, choose a size with more vCPU, memory and network bandwidth, such as Standard_D4s_v5 or above. Add mountpaths on larger disks as described later in this guide.

Deploy the virtual machine

To deploy from the Azure Portal, select the image in Azure Marketplace, choose your VM size, and supply your SSH public key for the azureuser account. To deploy from the Azure CLI instead, run the following commands on your own workstation, not on the VM:

# Accept the Marketplace terms once per subscription (required before the first CLI deploy)
az vm image terms accept --urn sac1647283583153:aistore-ubuntu-24-04:default:latest
az vm create \
  --resource-group my-resource-group \
  --name my-aistore-vm \
  --image sac1647283583153:aistore-ubuntu-24-04:default:latest \
  --size Standard_B2s \
  --admin-username azureuser \
  --generate-ssh-keys \
  --public-ip-sku Standard
az vm open-port --resource-group my-resource-group --name my-aistore-vm --port 8080

The dedicated data disk is part of the image. It is attached, formatted and mounted at /ais/disk0 automatically, so you do not create or attach it yourself.

When the VM is running, connect over SSH:

ssh azureuser@<vm-ip>

Confirm the cluster is healthy

First boot generates this VM's signing key and administrator password, starts the three services and forms the cluster. It normally completes within a minute of the VM becoming reachable.

Check that all three services are running:

systemctl is-active ais-authn.service ais-proxy.service ais-target.service

Expected output

active active active

ais-env reads the root-only credentials file, logs in to AuthN and prints ready-to-use exports for both the native client and any S3 client. Evaluate it once per shell, then ask the cluster for its status:

eval "$(sudo ais-env)" ais show cluster

Expected output

PROXY MEM USED(%) MEM AVAIL SYS CPU(%) UPTIME STATUS ALERT p[LYzPrJRY][P] 1.39% 3.21GiB 9% 9m30s online low-cpu TARGET MEM USED(%) MEM AVAIL CAP USED(%) CAP AVAIL SYS CPU(%) UPTIME STATUS ALERT t[jIDUkeFR] 1.35% 3.21GiB 5% 29.565GiB 4% 1m20s online low-cpu Cluster: CLI Endpoint: http://10.0.0.19:8080 Proxies: 1 Targets: 1 (one disk) Capacity: used 1.64GiB (5%), available 29.57GiB Cluster Map: version 276, UUID OY_9UiN0O, primary p[LYzPrJRY] Software: 4.9.a809c60 Backend: AWS, Azure Deployment: linux Status: 2 online Rebalance: - Authentication: enabled

The node identifiers, the cluster map UUID and the addresses are unique to your VM, so they differ from the example above.

Look for the line Authentication: enabled. It confirms that the cluster refuses unauthenticated requests.

The low-cpu alert is advisory. AIStore raises it because a 2 vCPU VM is below what it considers a production storage node, and the cluster is fully functional. The alert disappears on a larger VM size.

Confirm object data is on the dedicated volume and not the operating system disk:

eval "$(sudo ais-env)" ais show storage mountpath df -h /ais/disk0

Expected output

jIDUkeFR Used: min= 5%, avg= 5%, max= 5% /ais/disk0 /dev/sdc(ext4) Filesystem Size Used Avail Use% Mounted on /dev/sdc 32G 112K 30G 1% /ais/disk0

Retrieve your per-VM credentials

First boot generates the administrator password for this VM. It writes the password, with the endpoint addresses, to a root-only file. Read the file when you need to authenticate from another machine:

sudo cat /root/aistore-credentials.txt

The file records the administrator username and password, the gateway URL, the S3 endpoint URL, the AuthN URL and this VM's public address, along with copy-ready example commands. It is the only place the password is written. It is mode 0600, owned by root, and exists only on your VM.

On the VM itself you rarely need the password, because ais-env reads the file for you and exports a short-lived token:

sudo ais-env | sed -E "s/(TOKEN)='.*'/\1='<jwt>'/"

Expected output

export AIS_ENDPOINT='http://10.0.0.19:8080'
export AIS_AUTHN_URL='http://10.0.0.19:52001'
export AIS_AUTHN_TOKEN='<jwt>'
export AIS_S3_ENDPOINT='http://10.0.0.19:8080/s3'
export AWS_ACCESS_KEY_ID='aistore'
export AWS_SECRET_ACCESS_KEY='aistore'
export AWS_SESSION_TOKEN='<jwt>'
export AWS_DEFAULT_REGION='us-east-1'
export AWS_EC2_METADATA_DISABLED='true'

Tokens are valid for 24 hours by default. Run eval "$(sudo ais-env)" again to get a fresh one.

