TensorPool is the easiest way to deploy and manage GPU clusters, at a fraction of the cost of traditional cloud providers.
• Zero Cloud Setup: No GCP, no AWS, no Docker, no cloud accounts required
• Instant GPU Clusters: Deploy multi-node GPU clusters with a single command
• Flexible Storage: Attach and detach NFS volumes across your clusters
• >50% cheaper than traditional cloud providers: TensorPool aggregates demand across multiple cloud provides and thus offers GPUs at a fraction of market price. Check out our pricing here
• High-Performance Networking: All clusters come with high-speed interconnects for distributed training
pip install tensorpool
ssh-keygen -t rsa -b 4096 -f ~/.ssh/id_rsa
Deploy a single H100 node cluster:
tp cluster create -i ~/.ssh/id_rsa.pub -t 1xH100 --name my-training-cluster
For multi-node training, create a 4-node H100 cluster:
tp cluster create -i ~/.ssh/id_rsa.pub -t 8xH100 -n 4 --name distributed-training
tp cluster list
Once your cluster is ready, you'll receive the connection details. SSH into your nodes and start training!
When you're done, destroy your cluster:
tp cluster destroy <cluster_id>
tp cluster create
- Deploy a new GPU clustertp cluster list
- View all your clusterstp cluster info <cluster_id>
- Get detailed information about a clustertp cluster destroy <cluster_id>
- Terminate a clustertp nfs create
- Create a new NFS volumetp nfs list
- View all your NFS volumestp nfs attach <storage_id> <cluster_ids>
- Attach storage to one or more clusterstp nfs detach <storage_id> <cluster_ids>
- Detach storage from one or more clusterstp nfs destroy <storage_id>
- Delete an NFS volumetp me
- View your account information and usageInstance Type | GPUs | GPU Model |
---|---|---|
1xH100 | 1 | H100 |
2xH100 | 2 | H100 |
4xH100 | 4 | H100 |
8xH100 | 8 | H100 |
More instance types coming soon!
tp cluster create -i <public_key_path> -t <instance_type> [options]
Required Arguments:
-i, --public-key
: Path to your public SSH key (e.g., ~/.ssh/id_rsa.pub
)-t, --instance-type
: Instance type (1xH100
, 2xH100
, 4xH100
, 8xH100
)Optional Arguments:
--name
: Custom cluster name-n, --num-nodes
: Number of nodes (must be 8xH100
instance type for multi-node)Examples:
# Single node H100
tp cluster create -i ~/.ssh/id_rsa.pub -t 1xH100 --name dev-cluster
# 2-node cluster with 8xH100 each (16 GPUs total)
tp cluster create -i ~/.ssh/id_rsa.pub -t 8xH100 -n 2 --name large-training
tp cluster list [--org]
Optional Arguments:
--org, --organization
: List all clusters in your organizationtp cluster destroy <cluster_id>
Arguments:
cluster_id
: The ID of the cluster to destroytp nfs create -s <size_in_gb> [--name <name>]
Required Arguments:
-s, --size
: Size of the NFS volume in GBOptional Arguments:
--name
: Custom volume nameExamples:
# Create a 500GB volume
tp nfs create -s 500 --name training-data
# Create a 1TB volume with auto-generated name
tp nfs create -s 1000
# 1. Create a 1TB NFS volume named "shared-datasets"
tp nfs create -s 1000 --name shared-datasets
# 2. Attach the volume to a single cluster
tp nfs attach <storage_id> <cluster_id>
# 3. Attach the volume to multiple clusters
tp nfs attach <storage_id> <cluster_id_1> <cluster_id_2> <cluster_id_3>
# 4. Detach the volume from a single cluster
tp nfs detach <storage_id> <cluster_id>
# 5. Detach the volume from multiple clusters
tp nfs detach <storage_id> <cluster_id_1> <cluster_id_2>
You replace <storage_id>
and <cluster_id>
with your actual IDs as needed.
Each cluster node comes with high-performance local NVMe storage mounted at:
/mnt/local
When you attach an NFS volume to your cluster, it will be mounted at:
/mnt/nfs
For easy access, the storage locations are also symlinked in your home directory:
~/local
→ /mnt/local
~/nfs
→ /mnt/nfs
tp cluster list
to monitor your active resourcesReady to scale your ML training? Get started at tensorpool.dev!