Add local HPO cli execution (--local --args)

clearml/cli/hpo/__main__.py

@@ -60,7 +60,7 @@ def setup_parser(parser):
         default=[],
         help="List of parameters to search optimal value of. Each parameter must be a JSON having the following format:\n"
         "{\n"
-        '  "name": str,  # Name of the paramter you want to optimize\n'
+        '  "name": str,  # Name of the parameter you want to optimize\n'
         '  "type":  Union["LogUniformParameterRange", "UniformParameterRange", "UniformIntegerParameterRange", "DiscreteParameterRange"],\n'
         "  # other fields depending on type\n"
         "}\n"
@@ -93,7 +93,7 @@ def setup_parser(parser):
         "Each parameter must be a JSON having the following format:\n"
         "{\n"
         '  "name": str,  # name of the parameter to override\n'
-        '  "value": Any  # value of the paramter being overriden\n'
+        '  "value": Any  # value of the parameter being overriden\n'
         "}",
     )
     parser.add_argument(
@@ -138,7 +138,8 @@ def setup_parser(parser):
         help="The maximum compute time in minutes. When time limit is exceeded, all jobs aborted",
     )
     parser.add_argument(
-        "--pool-period-min", type=float, default=None, help="The time between two consecutive pools (minutes)"
+        "--pool-period-min", type=float, default=0.2,
+        help="The time between two consecutive pools (minutes) default 0.2 min"
     )
     parser.add_argument(
         "--total-max-jobs",
@@ -173,6 +174,22 @@ def setup_parser(parser):
         help="Maximum execution time per single job in minutes. When time limit is exceeded job is aborted."
         " Default: no time limit",
     )
+    parser.add_argument(
+        "--max-number-of-concurrent-tasks",
+        type=int,
+        default=None,
+        help="The maximum number of concurrent Tasks (experiments) running at the same time.",
+    )
+    parser.add_argument(
+        '--args', default=None, nargs='*',
+        help='Arguments to pass to the remote execution, list of <argument>=<value> strings.'
+             'Currently only argparse/click/hydra/fire arguments are supported. '
+             'Example: --args lr=0.003 batch_size=64')
+    parser.add_argument(
+        "--local", action='store_true', default=False,
+        help="If set, run the experiments locally, Notice no new python environment will be created, "
+             "--script must point to a local file entrypoint and all arguments must be passed with --args",
+    )
 
 
 def eval_params_search(params_search, params_override):
@@ -239,6 +256,7 @@ def build_opt_kwargs(args):
         "total_max_jobs",
         "min_iteration_per_job",
         "max_iteration_per_job",
+        "max_number_of_concurrent_tasks"
     ]
     for arg_name in optional_arg_names:
         arg_val = getattr(args, arg_name)
@@ -268,24 +286,35 @@ def cli():
     )
     task_id = args.task_id
     if not task_id:
-        if args.queue is None:
-            print("No queue supplied to run the script from")
-            exit(1)
         create_populate = CreateAndPopulate(script=args.script)
+        create_populate.update_task_args(args.args)
+        print('Creating new task')
         create_populate.create_task()
-        Task.enqueue(create_populate.task, queue_name=args.queue)
+        # update Task args
+        create_populate.update_task_args(args.args)
         task_id = create_populate.get_id()
+
     optimizer = HyperParameterOptimizer(
         base_task_id=task_id,
+        execution_queue=args.queue,
         hyper_parameters=eval_params_search(args.params_search, args.params_override),
         objective_metric_title=args.objective_metric_title,
         objective_metric_series=args.objective_metric_series,
         objective_metric_sign=args.objective_metric_sign,
         optimizer_class=eval_optimizer_class(args.optimizer_class),
         save_top_k_tasks_only=args.save_top_k_tasks_only,
-        **build_opt_kwargs(args),
+        **build_opt_kwargs(args)
     )
-    optimizer.start()
+
+    # make sure we sample every 30sec
+    optimizer.set_report_period(0.5)
+
+    print("Starting HPO process:")
+    if args.local:
+        optimizer.start_locally()
+    else:
+        optimizer.start()
+
     optimizer.wait()
     print("Optimization completed!")
     top_experiments_cnt = 10