[autoscaler] Add an initial_workers option (#3530)

## What do these changes do?

    This option goes along with `min_workers`, and `max_workers`.  When the
    cluster is first brought up (or when it is refreshed with a subsequent
    `ray up`) this number of nodes will be started.
    
    It's a workaround for issues of scaling (see related issues) where it
    can take a long time (or forever in the case where the head node has
    `--num-cpus 0`) to scale up a cluster in response to increasing demand.


## Related issue number

Workaround for https://github.com/ray-project/ray/issues/3339 and https://github.com/ray-project/ray/issues/2106

test/autoscaler_test.py

@@ -98,6 +98,7 @@ SMALL_CLUSTER = {
     "cluster_name": "default",
     "min_workers": 2,
     "max_workers": 2,
+    "initial_workers": 0,
     "target_utilization_fraction": 0.8,
     "idle_timeout_minutes": 5,
     "provider": {
@@ -314,6 +315,27 @@ class AutoscalingTest(unittest.TestCase):
         autoscaler.update()
         self.waitForNodes(10)
 
+    def testInitialWorkers(self):
+        config = SMALL_CLUSTER.copy()
+        config["min_workers"] = 0
+        config["max_workers"] = 20
+        config["initial_workers"] = 10
+        config_path = self.write_config(config)
+        self.provider = MockProvider()
+        autoscaler = StandardAutoscaler(
+            config_path,
+            LoadMetrics(),
+            max_launch_batch=5,
+            max_concurrent_launches=5,
+            max_failures=0,
+            update_interval_s=0)
+        self.waitForNodes(0)
+        autoscaler.update()
+        self.waitForNodes(5)  # expected due to batch sizes and concurrency
+        autoscaler.update()
+        self.waitForNodes(10)
+        autoscaler.update()
+
     def testDelayedLaunch(self):
         config_path = self.write_config(SMALL_CLUSTER)
         self.provider = MockProvider()