another API try for expe.py

expe.py

@@ -1,36 +1,87 @@
-import subprocess
+import os
+import random
 import itertools
+import subprocess
+
+class Run:
+    def __init__(self, cmd_tpl):
+        self.cmd_tpl = cmd_tpl
+
+    def single(self, doe):
+        for params in doe:
+            cmd = self.cmd_tpl.format(**params)
+            subprocess.run(cmd, shell=True)
 
 
-def batchsplit(axe, njobs):
-    for i in range(0,len(axe),njobs):
-        yield axe[i:i+njobs]
-
-
-def batchrun(key, jobs, params, cmd_tpl):
-        procs = []
-        for job in jobs:
-            params[key] = job
-            cmd = cmd_tpl.format(**params)
+    def all(self, doe):
+        queue = []
+        for params in doe:
+            cmd = self.cmd_tpl.format(**params)
             p = subprocess.Popen(cmd, shell=True)
-            procs.append(p)
+            queue.append(p)
 
-        for job in procs:
-            job.wait()
+        for p in queue:
+            p.wait()
 
 
-def expe(axes):
-    for p in itertools.product(*[axes[k] for k in axes]):
-        params = {}
-        for i in range(len(axes)):
-            params[list(axes.keys())[i]] = p[i]
-        yield params
+    def batch(self, doe, batch_size = None):
+        if batch_size is None:
+            batch_size = os.cpu_count()
+
+        nb = 0
+        queue = []
+        for params in doe:
+            if nb >= batch_size:
+                for p in queue:
+                    p.wait()
+                queue.clear()
+                nb = 0
+            else:
+                cmd = self.cmd_tpl.format(**params)
+                p = subprocess.Popen(cmd, shell=True)
+                queue.append(p)
+                nb += 1
+        # in case batch_size = float("inf")
+        for p in queue:
+            p.wait()
+
+
+    def qsub(self, doe):
+        for params in doe:
+            cmd = self.cmd_tpl.format(**params)
+            print("TODO qsub",cmd)
+
+
+class Expe:
+    def __init__(self, **kwargs):
+        self.names = []
+        self.axes = []
+        self.static_params = kwargs
+
+    def __iter__(self):
+        for p in itertools.product( *(self.axes) ):
+            params = self.static_params
+            for i in range(len(self.names)):
+                params[self.names[i]] = p[i]
+            yield params
+
+    def forall(self, name, iters):
+        self.names.append(name)
+        self.axes.append(iters)
+        return self
+
+    def random(self, name, nb, rmin=0, rmax=1):
+        """Note: this intentionally produce the SAME random sequence for each product of axes."""
+        self.names.append(name)
+        def rand():
+            for i in range(nb):
+                yield random.random() * (rmax-rmin) + rmin
+        self.axes.append(rand())
+        return self
 
 
 
 if __name__ == "__main__":
-    import os
-    import subprocess
 
     const_args=" --nb-sensors 5 --sensor-range 0.2 --domain-width 50 --iters 10000"
     solvers = ["num_greedy","bit_greedy","num_rand","bit_rand"]
@@ -40,10 +91,16 @@ if __name__ == "__main__":
     if not os.path.exists(outdir):
         os.mkdir(outdir)
 
-    cmd_tpl = "python3 snp.py --no-plot --dir {outdir} --seed {{seed}} --solver {{solver}}".format(outdir=outdir)
-    for seed in range(nbruns):
-        for params in expe({"seed":[seed]}):
-            for by_four in batchsplit(solvers, 4):
-                batchrun("solver", by_four, params, cmd_tpl)
+    cmd = "echo \"--no-plot --dir {outdir} --seed {seed} --solver {solver}\""
+    # for seed in range(nbruns):
+    #     for params in expe({"seed":[seed]}):
+    #         for by_four in batchsplit(solvers, 4):
+    #             batchrun("solver", by_four, params, cmd_tpl)
 
 
+    doe = Expe(outdir=outdir).forall("seed",range(nbruns)).forall("solver",solvers)
+
+    Run(cmd).single(doe)
+    Run(cmd).all(doe)
+    Run(cmd).batch(doe)
+    Run(cmd).qsub(doe)