Snakemake规则重复申请内存求助：如何规避默认设置干扰

我发现所有Snakemake规则都会重复申请内存，一次是低于请求值的mem_mb，另一次是实际设置的mem_gb。将规则作为localrule运行时速度更快，请问如何确保默认设置不会产生干扰？

资源信息 #

resources: mem_mb=100, disk_mb=8620, tmpdir=/tmp/pop071.54835, partition=h24, qos=normal, mem_gb=100, time=120:00:00

规则定义 #

rule bwa_mem2_mem:
    input:
        R1 = "data/results/qc/{species}.{population}.{individual}_1.fq.gz",
        R2 = "data/results/qc/{species}.{population}.{individual}_2.fq.gz", 
        R1_unp = "data/results/qc/{species}.{population}.{individual}_1_unp.fq.gz",
        R2_unp = "data/results/qc/{species}.{population}.{individual}_2_unp.fq.gz",
        idx= "data/results/genome/genome",
        ref = "data/results/genome/genome.fa"
    output:
        bam = "data/results/mapped_reads/{species}.{population}.{individual}.bam",
    log:
        bwa ="logs/bwa_mem2/{species}.{population}.{individual}.log",
        sam ="logs/samtools_view/{species}.{population}.{individual}.log",
    benchmark:
        "benchmark/bwa_mem2_mem/{species}.{population}.{individual}.tsv",
    resources:
        time = parameters["bwa_mem2"]["time"],
        mem_gb = parameters["bwa_mem2"]["mem_gb"],        
    params:
        extra = parameters["bwa_mem2"]["extra"],
        tag = compose_rg_tag,
    threads:
        parameters["bwa_mem2"]["threads"],
    shell:
        "bwa-mem2 mem -t {threads} -R '{params.tag}' {params.extra} {input.idx} {input.R1} {input.R2} | "
        "samtools sort -l 9 -o {output.bam} --reference {input.ref} --output-fmt CRAM -@ {threads} /dev/stdin 2> {log.sam}"

配置文件 #

cluster:
  mkdir -p logs/{rule} && # change the log file to logs/slurm/{rule}
  sbatch
    --partition={resources.partition}
    --time={resources.time}
    --qos={resources.qos}
    --cpus-per-task={threads}
    --mem={resources.mem_gb}
    --job-name=smk-{rule}-{wildcards}
    --output=logs/{rule}/{rule}-{wildcards}-%j.out
    --parsable # Required to pass job IDs to scancel
default-resources:
  - partition=h24
  - qos=normal
  - mem_gb=100
  - time="04:00:00"
restart-times: 3
max-jobs-per-second: 10
max-status-checks-per-second: 1
local-cores: 1
latency-wait: 60
jobs: 100
keep-going: True
rerun-incomplete: True
printshellcmds: True
scheduler: greedy
use-conda: True # Required to run with local conda enviroment
cluster-status: status-sacct.sh # Required to monitor the status of the submitted jobs
cluster-cancel: scancel # Required to cancel the jobs with Ctrl + C 
cluster-cancel-nargs: 50

1. 明确重复内存资源的来源 #

mem_mb是Snakemake内置的默认资源（默认值100MB），主要用于本地调度器的内存限制；你自定义的mem_gb是给集群调度器（如Slurm）使用的资源参数。两者同时存在是因为Snakemake会自动注入默认资源，除非你明确覆盖。

2. 同步mem_mb与mem_gb数值 #

在配置文件的default-resources中添加动态规则，让mem_mb自动跟随mem_gb的数值，确保两者一致：

default-resources:
  - partition=h24
  - qos=normal
  - mem_gb=100
  - mem_mb=lambda wildcards, resources: resources.mem_gb * 1024
  - time="04:00:00"

这样所有规则的mem_mb会自动换算为mem_gb对应的MB数，不再出现100MB的默认值。

3. 确认集群调度仅使用mem_gb #

你的cluster配置已经正确使用--mem={resources.mem_gb}，这一步无需修改，集群只会按照mem_gb的数值申请内存，mem_mb不会干扰集群调度逻辑。

4. 优化本地运行速度 #

本地运行更快的核心原因是：默认100MB的mem_mb会让本地调度器误判规则内存需求，导致资源调度受限或等待。同步mem_mb与mem_gb后，本地调度器能识别规则实际需要的内存量，不会再限制运行速度。

5. 规则级别单独设置（可选） #

如果部分规则需要自定义mem_mb，可在规则的resources块中明确指定：

resources:
    time = parameters["bwa_mem2"]["time"],
    mem_gb = parameters["bwa_mem2"]["mem_gb"],
    mem_mb = parameters["bwa_mem2"]["mem_gb"] * 1024

内容的提问来源于stack exchange，提问作者andapo