sglang tp_dp_matrix: run SGLang server/client in Docker on H200

- config.env: switch to /data/models/DeepSeek-V4-Flash, USE_DOCKER=1,
  CONTEXT_LENGTH=1M, CONCURRENCY_SAMPLES=2, add DATASET_PATH.
- start_sglang_docker.sh: add --ipc host --shm-size 16g for NCCL.
- start_sglang_dp.sh: use --model-path for Docker CLI.
- run_bench.sh: mount dataset into client container and pass --dataset-path
to avoid HF download on offline nodes.

experiments/dsv4_h200_sglang_tp_dp_matrix/config.env

@@ -1,4 +1,4 @@
-# TP×DP matrix experiment for DeepSeek-V4-Flash on H200 (8 GPUs) using SGLang Docker.
+# TP×DP matrix experiment for DeepSeek-V4-Flash on H200 (8 GPUs) using SGLang.
 # Tests SGLang with three parallel configurations:
 #   TP=2, DP=4  -> 2 GPUs per replica, 4 replicas
 #   TP=4, DP=2  -> 4 GPUs per replica, 2 replicas
@@ -6,7 +6,7 @@
 
 EXPERIMENT="dsv4_h200_sglang_tp_dp_matrix"
 MODEL_NAME="DeepSeek-V4-Flash"
-MODEL_PATH="/data3/hf_models/DeepSeek-V4-Flash"
+MODEL_PATH="/data/models/DeepSeek-V4-Flash"
 SERVED_MODEL_NAME="deepseek-v4-flash"
 
 SGLANG_PORT="${SGLANG_PORT:-30031}"
@@ -16,9 +16,7 @@ SGLANG_PORT="${SGLANG_PORT:-30031}"
 # does not exist on the host.
 VENV_CLIENT="${VENV_CLIENT:-/data/user1/yy/envs/sglang}"
 
-# When USE_DOCKER_CLIENT=1, the benchmark client (sglang.bench_serving) is run
-# inside the same SGLang Docker image used for the server. This avoids requiring
-# a native sglang installation on the host.
+# Run the benchmark client natively (0) or inside Docker (1).
 USE_DOCKER_CLIENT="${USE_DOCKER_CLIENT:-1}"
 
 export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
@@ -35,22 +33,28 @@ declare -a PARALLEL_CONFIGS=(
 )
 
 # SGLang server settings
-CONTEXT_LENGTH="${CONTEXT_LENGTH:-65536}"
-MAX_RUNNING_REQUESTS="${MAX_RUNNING_REQUESTS:-64}"
+CONTEXT_LENGTH="${CONTEXT_LENGTH:-1048576}"
+MAX_RUNNING_REQUESTS="${MAX_RUNNING_REQUESTS:-128}"
 MEM_FRACTION_STATIC="${MEM_FRACTION_STATIC:-0.88}"
 MOE_RUNNER_BACKEND="${MOE_RUNNER_BACKEND:-marlin}"
 
-# Docker deployment switch. SGLang is launched via Docker using the image below.
+# Deployment switch. 0 = native sglang venv, 1 = Docker.
 USE_DOCKER="${USE_DOCKER:-1}"
 DOCKER_IMAGE="${DOCKER_IMAGE:-lmsysorg/sglang:latest}"
 
+# Dataset used by sglang.bench_serving --dataset-name random.
+# The random sampler needs a ShareGPT-style JSON file locally; it falls back to
+# downloading from HuggingFace, which usually fails on offline H200 nodes.
+DATASET_PATH="${DATASET_PATH:-/data/user1/yy/datasets/ShareGPT_V4.3_unfiltered_cleaned_split.json}"
+
 # Matrix and concurrency rules are defined in matrix.json by default.
 MATRIX_FILE="${MATRIX_FILE:-${SCRIPT_DIR:-.}/matrix.json}"
 MATRIX_MODE="${MATRIX_MODE:-Y}"
 
-# Sampling density for concurrency. 0 means use the default heuristic in
-# generate_scenarios.py (6-8 points, or all integers when range is small).
-CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-0}"
+# Sampling density for concurrency.
+# 0 = use the default heuristic in generate_scenarios.py (6-8 points).
+# 2 = only test the low and high endpoints.
+export CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-2}"
 
 # Per-scenario timeout to avoid hangs (seconds).
 SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"

experiments/dsv4_h200_sglang_tp_dp_matrix/run_bench.sh

@@ -123,6 +123,7 @@ run_bench_serving() {
     docker run --rm \
       --network host \
       -v "${MODEL_PATH}:${MODEL_PATH}:ro" \
+      -v "${DATASET_PATH}:${DATASET_PATH}:ro" \
       -v "${RESULT_BASE}:${RESULT_BASE}" \
       -e PYTHONUNBUFFERED=1 \
       "${DOCKER_IMAGE}" \
@@ -142,6 +143,7 @@ run_warmup() {
     --host 127.0.0.1 \
     --port "$SGLANG_PORT" \
     --dataset-name random \
+    --dataset-path "$DATASET_PATH" \
     --random-input-len "$input_len" \
     --random-output-len "$output_len" \
     --num-prompts 1 \
@@ -419,6 +421,7 @@ run_parallel_config() {
         --host 127.0.0.1 \
         --port "$SGLANG_PORT" \
         --dataset-name random \
+        --dataset-path "$DATASET_PATH" \
         --random-input-len "$isl" \
         --random-output-len "$dsl" \
         --num-prompts "$num" \

experiments/dsv4_h200_sglang_tp_dp_matrix/start_sglang_docker.sh

@@ -28,7 +28,8 @@ rm -f "$PID_FILE"
 docker rm -f "$NAME" >/dev/null 2>&1 || true
 
 SERVER_ARGS=(
-  serve "$MODEL_PATH"
+  serve
+  --model-path "$MODEL_PATH"
   --trust-remote-code
   --tp-size "$TP"
   --moe-runner-backend "$MOE_RUNNER_BACKEND"
@@ -61,6 +62,8 @@ echo "Log: $LOG"
 nohup docker run --rm \
   --name "$NAME" \
   --gpus all \
+  --ipc host \
+  --shm-size 16g \
   --entrypoint sglang \
   -p "${PORT}:${PORT}" \
   -v "${MODEL_PATH}:${MODEL_PATH}:ro" \

experiments/dsv4_h200_sglang_tp_dp_matrix/start_sglang_dp.sh

@@ -33,7 +33,8 @@ PID_FILE="${RUNTIME_BASE}/${EXPERIMENT}_sglang_tp${TP}_dp${DP}.pid"
 rm -f "$PID_FILE"
 
 SERVER_ARGS=(
-  sglang serve "$MODEL_PATH"
+  sglang serve
+  --model-path "$MODEL_PATH"
   --trust-remote-code
   --tp-size "$TP"
   --moe-runner-backend "$MOE_RUNNER_BACKEND"