Update strategy flag in docs (#10000)

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>

docs/source/advanced/advanced_gpu.rst

@@ -71,9 +71,9 @@ To use Sharded Training, you need to first install FairScale using the command b
 .. code-block:: python
 
     # train using Sharded DDP
-    trainer = Trainer(plugins="ddp_sharded")
+    trainer = Trainer(strategy="ddp_sharded")
 
-Sharded Training can work across all DDP variants by adding the additional ``--plugins ddp_sharded`` flag.
+Sharded Training can work across all DDP variants by adding the additional ``--strategy ddp_sharded`` flag.
 
 Internally we re-initialize your optimizers and shard them across your machines and processes. We handle all communication using PyTorch distributed, so no code changes are required.
 
@@ -156,7 +156,7 @@ Below is an example of using both ``wrap`` and ``auto_wrap`` to create your mode
 
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="fsdp", precision=16)
+    trainer = Trainer(gpus=4, strategy="fsdp", precision=16)
     trainer.fit(model)
 
     trainer.test()
@@ -248,7 +248,7 @@ It is recommended to skip Stage 1 and use Stage 2, which comes with larger memor
     from pytorch_lightning import Trainer
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_1", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_1", precision=16)
     trainer.fit(model)
 
 
@@ -265,7 +265,7 @@ As a result, benefits can also be seen on a single GPU. Do note that the default
     from pytorch_lightning import Trainer
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_2", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_2", precision=16)
     trainer.fit(model)
 
 .. code-block:: bash
@@ -286,7 +286,7 @@ Below we show an example of running `ZeRO-Offload <https://www.deepspeed.ai/tuto
     from pytorch_lightning.plugins import DeepSpeedPlugin
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_2_offload", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_2_offload", precision=16)
     trainer.fit(model)
 
 
@@ -307,7 +307,7 @@ You can also modify the ZeRO-Offload parameters via the plugin as below.
     model = MyModel()
     trainer = Trainer(
         gpus=4,
-        plugins=DeepSpeedPlugin(offload_optimizer=True, allgather_bucket_size=5e8, reduce_bucket_size=5e8),
+        strategy=DeepSpeedPlugin(offload_optimizer=True, allgather_bucket_size=5e8, reduce_bucket_size=5e8),
         precision=16,
     )
     trainer.fit(model)
@@ -340,7 +340,7 @@ For even more speed benefit, DeepSpeed offers an optimized CPU version of ADAM c
 
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_2_offload", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_2_offload", precision=16)
     trainer.fit(model)
 
 
@@ -383,7 +383,7 @@ Also please have a look at our :ref:`deepspeed-zero-stage-3-tips` which contains
 
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3", precision=16)
     trainer.fit(model)
 
     trainer.test()
@@ -403,7 +403,7 @@ You can also use the Lightning Trainer to run predict or evaluate with DeepSpeed
 
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3", precision=16)
     trainer.test(ckpt_path="my_saved_deepspeed_checkpoint.ckpt")
 
 
@@ -438,7 +438,7 @@ This reduces the time taken to initialize very large models, as well as ensure w
 
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3", precision=16)
     trainer.fit(model)
 
     trainer.test()
@@ -463,14 +463,14 @@ DeepSpeed ZeRO Stage 3 Offloads optimizer state, gradients to the host CPU to re
 
     # Enable CPU Offloading
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3_offload", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3_offload", precision=16)
     trainer.fit(model)
 
     # Enable CPU Offloading, and offload parameters to CPU
     model = MyModel()
     trainer = Trainer(
         gpus=4,
-        plugins=DeepSpeedPlugin(
+        strategy=DeepSpeedPlugin(
             stage=3,
             offload_optimizer=True,
             offload_parameters=True,
@@ -492,14 +492,14 @@ Additionally, DeepSpeed supports offloading to NVMe drives for even larger model
 
     # Enable CPU Offloading
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3_offload", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3_offload", precision=16)
     trainer.fit(model)
 
     # Enable CPU Offloading, and offload parameters to CPU
     model = MyModel()
     trainer = Trainer(
         gpus=4,
-        plugins=DeepSpeedPlugin(
+        strategy=DeepSpeedPlugin(
             stage=3,
             offload_optimizer=True,
             offload_parameters=True,
@@ -576,12 +576,12 @@ This saves memory when training larger models, however requires using a checkpoi
     model = MyModel()
 
 
-    trainer = Trainer(gpus=4, plugins="deepspeed_stage_3_offload", precision=16)
+    trainer = Trainer(gpus=4, strategy="deepspeed_stage_3_offload", precision=16)
 
