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Skillstao-run-on-brev

tao-run-on-brev

Deployment & operations

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Updated 10-16 04:34

About this skill

Brev

NVIDIA Brev provides on-demand GPU instances across multiple cloud providers. Instances come pre-loaded with NVIDIA drivers, CUDA, Docker, and NVIDIA Container Toolkit.

Brev is instance-based (not job-based like Lepton). You create an instance, run commands on it via brev exec, and delete it when done. The TAO SDK's BrevHandler wraps this into the standard job interface.

Preflight

This skill needs the brev CLI, its companion agent skill (brev-cli), and an active login. Check before proceeding:

# 1. brev CLI installed
command -v brev >/dev/null 2>&1 || {
  echo "MISSING: brev CLI not installed. Install:"
  echo "  https://docs.nvidia.com/brev/"
  exit 1
}
2. brev-cli agent skill installed — provides the brev CLI's command reference to the agent

[ -d "$HOME/.claude/skills/brev-cli" ] || [ -d ".claude/skills/brev-cli" ] || { echo "MISSING: brev-cli agent skill not installed. Run:" echo " brev agent-skill install" exit 1 }

3. brev login active — always token-login first when running headless. Plain brev ls will hit an interactive auth prompt (read: EOF on stdin) even when BREV_API_TOKEN is set, so refresh the session up front.

if [ -n "$BREV_API_TOKEN" ]; then brev login --token "$BREV_API_TOKEN" >/dev/null 2>&1 || { echo "MISSING: brev token login failed. Verify BREV_API_TOKEN." exit 1 } fi

Retry once after a forced re-login: cached creds occasionally desync and the first brev ls returns auth EOF until the session is rebuilt.

brev ls >/dev/null 2>&1 || { [ -n "$BREV_API_TOKEN" ] && brev login --token "$BREV_API_TOKEN" >/dev/null 2>&1 brev ls >/dev/null 2>&1 || { echo "MISSING: not logged in to brev. Run:" echo " brev login # interactive (opens browser)" echo " # or set BREV_API_TOKEN in ~/.config/tao/.env (then 'brev login --token $BREV_API_TOKEN')" exit 1 } }


If any step fails, the agent prompts the user to authorize the fix via Bash, then re-runs the preflight before continuing. The TAO SDK is **not** required for Brev — `brev exec docker run …` is sufficient. Reach for the SDK only if you want Job handles, S3 I/O wrapping via `script_runner`, or state persistence; `nvidia-tao-sdk` is on public PyPI, install the pinned Brev extra from `versions.yaml`: `pip install "$("${TAO_SKILL_BANK_PATH:?}/scripts/resolve_versions_key.py" wheels.tao_sdk_brev)"`. **When going the SDK route, read `tao-skill-bank:tao-run-platform` for the `BrevSDK` kwarg reference, `build_entrypoint`, and `ActionWorkflow` patterns.**

## Authentication

Two options:

1. **Automated (recommended)**: Get an API token from the Brev console settings page. Set `BREV_API_TOKEN` as an environment variable (e.g., in `~/.config/tao/.env`). The handler auto-authenticates via `brev login --token` on first use — same UX as Lepton.

2. **Manual**: Run `brev login` (opens browser). Tokens expire hourly — the handler refreshes automatically.

S3 credentials (ACCESS_KEY, SECRET_KEY) are needed separately for data transfer.

### Headless / non-interactive

In a CI shell, container, or agent session with no controlling TTY, **always
run `brev login --token "$BREV_API_TOKEN"` before any other `brev` call** —
even when the token is exported. Otherwise the CLI prompts on stdin and
returns an `EOF` auth error on commands like `brev ls`, `brev create`, or
`brev exec`. Re-run the token login if a call returns auth-EOF; a single
refresh is usually enough.

## Launch Preflight

Before generating scripts or submitting jobs:

1. Verify `BREV_API_TOKEN` is set.
2. Verify the `brev` CLI is installed and can list instances, for example
   `brev ls --json`. If needed, authenticate with `brev login --token`.
3. For `s3://` datasets/results, verify `ACCESS_KEY` and `SECRET_KEY` are set
   and the exact paths are readable with `aws s3 ls`.
4. Do not accept local `/path` inputs for Brev unless the user has proven those
   paths exist on the target Brev instance or are mounted into it.
5. Verify model-specific credentials such as `HF_TOKEN` before launch.

## Instance Lifecycle

The agent controls instance lifecycle:

- **Reuse**: Pass `instance_id` in `backend_details` to run multiple jobs on the same instance. Efficient for multi-step workflows.
- **Ephemeral**: Omit `instance_id` — the handler creates a new instance per job. Clean but slower (instance boot ~2-5 min).

