-
Notifications
You must be signed in to change notification settings - Fork 1
/
spawner.py
583 lines (503 loc) · 26.1 KB
/
spawner.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
import argparse
from copy import deepcopy
import os
import sys
import numpy as np
import subprocess
import yaml
from datetime import datetime
from helpers import logger
from helpers.misc_util import zipsame, boolean_flag
from helpers.experiment import uuid as create_uuid
ENV_BUNDLES = {
'mujoco': {
'debug': ['Hopper-v3'],
'idp': ['InvertedDoublePendulum-v2'],
'walker': ['Walker2d-v3'],
'eevee': ['InvertedPendulum-v2',
'InvertedDoublePendulum-v2'],
'jolteon': ['Hopper-v3',
'Walker2d-v3',
'HalfCheetah-v3'],
'flareon': ['InvertedDoublePendulum-v2',
'Ant-v3'],
'glaceon': ['Hopper-v3',
'Walker2d-v3',
'HalfCheetah-v3',
'Ant-v3'],
'humanoid': ['Humanoid-v3'],
'ant': ['Ant-v3'],
'suite': ['InvertedDoublePendulum-v2',
'Hopper-v3',
'Walker2d-v3',
'HalfCheetah-v3',
'Ant-v3'],
},
'dmc': {
'debug': ['Hopper-Hop-Feat-v0'],
'flareon': ['Hopper-Hop-Feat-v0',
'Walker-Run-Feat-v0'],
'glaceon': ['Hopper-Hop-Feat-v0',
'Cheetah-Run-Feat-v0',
'Walker-Run-Feat-v0'],
'stacker': ['Stacker-Stack_2-Feat-v0',
'Stacker-Stack_4-Feat-v0'],
'humanoid': ['Humanoid-Walk-Feat-v0',
'Humanoid-Run-Feat-v0'],
'cmu': ['Humanoid_CMU-Stand-Feat-v0',
'Humanoid_CMU-Run-Feat-v0'],
'quad': ['Quadruped-Walk-Feat-v0',
'Quadruped-Run-Feat-v0',
'Quadruped-Escape-Feat-v0',
'Quadruped-Fetch-Feat-v0'],
'dog': ['Dog-Run-Feat-v0',
'Dog-Fetch-Feat-v0'],
},
}
MEMORY = 16
class Spawner(object):
def __init__(self, args):
self.args = args
# Retrieve config from filesystem
self.config = yaml.safe_load(open(self.args.config))
# Check if we need expert demos
self.need_demos = self.config['meta']['algo'] == 'sam-dac'
if self.need_demos:
self.num_demos = [int(i) for i in self.args.num_demos]
else:
self.num_demos = [0] # arbitrary, only used for dim checking
# Assemble wandb project name
self.wandb_project = '-'.join([self.config['logging']['wandb_project'].upper(),
self.args.deployment.upper(),
datetime.now().strftime('%B')[0:3].upper() + f"{datetime.now().year}"])
# Define spawn type
self.type = 'sweep' if self.args.sweep else 'fixed'
# Define the needed memory in GB
self.memory = MEMORY
# Write out the boolean arguments (using the 'boolean_flag' function)
self.bool_args = ['cuda', 'render', 'record', 'layer_norm',
'prioritized_replay', 'ranked', 'unreal',
'n_step_returns', 'ret_norm', 'popart',
'clipped_double', 'targ_actor_smoothing', 'use_c51', 'use_qr',
'state_only', 'minimax_only', 'spectral_norm', 'grad_pen', 'one_sided_pen',
'wrap_absorb', 'd_batch_norm', 'historical_patching', 'monitor_mods',
'red_batch_norm', 'use_purl']
if 'slurm' in self.args.deployment:
# Translate intuitive 'caliber' into actual duration and partition on the Baobab cluster
calibers = dict(short='0-06:00:00',
long='0-12:00:00',
verylong='1-00:00:00',
veryverylong='2-00:00:00',
veryveryverylong='4-00:00:00')
self.duration = calibers[self.args.caliber] # intended KeyError trigger if invalid caliber
if 'verylong' in self.args.caliber:
if self.config['resources']['cuda']:
self.partition = 'public-gpu'
else:
self.partition = 'public-cpu'
else:
if self.config['resources']['cuda']:
self.partition = 'shared-gpu'
else:
self.partition = 'shared-cpu'
# Define the set of considered environments from the considered suite
self.envs = ENV_BUNDLES[self.config['meta']['benchmark']][self.args.env_bundle]