     # Enable CPU Activation Checkpointing
     trainer = Trainer(
         gpus=4,
-        plugins=DeepSpeedPlugin(
+        strategy=DeepSpeedPlugin(
             stage=3,
             offload_optimizer=True,  # Enable CPU Offloading
             cpu_checkpointing=True,  # (Optional) offload activations to CPU
@@ -670,7 +670,7 @@ In some cases you may want to define your own DeepSpeed Config, to access all pa
     }
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins=DeepSpeedPlugin(deepspeed_config), precision=16)
+    trainer = Trainer(gpus=4, strategy=DeepSpeedPlugin(deepspeed_config), precision=16)
     trainer.fit(model)
 
 
@@ -682,7 +682,7 @@ We support taking the config as a json formatted file:
     from pytorch_lightning.plugins import DeepSpeedPlugin
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins=DeepSpeedPlugin("/path/to/deepspeed_config.json"), precision=16)
+    trainer = Trainer(gpus=4, strategy=DeepSpeedPlugin("/path/to/deepspeed_config.json"), precision=16)
     trainer.fit(model)
 
 
@@ -717,7 +717,7 @@ This can reduce peak memory usage and throughput as saved memory will be equal t
     from pytorch_lightning.plugins import DDPPlugin
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins=DDPPlugin(gradient_as_bucket_view=True))
+    trainer = Trainer(gpus=4, strategy=DDPPlugin(gradient_as_bucket_view=True))
     trainer.fit(model)
 
 DDP Communication Hooks
@@ -740,7 +740,7 @@ Enable `FP16 Compress Hook for multi-node throughput improvement <https://pytorc
     )
 
     model = MyModel()
-    trainer = Trainer(gpus=4, plugins=DDPPlugin(ddp_comm_hook=default.fp16_compress_hook))
+    trainer = Trainer(gpus=4, strategy=DDPPlugin(ddp_comm_hook=default.fp16_compress_hook))
     trainer.fit(model)
 
 Enable `PowerSGD for multi-node throughput improvement <https://pytorch.org/docs/stable/ddp_comm_hooks.html#powersgd-communication-hook>`__:
@@ -758,7 +758,7 @@ Enable `PowerSGD for multi-node throughput improvement <https://pytorch.org/docs
     model = MyModel()
     trainer = Trainer(
         gpus=4,
-        plugins=DDPPlugin(
+        strategy=DDPPlugin(
             ddp_comm_state=powerSGD.PowerSGDState(
                 process_group=None,
                 matrix_approximation_rank=1,
@@ -787,7 +787,7 @@ Combine hooks for accumulated benefit:
     model = MyModel()
     trainer = Trainer(
         gpus=4,
-        plugins=DDPPlugin(
+        strategy=DDPPlugin(
             ddp_comm_state=powerSGD.PowerSGDState(
                 process_group=None,
                 matrix_approximation_rank=1,

docs/source/advanced/ipu.rst

@@ -83,7 +83,7 @@ IPUs provide further optimizations to speed up training. By using the ``IPUPlugi
     from pytorch_lightning.plugins import IPUPlugin
 
     model = MyLightningModule()
-    trainer = pl.Trainer(ipus=8, plugins=IPUPlugin(device_iterations=32))
+    trainer = pl.Trainer(ipus=8, strategy=IPUPlugin(device_iterations=32))
     trainer.fit(model)
 
 Note that by default we return the last device iteration loss. You can override this by passing in your own ``poptorch.Options`` and setting the AnchorMode as described in the `PopTorch documentation <https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/reference.html#poptorch.Options.anchorMode>`__.
@@ -102,7 +102,7 @@ Note that by default we return the last device iteration loss. You can override
     training_opts.anchorMode(poptorch.AnchorMode.All)
     training_opts.deviceIterations(32)
 
-    trainer = Trainer(ipus=8, plugins=IPUPlugin(inference_opts=inference_opts, training_opts=training_opts))
+    trainer = Trainer(ipus=8, strategy=IPUPlugin(inference_opts=inference_opts, training_opts=training_opts))
     trainer.fit(model)
 
 You can also override all options by passing the ``poptorch.Options`` to the plugin. See `PopTorch options documentation <https://docs.graphcore.ai/projects/poptorch-user-guide/en/latest/batching.html>`__ for more information.
@@ -124,7 +124,7 @@ Lightning supports dumping all reports to a directory to open using the tool.
     from pytorch_lightning.plugins import IPUPlugin
 
     model = MyLightningModule()
-    trainer = pl.Trainer(ipus=8, plugins=IPUPlugin(autoreport_dir="report_dir/"))
+    trainer = pl.Trainer(ipus=8, strategy=IPUPlugin(autoreport_dir="report_dir/"))
     trainer.fit(model)
 
