115 lines
4.3 KiB
YAML
115 lines
4.3 KiB
YAML
apiVersion: serving.kserve.io/v1beta1
|
|
kind: InferenceService
|
|
metadata:
|
|
annotations:
|
|
serving.kserve.io/deploymentMode: RawDeployment
|
|
labels:
|
|
app.kubernetes.io/name: llm-reasoning
|
|
app.kubernetes.io/part-of: llm-serving
|
|
name: reasoning
|
|
namespace: llm-serving
|
|
spec:
|
|
predictor:
|
|
containers:
|
|
- args:
|
|
- --model=unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit
|
|
- --served-model-name=reasoning
|
|
- --quantization=bitsandbytes
|
|
- --dtype=float16
|
|
- --kv-cache-dtype=auto
|
|
- --tensor-parallel-size=1
|
|
- --max-model-len=16384
|
|
- --gpu-memory-utilization=0.90
|
|
- --max-num-seqs=4
|
|
- --enable-chunked-prefill
|
|
- --enable-prefix-caching
|
|
- --reasoning-parser=deepseek_r1
|
|
# Without these, any client sending tool_choice="auto" (pi does, for
|
|
# Read/Bash/etc.) gets a 400: "auto" tool choice requires
|
|
# --enable-auto-tool-choice and --tool-call-parser to be set.
|
|
# deepseek_v3 (matching --reasoning-parser above) 400s here --
|
|
# "DeepSeek-V3 Tool parser could not locate tool call start/end
|
|
# tokens in the tokenizer" -- this checkpoint is a Qwen2.5-32B base
|
|
# distilled on R1 traces, so its tokenizer never got DeepSeek-V3's
|
|
# own special tool-call tokens registered even though it kept R1's
|
|
# <think> reasoning format. hermes parses tool calls from plain text
|
|
# patterns instead of special tokens, so it works against the
|
|
# underlying Qwen tokenizer regardless. Verified live: deepseek_v3
|
|
# 400s, hermes returns a real tool_calls response.
|
|
- --enable-auto-tool-choice
|
|
- --tool-call-parser=hermes
|
|
# vLLM 0.11.0's native OffloadingConnector -- spills KV cache blocks
|
|
# to CPU DRAM instead of discarding them on preemption (max-num-seqs=4
|
|
# + max-model-len=16384 means concurrent long sequences compete for
|
|
# the same GPU KV space). No extra dependency, built into vLLM core.
|
|
# num_cpu_blocks=2000 hung the pod at startup ("Allocating 64 CPU
|
|
# tensors..." then nothing -- 64 is this model's layer count, one
|
|
# pinned host tensor per layer, each sized for every CPU block; 2000
|
|
# was oversized enough to stall pinning that much host memory, likely
|
|
# blowing well past the pod's memory limit). Dropped to a small,
|
|
# known-safe starting point -- confirm it actually comes up healthy,
|
|
# then watch real host memory usage and raise it deliberately rather
|
|
# than guessing a round number again. block_size=128 tokens matches
|
|
# vLLM's own example.
|
|
# Note: 0.11.0 ships the original (fragmented, small-transfer-block)
|
|
# version of this connector -- 0.12.0 consolidates KV data into one
|
|
# contiguous block per request and is reported an order of magnitude
|
|
# faster for this specific feature, so this is a real but not yet
|
|
# optimal implementation until the image gets bumped.
|
|
- --kv-transfer-config={"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"num_cpu_blocks":32,"block_size":128}}
|
|
- --host=0.0.0.0
|
|
- --port=8080
|
|
env:
|
|
- name: VLLM_USE_FLASHINFER_SAMPLER
|
|
value: '0'
|
|
- name: VLLM_ATTENTION_BACKEND
|
|
value: TRITON_ATTN
|
|
- name: HF_HOME
|
|
value: /mnt/models
|
|
image: vllm/vllm-openai:v0.11.0@sha256:014a95f21c9edf6abe0aea6b07353f96baa4ec291c427bb1176dc7c93a85845c
|
|
name: kserve-container
|
|
ports:
|
|
- containerPort: 8080
|
|
protocol: TCP
|
|
readinessProbe:
|
|
httpGet:
|
|
path: /health
|
|
port: 8080
|
|
periodSeconds: 10
|
|
resources:
|
|
limits:
|
|
cpu: '16'
|
|
memory: 36Gi
|
|
nvidia.com/gpu: '1'
|
|
requests:
|
|
cpu: '8'
|
|
memory: 12Gi
|
|
nvidia.com/gpu: '1'
|
|
startupProbe:
|
|
failureThreshold: 80
|
|
httpGet:
|
|
path: /health
|
|
port: 8080
|
|
periodSeconds: 15
|
|
volumeMounts:
|
|
- mountPath: /mnt/models
|
|
name: models
|
|
- mountPath: /dev/shm
|
|
name: shm
|
|
deploymentStrategy:
|
|
type: Recreate
|
|
maxReplicas: 2
|
|
minReplicas: 2
|
|
nodeSelector:
|
|
kubernetes.io/hostname: worker-1
|
|
runtimeClassName: nvidia
|
|
volumes:
|
|
- name: models
|
|
persistentVolumeClaim:
|
|
claimName: llm-models
|
|
- emptyDir:
|
|
medium: Memory
|
|
sizeLimit: 2Gi
|
|
name: shm
|
|
|