Store and read your first objects

Create a bucket, write an object, read it back, and confirm that the bytes that came back are the bytes that went in.

eval "$(sudo ais-env)" head -c 4194304 /dev/urandom > /tmp/dataset.bin ais bucket create ais://sac-demo ais put /tmp/dataset.bin ais://sac-demo/dataset.bin ais ls ais://sac-demo ais get ais://sac-demo/dataset.bin /tmp/dataset-out.bin sha256sum /tmp/dataset.bin /tmp/dataset-out.bin

Expected output

"ais://sac-demo" created PUT "/tmp/dataset.bin" => ais://sac-demo/dataset.bin NAME SIZE dataset.bin 4.00MiB GET dataset.bin from ais://sac-demo as /tmp/dataset-out.bin (4.00MiB) 982049437e976ec56e3a31d2325080edd50fe55289de806db92ac99495f741aa /tmp/dataset.bin 982049437e976ec56e3a31d2325080edd50fe55289de806db92ac99495f741aa /tmp/dataset-out.bin

The two checksums match, which proves that the object made the round trip through storage unchanged. Your checksum differs from the example because the source file is random.

When you are finished, delete the object and the bucket:

eval "$(sudo ais-env)" ais object rm ais://sac-demo/dataset.bin ais ls ais://sac-demo

Expected output

deleted "dataset.bin" from ais://sac-demo NAME SIZE

Use the S3-compatible API

Everything you just did through the native client is also available through the S3-compatible API on the same gateway, at http://<vm-address>:8080/s3. Unmodified S3 clients work. AIStore accepts its token in the standard AWS session token header, so you pass the token as AWS_SESSION_TOKEN and use dummy values for the access key and secret. ais-env has already set all of this for you.

The AWS CLI v2 is installed on the image:

eval "$(sudo ais-env)" head -c 4194304 /dev/urandom > /tmp/s3-src.bin aws --endpoint-url "$AIS_S3_ENDPOINT" s3 mb s3://sac-s3-demo aws --no-progress --endpoint-url "$AIS_S3_ENDPOINT" s3 cp /tmp/s3-src.bin s3://sac-s3-demo/via-s3.bin aws --endpoint-url "$AIS_S3_ENDPOINT" s3 ls s3://sac-s3-demo/ aws --no-progress --endpoint-url "$AIS_S3_ENDPOINT" s3 cp s3://sac-s3-demo/via-s3.bin /tmp/s3-out.bin sha256sum /tmp/s3-src.bin /tmp/s3-out.bin

Expected output

make_bucket: sac-s3-demo upload: ../../tmp/s3-src.bin to s3://sac-s3-demo/via-s3.bin 2026-08-07 06:57:07 4194304 via-s3.bin download: s3://sac-s3-demo/via-s3.bin to ../../tmp/s3-out.bin bcf7b906cef78ddb7c4bb09efc77bf312c62a4775d787c758b59c4f96e7003e2 /tmp/s3-src.bin bcf7b906cef78ddb7c4bb09efc77bf312c62a4775d787c758b59c4f96e7003e2 /tmp/s3-out.bin

The same buckets and objects are visible from either API. You can write with an S3 tool and read with the native client, or the other way round.

boto3, the official AWS SDK for Python, is installed as well:

eval "$(sudo ais-env)" python3 - <<'PY' import boto3, os s3 = boto3.client( "s3", endpoint_url=os.environ["AIS_S3_ENDPOINT"], aws_access_key_id="aistore", aws_secret_access_key="aistore", aws_session_token=os.environ["AIS_AUTHN_TOKEN"], region_name="us-east-1", ) print([b["Name"] for b in s3.list_buckets()["Buckets"]]) PY

Expected output

['sac-s3-demo', 'sac-demo']

Both buckets appear, including the one you created earlier with the native client, because one namespace sits behind both APIs.

When you are done, remove the demonstration buckets:

eval "$(sudo ais-env)" aws --endpoint-url "$AIS_S3_ENDPOINT" s3 rm s3://sac-s3-demo/via-s3.bin aws --endpoint-url "$AIS_S3_ENDPOINT" s3 rb s3://sac-s3-demo ais bucket rm ais://sac-demo --yes

To point an existing application at the appliance, set its S3 endpoint to http://<vm-address>:8080/s3. Set its region to any value, its access key and secret to any dummy value, and its session token to a token from ais-env. No client patching is required.

