GPU Configuration Wizard

GPU Configuration Wizard#

Auto-configure optimal GPU settings for your Graphistry deployment based on your hardware.

Overview#

The GPU Configuration Wizard simplifies multi-GPU configuration by:

  • Auto-detecting available GPUs via nvidia-smi

  • Supporting 140+ hardware presets for cloud and on-prem environments

  • Generating optimal worker counts using a simple replication model

  • Exporting settings directly to custom.env

Usage#

# Interactive mode - displays recommended settings
./etc/scripts/gpu-config-wizard.sh

# Export mode - print to stdout (for copying to custom.env)
./etc/scripts/gpu-config-wizard.sh -E

# Export mode - append directly to custom.env file
./etc/scripts/gpu-config-wizard.sh -E ./data/config/custom.env

# Use hardware preset
./etc/scripts/gpu-config-wizard.sh -p aws-p3-8xlarge

# Custom GPU count
./etc/scripts/gpu-config-wizard.sh -n 4

# Specific GPU indices
./etc/scripts/gpu-config-wizard.sh -g 0,2,5-7

# Custom worker multipliers
./etc/scripts/gpu-config-wizard.sh -w 8,8,2

# List all available presets
./etc/scripts/gpu-config-wizard.sh -l

Options#

Option

Description

-n, --gpu-count N

Use N GPUs (indices 0 to N-1)

-g, --gpu-indices LIST

Use specific GPUs (e.g., 0,2,5-7 or GPU-xxx)

-w, --workers F,S,D

Worker multipliers: forge,streamgl,dask (default: 4,4,1)

-p, --preset <name>

Use hardware preset (140+ available)

-E, --export [FILE]

Export env vars (stdout or append to file)

-l, --list-presets

List all available presets

-h, --help

Show help message

Replication Model#

The wizard uses a simple replication model:

Total workers = N GPUs x multiplier

Default multipliers (per GPU):

  • forge-etl-python: 4 workers

  • streamgl-gpu: 4 workers

  • dask-cuda-worker: 1 worker

Workers > GPUs is intentional for multi-user concurrent handling:

  • Multiple users can run blocking tasks simultaneously

  • Cached operations can be served while compute runs

  • Round-robin GPU assignment distributes load

Examples#

# Auto-detect GPUs, use default multipliers
./etc/scripts/gpu-config-wizard.sh

# Use 4 GPUs with default multipliers (16 forge, 16 streamgl, 4 dask)
./etc/scripts/gpu-config-wizard.sh -n 4

# Use specific GPUs with custom multipliers
./etc/scripts/gpu-config-wizard.sh -g 2,3,5-7 -w 2,2,1

# Simulate DGX A100 configuration
./etc/scripts/gpu-config-wizard.sh -p dgx-a100

# AWS p3.8xlarge preset with custom multipliers
./etc/scripts/gpu-config-wizard.sh -p aws-p3-8xlarge -w 2,2,1

# Print configuration to stdout (for copying)
./etc/scripts/gpu-config-wizard.sh -p dgx-a100 -E

# Export configuration directly to custom.env file
./etc/scripts/gpu-config-wizard.sh -p dgx-a100 -E ./data/config/custom.env

Hardware Presets#

NVIDIA DGX Systems#

Preset

GPUs

Description

dgx-a100

8

NVIDIA DGX A100 (8x A100 80GB)

dgx-h100

8

NVIDIA DGX H100 (8x H100 80GB)

dgx-station-a100

4

NVIDIA DGX Station A100 (4x A100 80GB)

dgx-1

8

NVIDIA DGX-1 (8x V100 32GB)

dgx-2

16

NVIDIA DGX-2 (16x V100 32GB)

dgx-spark

1

NVIDIA DGX Spark ARM (1x GB200 128GB)

dgx-b200

8

NVIDIA DGX B200 (8x B200 192GB)

NVIDIA Grace Hopper / Blackwell#

Preset

GPUs

Description

gh200

1

NVIDIA GH200 Grace Hopper ARM (1x GH200 96GB)

gh200-nvl

2

NVIDIA GH200 NVL ARM (2x GH200 96GB)

gb200-nvl2

2

NVIDIA GB200 NVL2 ARM (2x GB200 384GB)

gb200-nvl72

72

NVIDIA GB200 NVL72 ARM (72x GB200 13.5TB total)

hgx-b200

8

NVIDIA HGX B200 (8x B200 192GB)

hgx-b100

8

NVIDIA HGX B100 (8x B100 192GB)

