static void Main(string[] args)
{
// 1. allocate a new dataflow computation.
using (var computation = NewComputation.FromArgs(ref args))
{
if (args.Length != 5)
{
PrintHelp();
return;
}
Int32 procid = computation.Configuration.ProcessID;
Int32 thread_num = computation.Configuration.WorkerCount;
Int32 worker_num = computation.Configuration.Processes;
Int32 dimension = Int32.Parse(args[0]);
Int32 iteration_num = Int32.Parse(args[1]);
Int32 partition_num = Int32.Parse(args[2]);
double sample_num_m = Convert.ToDouble(args[3]);
Int64 spin_wait = Int64.Parse(args[4]);
Console.Out.WriteLine("dimension: " + dimension);
Console.Out.WriteLine("iteration_num: " + iteration_num);
Console.Out.WriteLine("partition_num: " + partition_num);
Console.Out.WriteLine("sample_num_m: " + sample_num_m);
Console.Out.WriteLine("spin_wait: " + spin_wait);
Console.Out.WriteLine("procid: " + procid);
Console.Out.WriteLine("worker_num: " + worker_num);
Console.Out.WriteLine("thread_num: " + thread_num);
Console.Out.Flush();
LogisticRegression lr =
new LogisticRegression(dimension,
iteration_num,
partition_num,
sample_num_m,
spin_wait,
procid,
worker_num,
thread_num);
Stream <SampleBatch, Epoch> samples = lr.GenerateSamples().AsNaiadStream(computation);
samples = samples.PartitionBy(s => (int)(s[0][0]));
var end_samples = samples.Iterate((lc, s) => lr.Advance(s), iteration_num, "LogisticRegression");
// var output = end_samples.Subscribe(x => {
// Console.Out.WriteLine("Final weight: " + PrintList(lr.weight_));
// Console.Out.Flush();
// });
Console.Out.WriteLine("Before Activate!");
Console.Out.Flush();
// start the computation, fixing the structure of the dataflow graph.
computation.Activate();
Console.Out.WriteLine("After Activate!");
Console.Out.Flush();
// block until all work is finished.
computation.Join();
Console.Out.WriteLine("After Join!");
double average_total = lr.total_times_.GetRange(truncate_index_, iteration_num - truncate_index_).Average();
double average_compute = lr.compute_times_.GetRange(truncate_index_, iteration_num - truncate_index_).Average();
double average_idle = average_total - average_compute;
Console.Out.WriteLine("*** Average for the last {0:D2} iterations: compute(ms): {1:F2} total(ms): {2:F2} (idle(ms): {3:F2})",
iteration_num - truncate_index_, 1000 * average_compute, 1000 * average_total, 1000 * average_idle);
Console.Out.WriteLine("Final Weight: " + PrintList(lr.weight_));
Console.Out.WriteLine("Samples Counts: " + PrintList(lr.sample_counter));
Console.Out.WriteLine("Reduce Level 1 Counts: " + PrintList(lr.reduce_l1_counter_));
Console.Out.WriteLine("Reduce Level 2 Counts: " + PrintList(lr.reduce_l2_counter_));
Console.Out.WriteLine("Sync Level 1 Counts: " + PrintList(lr.sync_l1_counter_));
Console.Out.WriteLine("Sync Level 2 Counts: " + PrintList(lr.sync_l2_counter_));
Console.Out.WriteLine("Sync Tags: " + PrintHashSet(lr.sync_tags_));
Console.Out.WriteLine("Reduce Tags: " + PrintHashSet(lr.reduce_tags_));
Console.Out.WriteLine("Gradient Tags: " + PrintHashSet(lr.gradient_tags_));
Console.Out.Flush();
}
}
static void Main(string[] args)
{
// 1. allocate a new dataflow computation.
using (var computation = NewComputation.FromArgs(ref args))
{
if (args.Length != 5)
{
PrintHelp();
return;
}
else if (args.Length == 1)
{
if (args[0] == "--help" || args[0] == "-h")
{
PrintHelp();
return;
}
}
Int32 procid = computation.Configuration.ProcessID;
Int32 dimension = Int32.Parse(args[0]);
Int32 iteration_num = Int32.Parse(args[1]);
Int64 partition_num = Int32.Parse(args[2]);
Int64 sample_num_m = Int64.Parse(args[3]);
Int64 worker_num = Int64.Parse(args[4]);
Console.Out.WriteLine("**NOTE: Worker num should be equal to core num!");
Console.Out.WriteLine("procid: " + procid);
Console.Out.WriteLine("dimension: " + dimension);
Console.Out.WriteLine("iteration_num: " + iteration_num);
Console.Out.WriteLine("partition_num: " + partition_num);
Console.Out.WriteLine("sample_num_m: " + sample_num_m);
Console.Out.WriteLine("worker_num (should be core num): " + worker_num);
Console.Out.Flush();
LogisticRegression lr =
new LogisticRegression(procid,
dimension,
iteration_num,
partition_num,
sample_num_m,
worker_num);
Stream <Sample, Epoch> samples = lr.GenerateSamples().AsNaiadStream(computation);
// partition the samples based on the first element.
samples = samples.PartitionBy(s => (int)(s[0]));
var end_samples = samples.Iterate((lc, s) => lr.Advance(s), iteration_num, "LogisticRegression");
var output = end_samples.Subscribe(x => {
Console.Out.WriteLine("Final weight: " + PrintList(lr.weight_));
Console.Out.Flush();
});
Console.Out.WriteLine("Before Activate!");
Console.Out.Flush();
// start the computation, fixing the structure of the dataflow graph.
computation.Activate();
Console.Out.WriteLine("After Activate!");
Console.Out.Flush();
// block until all work is finished.
computation.Join();
Console.Out.WriteLine("After Join!");
Console.Out.WriteLine("Counter 1 from procid: " + lr.procid_ + " " + PrintList(lr.counter_1_));
Console.Out.WriteLine("Counter 2 from procid: " + lr.procid_ + " " + PrintList(lr.counter_2_));
Console.Out.WriteLine("Counter 3 from procid: " + lr.procid_ + " " + PrintList(lr.counter_3_));
Console.Out.Flush();
}
}
