mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
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131 lines
4.8 KiB
Bash
Executable File
131 lines
4.8 KiB
Bash
Executable File
#!/bin/bash
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Launch vLLM with NVIDIA Triton Inference Server optimized build
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# This should have proper support for compute capability 12.1 (DGX Spark)
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# Enable unified memory usage for DGX Spark
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export CUDA_MANAGED_FORCE_DEVICE_ALLOC=1
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export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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# Enable CUDA unified memory and oversubscription
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export CUDA_VISIBLE_DEVICES=0
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export PYTORCH_NO_CUDA_MEMORY_CACHING=0
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# Force vLLM to use CPU offloading for large models
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export VLLM_CPU_OFFLOAD_GB=50
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export VLLM_ALLOW_RUNTIME_LORA_UPDATES_WITH_SGD_LORA=1
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export VLLM_SKIP_WARMUP=0
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# Optimized environment for performance
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export VLLM_LOGGING_LEVEL=INFO
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export PYTHONUNBUFFERED=1
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# Enable CUDA optimizations
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export VLLM_USE_MODELSCOPE=false
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# Enable unified memory in vLLM
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export VLLM_USE_V1=0
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# First, test basic CUDA functionality
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echo "=== Testing CUDA functionality ==="
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python3 -c "
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import torch
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print(f'PyTorch version: {torch.__version__}')
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print(f'CUDA available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f'CUDA version: {torch.version.cuda}')
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print(f'GPU count: {torch.cuda.device_count()}')
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for i in range(torch.cuda.device_count()):
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props = torch.cuda.get_device_properties(i)
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print(f'GPU {i}: {props.name} (compute capability {props.major}.{props.minor})')
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# Try basic CUDA operation
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try:
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x = torch.randn(10, 10).cuda(i)
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y = torch.matmul(x, x.T)
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print(f'GPU {i}: Basic CUDA operations work')
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except Exception as e:
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print(f'GPU {i}: CUDA operation failed: {e}')
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"
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echo "=== Starting optimized vLLM server ==="
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# Optimized configuration for DGX Spark performance with NVFP4 quantization
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# Available quantized models from NVIDIA
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NVFP4_MODEL="nvidia/Llama-3.3-70B-Instruct-FP4"
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NVFP8_MODEL="nvidia/Llama-3.1-8B-Instruct-FP8"
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STANDARD_MODEL="meta-llama/Llama-3.1-70B-Instruct"
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# Check GPU compute capability for optimal quantization
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COMPUTE_CAPABILITY=$(nvidia-smi -i 0 --query-gpu=compute_cap --format=csv,noheader,nounits 2>/dev/null || echo "unknown")
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echo "Detected GPU compute capability: $COMPUTE_CAPABILITY"
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# Configure quantization based on GPU architecture
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if [[ "$COMPUTE_CAPABILITY" == "12.1" ]] || [[ "$COMPUTE_CAPABILITY" == "10.0" ]]; then
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# Blackwell/DGX Spark architecture - use standard 70B model with CPU offloading
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echo "Using standard Llama-3.1-70B model for Blackwell/DGX Spark with CPU offloading"
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QUANTIZATION_FLAG=""
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MODEL_TO_USE="$STANDARD_MODEL" # Use standard 70B model
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GPU_MEMORY_UTIL="0.7" # Lower GPU memory to allow unified memory
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MAX_MODEL_LEN="4096" # Shorter sequences for memory efficiency
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MAX_NUM_SEQS="16" # Lower concurrent sequences for 70B
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MAX_BATCHED_TOKENS="4096"
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CPU_OFFLOAD_GB="50" # Offload 50GB to CPU/unified memory
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elif [[ "$COMPUTE_CAPABILITY" == "9.0" ]]; then
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# Hopper architecture - use standard model
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echo "Using standard 70B model for Hopper architecture"
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QUANTIZATION_FLAG=""
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MODEL_TO_USE="$STANDARD_MODEL"
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GPU_MEMORY_UTIL="0.7"
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MAX_MODEL_LEN="4096"
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MAX_NUM_SEQS="16"
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MAX_BATCHED_TOKENS="4096"
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CPU_OFFLOAD_GB="40"
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else
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# Other architectures - use standard precision
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echo "Using standard 70B model for GPU architecture: $COMPUTE_CAPABILITY"
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QUANTIZATION_FLAG=""
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MODEL_TO_USE="$STANDARD_MODEL"
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GPU_MEMORY_UTIL="0.7"
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MAX_MODEL_LEN="2048"
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MAX_NUM_SEQS="16"
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MAX_BATCHED_TOKENS="2048"
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CPU_OFFLOAD_GB="40"
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fi
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echo "Using model: $MODEL_TO_USE"
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echo "Quantization: ${QUANTIZATION_FLAG:-'disabled'}"
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echo "GPU memory utilization: $GPU_MEMORY_UTIL"
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echo "CPU Offload: ${CPU_OFFLOAD_GB}GB"
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vllm serve "$MODEL_TO_USE" \
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--host 0.0.0.0 \
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--port 8001 \
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--tensor-parallel-size 1 \
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--max-model-len "$MAX_MODEL_LEN" \
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--max-num-seqs "$MAX_NUM_SEQS" \
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--max-num-batched-tokens "$MAX_BATCHED_TOKENS" \
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--gpu-memory-utilization "$GPU_MEMORY_UTIL" \
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--cpu-offload-gb "$CPU_OFFLOAD_GB" \
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--kv-cache-dtype auto \
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--trust-remote-code \
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--served-model-name "$MODEL_TO_USE" \
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--enable-chunked-prefill \
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--disable-custom-all-reduce \
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--disable-async-output-proc \
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$QUANTIZATION_FLAG |