AWS EC2 Instances#

Preset

GPUs

Description

aws-p5.48xlarge

8

AWS p5.48xlarge (8x H100 80GB)

aws-p4d.24xlarge

8

AWS p4d.24xlarge (8x A100 40GB)

aws-p4de.24xlarge

8

AWS p4de.24xlarge (8x A100 80GB)

aws-p3.16xlarge

8

AWS p3.16xlarge (8x V100 16GB)

aws-p3.8xlarge

4

AWS p3.8xlarge (4x V100 16GB)

aws-p3.2xlarge

1

AWS p3.2xlarge (1x V100 16GB)

aws-g5.xlarge

1

AWS g5.xlarge (1x A10G 24GB)

aws-g5.12xlarge

4

AWS g5.12xlarge (4x A10G 24GB)

aws-g5.48xlarge

8

AWS g5.48xlarge (8x A10G 24GB)

aws-g4dn.xlarge

1

AWS g4dn.xlarge (1x T4 16GB)

aws-g4dn.12xlarge

4

AWS g4dn.12xlarge (4x T4 16GB)

aws-g5g.xlarge

1

AWS g5g.xlarge Graviton2 ARM (1x T4 16GB)

aws-g5g.16xlarge

2

AWS g5g.16xlarge Graviton2 ARM (2x T4 16GB)

aws-g5g.metal

2

AWS g5g.metal Graviton2 ARM (2x T4 16GB)

Microsoft Azure#

Preset

GPUs

Description

azure-nd96asr-v4

8

Azure ND96asr v4 (8x A100 40GB)

azure-nd96amsr-a100-v4

8

Azure ND96amsr A100 v4 (8x A100 80GB)

azure-nd96isr-h100-v5

8

Azure ND96isr H100 v5 (8x H100 80GB)

azure-nc24ads-a100-v4

1

Azure NC24ads A100 v4 (1x A100 80GB)

azure-nc48ads-a100-v4

2

Azure NC48ads A100 v4 (2x A100 80GB)

azure-nc96ads-a100-v4

4

Azure NC96ads A100 v4 (4x A100 80GB)

azure-nc6s-v3

1

Azure NC6s v3 (1x V100 16GB)

azure-nc12s-v3

2

Azure NC12s v3 (2x V100 16GB)

azure-nc24s-v3

4

Azure NC24s v3 (4x V100 16GB)

azure-nc4as-t4-v3

1

Azure NC4as T4 v3 (1x T4 16GB)

azure-nc64as-t4-v3

4

Azure NC64as T4 v3 (4x T4 16GB)

azure-nv6ads-a10-v5

1

Azure NV6ads A10 v5 (1x A10 4GB, 1/6 GPU)

azure-nv36ads-a10-v5

1

Azure NV36ads A10 v5 (1x A10 24GB)

azure-nv72ads-a10-v5

2

Azure NV72ads A10 v5 (2x A10 24GB)

azure-ncads-h100-v5

1

Azure NCads H100 v5 ARM (1x H100 80GB)

Google Cloud Platform#

Preset

GPUs

Description

gcp-a2-highgpu-1g

1

GCP a2-highgpu-1g (1x A100 40GB)

gcp-a2-highgpu-2g

2

GCP a2-highgpu-2g (2x A100 40GB)

gcp-a2-highgpu-4g

4

GCP a2-highgpu-4g (4x A100 40GB)

gcp-a2-highgpu-8g

8

GCP a2-highgpu-8g (8x A100 40GB)

gcp-a2-megagpu-16g

16

GCP a2-megagpu-16g (16x A100 40GB)

gcp-a2-ultragpu-1g

1

GCP a2-ultragpu-1g (1x A100 80GB)

gcp-a2-ultragpu-2g

2

GCP a2-ultragpu-2g (2x A100 80GB)

gcp-a2-ultragpu-4g

4

GCP a2-ultragpu-4g (4x A100 80GB)

gcp-a2-ultragpu-8g

8

GCP a2-ultragpu-8g (8x A100 80GB)

gcp-a3-highgpu-8g

8

GCP a3-highgpu-8g (8x H100 80GB)

gcp-n1-t4-1

1

GCP n1-standard (1x T4 16GB)

gcp-n1-t4-4

4

GCP n1-standard (4x T4 16GB)

gcp-g2-standard-4-arm

1

GCP g2-standard-4 ARM (1x L4 24GB)

gcp-g2-standard-16-arm

2

GCP g2-standard-16 ARM (2x L4 24GB)

Oracle Cloud Infrastructure#

Preset

GPUs

Description

oci-bm-gpu-a100-4

4

OCI BM.GPU.A100-v2.8 (4x A100 40GB)

oci-bm-gpu-h100-8

8

OCI BM.GPU.H100.8 (8x H100 80GB)

oci-bm-gpu-a10-4

4

OCI BM.GPU.A10.4 (4x A10 24GB)

oci-vm-gpu-a10-1

1

OCI VM.GPU.A10.1 (1x A10 24GB)

oci-vm-gpu-a10-2

2

OCI VM.GPU.A10.2 (2x A10 24GB)

oci-bm-gpu-a10-4-arm

4

OCI BM.GPU.A10.4 ARM (4x A10 24GB)