if self.need_demos:
# Create the list of demonstrations associated with the environments
demo_dir = os.environ['DEMO_DIR']
self.demos = {k: os.path.join(demo_dir, k) for k in self.envs}
def copy_and_add_seed(self, hpmap, seed):
hpmap_ = deepcopy(hpmap)
# Add the seed and edit the job uuid to only differ by the seed
hpmap_.update({'seed': seed})
# Enrich the uuid with extra information
try:
out = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'])
gitsha = "gitSHA_{}".format(out.strip().decode('ascii'))
except OSError:
pass
uuid = f"{hpmap['uuid']}.{gitsha}.{hpmap['env_id']}.{hpmap['algo']}_{self.args.num_workers}"
if self.need_demos:
uuid += f".demos{str(hpmap['num_demos']).zfill(3)}"
uuid += f".seed{str(seed).zfill(2)}"
hpmap_.update({'uuid': uuid})
return hpmap_
def copy_and_add_env(self, hpmap, env):
hpmap_ = deepcopy(hpmap)
# Add the env and demos
hpmap_.update({'env_id': env})
if self.need_demos:
hpmap_.update({'expert_path': self.demos[env]})
# Overwrite discount factor per environment
if env == 'Hopper-v3':
old_gamma = hpmap_['gamma']
new_gamma = 0.995
logger.info(f"overwrite discount for {env}: {old_gamma} -> {new_gamma}")
hpmap_.update({'gamma': 0.995})
return hpmap_
def copy_and_add_num_demos(self, hpmap, num_demos):
assert self.need_demos
hpmap_ = deepcopy(hpmap)
# Add the num of demos
hpmap_.update({'num_demos': num_demos})
return hpmap_
def get_hps(self):
"""Return a list of maps of hyperparameters"""
# Create a uuid to identify the current job
uuid = create_uuid()
# Assemble the hyperparameter map
if self.args.sweep:
# Random search
hpmap = {
'wandb_project': self.wandb_project,
'uuid': uuid,
'cuda': self.config['resources']['cuda'],
'render': False,
'record': self.config['logging'].get('record', False),
'task': self.config['meta']['task'],
'algo': self.config['meta']['algo'],
# Training
'num_timesteps': int(float(self.config.get('num_timesteps', 2e7))),
'training_steps_per_iter': self.config.get('training_steps_per_iter', 2),
'eval_steps_per_iter': self.config.get('eval_steps_per_iter', 10),
'eval_frequency': self.config.get('eval_frequency', 10),
# Model
'layer_norm': self.config['layer_norm'],
# Optimization
'actor_lr': float(np.random.choice([1e-4, 3e-4])),
'critic_lr': float(np.random.choice([1e-4, 3e-4])),
'lr_schedule': self.config['lr_schedule'],
'clip_norm': self.config['clip_norm'],
'wd_scale': float(np.random.choice([1e-4, 3e-4, 1e-3])),
# Algorithm
'rollout_len': np.random.choice([2, 5]),
'batch_size': np.random.choice([32, 64, 128]),
'gamma': np.random.choice([0.99, 0.995]),
'mem_size': np.random.choice([10000, 50000, 100000]),
'noise_type': np.random.choice(['"adaptive-param_0.2, normal_0.2"',
'"adaptive-param_0.2, ou_0.2"',
'"normal_0.2"',
'"ou_0.2"']),
'pn_adapt_frequency': self.config.get('pn_adapt_frequency', 50),
'polyak': np.random.choice([0.001, 0.005, 0.01]),
'targ_up_freq': np.random.choice([10, 1000]),
'n_step_returns': self.config.get('n_step_returns', False),
'lookahead': np.random.choice([5, 10, 20, 40, 60]),
'ret_norm': self.config.get('ret_norm', False),
'popart': self.config.get('popart', False),
# TD3
'clipped_double': self.config.get('clipped_double', False),
'targ_actor_smoothing': self.config.get('targ_actor_smoothing', False),
'td3_std': self.config.get('td3_std', 0.2),
'td3_c': self.config.get('td3_c', 0.5),
