How can I execute a series of workflow steps on many simulations?
Demonstrate how to use signac-flow to define workflow steps and their associated pre-conditions and post-conditions.
Execute the workflow to randomize and compress all state points in the data space.
import math import hoomd import signac
The Introducing HOOMD-blue tutorial employs distinct workflow steps that must be performed in sequence to complete the self-assembly study at a given state point. These are initialization, randomization, compression, equilibration, and analysis. The previous section in this tutorial initialized every state point in the data space. This section will randomize and compress them and the next section will equilibrate them. Analysis can also be implemented as a workflow step, but this is left as an exercise for the reader.
Use signac-flow to define these workflow steps as Python functions and execute them in the proper sequence on all state points in the data space.
Define a function that creates a Simulation object based on the signac job:
def create_simulation(job): cpu = hoomd.device.CPU() # Set the simulation seed from the statepoint. sim = hoomd.Simulation(device=cpu, seed=job.statepoint.seed) mc = hoomd.hpmc.integrate.ConvexPolyhedron() mc.shape['octahedron'] = dict(vertices=[ (-0.5, 0, 0), (0.5, 0, 0), (0, -0.5, 0), (0, 0.5, 0), (0, 0, -0.5), (0, 0, 0.5), ]) sim.operations.integrator = mc return sim
This is the code from Introducing HOOMD-blue, adapted to use the seed for the current signac job with
Subclass FlowProject by convention.
class Project(flow.FlowProject): pass
Define a function that executes the randomization step:
@Project.operation # Workflow step. @Project.pre.true('initialized') # Pre-condition in job document. @Project.post.true('randomized') # Post-condition in job document. def randomize(job): sim = create_simulation(job) # Read `lattice.gsd` from the signac job's directory. sim.create_state_from_gsd(filename=job.fn('lattice.gsd')) sim.run(10e3) # Write `random.gsd` to the signac job's directory. hoomd.write.GSD.write(state=sim.state, mode='xb', filename=job.fn('random.gsd')) # Set the 'randomized' to satisfy the post-condition. job.document['randomized'] = True
In signac-flow’s terminology, a project operation is a complete workflow step that modifies the signac job’s state. Recall that the similarly named HOOMD-blue Operation is a class that acts on the state of the simulation at defined time steps. This tutorial uses the term workflow step wherever possible to avoid ambiguity.
@Project.operation is a decorator that declares the randomize function is a workflow step.
@Project.post define pre-conditions and post-conditions for this step, which return a boolean to indicate whether a workflow step is ready to start (pre-condition) or complete (post-condition). In this block, both conditions are evaluated by
post.true which examine the job document and check whether the item with the given key
True. Use pre- and post-conditions to define the sequence in which the workflow steps will execute. Here, the pre-condition checking
'initialized' is satisfied for those signac jobs that were initialized in the previous section which set
job.document['initialize'] = True.
