runtime-by-region.md

Runtime by region profiling for make_examples

Starting in Deepvariant v1.1, make_examples has an optional parameter called --runtime_by_region, which enables make_examples to output the runtime for each region, broken up into the different stages of processing each region: "get reads", "find candidates", "make pileup images", and "write outputs". These stats, along with the number of reads, candidates, and examples processed for each region, are output into a tab-separated value (TSV) file, or one TSV file per task if the examples are sharded.

This TSV file can then be visualized using the runtime_by_region_vis script, creating a visual report.

Example reports for typical runs:

• WGS
• WES
• PacBio
• Hybrid

How to enable runtime profiling during a DeepVariant run

When using the one-step run_deepvariant script, supply a --logging_dir directory. For example, when following the quick start, including --logging_dir=/output/logs would produce the following runtime profiling output file:

/output/logs/make_examples_runtime_by_region/make_examples_runtime-00000-of-00001.tsv

If you are using make_examples itself without the run_deepvariant script, supply make_examples with --runtime_by_region= a filename (.tsv). If the examples are sharded then the runtime profile should be sharded into the same number of shards, e.g. when using examples@64 then runtimes could be runtimes@64.tsv.

Creating the visual runtime report

Use the runtime_by_region_vis script to create a visual report of the make_examples runtime by region data.

BIN_VERSION="1.1.0" # Only available in v1.1+.
docker run \
  -v "INPUT_DIR":"/input" \
  -v "OUTPUT_DIR:/output" \
  google/deepvariant:"${BIN_VERSION}" \
  /opt/deepvariant/bin/runtime_by_region_vis  \
    --input=/output/logs/make_examples_runtime_by_region/make_examples_runtime-00000-of-00001.tsv \
    --output=/output/logs/make_examples_runtime_by_region/make_examples_runtime_vis.html \
    --title="Quick start runtime profiling"

Interpreting the runtime report

Where the data comes from

make_examples, the first stage of DeepVariant, starts from a BAM file and corresponding reference FASTA file. One 1000 bp region at a time (set by --partition_size), make_examples will:

1. Get reads: Query the bam files for all the reads in the region. Optionally (--realign_reads) do a local assembly of the reads and realign the reads to the resulting haplotype graph.

2. Find candidates: Catalogue all the putative alternate alleles for all those reads, and compare the accumulated evidence for each alt against thresholds (see --vsc_min* parameters for the thresholds).

3. Make pileup images: Represent the reads as a pileup image tensor. When --alt_aligned_pileup is enabled, those alignments are included in this step.

4. Write output: Write the examples composed of candidates and pileup images out to a tfrecord file.

This is the top of one example file (tab-delimited but shown nicely spaced here for readability):

region              get reads  find candidates  make pileup images  write outputs  num reads  num candidates  num examples
chr1:1-1000         0.025      0.016            0.0                 0.0            0          0               0
chr1:64001-65000    0.032      0.044            0.027               0.001          25         1               1
chr1:128001-129000  0.059      0.047            0.081               0.01           59         2               2
chr1:192001-193000  0.058      0.045            0.261               0.078          56         13              13
...

The runtimes for the four stages are all captured in seconds.

Interpreting the charts

All the charts are built from the same types of data as shown above. Here "task" will refer to the runtimes captured when producing one shard of examples. For example, if there are 64 shards, task 1 is the task that produced examples-00001-of-00064.tfrecord.gz.

1. "Overall runtime by stage": Total runtime across all regions and all tasks.
2. "Pareto curve for each task": Each task is treated separately here. For the Pareto curve, the regions are sorted by longest total runtime (sum of the four stages) and ordered from left to right, longest runtime to shortest runtime. These are shown as a percentage of all regions. The cumulative runtime is shown on the y-axis, as a percentage of the total runtime for the given task. If all regions had the same runtime, the curve would be a straight diagonal line. The extent to which the curve bends to the upper left corner shows how much some regions take disproportionately longer than others. Hover the cursor over the lines to see the exact percentages.
3. "Total runtime for each task": Each point is a task. Hover over each point to see the runtime calculated into hours, minutes, and seconds. Drag a rectangle around some of the tasks to see them highlighted in the Pareto curve. Often the tasks with longer runtimes in the chart will be the same tasks with Pareto curves leaning to the upper left, indicating that for tasks than run longer than others, the cause is with a subset of the regions not with an overall slowdown of all regions.
4. "Stage runtimes for each task": A histogram of how long each stage takes for the different tasks. Often the make pileup images stage will show more variability here than other stages.
5. "Top runtime regions" and "Median runtime regions": This shows some individual regions to give more context for some of the trends seen in other charts. Pay attention especially to the differences between the y-axis limits in these two charts. The long-running regions are often taking hundreds of times longer than median regions, with the runtime also taken up by different stages.
6. "The longest-running regions that produced no examples": This profiles some individual regions that yielded zero output examples. Also look at the subtitle to see what percentage of the total runtime is taken up by processing these zero-example regions.
7. "Runtime by stage for ...": When there are more than 5000 regions, there will be two charts here, one for the bottom 99% of regions and one for the top 100 regions (both by total runtime). If fewer than 5000 regions, there will only be one chart showing all the regions. This is similar to the "Stage runtimes for each task" except that regions are shown individually here instead of being combined into tasks. This shows the spread of runtimes across regions for the different stages.
8. "Trends for ...": This is in one or two sets of charts by the same logic as the "Runtime by stage" charts. This shows a grid of charts intersecting counts of reads, candidates, and examples (rows) with the runtime for the four stages (columns) in seconds. It is common that some of these runtimes will correlate nicely with the counts. For example, the write outputs runtime is closely tied to the number of examples, which is not surprising.