Confirm the appliance is closed

Every path except the liveness probe refuses a request that carries no token. This applies on the VM's own routable address as well as on loopback. Verify it yourself:

AIS_IP=$(ip -4 -o addr show scope global | awk '{print $4}' | cut -d/ -f1 | head -1) printf 'anonymous native /v1/cluster : HTTP '; curl -s -o /dev/null -w '%{http_code}\n' "http://${AIS_IP}:8080/v1/cluster" printf 'anonymous S3 /s3 : HTTP '; curl -s -o /dev/null -w '%{http_code}\n' "http://${AIS_IP}:8080/s3" printf 'forged token /v1/cluster : HTTP '; curl -s -o /dev/null -w '%{http_code}\n' -H 'Authorization: Bearer eyJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJmb3JnZWQifQ.forged' "http://${AIS_IP}:8080/v1/cluster" printf 'liveness probe /v1/health : HTTP '; curl -s -o /dev/null -w '%{http_code}\n' "http://${AIS_IP}:8080/v1/health"

Expected output

anonymous native /v1/cluster : HTTP 401 anonymous S3 /s3 : HTTP 403 forged token /v1/cluster : HTTP 401 liveness probe /v1/health : HTTP 200

An unmodified S3 client with no token is refused in the same way:

AIS_IP=$(ip -4 -o addr show scope global | awk '{print $4}' | cut -d/ -f1 | head -1) if env -u AWS_SESSION_TOKEN AWS_ACCESS_KEY_ID=x AWS_SECRET_ACCESS_KEY=x AWS_DEFAULT_REGION=us-east-1 \
  aws --endpoint-url "http://${AIS_IP}:8080/s3" s3 ls >/dev/null 2>&1; then echo "UNEXPECTED: an unauthenticated client listed buckets" else echo "refused: an unauthenticated S3 client cannot list buckets" fi

Expected output

refused: an unauthenticated S3 client cannot list buckets

The client's own error message is An error occurred (None) when calling the ListBuckets operation: token required.

The per-VM secrets are readable only by root and the service account:

sudo stat -c '%a %U:%G %n' /root/aistore-credentials.txt /etc/ais/ais-secret.env /etc/ais/authn-secret.env

Expected output

600 root:root /root/aistore-credentials.txt 640 root:ais /etc/ais/ais-secret.env 640 root:ais /etc/ais/authn-secret.env

Run the bundled self-check

aistore-selfcheck verifies the entire appliance end to end, and you can run it at any time. It authenticates with your VM's own credential and creates a bucket. It writes a 4 MiB object, reads it back, compares checksums, lists the object and deletes it. It does this over both the native API and the S3-compatible API. It then confirms that anonymous and forged requests are refused on the routable address:

sudo aistore-selfcheck

Expected output

selfcheck: authenticated as admin with the per-VM credential selfcheck: wrong password refused by AuthN selfcheck: native: created ais://sac-selfcheck-53663 selfcheck: native: PUT 4 MiB object selfcheck: native: GET byte-compare OK (sha256 34481059ef4221a4...) selfcheck: native: LIST shows native.bin selfcheck: native: DELETE removed the object selfcheck: s3: created s3://sac-s3-selfcheck-53663 selfcheck: s3: PUT 4 MiB object selfcheck: s3: LIST shows s3.bin selfcheck: s3: GET byte-compare OK (sha256 34481059ef4221a4...) selfcheck: s3: DELETE removed the object selfcheck: anonymous native request to 10.0.0.19:8080 refused (HTTP 401) selfcheck: anonymous S3 request to 10.0.0.19:8080/s3 refused (HTTP 403) selfcheck: unauthenticated aws CLI over the routable address refused selfcheck: forged token refused (HTTP 401) selfcheck: liveness probe /v1/health returns 200 (no version, no config, no data) selfcheck: object data volume /ais/disk0 is mounted selfcheck: ALL CHECKS PASSED — native and S3 round-trips proved, anonymous access refused

The bucket names contain the process ID, and the checksums and address are your VM's own. Those lines differ from the example above.

