Deploy or smoke-test the Material Agent with Brev-hosted dependency endpoints. Use when the user asks to test material agent on Brev, deploy material agent with Brev, use RTX/L40S for rendering and A100/H100-grade GPU for VLM, run a Brev hybrid material-agent test, or recreate the Brev material-agent deployment. This workflow keeps the main pipeline local and uses Brev port-forwards to reach OVRTX and Qwen-family VLM endpoints.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Deploy or smoke-test the Material Agent with Brev-hosted dependency endpoints. Use when the user asks to test material agent on Brev, deploy material agent with Brev, use RTX/L40S for rendering and A100/H100-grade GPU for VLM, run a Brev hybrid material-agent test, or recreate the Brev material-agent deployment. This workflow keeps the main pipeline local and uses Brev port-forwards to reach OVRTX and Qwen-family VLM endpoints.
Requires Brev CLI access, a remote RTX/L40S-class render GPU for OVRTX, an A100/H100-class VLM endpoint, local port-forwards, and Material Agent provider credentials.
Deploy Material Agent With Brev
When to Use
Use this workflow for the credit-conscious hybrid path:
Local machine runs the material-agent pipeline.
Brev wu-ma-render runs standalone OVRTX on an RTX GPU. Prefer AWS
g7e.2xlarge / RTX PRO Server 6000 for fast validation, or AWS
g6e.xlarge / L40S when lower cost matters more than cold-start latency.
Brev wu-ma-vlm-a100 or wu-ma-vlm-h100 runs an OpenAI-compatible Qwen
VLM/LLM endpoint. The validated larger path is H100 + Qwen3.5 35B NIM with
NIM_MAX_MODEL_LEN=65536; it passed Material Agent end-to-end with local
data-URI rendered image payloads.
Local port-forwards expose RENDER_ENDPOINT=http://localhost:8001 and
MA_VLM_NIM_BASE_URL=http://localhost:8003/v1.
Optional image-generation endpoints exposed through MA_IMAGE_GEN_* are used
by generated reference images and generated material-library textures. Reuse a
Texture Agent image-generation endpoint when one is already running.
Limitations
Keep provider credentials in .env or remote env files with restrictive
permissions; never print or commit them.
Prefer local port-forwards for this hybrid path; do not assume Brev
instance-to-instance networking is available.
Delete GPU nodes after validation unless the user asks to keep them.
Prerequisites
Brev CLI access and SSH-ready render and VLM nodes.
Docker, NVIDIA Container Toolkit, and writable storage on each remote node.
Local Material Agent environment with the provider keys and optional Scene
Optimizer package required by the selected config.
Instructions
Use brev-cli for generic Brev inventory, dry-run, create, port-forward,
stop, delete, and cleanup guardrails.
Create or reuse the Brev render node, then validate OVRTX health and render.
Create or reuse the VLM node, then validate model listing and image chat.
Set local Material Agent render, VLM, LLM, and optional image-generation environment variables.
Run the local pipeline with the forwarded endpoints, then clean up nodes and
port-forwards.
Credit Safety
Always start with:
brev --version
brev healthcheck
brev ls --json
python scripts/brev_agent_services.py --service material --preset hybrid --render-gpu-name RTX --vlm-gpu-name A100
If instances already exist, inspect and reuse them instead of creating new
ones. Use brev create --dry-run before real creation unless the user has
already confirmed the exact spend.
For service-on-Brev presets, the planner excludes local .env files during
worktree copy, then writes a minimal remote .env with generated endpoint/model
wiring and starts Docker Compose with --env-file .env. Edit that remote
.env before the Compose step when a generated comment asks for a real API key.
Wait until brev ls --json shows shell access ready, then copy the repo to a
writable path. Brev's default user is ubuntu; /workspace was not writable
on observed images, so use ~/world-understanding.
Keep the port-forward running while the local pipeline runs. OVRTX can take
several minutes to warm up before gpu_initialized is true.
The empty .build-resources directory is required because the OVRTX Dockerfile
copies that path during build; keep the rsync exclusion so local/internal Scene
Optimizer bundles and other build artifacts are not copied to the render node.
Do not use rt2 for Material Agent validation. For a first render-service
return-path smoke, it is acceptable to add OVRTX_NUM_SENSOR_UPDATES=1 while
keeping OVRTX_RENDER_MODE=pt; restore the default before quality-sensitive
pipeline runs. Avoid very short cold-start render timeouts. A 900 second
timeout allowed warm-up and an OVRTX /render smoke to pass on validated AWS
RTX PRO Server 6000 and L40S nodes, but L40S can be noticeably slower.
A100/H100 VLM Node
Start a Qwen-family OpenAI-compatible VLM/LLM endpoint on the model node. The
validated path is Denvr denvr_A100_sxm4_80G with Qwen/Qwen2.5-VL-7B-Instruct. Promote
H100 providers or larger Qwen 3.5/3.6 models only after the selected image
passes disk qualification, endpoint readiness, text chat, image chat, and a
Material Agent predict smoke.