'actor_update_delay': np.random.choice([2, 3, 4]),
# Prioritized replay
'prioritized_replay': self.config.get('prioritized_replay', False),
'alpha': self.config.get('alpha', 0.3),
'beta': self.config.get('beta', 1.),
'ranked': self.config.get('ranked', False),
'unreal': self.config.get('unreal', False),
# Distributional RL
'use_c51': self.config.get('use_c51', False),
'use_qr': self.config.get('use_qr', False),
'c51_num_atoms': self.config.get('c51_num_atoms', 51),
'c51_vmin': self.config.get('c51_vmin', -10.),
'c51_vmax': self.config.get('c51_vmax', 10.),
'num_tau': np.random.choice([100, 200]),
# Adversarial imitation
'g_steps': self.config.get('g_steps', 3),
'd_steps': self.config.get('d_steps', 1),
'd_lr': float(self.config.get('d_lr', 1e-5)),
'state_only': self.config.get('state_only', True),
'minimax_only': self.config.get('minimax_only', True),
'ent_reg_scale': self.config.get('ent_reg_scale', 0.001),
'spectral_norm': self.config.get('spectral_norm', True),
'grad_pen': self.config.get('grad_pen', True),
'grad_pen_type': self.config.get('grad_pen_type', 'wgan'),
'grad_pen_targ': self.config.get('grad_pen_targ', 1.),
'grad_pen_scale': self.config.get('grad_pen_scale', 10.),
'one_sided_pen': self.config.get('one_sided_pen', True),
'historical_patching': self.config.get('historical_patching', True),
'fake_ls_type': np.random.choice(['"random-uniform_0.7_1.2"',
'"soft_labels_0.1"',
'"none"']),
'real_ls_type': np.random.choice(['"random-uniform_0.7_1.2"',
'"soft_labels_0.1"',
'"none"']),
'wrap_absorb': self.config.get('wrap_absorb', False),
'd_batch_norm': self.config.get('d_batch_norm', False),
'reward_type': self.config.get('reward_type', 'gail'),
'f_grad_pen_targ': self.config.get('f_grad_pen_targ', 9.0),
'monitor_mods': self.config.get('monitor_mods', False),
'red_epochs': self.config.get('red_epochs', 200),
'red_lr': self.config.get('red_lr', 5e-4),
'proportion_of_exp_per_red_update': self.config.get(
'proportion_of_exp_per_red_update', 1.),
'use_purl': self.config.get('use_purl', False),
'purl_eta': float(self.config.get('purl_eta', 0.25)),
}
else:
# No search, fixed hyper-parameters
hpmap = {
'wandb_project': self.wandb_project,
'uuid': uuid,
'cuda': self.config['resources']['cuda'],
'render': False,
'record': self.config['logging'].get('record', False),
'task': self.config['meta']['task'],
'algo': self.config['meta']['algo'],
# Training
'num_timesteps': int(float(self.config.get('num_timesteps', 2e7))),
'training_steps_per_iter': self.config.get('training_steps_per_iter', 2),
'eval_steps_per_iter': self.config.get('eval_steps_per_iter', 10),
'eval_frequency': self.config.get('eval_frequency', 10),
# Model
'layer_norm': self.config['layer_norm'],
# Optimization
'actor_lr': float(self.config.get('actor_lr', 3e-4)),
'critic_lr': float(self.config.get('critic_lr', 3e-4)),
'lr_schedule': self.config['lr_schedule'],
'clip_norm': self.config['clip_norm'],
'wd_scale': float(self.config.get('wd_scale', 3e-4)),
# Algorithm
'rollout_len': self.config.get('rollout_len', 2),
'batch_size': self.config.get('batch_size', 128),
'gamma': self.config.get('gamma', 0.99),
'mem_size': int(self.config.get('mem_size', 100000)),
'noise_type': self.config['noise_type'],
'pn_adapt_frequency': self.config.get('pn_adapt_frequency', 50),
'polyak': self.config.get('polyak', 0.005),
'targ_up_freq': self.config.get('targ_up_freq', 100),
'n_step_returns': self.config.get('n_step_returns', False),