Lambda Labs#

Preset

GPUs

Description

lambda-1xa100

1

Lambda Labs (1x A100 40GB)

lambda-2xa100

2

Lambda Labs (2x A100 40GB)

lambda-4xa100

4

Lambda Labs (4x A100 40GB)

lambda-8xa100

8

Lambda Labs (8x A100 40GB)

lambda-1xh100

1

Lambda Labs (1x H100 80GB)

lambda-8xh100

8

Lambda Labs (8x H100 80GB)

lambda-1xa10

1

Lambda Labs (1x A10 24GB)

lambda-4xa6000

4

Lambda Labs (4x RTX A6000 48GB)

CoreWeave#

Preset

GPUs

Description

coreweave-1xa100

1

CoreWeave (1x A100 40GB)

coreweave-1xa100-80

1

CoreWeave (1x A100 80GB)

coreweave-8xa100

8

CoreWeave (8x A100 40GB)

coreweave-1xh100

1

CoreWeave (1x H100 80GB)

Workstations#

Preset

GPUs

Description

workstation-rtx4090

1

Workstation (1x RTX 4090 24GB)

workstation-rtx4090-2

2

Workstation (2x RTX 4090 24GB)

workstation-a6000

1

Workstation (1x RTX A6000 48GB)

workstation-a6000-2

2

Workstation (2x RTX A6000 48GB)

workstation-a6000-4

4

Workstation (4x RTX A6000 48GB)

Consumer / Hobbyist#

Preset

GPUs

Description

rtx5090

1

Consumer (1x RTX 5090 32GB)

rtx5090-2

2

Consumer (2x RTX 5090 32GB)

rtx4090

1

Consumer (1x RTX 4090 24GB)

rtx4090-2

2

Consumer (2x RTX 4090 24GB)

rtx4090-4

4

Consumer (4x RTX 4090 24GB)

rtx3090

1

Consumer (1x RTX 3090 24GB)

rtx3090-2

2

Consumer (2x RTX 3090 24GB)

rtx3090-4

4

Consumer (4x RTX 3090 24GB)

hobbyist-8gpu

8

Hobbyist (8x RTX 3090 24GB)

hobbyist-10gpu

10

Hobbyist (10x RTX 3090 24GB)

Rack Servers#

Preset

GPUs

Description

rack-24gpu

24

Rack server (24x A100 40GB)

rack-32gpu

32

Rack server (32x A100 40GB)

Development#

Preset

GPUs

Description

dev-single

1

Development (1x RTX 4080 16GB)

dev-dual

2

Development (2x RTX 3060 12GB)

Supercomputers#

Preset

GPUs

Description

dgx-superpod-h100

256

NVIDIA DGX SuperPOD (32x DGX H100)

dgx-superpod-b200

256

NVIDIA DGX SuperPOD (32x DGX B200)

dgx-basepod-h100

32

NVIDIA DGX BasePOD (4x DGX H100)

xai-colossus

100000

xAI Colossus Supercomputer (100k H100)

meta-rsc

16000

Meta Research SuperCluster (16000x A100 80GB)

microsoft-eagle

14400

Microsoft Eagle Supercomputer (14400x H100 80GB)

Generated Configuration#

The wizard generates environment variables for data/config/custom.env:

# GPU Assignment (all services share all GPUs)
CUDA_VISIBLE_DEVICES=0,1,2,3

# Worker Configuration (N GPUs x multiplier)
FORGE_NUM_WORKERS=16
STREAMGL_NUM_WORKERS=16
DASK_NUM_WORKERS=4

Applying Configuration#

After generating settings:

  1. Copy the settings to data/config/custom.env (or use -E to export directly)

  2. Restart GPU services:

./graphistry up --force-recreate forge-etl-python streamgl-gpu dask-cuda-worker

Fine-Grained Control#

For advanced tuning beyond the wizard, set these environment variables directly in data/config/custom.env:

Worker counts:

  • FORGE_NUM_WORKERS - forge-etl-python workers

  • STREAMGL_NUM_WORKERS - streamgl-gpu workers

  • DASK_NUM_WORKERS - dask-cuda-worker instances

Per-service GPU assignment:

  • FORGE_CUDA_VISIBLE_DEVICES - forge-etl-python

  • DCW_CUDA_VISIBLE_DEVICES - dask-cuda-worker

  • STREAMGL_CUDA_VISIBLE_DEVICES - streamgl-gpu

  • DASK_SCHEDULER_CUDA_VISIBLE_DEVICES - dask-scheduler

  • GAK_PUBLIC_CUDA_VISIBLE_DEVICES - graph-app-kit-public

  • GAK_PRIVATE_CUDA_VISIBLE_DEVICES - graph-app-kit-private

  • NOTEBOOK_CUDA_VISIBLE_DEVICES - notebook

See Performance Tuning - Multi-GPU for detailed configuration guidance.