mirror of
https://github.com/trailofbits/buttercup
synced 2026-06-21 14:11:39 +00:00
Ronald Eytchison
77a3e77936
* Create minikube config for a large machine (16 CPU, 128 GB) * Address PR feedback and scale services - Increase redis memory - Disable redis in-memory backups to conserve memory - Increase build bot replicas - Increase dind CPU/Memory given build bot scaling - Set reasonable limits for merger-bot and pov-reproducer - increase coverage bot memory * Configure /dev/shm size for corpus tmpfs
108 lines
4.9 KiB
Plaintext
108 lines
4.9 KiB
Plaintext
# Reference a values file for kubernetes deployment. This should be a template
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# file, filled by crs-architecture.sh with the correct variables.
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# See values-upstream-minikube.template, values-minikube.template, values-aks.template, values-prod.template
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# For a large machine, see values-upstream-minikube-16cpu-128gb.template
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export BUTTERCUP_K8S_VALUES_TEMPLATE="k8s/values-upstream-minikube.template"
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# Namespace used to install the whole CRS in.
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export BUTTERCUP_NAMESPACE=crs
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# Choose whether to deploy the k8s cluster. If not, the cluster should already
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# be accessible with kubectl
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export DEPLOY_CLUSTER=true # or false
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# When DEPLOY_CLUSTER is set to true, this variable defines what kind of cluster
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# to deploy
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export CLUSTER_TYPE=minikube # or "aks"
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# These are terraform variables, necessary only for actual deployment in AKS
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# See README.md for instructions on how to set them up.
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# export TF_VAR_ARM_CLIENT_ID="<your-client-id>"
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# export TF_VAR_ARM_CLIENT_SECRET="<your-client-secret>"
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# export TF_VAR_ARM_TENANT_ID="<your-tenant-id>"
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# export TF_VAR_ARM_SUBSCRIPTION_ID="<your-sub-id>"
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# export TF_VAR_usr_node_count=50
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# export TF_VAR_resource_group_name_prefix="<resource-prefix>"
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# export TF_VAR_resource_group_name="<existing-resource-group-name>"
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# TailScale variables, necessary only for production/staging deployments
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export TAILSCALE_ENABLED=false # or true
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# export TS_CLIENT_ID="<your-tailscale-oauth-client-id>"
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# export TS_CLIENT_SECRET="<your-tailscale-oauth-client-secret>"
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# export TS_OP_TAG="<your-tailscale-operator-tag>"
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# export TAILSCALE_DOMAIN="tail123456.ts.net"
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# AIxCC - Competition API settings, used to properly connect to the right endpoint and
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# with the right authentication. If COMPETITION_API_ENABLED is true, a test API
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# will be deployed and configured according to the variables.
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# SCANTRON_GITHUB_PAT is a GitHub PAT with at least package:read and repo:write
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# permissions.
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# export COMPETITION_API_ENABLED=false # or true
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# export COMPETITION_API_KEY_ID="11111111-1111-1111-1111-111111111111"
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# export COMPETITION_API_KEY_TOKEN="secret"
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# export COMPETITION_API_URL="<competition-api-url>"
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# export SCANTRON_GITHUB_PAT="<gh-pat-with-repo-read+packages-read>"
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# Start an additional pod for running challenges from blobs and in the same times and using same ids
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# as in the organized round. See the orchestrator/src/buttercup/orchestrator/mock_competition_api/README.md
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# for more details.
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export MOCK_COMPETITION_API_ENABLED=false # or true
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# CRS settings
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export CRS_KEY_ID="515cc8a0-3019-4c9f-8c1c-72d0b54ae561"
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export CRS_KEY_TOKEN="VGuAC8axfOnFXKBB7irpNDOKcDjOlnyB"
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export CRS_KEY_TOKEN_HASH='$argon2id$v=19$m=65536,t=3,p=4$Dg1v6NPGTyXPoOPF4ozD5A$wa/85ttk17bBsIASSwdR/uGz5UKN/bZuu4wu+JIy1iA'
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# export CRS_URL="<your-crs-api-url>"
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# export CRS_API_HOSTNAME="<your-crs-api-hostname>"
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# GitHub Container Registry auth, base64 encoded
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# echo "USERNAME:$GHCR_PAT" | base64 --wrap=0
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# The PAT should have at least package:read permissions
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# NOTE: Comment out for local builds - images will be built locally without GHCR access.
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# Only needed when pulling pre-built images from GHCR.
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# export GHCR_AUTH="<your-ghcr-base64-auth>"
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# LiteLLM/LLMs settings
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export LITELLM_MASTER_KEY="d5179c62ae1c7366e3ee09775d0993d5" # Generate with: openssl rand -hex 16
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export LITELLM_MAX_BUDGET="100"
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export AZURE_API_BASE="<disabled>"
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export AZURE_API_KEY="<disabled>"
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export OPENAI_API_KEY="<your-openai-api-key>"
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export ANTHROPIC_API_KEY="<your-anthropic-api-key>"
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# export GEMINI_API_KEY="<your-gemini-api-key>"
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# LangFuse settings, instructing LLM-applications to log their LLM traces
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# export LANGFUSE_ENABLED=true
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# export LANGFUSE_HOST="<your-langfuse-host-url>"
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# export LANGFUSE_PUBLIC_KEY="<your-langfuse-public-key>"
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# export LANGFUSE_SECRET_KEY="<your-langfuse-secret-key>"
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# OpenTelemetry endpoint settings (e.g. SigNoz)
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export DEPLOY_SIGNOZ=false
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# Your otel endpoint
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# export OTEL_ENDPOINT="<your-otel-endpoint>"
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# Your otel token, including basic or bearer
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# export OTEL_TOKEN="dXNlcm5hbWU6cGFzc3dvcmQ="
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# export OTEL_PROTOCOL=grpc
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# Docker Hub credentials for doing logged requests while getting Container
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# Images from DockerHub. If not set, the system will perform unauthenticated requests.
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# export DOCKER_USERNAME="<your-docker-username>"
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# export DOCKER_PAT="<your-docker-pat>"
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# Docker build arguments, useful for local deployment
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export FUZZER_BASE_IMAGE="gcr.io/oss-fuzz-base/base-runner"
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# Minikube cluster size
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# NOTE: Docker Desktop typically limits resources. Check Docker Desktop > Settings > Resources.
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# For macOS Docker Desktop with 8GB RAM allocated to Docker:
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# - Use MINIKUBE_CPU=4 and MINIKUBE_MEMORY_GB=7
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# - Some pods may remain Pending due to resource constraints (this is expected)
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# For larger systems or dedicated Linux machines with 16GB+ RAM:
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# - Use MINIKUBE_CPU=6 and MINIKUBE_MEMORY_GB=10
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export MINIKUBE_CPU=6
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export MINIKUBE_MEMORY_GB=10
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export MINIKUBE_DISK_GB=80
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# Increase /dev/shm size if corpus tmpfs is enabled
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# export MINIKUBE_SHM_SIZE_GB=60
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