### Creating an instance — placement info

For accounts with more than one cloud credential or workspace group, plain
`brev create` rejects the call with a placement error. Pass the account-specific
IDs explicitly:

```bash
brev create my-instance \
  --gpu L40S:1 \
  --cloud-cred-id <cloudCredId> \
  --workspace-group-id <workspaceGroupId>

Discover the values once and stash them in ~/.config/tao/.env:

brev ls --json | jq -r '.workspaces[0].workspaceGroupId'   # default group
brev orgs --json | jq -r '.[0].cloudCredentials[].id'      # cloud credential

When using the SDK, pass them through backend_details:

BrevSDK().create_job(
    ...,
    backend_details={
        "cloud_cred_id": "<cloudCredId>",
        "workspace_group_id": "<workspaceGroupId>",
    },
)

Multi-GPU and multi-node

Multi-node is not supported on Brev. Brev is instance-based — one job runs on one instance, with no cross-instance coordination.

Multi-GPU on a single instance is supported (instances available with up to 8× H100 / A100 / L40S). gpu_count maps to the GPU count on the instance; torchrun --nproc-per-node=N or PyTorch DDP work within the instance.

GPU Types

Available via brev search:

  • L40S, A100 80GB, H100 (availability varies by provider)
  • Use --gpu-name to filter, --min-vram for memory requirements

Storage

No shared NFS/Lustre. All data flows through S3 via the script_runner's fsspec integration. Instance-local disk under the login user's home directory ($HOME) persists across stop/start but not across delete/create.

Docker on Brev

VM Mode instances have Docker pre-installed. For TAO container images:

# NGC auth (one-time per instance)
brev exec <instance> -- docker login nvcr.io -u '$oauthtoken' -p <NGC_KEY>

# Run a TAO training job
brev exec <instance> -- docker run --gpus all --rm \
  -v $HOME/data:/data \
  nvcr.io/nvidia/tao/tao-toolkit:6.26.3-pyt \
  visual_changenet train -e /data/spec.yaml

Wait for instance readiness before the first brev exec

A freshly created instance reports RUNNING long before sshd, hostname resolution, and the user shell are ready. The first brev exec against an unsettled instance fails with hostname not resolvable, Connection refused, or a silent timeout. Always poll until a trivial exec succeeds before issuing real work:

# Wait up to 5 minutes for shell readiness — covers the SSH bring-up window.
for i in $(seq 1 60); do
  brev exec <instance> -- true >/dev/null 2>&1 && break
  sleep 5
done
brev exec <instance> -- true >/dev/null 2>&1 || {
  echo "instance <instance> never became exec-ready"; exit 1;
}

brev exec timeout for cold-start workloads

brev exec inherits no default timeout, but anything that wraps it (the SDK handler, CI step wrappers, timeout shell builtins) must allow time for both the SSH bring-up window and the container pull on a fresh instance. Use ≥ 600 s (10 min) for the first exec on a new instance; the previous 60–120 s default truncates remote startup and surfaces as a spurious exec failed even though the remote command is still progressing.

Mixed-Platform Workflows

Brev can be mixed with Lepton in the same workflow. Per-stage platform assignment:

{"skill": "vcn-gap-analysis", "action": "analyze", "platform": "brev"},
{"skill": "visual-changenet", "action": "train", "platform": "lepton"}

CPU stages (gap analysis, data merge) run cheaply on Brev. GPU stages (training) run on Lepton H100s.

Cleanup

brev delete <instance>      # plain delete — no flags

The CLI does not accept --yes / -y; passing it errors with unknown flag: --yes. brev delete <instance> is already non-interactive on recent CLIs, so no confirmation flag is needed.

Error Patterns

brev CLI not found: Install from https://docs.nvidia.com/brev/.

brev ls returns auth EOF even with BREV_API_TOKEN set: Headless shell has no stdin for the interactive auth prompt. Run brev login --token "$BREV_API_TOKEN" first, then retry. If the failure persists across a single retry, the token itself is stale — mint a fresh one.

Token expired: Handler auto-refreshes via brev login --token. If persistent, run brev login manually.

brev create rejected with placement error (cloudCredId / workspaceGroupId required): Multi-credential or multi-workspace accounts must pass --cloud-cred-id and/or --workspace-group-id. See Creating an instance — placement info above.

brev exec fails with hostname not resolvable or Connection refused right after create: Instance reports RUNNING before sshd is up. Use the readiness-wait loop in Wait for instance readiness before the first brev exec before issuing the real command.

SDK exec timeout / exec failed on a fresh instance: The SDK's brev exec wrapper timed out before remote startup finished. Raise the timeout to ≥ 600 s for cold-start runs (see brev exec timeout for cold-start workloads).

brev delete --yes: unknown flag: --yes: The CLI has no confirmation flag. Use plain brev delete <instance>.

Instance stuck in provisioning: Some GPU types have limited availability. Try a different --gpu-name or provider.

Docker pull fails on nvcr.io: NGC_KEY not set or expired. Run docker login nvcr.io on the instance.