Reach AIStore from another machine

From the same virtual network, open TCP 8080 in the network security group and nothing more. Point your client at http://<vm-private-address>:8080 for the native API or http://<vm-private-address>:8080/s3 for the S3 API. Pass a token from ais-env on the VM. This is the normal deployment shape: an object store serving datasets to compute in the same network.

From your own workstation, over the S3 API, the public address works directly once TCP 8080 is open to your address. The gateway serves the S3 API entirely by itself:

aws --endpoint-url "http://<vm-ip>:8080/s3" s3 ls

Set AWS_SESSION_TOKEN to a token you obtained on the VM, and use dummy values for the access key and secret.

From your own workstation, over the native API, use an SSH SOCKS tunnel. The native API redirects object reads and writes to the storage target using the VM's own network address. A plain port forward is not enough, but a SOCKS proxy routes the redirect correctly. Run this on your workstation:

ssh -D 1080 -N azureuser@<vm-ip>

Then, in another terminal on your workstation, with the ais client installed:

export ALL_PROXY=socks5h://127.0.0.1:1080
export AIS_ENDPOINT=http://<private-ip>:8080
export AIS_AUTHN_TOKEN=<your-token> ais show cluster

Add more storage

The storage target uses /ais/disk0 as its only mountpath. To grow capacity, attach another Azure data disk to the VM, format and mount it, then tell AIStore about it. A freshly deployed VM has no second disk, so run these steps only after you have attached one:

# after attaching and mounting a new disk at /ais/disk1, owned by the ais user eval "$(sudo ais-env)" ais storage mountpath attach /ais/disk1 ais show storage mountpath

Mount the new filesystem with an /etc/fstab entry so that it survives a reboot, and make sure the ais user owns it. AIStore spreads objects across all attached mountpaths.

Server components

Component Version Purpose

AIStore gateway (aisnode -role=proxy) 1.4.9 Serves the native API and the S3 compatible API, holds the cluster map

AIStore target (aisnode -role=target) 1.4.9 Stores objects on /ais/disk0

AIStore AuthN 1.4.9 Issues and validates the JSON Web Tokens the cluster requires

ais command line client 1.4.9 Native client, built from the same source revision as the cluster

AWS CLI 2.36.18 Unmodified S3 client for the S3 compatible API

boto3 1.34.46 Official AWS SDK for Python

Ubuntu Server 24.04 LTS Operating system

Filesystem layout

Path Size Purpose

/ 29 GB Root filesystem

/boot 913 MB Operating system kernel files

/boot/efi 106 MB UEFI boot partition (Gen2 Hyper V)

/ais/disk0 32 GB Dedicated AIStore object data volume

/mnt varies Azure temporary resource disk

Key directories

Path Purpose

/usr/local/bin/aisnode The AIStore node binary, run as both gateway and target

/usr/local/bin/ais The native command line client

/usr/local/bin/authn The AuthN token service

/usr/local/bin/ais-env Prints authenticated shell exports

/usr/local/sbin/aistore-selfcheck End to end appliance self check

/etc/ais/ais.json Cluster configuration, including auth.enabled

/etc/ais/ais_local_proxy.json Gateway node configuration

/etc/ais/ais_local_target.json Storage target configuration, including mountpaths

/etc/ais/authn/authn.json AuthN configuration

/var/lib/ais/proxy, /var/lib/ais/target Cluster metadata and node identity

/var/log/ais/ AIStore logs, one directory per node

/root/aistore-credentials.txt Per VM credentials, mode 0600

/usr/share/doc/aistore/LICENSE The upstream MIT licence

Network ports

Port Bound to Purpose

22 all interfaces SSH administration

8080 all interfaces AIStore gateway: native API and S3 compatible API

8081 all interfaces AIStore storage target, native API object transfers

52001 all interfaces AuthN token service, requires the administrator password

9080, 10080 VM network address Gateway intra cluster control and data planes

9081, 10081 VM network address Target intra cluster control and data planes

Open only 22 and 8080 in your Network Security Group unless you use the native API from another machine, in which case also open 8081. Never open the intra cluster ports.

Managing the services

sudo systemctl status ais-proxy.service --no-pager || true
sudo systemctl restart ais-target.service
sudo journalctl -u ais-proxy.service -n 20 --no-pager

Start order matters: ais-authn.service first, then ais-proxy.service, then ais-target.service. The unit dependencies encode this order, so a plain sudo systemctl restart ais-target.service is safe.

Stopping ais-authn.service takes authentication offline, and every subsequent request is refused. Restart it before you use the cluster again.