If the user asks for NVIDIA NIM, use deploy-qwen-vlm-brev. The validated
larger Material Agent path is hyperstack_H100 with
nvcr.io/nim/qwen/qwen3.5-35b-a3b:1.7.0-variant, model ID
qwen/qwen3.5-35b-a3b, NIM_MAX_MODEL_LEN=65536,
NIM_MAX_IMAGES_PER_PROMPT=20, /ephemeral for Docker/NIM cache, and
VLLM_ENABLE_CUDA_COMPATIBILITY=1. That NIM requires request-level
chat_template_kwargs: {"enable_thinking": false} for direct answers.
The nvcr.io/nim/qwen/qwen3.6-35b-a3b:1.7.0-variant H100 path can serve text
and public image URLs, but it rejects Material Agent's local/base64 image
payloads with HTTP 400 (Input should be a valid string). Do not use Qwen3.6
35B A3B as the Material Agent default until the image transport is changed or a
compatible container is found. Prefer the validated Qwen3.5 35B NIM endpoint
for Material Agent VLM tests that need the larger model.
Qualify the provider image before pulling model weights. Require a real
writable Docker/Hugging Face cache location with enough free disk; use at least
250 GB free for first small-Qwen smoke tests and more for 9B+ experiments.
If the requested Brev disk size is not reflected in a writable filesystem,
delete the VM and try another provider/type. Do not promote a VLM VM type to
the preferred path until disk qualification, /v1/models, text chat, and image
chat all pass.
On the validated Denvr A100 image, /dev/vdc is the blank large data disk.
Only format it after confirming it is blank:
This merge step preserves existing Docker daemon keys such as runtimes and
default-runtime; nvidia-ctk runs before Docker is restarted so the daemon
comes back with the NVIDIA runtime still registered after the data-root move.
Do not replace
/etc/docker/daemon.json with a one-key file on provider images.
Serve the pinned Qwen vLLM endpoint with multimodal prompts enabled:
brev exec wu-ma-vlm-a100 "curl -fsS http://localhost:8000/v1/models"
brev port-forward wu-ma-vlm-a100 -p 8003:8000
curl -fsS http://localhost:8003/v1/models
curl -fsS http://localhost:8003/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{"model":"Qwen/Qwen2.5-VL-7B-Instruct","messages":[{"role":"user","content":[{"type":"text","text":"What is the dominant color in this image? Answer with one word."},{"type":"image_url","image_url":{"url":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAIAAAD8GO2jAAAAKElEQVR4nO3NsQ0AAAzCMP5/un0CNkuZ41wybXsHAAAAAAAAAAAAxR4yw/wuPL6QkAAAAABJRU5ErkJggg=="}}]}],"max_tokens":16,"temperature":0}'
Keep the port-forward running while the local pipeline runs.
Local Material Pipeline Environment
Set these locally before running the material-agent CLI or service client:
MA_RENDERING_USE_DATA_URI=true is required when the local Material Agent
pipeline uses the NVCF-compatible remote render backend against a
port-forwarded OVRTX service. It embeds prepared USD stages in the render
request and avoids S3/NVCF staging.
Use MA_NIM_API_KEY=not-used only for local no-auth VLM/LLM endpoints such as
a Brev port-forward. For tunnel, external URL, private-IP, or otherwise
authenticated endpoints, put the real MA_NIM_API_KEY in .env instead of the
dummy key.
If the local Scene Optimizer package is not installed, either fetch it before
the run or skip optimization for small smoke assets:
material-agent run <config.yaml> --skip optimize_usd
For the validated 35B NIM path, set MA_VLM_MODEL and MA_LLM_MODEL to
qwen/qwen3.5-35b-a3b and ensure the model config or request extra body passes
chat_template_kwargs.enable_thinking=false. A ladder end-to-end smoke passed
with RTX PRO Server 6000 OVRTX plus H100 Qwen3.5 35B NIM: 20 dataset renders,
4 VLM predictions, material application, output validation, and 2 final
renders.
This path does not require Brev-to-Brev networking because the local machine is
the caller for both remote endpoints.
Output Format
Return the Brev instance names, render and VLM endpoint URLs, selected model
IDs, smoke-test results, Material Agent environment exports, pipeline result,
and cleanup commands.
Troubleshooting
Use local port-forwards for this hybrid path; do not rely on Brev private
instance-to-instance networking unless it has been proven for the selected
provider.
Wait for shell_status: READY before brev exec or rsync.
Use ~/world-understanding and rsync with secret/cache excludes; /workspace
may not be writable.
If a render provider image lacks NVIDIA GL/Vulkan libraries, install the
matching libnvidia-gl package, add vulkan-tools, regenerate CDI, and
recreate the OVRTX container.
Start with Qwen/Qwen2.5-VL-7B-Instruct for self-hosted endpoint smoke tests. Move to
Qwen/Qwen3.5-9B or larger models only after the endpoint is proven on the
selected Brev image.
Check usable writable disk before downloading Hugging Face models; requested
Brev disk size may not be reflected in the root filesystem.
If Brev auth fails before deletion, power off reachable VMs over SSH
(ssh <name> "sudo poweroff") and re-login so brev delete can finish.
Cleanup
When the test is complete, stop port-forward sessions and delete the GPU nodes
unless the user explicitly wants to keep them:
