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https://github.com/vale981/ray
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c8f207f746
This PR makes a number of major overhauls to the Ray core docs:
Add a key-concepts section for {Tasks, Actors, Objects, Placement Groups, Env Deps}.
Re-org the user guide to align with key concepts.
Rewrite the walkthrough to link to mini-walkthroughs in the key concept sections.
Minor tweaks and additional transition material.
108 lines
3.2 KiB
ReStructuredText
108 lines
3.2 KiB
ReStructuredText
.. _resource-requirements:
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Specifying Required Resources
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=============================
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Oftentimes, you may want to specify a task's resource requirements (for example
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one task may require a GPU). Ray will automatically
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detect the available GPUs and CPUs on the machine. However, you can override
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this default behavior by passing in specific resources.
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.. tabbed:: Python
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.. code-block:: python
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ray.init(num_cpus=8, num_gpus=4, resources={'Custom': 2})
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.. tabbed:: Java
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Set Java system property: ``-Dray.resources=CPU:8,GPU:4,Custom:2``.
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.. tabbed:: C++
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.. code-block:: c++
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RayConfig config;
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config.num_cpus = 8;
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config.num_gpus = 4;
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config.resources = {{"Custom", 2}};
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ray::Init(config);
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Ray also allows specifying a task's resources requirements (e.g., CPU, GPU, and custom resources).
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The task will only run on a machine if there are enough resources
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available to execute the task.
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.. tabbed:: Python
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.. code-block:: python
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# Specify required resources.
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@ray.remote(num_cpus=4, num_gpus=2)
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def my_function():
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return 1
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.. tabbed:: Java
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.. code-block:: java
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// Specify required resources.
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Ray.task(MyRayApp::myFunction).setResource("CPU", 1.0).setResource("GPU", 4.0).remote();
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.. tabbed:: C++
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.. code-block:: c++
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// Specify required resources.
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ray::Task(MyFunction).SetResource("CPU", 1.0).SetResource("GPU", 4.0).Remote();
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.. note::
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* If you do not specify any resources, the default is 1 CPU resource and
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no other resources.
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* If specifying CPUs, Ray does not enforce isolation (i.e., your task is
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expected to honor its request).
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* If specifying GPUs, Ray does provide isolation in forms of visible devices
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(setting the environment variable ``CUDA_VISIBLE_DEVICES``), but it is the
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task's responsibility to actually use the GPUs (e.g., through a deep
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learning framework like TensorFlow or PyTorch).
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The resource requirements of a task have implications for the Ray's scheduling
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concurrency. In particular, the sum of the resource requirements of all of the
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concurrently executing tasks on a given node cannot exceed the node's total
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resources.
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Below are more examples of resource specifications:
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.. tabbed:: Python
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.. code-block:: python
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# Ray also supports fractional resource requirements.
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@ray.remote(num_gpus=0.5)
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def h():
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return 1
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# Ray support custom resources too.
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@ray.remote(resources={'Custom': 1})
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def f():
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return 1
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.. tabbed:: Java
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.. code-block:: java
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// Ray aslo supports fractional and custom resources.
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Ray.task(MyRayApp::myFunction).setResource("GPU", 0.5).setResource("Custom", 1.0).remote();
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.. tabbed:: C++
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.. code-block:: c++
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// Ray aslo supports fractional and custom resources.
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ray::Task(MyFunction).SetResource("GPU", 0.5).SetResource("Custom", 1.0).Remote();
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.. tip::
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Besides compute resources, you can also specify an environment for a task to run in,
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which can include Python packages, local files, environment variables, and more---see :ref:`Runtime Environments <runtime-environments>` for details.
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