add multi_xpu configuration

code_samples/training_configuration/multi_xpu

@@ -0,0 +1,42 @@
+##
+<pre>
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: MULTI_XPU
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+gpu_ids: 0,1,2,3
+ipex_config:
+  ipex: true
+machine_rank: 0
+main_training_function: main
+mixed_precision: 'no'
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false</pre>
+##
+None
+##
+If the YAML was generated through the `accelerate config` command:
+```
+accelerate launch {script_name.py} {--arg1} {--arg2} ...
+```
+
+If the YAML is saved to a `~/config.yaml` file:
+```
+accelerate launch --config_file ~/config.yaml {script_name.py} {--arg1} {--arg2} ...
+```
+
+##
+Launching on multi-XPU instances requires a different launch command than just `python myscript.py`. Accelerate will wrap around the proper launching script to delegate and call, reading in how to set their configuration based on the parameters passed in. It is a passthrough to the `torchrun` command.
+
+**Remember that you can always use the `accelerate launch` functionality, even if the code in your script does not use the `Accelerator`**
+##
+To learn more checkout the related documentation:
+- <a href="https://huggingface.co/docs/accelerate/main/en/basic_tutorials/launch" target="_blank">Launching distributed code</a>
+- <a href="https://huggingface.co/docs/accelerate/main/en/package_reference/cli" target="_blank">The Command Line</a>