'lookahead': self.config.get('lookahead', 10),
'ret_norm': self.config.get('ret_norm', False),
'popart': self.config.get('popart', False),
# TD3
'clipped_double': self.config.get('clipped_double', False),
'targ_actor_smoothing': self.config.get('targ_actor_smoothing', False),
'td3_std': self.config.get('td3_std', 0.2),
'td3_c': self.config.get('td3_c', 0.5),
'actor_update_delay': self.config.get('actor_update_delay', 2),
# Prioritized replay
'prioritized_replay': self.config.get('prioritized_replay', False),
'alpha': self.config.get('alpha', 0.3),
'beta': self.config.get('beta', 1.),
'ranked': self.config.get('ranked', False),
'unreal': self.config.get('unreal', False),
# Distributional RL
'use_c51': self.config.get('use_c51', False),
'use_qr': self.config.get('use_qr', False),
'c51_num_atoms': self.config.get('c51_num_atoms', 51),
'c51_vmin': self.config.get('c51_vmin', -10.),
'c51_vmax': self.config.get('c51_vmax', 10.),
'num_tau': self.config.get('num_tau', 200),
# Adversarial imitation
'g_steps': self.config.get('g_steps', 3),
'd_steps': self.config.get('d_steps', 1),
'd_lr': float(self.config.get('d_lr', 1e-5)),
'state_only': self.config.get('state_only', True),
'minimax_only': self.config.get('minimax_only', True),
'ent_reg_scale': self.config.get('ent_reg_scale', 0.001),
'spectral_norm': self.config.get('spectral_norm', True),
'grad_pen': self.config.get('grad_pen', True),
'grad_pen_type': self.config.get('grad_pen_type', 'wgan'),
'grad_pen_targ': self.config.get('grad_pen_targ', 1.),
'grad_pen_scale': self.config.get('grad_pen_scale', 10.),
'one_sided_pen': self.config.get('one_sided_pen', True),
'historical_patching': self.config.get('historical_patching', True),
'fake_ls_type': self.config.get('fake_ls_type', 'none'),
'real_ls_type': self.config.get('real_ls_type', 'random-uniform_0.7_1.2'),
'wrap_absorb': self.config.get('wrap_absorb', False),
'd_batch_norm': self.config.get('d_batch_norm', False),
'reward_type': self.config.get('reward_type', 'gail'),
'f_grad_pen_targ': self.config.get('f_grad_pen_targ', 9.0),
'monitor_mods': self.config.get('monitor_mods', False),
'red_epochs': self.config.get('red_epochs', 200),
'red_lr': self.config.get('red_lr', 5e-4),
'proportion_of_exp_per_red_update': self.config.get(
'proportion_of_exp_per_red_update', 1.),
'use_purl': self.config.get('use_purl', False),
'purl_eta': float(self.config.get('purl_eta', 0.25)),
}
# Duplicate for each environment
hpmaps = [self.copy_and_add_env(hpmap, env)
for env in self.envs]
if self.need_demos:
# Duplicate for each number of demos
hpmaps = [self.copy_and_add_num_demos(hpmap_, num_demos)
for hpmap_ in hpmaps
for num_demos in self.num_demos]
# Duplicate for each seed
hpmaps = [self.copy_and_add_seed(hpmap_, seed)
for hpmap_ in hpmaps
for seed in range(self.args.num_seeds)]
# Verify that the correct number of configs have been created
assert len(hpmaps) == self.args.num_seeds * len(self.envs) * len(self.num_demos)
return hpmaps
def unroll_options(self, hpmap):
"""Transform the dictionary of hyperparameters into a string of bash options"""
indent = 4 * ' ' # choice: indents are defined as 4 spaces
arguments = ""
for k, v in hpmap.items():
if k in self.bool_args:
if v is False:
argument = f"no-{k}"
else:
argument = f"{k}"
else:
argument = f"{k}={v}"
arguments += f"{indent}--{argument} \\\n"
return arguments
