Replace all instances of ray.readthedocs.io with ray.io (#7994)

python/ray/util/joblib/__init__.py

@@ -9,7 +9,7 @@ def register_ray():
     except ImportError:
         msg = ("To use the ray backend you must install ray."
                "Try running 'pip install ray'."
-               "See https://ray.readthedocs.io/en/latest/installation.html"
+               "See https://docs.ray.io/en/latest/installation.html"
                "for more information.")
         raise ImportError(msg)
 

python/ray/util/joblib/ray_backend.py

@@ -12,7 +12,7 @@ logger = logging.getLogger(__name__)
 
 class RayBackend(MultiprocessingBackend):
     """Ray backend uses ray, a system for scalable distributed computing.
-    More info about Ray is available here: https://ray.readthedocs.io.
+    More info about Ray is available here: https://docs.ray.io.
     """
 
     def configure(self,

python/ray/util/sgd/torch/examples/benchmarks/README.rst

@@ -3,7 +3,7 @@ Running benchmarks
 
 RaySGD provides comparable or better performance than other existing solutions for parallel or distributed training.
 
-You can run ``ray/python/ray/util/sgd/torch/examples/benchmarks/benchmark.py`` for benchmarking the RaySGD TorchTrainer implementation. To benchmark training on a multi-node multi-gpu cluster, you can use the `Ray Autoscaler <https://ray.readthedocs.io/en/latest/autoscaling.html#aws>`_.
+You can run ``ray/python/ray/util/sgd/torch/examples/benchmarks/benchmark.py`` for benchmarking the RaySGD TorchTrainer implementation. To benchmark training on a multi-node multi-gpu cluster, you can use the `Ray Autoscaler <https://docs.ray.io/en/latest/autoscaling.html#aws>`_.
 
 DISCLAIMER: RaySGD does not provide any custom communication primitives. If you see any performance issues, you may need to file them on the PyTorch github repository.