Autodetect the number of GPUs when starting Ray. (#1293)

* autodetect

* Wed Dec  6 12:46:52 PST 2017

* Wed Dec  6 12:47:54 PST 2017

* Move GPU autodetection into services.py.

* Fix capitalization of Nvidia.

* Update documentation.

doc/source/api.rst

@@ -32,8 +32,8 @@ If there are GPUs available on the machine, you should specify this with the
 
   ray.init(num_cpus=20, num_gpus=2)
 
-By default, Ray will use ``psutil.cpu_count()`` to determine the number of CPUs,
-and by default the number of GPUs will be zero.
+By default, Ray will use ``psutil.cpu_count()`` to determine the number of CPUs.
+Ray will also attempt to automatically determine the number of GPUs.
 
 Instead of thinking about the number of "worker" processes on each node, we
 prefer to think in terms of the quantities of CPU and GPU resources on each

doc/source/resources.rst

@@ -33,7 +33,7 @@ through ``ray.init``, do the following.
 
 If the number of CPUs is unspecified, Ray will automatically determine the
 number by running ``psutil.cpu_count()``. If the number of GPUs is unspecified,
-Ray will default to 0 GPUs.
+Ray will attempt to automatically detect the number of GPUs.
 
 Specifying a task's CPU and GPU requirements
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

python/ray/services.py

@@ -279,6 +279,20 @@ def wait_for_redis_to_start(redis_ip_address, redis_port, num_retries=5):
                         "configured properly.")
 
 
+def _autodetect_num_gpus():
+    """Attempt to detect the number of GPUs on this machine.
+
+    TODO(rkn): This currently assumes Nvidia GPUs and Linux.
+
+    Returns:
+        The number of GPUs if any were detected, otherwise 0.
+    """
+    proc_gpus_path = "/proc/driver/nvidia/gpus"
+    if os.path.isdir(proc_gpus_path):
+        return len(os.listdir(proc_gpus_path))
+    return 0
+
+
 def _compute_version_info():
     """Compute the versions of Python, cloudpickle, pyarrow, and Ray.
 
@@ -679,6 +693,9 @@ def start_local_scheduler(redis_address,
         # By default, use the number of hardware execution threads for the
         # number of cores.
         resources["CPU"] = psutil.cpu_count()
+    if "GPU" not in resources:
+        # Try to automatically detect the number of GPUs.
+        resources["GPU"] = _autodetect_num_gpus()
     print("Starting local scheduler with the following resources: {}."
           .format(resources))
     local_scheduler_name, p = ray.local_scheduler.start_local_scheduler(

python/ray/worker.py

@@ -1224,7 +1224,9 @@ def _init(address_info=None,
         num_cpus (int): Number of cpus the user wishes all local schedulers to
             be configured with.
         num_gpus (int): Number of gpus the user wishes all local schedulers to
-            be configured with.
+            be configured with. If unspecified, Ray will attempt to autodetect
+            the number of GPUs available on the node (note that autodetection
+            currently only works for Nvidia GPUs).
         resources: A dictionary mapping resource names to the quantity of that
             resource available.
         num_redis_shards: The number of Redis shards to start in addition to