def create_job_str(self, name, command):
"""Build the batch script that launches a job"""
# Prepend python command with python binary path
command = os.path.join(os.environ['CONDA_PREFIX'], "bin", command)
if 'slurm' in self.args.deployment:
os.makedirs("./out", exist_ok=True)
# Set sbatch config
bash_script_str = ('#!/usr/bin/env bash\n\n')
bash_script_str += (f"#SBATCH --job-name={name}\n"
f"#SBATCH --partition={self.partition}\n"
f"#SBATCH --ntasks={self.args.num_workers}\n"
"#SBATCH --cpus-per-task=1\n"
f"#SBATCH --time={self.duration}\n"
f"#SBATCH --mem={self.memory}000\n"
"#SBATCH --output=./out/run_%j.out\n")
if self.args.deployment == 'slurm':
bash_script_str += '#SBATCH --constraint="V3|V4|V5|V6|V7"\n' # single quote to escape
if self.config['resources']['cuda']:
bash_script_str += f'#SBATCH --gres=gpu:"{self.args.num_workers}"\n' # single quote to escape
if self.args.deployment == 'slurm':
contraint = "COMPUTE_CAPABILITY_6_0|COMPUTE_CAPABILITY_6_1"
bash_script_str += f'#SBATCH --constraint="{contraint}"\n' # single quote to escape
bash_script_str += ('\n')
# Load modules
bash_script_str += ("module load GCC/8.3.0 OpenMPI/3.1.4\n")
if self.config['meta']['benchmark'] == 'dmc': # legacy comment: needed for dmc too
bash_script_str += ("module load Mesa/19.2.1\n")
if self.config['resources']['cuda']:
bash_script_str += ("module load CUDA/11.1.1\n")
bash_script_str += ('\n')
# Launch command
if self.args.deployment == 'slurm':
bash_script_str += (f"srun {command}")
else:
bash_script_str += (f"mpirun {command}")
elif self.args.deployment == 'tmux':
# Set header
bash_script_str = ("#!/usr/bin/env bash\n\n")
bash_script_str += (f"# job name: {name}\n\n")
# Launch command
bash_script_str += (f"mpiexec -n {self.args.num_workers} {command}")
else:
raise NotImplementedError("cluster selected is not covered.")
return bash_script_str[:-2] # remove the last `\` and `\n` tokens
def run(args):
"""Spawn jobs"""
if args.wandb_upgrade:
# Upgrade the wandb package
logger.info(">>>>>>>>>>>>>>>>>>>> Upgrading wandb pip package")
out = subprocess.check_output([sys.executable, '-m', 'pip', 'install', 'wandb', '--upgrade'])
logger.info(out.decode("utf-8"))
# Create a spawner object
spawner = Spawner(args)
# Create directory for spawned jobs
root = os.path.dirname(os.path.abspath(__file__))
spawn_dir = os.path.join(root, 'spawn')
os.makedirs(spawn_dir, exist_ok=True)
if args.deployment == 'tmux':
tmux_dir = os.path.join(root, 'tmux')
os.makedirs(tmux_dir, exist_ok=True)
# Get the hyperparameter set(s)
if args.sweep:
hpmaps_ = [spawner.get_hps()
for _ in range(spawner.config['num_trials'])]
# Flatten into a 1-dim list
hpmaps = [x for hpmap in hpmaps_ for x in hpmap]
else:
hpmaps = spawner.get_hps()
# Create associated task strings
commands = ["python main.py \\\n{}".format(spawner.unroll_options(hpmap)) for hpmap in hpmaps]
if not len(commands) == len(set(commands)):
# Terminate in case of duplicate experiment (extremely unlikely though)
raise ValueError("bad luck, there are dupes -> Try again (:")
# Create the job maps
names = [f"{spawner.type}.{hpmap['uuid']}" for i, hpmap in enumerate(hpmaps)]
# Finally get all the required job strings
jobs = [spawner.create_job_str(name, command)
for name, command in zipsame(names, commands)]
# Spawn the jobs
for i, (name, job) in enumerate(zipsame(names, jobs)):
logger.info(f"job#={i},name={name} -> ready to be deployed.")