The body of the function creates the Simulation object using the
create_simulation method from above, completes the randomization as in the Introducing HOOMD-blue tutorial, and sets the
'randomized' item in the job document to
True. The function writes
randomized.gsd to the signac job’s assigned directory using the
Similarly define a function that executes the compression step:
@Project.operation @Project.pre.after(randomize) # Execute after randomize completes. @Project.post.true('compressed_step') def compress(job): sim = create_simulation(job) # Read `random.gsd` from the signac job directory. sim.create_state_from_gsd(filename=job.fn('random.gsd')) a = math.sqrt(2) / 2 V_particle = 1 / 3 * math.sqrt(2) * a**3 initial_box = sim.state.box final_box = hoomd.Box.from_box(initial_box) # Set the final box volume to the volume fraction for this signac job. final_box.volume = (sim.state.N_particles * V_particle / job.statepoint.volume_fraction) compress = hoomd.hpmc.update.QuickCompress( trigger=hoomd.trigger.Periodic(10), target_box=final_box) sim.operations.updaters.append(compress) periodic = hoomd.trigger.Periodic(10) tune = hoomd.hpmc.tune.MoveSize.scale_solver(moves=['a', 'd'], target=0.2, trigger=periodic, max_translation_move=0.2, max_rotation_move=0.2) sim.operations.tuners.append(tune) while not compress.complete and sim.timestep < 1e6: sim.run(1000) if not compress.complete: raise RuntimeError("Compression failed to complete") # Write `compressed.gsd` to the job document. hoomd.write.GSD.write(state=sim.state, mode='xb', filename=job.fn('compressed.gsd')) # Set 'compressed step' in the signac job document. job.document['compressed_step'] = sim.timestep
This workflow step executes after the randomize step completes using the
pre.after(randomize) pre-condition. The body of the function contains the code from the Introducing HOOMD-blue tutorial, changed to use the volume fraction for the current signac job with
job.statepoint.volume_fraction and to read and write files from the signac job’s directory with
The compress operation sets the
compressed_step item in the job document and uses that to evaluate the post-condition. The next section of the tutorial will use the value of
Run the workflow
Now that you have defined the workflow steps, check the status of the workflow:
project = Project() project.print_status(overview=False, detailed=True, parameters=['volume_fraction'])
Detailed View: job id operation volume_fraction labels -------------------------------- ------------- ----------------- -------- 59363805e6f46a715bc154b38dffc4e4 randomize [U] 0.6 972b10bd6b308f65f0bc3a06db58cf9d randomize [U] 0.4 c1a59a95a0e8b4526b28cf12aa0a689e randomize [U] 0.5 [U]:unknown [R]:registered [I]:inactive [S]:submitted [H]:held [Q]:queued [A]:active [E]:error
Each signac job is ready to execute randomize, the first step in the workflow. Run it:
Every signac job directory in the data space now has a
random.gsd file produced by randomize:
workspace/59363805e6f46a715bc154b38dffc4e4: lattice.gsd signac_job_document.json random.gsd signac_statepoint.json workspace/972b10bd6b308f65f0bc3a06db58cf9d: lattice.gsd signac_job_document.json random.gsd signac_statepoint.json workspace/c1a59a95a0e8b4526b28cf12aa0a689e: lattice.gsd signac_job_document.json random.gsd signac_statepoint.json
Now, the status shows that the compress step is ready:
project.print_status(overview=False, detailed=True, parameters=['volume_fraction'])
Detailed View: job id operation volume_fraction labels -------------------------------- ------------ ----------------- -------- 59363805e6f46a715bc154b38dffc4e4 compress [U] 0.6 972b10bd6b308f65f0bc3a06db58cf9d compress [U] 0.4 c1a59a95a0e8b4526b28cf12aa0a689e compress [U] 0.5 [U]:unknown [R]:registered [I]:inactive [S]:submitted [H]:held [Q]:queued [A]:active [E]:error
Every signac job directory in the data space now has a
compressed.gsd file produced by compress:
workspace/59363805e6f46a715bc154b38dffc4e4: compressed.gsd random.gsd signac_statepoint.json lattice.gsd signac_job_document.json workspace/972b10bd6b308f65f0bc3a06db58cf9d: compressed.gsd random.gsd signac_statepoint.json lattice.gsd signac_job_document.json workspace/c1a59a95a0e8b4526b28cf12aa0a689e: compressed.gsd random.gsd signac_statepoint.json lattice.gsd signac_job_document.json
In this section of the tutorial, you defined the workflow steps to randomize and compress the initial configuration using signac-flow, along with the pre- and post-conditions needed to sequence the steps. Then you executed the workflow steps on all state points in the dataset. The directory for each simulation now contains
compressed.gsd and is ready for equilibration at the target volume fraction.
The next section in this tutorial teaches you how to write a workflow step that can continue itself and complete over several submissions.
This tutorial only teaches the basics of signac-flow. Read the signac-flow documentation to learn more.