Off-memory processing

When the dimensionality of a model is large and/or the number of MCMC samples is large, the samples may not fit in memory. In some situations, it may be possible to compute the output in finite memory, as described in the online statistics documentation page. However not all situations admit sufficient statistics and in this case it is necessary to store samples to disk. We show here how to do so when pigeons is run on a single machine, but the interface is similar over MPI and described in the MPI sample processing documentation page.

Prepare the PT run with the disk recorder

Two options need to be enabled. First, checkpoint = true, which saves a snapshot at the end of each round in the directory results/all/[unique directory] and symlinked to results/latest. Second, the disk recorder:

using Pigeons

# example target: a 1000 dimensional target
high_d_target = Pigeons.toy_mvn_target(1000)

pt = pigeons(target = high_d_target,
                checkpoint = true,
                record = [disk])

PT("/home/runner/work/Pigeons.jl/Pigeons.jl/docs/build/results/all/2024-12-20-18-05-36-Q8ZePC77")

Accessing the disk samples

Use the function process_sample() which processes the samples one by one and passes it to a user-provided function. Here we will extract the first dimension of each 1000-dimensional vector:

# load the samples from disk one by one, keeping only the first dimension
first_dim_of_each = Vector{Float64}()
process_sample(pt) do chain, scan, sample # ordered as if we had an inner loop over scans
    # each sample here is a Vector{Float64} of length 1000
    # in general, it will is produced by extract_sample()
    push!(first_dim_of_each, sample[1])
end

using Plots
plotlyjs()
myplot = Plots.plot(first_dim_of_each)
Plots.savefig(myplot, "first_dim_of_each.html");

Internal organization of the samples

This section can be skipped.

The samples are produced in compressed zip folders, one for each replica having visited the target:

readdir("$(pt.exec_folder)/round=10/samples")

2-element Vector{String}:
 "replica=6.jls.zip"
 "replica=7.jls.zip"

In the above example we see that only replicas 6 and 7 visited the target. Each zip file contains serialized .jl files. This output organization is used to support concurrent and distributed processing.

Internally, two passes are made, a first one to index the samples which are shuffled across many files. Then they are visited in the correct order and passed to the processing function.