From e25d1595487ade2b5fd9ccd47113c89b84b33b73 Mon Sep 17 00:00:00 2001 From: Kiuk Chung Date: Mon, 26 Apr 2021 16:03:26 -0700 Subject: [PATCH] Move torchelastic docs *.rst (#56811) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/56811 Moves docs sphinx `*.rst` files from the torchelastic repository to torch. Note: only moves the rst files the next step is to link it to the main pytorch `index.rst` and write new `examples.rst` Reviewed By: H-Huang Differential Revision: D27974751 fbshipit-source-id: 47a32f060b0cfb624a00afe94191b4e86e4eb813 --- docs/Makefile | 2 +- docs/source/agent.rst | 61 --------------- docs/source/agent_diagram.jpg | Bin 154004 -> 0 bytes docs/source/conf.py | 4 +- docs/source/customization.rst | 118 ------------------------------ docs/source/distributed.rst | 11 --- docs/source/errors.rst | 17 ----- docs/source/etcd_rdzv_diagram.png | Bin 435037 -> 0 bytes docs/source/events.rst | 27 ------- docs/source/index.rst | 49 ++----------- docs/source/kubernetes.rst | 5 -- docs/source/metrics.rst | 31 -------- docs/source/multiprocessing.rst | 24 ------ docs/source/quickstart.rst | 50 ------------- docs/source/rendezvous.rst | 57 --------------- docs/source/runtime.rst | 18 ----- docs/source/timer.rst | 41 ----------- docs/source/train_script.rst | 46 ------------ 18 files changed, 9 insertions(+), 552 deletions(-) delete mode 100644 docs/source/agent.rst delete mode 100644 docs/source/agent_diagram.jpg delete mode 100644 docs/source/customization.rst delete mode 100644 docs/source/distributed.rst delete mode 100644 docs/source/errors.rst delete mode 100644 docs/source/etcd_rdzv_diagram.png delete mode 100644 docs/source/events.rst delete mode 100644 docs/source/kubernetes.rst delete mode 100644 docs/source/metrics.rst delete mode 100644 docs/source/multiprocessing.rst delete mode 100644 docs/source/quickstart.rst delete mode 100644 docs/source/rendezvous.rst delete mode 100644 docs/source/runtime.rst delete mode 100644 docs/source/timer.rst delete mode 100644 docs/source/train_script.rst diff --git a/docs/Makefile b/docs/Makefile index e520c7e1..c893a632 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -10,7 +10,7 @@ SPHINXBUILD = sphinx-build SPHINXPROJ = torchelastic SOURCEDIR = source BUILDDIR = build -VERSION := $(shell python -c "import torchelastic; print(torchelastic.__version__)") +VERSION := "0.2.3.dev0" # Put it first so that "make" without argument is like "make help". help: diff --git a/docs/source/agent.rst b/docs/source/agent.rst deleted file mode 100644 index 26620293..00000000 --- a/docs/source/agent.rst +++ /dev/null @@ -1,61 +0,0 @@ -Elastic Agent -============== - -.. automodule:: torchelastic.agent -.. currentmodule:: torchelastic.agent - -Server --------- - -.. automodule:: torchelastic.agent.server - -Below is a diagram of an agent that manages a local group of workers. - -.. image:: agent_diagram.jpg - -Concepts --------- - -This section describes the high-level classes and concepts that -are relevant to understanding the role of the ``agent`` in torchelastic. - -.. currentmodule:: torchelastic.agent.server - -.. autoclass:: ElasticAgent - :members: - -.. autoclass:: WorkerSpec - :members: - -.. autoclass:: WorkerState - :members: - -.. autoclass:: Worker - :members: - -.. autoclass:: WorkerGroup - :members: - -Implementations -------------------- - -Below are the agent implementations provided by torchelastic. - -.. currentmodule:: torchelastic.agent.server.local_elastic_agent -.. autoclass:: LocalElasticAgent - - -Extending the Agent ---------------------- - -To extend the agent you can implement ```ElasticAgent`` directly, however -we recommend you extend ``SimpleElasticAgent`` instead, which provides -most of the scaffolding and leaves you with a few specific abstract methods -to implement. - -.. currentmodule:: torchelastic.agent.server -.. autoclass:: SimpleElasticAgent - :members: - :private-members: - -.. autoclass:: torchelastic.agent.server.api.RunResult diff --git a/docs/source/agent_diagram.jpg b/docs/source/agent_diagram.jpg deleted file mode 100644 index 79fad343280362d542b53118bc686cdeb1cb8bcd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 154004 zcmeFZdsI`|mN&W)5fLFg1Qdj*sHli3OOO&V(V`HM!l)<+2r0`4uY!