if args.debug:
logger.info("config below.")
logger.info(job + "\n")
dirname = name.split('.')[1]
full_dirname = os.path.join(spawn_dir, dirname)
os.makedirs(full_dirname, exist_ok=True)
job_name = os.path.join(full_dirname, f"{name}.sh")
with open(job_name, 'w') as f:
f.write(job)
if args.deploy_now and not args.deployment == 'tmux':
# Spawn the job!
stdout = subprocess.run(["sbatch", job_name]).stdout
if args.debug:
logger.info(f"[STDOUT]\n{stdout}")
logger.info(f"job#={i},name={name} -> deployed on slurm.")
if args.deployment == 'tmux':
dir_ = hpmaps[0]['uuid'].split('.')[0] # arbitrarilly picked index 0
session_name = f"{spawner.type}-{str(args.num_seeds).zfill(2)}seeds-{dir_}"
yaml_content = {'session_name': session_name,
'windows': []}
if spawner.need_demos:
yaml_content.update({'environment': {'DEMO_DIR': os.environ['DEMO_DIR']}})
for i, name in enumerate(names):
executable = f"{name}.sh"
pane = {'shell_command': [f"source activate {args.conda_env}",
f"chmod u+x spawn/{dir_}/{executable}",
f"spawn/{dir_}/{executable}"]}
window = {'window_name': f"job{str(i).zfill(2)}",
'focus': False,
'panes': [pane]}
yaml_content['windows'].append(window)
logger.info(f"job#={i},name={name} -> will run in tmux, session={session_name},window={i}.")
# Dump the assembled tmux config into a yaml file
job_config = os.path.join(tmux_dir, f"{session_name}.yaml")
with open(job_config, "w") as f:
yaml.dump(yaml_content, f, default_flow_style=False)
if args.deploy_now:
# Spawn all the jobs in the tmux session!
stdout = subprocess.run(["tmuxp", "load", "-d", job_config]).stdout
if args.debug:
logger.info(f"[STDOUT]\n{stdout}")
logger.info(f"[{len(jobs)}] jobs are now running in tmux session '{session_name}'.")
else:
# Summarize the number of jobs spawned
logger.info(f"[{len(jobs)}] jobs were spawned.")
if __name__ == "__main__":
# Parse the arguments
parser = argparse.ArgumentParser(description="Job Spawner")
parser.add_argument('--config', type=str, default=None)
parser.add_argument('--conda_env', type=str, default=None)
parser.add_argument('--env_bundle', type=str, default=None)
parser.add_argument('--num_workers', type=int, default=None)
parser.add_argument('--deployment', type=str, choices=['tmux', 'slurm', 'slurm2'],
default='tmux', help='deploy how?')
parser.add_argument('--num_seeds', type=int, default=None)
parser.add_argument('--caliber', type=str, default=None)
boolean_flag(parser, 'deploy_now', default=True, help="deploy immediately?")
boolean_flag(parser, 'sweep', default=False, help="hp search?")
boolean_flag(parser, 'wandb_upgrade', default=True, help="upgrade wandb?")
parser.add_argument('--num_demos', '--list', nargs='+', type=str, default=None)
boolean_flag(parser, 'debug', default=False, help="toggle debug/verbose mode in spawner")
boolean_flag(parser, 'wandb_dryrun', default=True, help="toggle wandb offline mode")
parser.add_argument('--debug_lvl', type=int, default=0, help="set the debug level for the spawned runs")
args = parser.parse_args()
if args.wandb_dryrun:
# Run wandb in offline mode (does not sync with wandb servers in real time,
# use `wandb sync` later on the local directory in `wandb/` to sync to the wandb cloud hosted app)
os.environ["WANDB_MODE"] = "dryrun"
# Set the debug level for the spawned runs
os.environ["DEBUG_LVL"] = str(args.debug_lvl)
# Create (and optionally deploy) the jobs
run(args)