 This will dump all reports to ``report_dir/`` which can then be opened using the Graph Analyser Tool, see `Opening Reports <https://docs.graphcore.ai/projects/graphcore-popvision-user-guide/en/latest/graph/graph.html#opening-reports>`__.
@@ -174,7 +174,7 @@ Below is an example using the block annotation in a LightningModule.
 
 
     model = MyLightningModule()
-    trainer = pl.Trainer(ipus=8, plugins=IPUPlugin(device_iterations=20))
+    trainer = pl.Trainer(ipus=8, strategy=IPUPlugin(device_iterations=20))
     trainer.fit(model)
 
 
@@ -217,7 +217,7 @@ You can also use the block context manager within the forward function, or any o
 
 
     model = MyLightningModule()
-    trainer = pl.Trainer(ipus=8, plugins=IPUPlugin(device_iterations=20))
+    trainer = pl.Trainer(ipus=8, strategy=IPUPlugin(device_iterations=20))
     trainer.fit(model)
 
 

docs/source/advanced/multi_gpu.rst

@@ -253,11 +253,11 @@ Distributed modes
 -----------------
 Lightning allows multiple ways of training
 
-- Data Parallel (``accelerator='dp'``) (multiple-gpus, 1 machine)
-- DistributedDataParallel (``accelerator='ddp'``) (multiple-gpus across many machines (python script based)).
-- DistributedDataParallel (``accelerator='ddp_spawn'``) (multiple-gpus across many machines (spawn based)).
-- DistributedDataParallel 2 (``accelerator='ddp2'``) (DP in a machine, DDP across machines).
-- Horovod (``accelerator='horovod'``) (multi-machine, multi-gpu, configured at runtime)
+- Data Parallel (``strategy='dp'``) (multiple-gpus, 1 machine)
+- DistributedDataParallel (``strategy='ddp'``) (multiple-gpus across many machines (python script based)).
+- DistributedDataParallel (``strategy='ddp_spawn'``) (multiple-gpus across many machines (spawn based)).
+- DistributedDataParallel 2 (``strategy='ddp2'``) (DP in a machine, DDP across machines).
+- Horovod (``strategy='horovod'``) (multi-machine, multi-gpu, configured at runtime)
 - TPUs (``tpu_cores=8|x``) (tpu or TPU pod)
 
 .. note::
@@ -287,7 +287,7 @@ after which the root node will aggregate the results.
     :skipif: torch.cuda.device_count() < 2
 
     # train on 2 GPUs (using DP mode)
-    trainer = Trainer(gpus=2, accelerator="dp")
+    trainer = Trainer(gpus=2, strategy="dp")
 
 Distributed Data Parallel
 ^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -308,10 +308,10 @@ Distributed Data Parallel
 .. code-block:: python
 
     # train on 8 GPUs (same machine (ie: node))
-    trainer = Trainer(gpus=8, accelerator="ddp")
+    trainer = Trainer(gpus=8, strategy="ddp")
 
     # train on 32 GPUs (4 nodes)
-    trainer = Trainer(gpus=8, accelerator="ddp", num_nodes=4)
+    trainer = Trainer(gpus=8, strategy="ddp", num_nodes=4)
 
 This Lightning implementation of DDP calls your script under the hood multiple times with the correct environment
 variables:
@@ -356,7 +356,7 @@ In  this case, we can use DDP2 which behaves like DP in a machine and DDP across
 .. code-block:: python
 
     # train on 32 GPUs (4 nodes)
-    trainer = Trainer(gpus=8, accelerator="ddp2", num_nodes=4)
+    trainer = Trainer(gpus=8, strategy="ddp2", num_nodes=4)
 
 Distributed Data Parallel Spawn
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -374,7 +374,7 @@ project module) you can use the following method:
 .. code-block:: python
 
     # train on 8 GPUs (same machine (ie: node))
-    trainer = Trainer(gpus=8, accelerator="ddp_spawn")
+    trainer = Trainer(gpus=8, strategy="ddp_spawn")
 
 We STRONGLY discourage this use because it has limitations (due to Python and PyTorch):
 
@@ -446,10 +446,10 @@ Horovod can be configured in the training script to run with any number of GPUs
 .. code-block:: python
 
     # train Horovod on GPU (number of GPUs / machines provided on command-line)
-    trainer = Trainer(accelerator="horovod", gpus=1)
+    trainer = Trainer(strategy="horovod", gpus=1)
 
     # train Horovod on CPU (number of processes / machines provided on command-line)
-    trainer = Trainer(accelerator="horovod")
+    trainer = Trainer(strategy="horovod")
 