~%NR*I`ydogx 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torchelastic.events.get_logging_handler - -Event Objects ------------------ - -.. currentmodule:: torchelastic.events.api - -.. autoclass:: Event - -.. autoclass:: EventSource - -.. autoclass:: EventMetadataValue - - - diff --git a/docs/source/index.rst b/docs/source/index.rst index 428d0ded..47af3d2f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,49 +3,14 @@ TorchElastic ================== -Makes distributed PyTorch fault-tolerant and elastic. - -Get Started ---------------- -.. toctree:: - :maxdepth: 1 - :caption: Usage - - quickstart - train_script - examples - -Documentation ---------------- - -.. toctree:: - :maxdepth: 1 - :caption: API - - distributed - agent - multiprocessing - errors - rendezvous - timer - metrics - events - -.. toctree:: - :maxdepth: 1 - :caption: Advanced - - customization - -.. toctree:: - :maxdepth: 1 - :caption: Plugins - - kubernetes - runtime - +.. important:: TorchElastic has been upstreamed to PyTorch (``torch.distributed.elastic``) and will be + included as part of the next PyTorch release (``torch-1.9``). In the meanwhile please use + `pytorch-nightly `_ or + `torchelastic-0.2.2 `_. For documentation please refer to our + `v0.2.2 docs `_ for now. + Training Session Manager(TSM) ---------------- +------------------------------ .. toctree:: :maxdepth: 1 :caption: Training Session Manager(TSM) diff --git a/docs/source/kubernetes.rst b/docs/source/kubernetes.rst deleted file mode 100644 index 55a051b5..00000000 --- a/docs/source/kubernetes.rst +++ /dev/null @@ -1,5 +0,0 @@ -TorchElastic Kubernetes -========================== - -Please refer to our github's `Kubernetes README `_ -for more information on Elastic Job Controller and custom resource definition. diff --git a/docs/source/metrics.rst b/docs/source/metrics.rst deleted file mode 100644 index c4a3d4ec..00000000 --- a/docs/source/metrics.rst +++ /dev/null @@ -1,31 +0,0 @@ -.. _metrics-api: - -Metrics -========= - -.. automodule:: torchelastic.metrics - - -Metric Handlers ------------------ - -.. currentmodule:: torchelastic.metrics.api - -Below are the metric handlers that come included with torchelastic. - -.. autoclass:: MetricHandler - -.. autoclass:: ConsoleMetricHandler - -.. autoclass:: NullMetricHandler - - - -Methods ------------- - -.. autofunction:: torchelastic.metrics.configure - -.. autofunction:: torchelastic.metrics.prof - -.. autofunction:: torchelastic.metrics.put_metric diff --git a/docs/source/multiprocessing.rst b/docs/source/multiprocessing.rst deleted file mode 100644 index fc5866c0..00000000 --- a/docs/source/multiprocessing.rst +++ /dev/null @@ -1,24 +0,0 @@ -:github_url: https://github.com/pytorch/elastic - -Multiprocessing -================ - -.. automodule:: torch.distributed.elastic.multiprocessing - -Starting Multiple Workers ---------------------------- - -.. autofunction:: torch.distributed.elastic.multiprocessing.start_processes - -Process Context ----------------- - -.. currentmodule:: torch.distributed.elastic.multiprocessing.api - -.. autoclass:: PContext - -.. autoclass:: MultiprocessContext - -.. autoclass:: SubprocessContext - -.. autoclass:: RunProcsResult diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst deleted file mode 100644 index 81f8c20d..00000000 --- a/docs/source/quickstart.rst +++ /dev/null @@ -1,50 +0,0 @@ -Quickstart -=========== - -.. code-block:: bash - - pip install torchelastic - - # start a single-node etcd server on ONE host - etcd --enable-v2 - --listen-client-urls http://0.0.0.0:2379,http://127.0.0.1:4001 - --advertise-client-urls PUBLIC_HOSTNAME:2379 - -To launch a **fault-tolerant** job, run the following on all nodes. - -.. code-block:: bash - - python -m torchelastic.distributed.launch - --nnodes=NUM_NODES - --nproc_per_node=TRAINERS_PER_NODE - --rdzv_id=JOB_ID - --rdzv_backend=etcd - --rdzv_endpoint=ETCD_HOST:ETCD_PORT - YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) - - -To launch an **elastic** job, run the following on at least ``MIN_SIZE`` nodes -and at most ``MAX_SIZE`` nodes. - -.. code-block:: bash - - python -m torchelastic.distributed.launch - --nnodes=MIN_SIZE:MAX_SIZE - --nproc_per_node=TRAINERS_PER_NODE - --rdzv_id=JOB_ID - --rdzv_backend=etcd - --rdzv_endpoint=ETCD_HOST:ETCD_PORT - YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...) - - -.. note:: The `--standalone` option can be passed to launch a single node job with - a sidecar rendezvous server. You don’t have to pass —rdzv_id, —rdzv_endpoint, - and —rdzv_backend when the —standalone option is used - - -.. note:: Learn more about writing your distributed training script - `here `_. - -If ``torchelastic.distributed.launch`` does not meet your requirements -you may use our APIs directly for more powerful customization. Start by -taking a look at the `elastic agent `_ API). diff --git a/docs/source/rendezvous.rst b/docs/source/rendezvous.rst deleted file mode 100644 index 8eb99a52..00000000 --- a/docs/source/rendezvous.rst +++ /dev/null @@ -1,57 +0,0 @@ -.. _rendezvous-api: - -Rendezvous -========== - -.. automodule:: torchelastic.rendezvous - -Below is a state diagram describing how rendezvous works. - -.. image:: etcd_rdzv_diagram.png - - -Handler --------------------- - -.. currentmodule:: torchelastic.rendezvous - -.. autoclass:: RendezvousHandler - :members: - -Exceptions -------------- -.. autoclass:: RendezvousClosedException -.. autoclass:: RendezvousTimeoutException -.. autoclass:: RendezvousNonRetryableError - -Implmentations ----------------- - -Etcd Rendezvous -**************** - -.. currentmodule:: torchelastic.rendezvous.etcd_rendezvous - -.. autoclass:: EtcdRendezvousHandler - -.. autoclass:: EtcdRendezvous - :members: - -.. autoclass:: EtcdStore - :members: - -Etcd Server -************* - -The ``EtcdServer`` is a convenience class that makes it easy for you to -start and stop an etcd server on a subprocess. This is useful for testing -or single-node (multi-worker) deployments where manually setting up an -etcd server on the side is cumbersome. - -.. warning:: For production and multi-node deployments please consider - properly deploying a highly available etcd server as this is - the single point of failure for your distributed jobs. - -.. currentmodule:: torchelastic.rendezvous.etcd_server - -.. autoclass:: EtcdServer diff --git a/docs/source/runtime.rst b/docs/source/runtime.rst deleted file mode 100644 index e32d7c50..00000000 --- a/docs/source/runtime.rst +++ /dev/null @@ -1,18 +0,0 @@ -Cloud Provider Support (Deprecated) -==================================== - -Sections below are instructions on running torchelastic on Kubernetes and -various cloud providers. - -.. warning:: The instructions below will only work on torchelastic ``0.1.0rc``. - We highly encourage you to use our kubernetes - `elastic job controller `_ and use a kubernetes - cluster setup on your preferred cloud provider. - -AWS (EC2) ------------------- -Refer to our github's `ec2 README `_ - -Azure ------- -Refer to our github's `azure README `_ diff --git a/docs/source/timer.rst b/docs/source/timer.rst deleted file mode 100644 index ca5b251c..00000000 --- a/docs/source/timer.rst +++ /dev/null @@ -1,41 +0,0 @@ -Expiration Timers -================== - -.. automodule:: torchelastic.timer -.. currentmodule:: torchelastic.timer - -Client Methods ---------------- -.. autofunction:: torchelastic.timer.configure - -.. autofunction:: torchelastic.timer.expires - -Server/Client Implementations ------------------------------- -Below are the timer server and client pairs that are provided by torchelastic. - -.. note:: Timer server and clients always have to be implemented and used - in pairs since there is a messaging protocol between the server - and client. - -.. autoclass:: LocalTimerServer - -.. autoclass:: LocalTimerClient - -Writing a custom timer server/client --------------------------------------- - -To write your own timer server and client extend the -``torchelastic.timer.TimerServer`` for the server and -``torchelastic.timer.TimerClient`` for the client. The -``TimerRequest`` object is used to pass messages between -the server and client. - -.. autoclass:: TimerRequest - :members: - -.. autoclass:: TimerServer - :members: - -.. autoclass:: TimerClient - :members: diff --git a/docs/source/train_script.rst b/docs/source/train_script.rst deleted file mode 100644 index 8134a648..00000000 --- a/docs/source/train_script.rst +++ /dev/null @@ -1,46 +0,0 @@ -Train script -------------- - -If your train script works with ``torch.distributed.launch`` it will continue -working with ``torchelastic.distributed.launch`` with these differences: - -1. No need to manually pass ``RANK``, ``WORLD_SIZE``, - ``MASTER_ADDR``, and ``MASTER_PORT``. - -2. ``rdzv_backend`` and ``rdzv_endpoint`` must be provided. For most users - this will be set to ``etcd`` (see `rendezvous `_). - -3. Make sure you have a ``load_checkpoint(path)`` and - ``save_checkpoint(path)`` logic in your script. When workers fail - we restart all the workers with the same program arguments so you will - lose progress up to the most recent checkpoint - (see `elastic launch `_). - -4. ``use_env`` flag has been removed. If you were parsing local rank by parsing - the ``--local_rank`` option, you need to get the local rank from the - environment variable ``LOCAL_RANK`` (e.g. ``os.environ["LOCAL_RANK"]``). - -Below is an expository example of a training script that checkpoints on each -epoch, hence the worst-case progress lost on failure is one full epoch worth -of training. - -.. code-block:: python - - def main(): - args = parse_args(sys.argv[1:]) - state = load_checkpoint(args.checkpoint_path) - initialize(state) - - # torchelastic.distributed.launch ensure that this will work - # by exporting all the env vars needed to initialize the process group - torch.distributed.init_process_group(backend=args.backend) - - for i in range(state.epoch, state.total_num_epochs) - for batch in iter(state.dataset) - train(batch, state.model) - - state.epoch += 1 - save_checkpoint(state) - -For concrete examples of torchelastic-compliant train scripts, visit -our `examples `_ page.