Scripts and log files

Path Purpose

/usr/local/sbin/aistore-firstboot.sh First boot bootstrap: mints the per VM secrets and starts the cluster

/usr/local/sbin/ais-netenv Resolves this VM's address at every boot and pins it into the node configuration

/usr/local/sbin/ais-token Exchanges a username and password for a token

/usr/local/bin/ais-env Prints authenticated shell exports

/usr/local/sbin/aistore-selfcheck End to end appliance self check

/var/log/sac/sac-firstboot.log First boot log

/var/log/ais/proxy/, /var/log/ais/target/, /var/log/ais/authn/ Per node AIStore logs

On startup

On the first boot only, aistore-firstboot.service runs aistore-firstboot.sh. The script:

  • generates this VM's signing key and administrator password
  • creates the bootstrap marker that permits the AIStore services to start
  • brings up AuthN, the gateway and the target in order, and registers the cluster with AuthN
  • writes /root/aistore-credentials.txt and then disables itself

A marker file at /var/lib/sac/data/aistore-firstboot.done prevents the script from running again.

Until the bootstrap marker exists, the three AIStore units refuse to start. This guarantees that the shipped image never listens on any address without your own credentials in place.

On every boot, ais-netenv.service resolves the VM's current network address and writes it into the node configuration before the cluster starts. The appliance keeps working if the VM is reassigned a different address.

Troubleshooting

The services are not active after first boot. Read the first boot log, which records each step:

sudo tail -40 /var/log/sac/sac-firstboot.log
sudo systemctl status aistore-firstboot.service --no-pager || true

Once it has completed successfully, aistore-firstboot.service reports inactive (dead) and disabled, which is why the command includes || true. That is the healthy state, not a fault.

ais-env reports it cannot read the credentials file. Run it with sudo, because the credentials are mode 0600 and owned by root.

ais commands return "token required" or HTTP 401. Your token has expired, or this shell never had one. Run eval "$(sudo ais-env)" again.

A service will not start and the journal mentions the bootstrap marker. The units are gated on /var/lib/aistore/.bootstrap-ready, which only first boot creates. If it is missing, first boot did not complete; check the first boot log above.

The storage target will not start. It requires the data volume. Confirm that the volume is mounted:

mountpoint /ais/disk0 && df -h /ais/disk0

An S3 client reports a signature error. AIStore does not validate AWS signatures. It authenticates on the session token. Use any dummy access key and secret, and set AWS_SESSION_TOKEN to a token from ais-env.

Native API object transfers time out from a machine outside the virtual network. The native API redirects object reads and writes to the storage target's own network address. Use the S3 API, which does not redirect, or use an SSH SOCKS tunnel as shown earlier.

Check cluster health at any time:

eval "$(sudo ais-env)" ais show cluster
sudo journalctl -u ais-proxy.service -u ais-target.service -n 60 --no-pager

Security recommendations

  • Restrict TCP 8080 to the network your clients run on. Authentication is enforced, but there is no reason to expose an object store to the whole internet. Use NSG rules scoped to your subnet or to specific source addresses.
  • Keep the credentials file where it is. /root/aistore-credentials.txt is mode 0600 and owned by root. Copy the password into your secret manager rather than moving or relaxing the file.
  • Create per-application users instead of sharing the administrator. AuthN supports additional users and roles. See ais auth --help. Reserve the administrator account for administration.
  • Put TLS in front of the gateway for any untrusted network. The appliance speaks HTTP. Terminate TLS on an Azure Application Gateway or a reverse proxy, or enable AIStore's own HTTPS support with your certificate.
  • Rotate the administrator password with ais auth update user, and update your stored copy.
  • Leave unattended security upgrades enabled so the operating system keeps receiving patches.
  • Take Azure snapshots of the data disk on a schedule that matches how much data you can afford to re-create. A single-node cluster has no replication.

Support

SecureAnyCloud images come with 24/7 support. For help with this image, contact support through the Azure Marketplace listing or visit secureanycloud.com.

AIStore and NVIDIA are trademarks of NVIDIA Corporation. Amazon S3 and AWS are trademarks of Amazon.com, Inc. or its affiliates. SecureAnyCloud is not affiliated with, endorsed by, or sponsored by NVIDIA Corporation or Amazon. AIStore is distributed under the MIT licence; the licence text ships on the image at /usr/share/doc/aistore/LICENSE.

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