 When starting the training job, the driver application will then be used to specify the total
 number of worker processes:
@@ -583,11 +583,11 @@ Below are the possible configurations we support.
 +-------+---------+----+-----+--------+------------------------------------------------------------+
 | Y     |         |    |     | Y      | `Trainer(gpus=1, precision=16)`                            |
 +-------+---------+----+-----+--------+------------------------------------------------------------+
-|       | Y       | Y  |     |        | `Trainer(gpus=k, accelerator='dp')`                        |
+|       | Y       | Y  |     |        | `Trainer(gpus=k, strategy='dp')`                           |
 +-------+---------+----+-----+--------+------------------------------------------------------------+
-|       | Y       |    | Y   |        | `Trainer(gpus=k, accelerator='ddp')`                       |
+|       | Y       |    | Y   |        | `Trainer(gpus=k, strategy='ddp')`                          |
 +-------+---------+----+-----+--------+------------------------------------------------------------+
-|       | Y       |    | Y   | Y      | `Trainer(gpus=k, accelerator='ddp', precision=16)`         |
+|       | Y       |    | Y   | Y      | `Trainer(gpus=k, strategy='ddp', precision=16)`            |
 +-------+---------+----+-----+--------+------------------------------------------------------------+
 
 
@@ -616,29 +616,29 @@ In DDP, DDP_SPAWN, Deepspeed, DDP_SHARDED, or Horovod your effective batch size
 .. code-block:: python
 
     # effective batch size = 7 * 8
-    Trainer(gpus=8, accelerator="ddp")
-    Trainer(gpus=8, accelerator="ddp_spawn")
-    Trainer(gpus=8, accelerator="ddp_sharded")
-    Trainer(gpus=8, accelerator="horovod")
+    Trainer(gpus=8, strategy="ddp")
+    Trainer(gpus=8, strategy="ddp_spawn")
+    Trainer(gpus=8, strategy="ddp_sharded")
+    Trainer(gpus=8, strategy="horovod")
 
     # effective batch size = 7 * 8 * 10
-    Trainer(gpus=8, num_nodes=10, accelerator="ddp")
-    Trainer(gpus=8, num_nodes=10, accelerator="ddp_spawn")
-    Trainer(gpus=8, num_nodes=10, accelerator="ddp_sharded")
-    Trainer(gpus=8, num_nodes=10, accelerator="horovod")
+    Trainer(gpus=8, num_nodes=10, strategy="ddp")
+    Trainer(gpus=8, num_nodes=10, strategy="ddp_spawn")
+    Trainer(gpus=8, num_nodes=10, strategy="ddp_sharded")
+    Trainer(gpus=8, num_nodes=10, strategy="horovod")
 
 In DDP2 or DP, your effective batch size will be 7 * num_nodes.
 The reason is that the full batch is visible to all GPUs on the node when using DDP2.
 
 .. code-block:: python
 
     # effective batch size = 7
-    Trainer(gpus=8, accelerator="ddp2")
-    Trainer(gpus=8, accelerator="dp")
+    Trainer(gpus=8, strategy="ddp2")
+    Trainer(gpus=8, strategy="dp")
 
     # effective batch size = 7 * 10
-    Trainer(gpus=8, num_nodes=10, accelerator="ddp2")
-    Trainer(gpus=8, accelerator="dp")
+    Trainer(gpus=8, num_nodes=10, strategy="ddp2")
+    Trainer(gpus=8, strategy="dp")
 
 
 .. note:: Huge batch sizes are actually really bad for convergence. Check out:
@@ -652,7 +652,7 @@ Lightning supports the use of Torch Distributed Elastic to enable fault-tolerant
 
 .. code-block:: python
 
-    Trainer(gpus=8, accelerator="ddp")
+    Trainer(gpus=8, strategy="ddp")
 
 To launch a fault-tolerant job, run the following on all nodes.
 

docs/source/advanced/tpu.rst

@@ -349,14 +349,14 @@ Don't use ``xm.xla_device()`` while working on Lightning + TPUs!
 
 PyTorch XLA only supports Tensor objects for CPU to TPU data transfer. Might cause issues if the User is trying to send some non-tensor objects through the DataLoader or during saving states.
 
-- **Using `tpu_spawn_debug` Plugin**
+- **Using `tpu_spawn_debug` Plugin alias**
 
 .. code-block:: python
 
     import pytorch_lightning as pl
 
     my_model = MyLightningModule()
-    trainer = pl.Trainer(tpu_cores=8, plugins="tpu_spawn_debug")
+    trainer = pl.Trainer(tpu_cores=8, strategy="tpu_spawn_debug")
     trainer.fit(my_model)
 
 Example Metrics report: