def launch_experiment(script, run_slot, affinity_code, log_dir, variant,
run_ID, args):
"""Launches one learning run using ``subprocess.Popen()`` to call the
python script. Calls the script as:
``python {script} {slot_affinity_code} {log_dir} {run_ID} {*args}``
If ``affinity_code["all_cpus"]`` is provided, then the call is prepended
with ``tasket -c ..`` and the listed cpus (this is the most sure way to
keep the run limited to these CPU cores). Also saves the `variant` file.
Returns the process handle, which can be monitored.
"""
slot_affinity_code = prepend_run_slot(run_slot, affinity_code)
affinity = affinity_from_code(slot_affinity_code)
call_list = list()
if isinstance(affinity, dict) and affinity.get("all_cpus", False):
cpus = ",".join(str(c) for c in affinity["all_cpus"])
elif isinstance(affinity, list) and affinity[0].get("all_cpus", False):
cpus = ",".join(str(c) for aff in affinity for c in aff["all_cpus"])
else:
cpus = ()
if cpus:
call_list += ["taskset", "-c",
cpus] # PyTorch obeys better than just psutil.
call_list += ["python", script, slot_affinity_code, log_dir, str(run_ID)]
call_list += [str(a) for a in args]
save_variant(variant, log_dir)
print("\ncall string:\n", " ".join(call_list))
p = subprocess.Popen(call_list)
return p
def launch_experiment(
script,
run_slot,
affinity_code,
log_dir,
variant,
run_ID,
args,
python_executable=None,
set_egl_device=False,
):
"""Launches one learning run using ``subprocess.Popen()`` to call the
python script. Calls the script as:
``python {script} {slot_affinity_code} {log_dir} {run_ID} {*args}``
If ``affinity_code["all_cpus"]`` is provided, then the call is prepended
with ``tasket -c ..`` and the listed cpus (this is the most sure way to
keep the run limited to these CPU cores). Also saves the `variant` file.
Returns the process handle, which can be monitored.
Use ``set_egl_device=True`` to set an environment variable
``EGL_DEVICE_ID`` equal to the same value as the cuda index for the
algorithm. For example, can use with DMControl environment modified
to look for this environment variable when selecting a GPU for headless
rendering.
"""
slot_affinity_code = prepend_run_slot(run_slot, affinity_code)
affinity = affinity_from_code(slot_affinity_code)
call_list = list()
if isinstance(affinity, dict) and affinity.get("all_cpus", False):
cpus = ",".join(str(c) for c in affinity["all_cpus"])
elif isinstance(affinity, list) and affinity[0].get("all_cpus", False):
cpus = ",".join(str(c) for aff in affinity for c in aff["all_cpus"])
else:
cpus = ()
if cpus:
call_list += ["taskset", "-c",
cpus] # PyTorch obeys better than just psutil.
py = python_executable if python_executable else sys.executable or "python"
call_list += [py, script, slot_affinity_code, log_dir, str(run_ID)]
call_list += [str(a) for a in args]
save_variant(variant, log_dir)
print("\ncall string:\n", " ".join(call_list))
print(os.getcwd())
if set_egl_device and affinity.get("cuda_idx", None) is not None:
egl_device_id = str(affinity["cuda_idx"])
egl_env = os.environ.copy()
egl_env["EGL_DEVICE_ID"] = egl_device_id
print(f"Assigning EGL_DEVICE_ID={egl_device_id}")
p = subprocess.Popen(call_list, env=egl_env)
else:
p = subprocess.Popen(call_list)
return p
def launch_experiment(script, run_slot, affinity_code, log_dir, variant,
run_ID, args):
slot_affinity_code = prepend_run_slot(run_slot, affinity_code)
affinity = affinity_from_code(slot_affinity_code)
call_list = list()
if isinstance(affinity, dict) and affinity.get("all_cpus", False):
cpus = ",".join(str(c) for c in affinity["all_cpus"])
elif isinstance(affinity, list) and affinity[0].get("all_cpus", False):
cpus = ",".join(str(c) for aff in affinity for c in aff["all_cpus"])
else:
cpus = ()
if cpus:
call_list += ["taskset", "-c",
cpus] # PyTorch obeys better than just psutil.
call_list += ["python", script, slot_affinity_code, log_dir, str(run_ID)]
call_list += [str(a) for a in args]
save_variant(variant, log_dir)
print("\ncall string:\n", " ".join(call_list))
p = subprocess.Popen(call_list)
return p