examples,docs: adjust ddp example timeout and docs (#93)

README.md

@@ -13,7 +13,7 @@ Easy Per Step Fault Tolerance for PyTorch
   | <a href="https://pytorch.org/torchft/"><b>Documentation</b></a>
   | <a href="https://github.com/pytorch-labs/torchft/blob/main/media/fault_tolerance_poster.pdf"><b>Poster</b></a>
   | <a href="https://docs.google.com/document/d/1OZsOsz34gRDSxYXiKkj4WqcD9x0lP9TcsfBeu_SsOY4/edit"><b>Design Doc</b></a>
-  | 
+  |
 </p>
 <p align="center">
   <a href="https://pypi.org/project/torchft-nightly/"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/torchft-nightly"></a>
@@ -98,7 +98,7 @@ when using synchronous training.
 You can start a lighthouse server by running:
 
 ```sh
-$ RUST_BACKTRACE=1 torchft_lighthouse --min_replicas 1 --quorum_tick_ms 100 --join_timeout_ms 1000
+$ RUST_BACKTRACE=1 torchft_lighthouse --min_replicas 1 --quorum_tick_ms 100 --join_timeout_ms 10000
 ```
 
 ### Example Training Loop (DDP)
@@ -108,7 +108,7 @@ See [train_ddp.py](./train_ddp.py) for the full example.
 Invoke with:
 
 ```sh
-$ TORCHFT_MANAGER_PORT=29512 TORCHFT_LIGHTHOUSE=http://localhost:29510 torchrun --master_port 29501 --nnodes 1 --nproc_per_node 1 train.py
+$ TORCHFT_LIGHTHOUSE=http://localhost:29510 torchrun --master_port 29501 --nnodes 1 --nproc_per_node 1 train.py
 ```
 
 train.py:

src/lighthouse.rs

@@ -77,7 +77,7 @@ pub struct LighthouseOpt {
     #[structopt(
         long = "join_timeout_ms",
         default_value = "60000",
-        help = "How long to wait for new replicas to join before considering a quorum"
+        help = "How long to wait for heartbeating stragglers to join before issuing quorum"
     )]
     pub join_timeout_ms: u64,
 
@@ -90,14 +90,14 @@ pub struct LighthouseOpt {
     #[structopt(
         long = "quorum_tick_ms",
         default_value = "100",
-        help = "How frequently to check for quorum when waiting for workers."
+        help = "How frequently to check for quorum when waiting for stragglers."
     )]
     pub quorum_tick_ms: u64,
 
     #[structopt(
         long = "heartbeat_timeout_ms",
         default_value = "5000",
-        help = "how long to wait for a heartbeat before considering a replica dead."
+        help = "How long to wait for a heartbeat before considering a replica dead."
     )]
     pub heartbeat_timeout_ms: u64,
 }

train_ddp.py

@@ -7,6 +7,7 @@
 import logging
 import os
 import sys
+from datetime import timedelta
 
 import torch
 import torch.nn.functional as F
@@ -70,14 +71,21 @@ def state_dict():
         }
 
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    pg = ProcessGroupBabyNCCL() if torch.cuda.is_available() else ProcessGroupGloo()
+    pg = (
+        ProcessGroupBabyNCCL(
+            timeout=timedelta(seconds=5),
+        )
+        if torch.cuda.is_available()
+        else ProcessGroupGloo(timeout=timedelta(seconds=5))
+    )
 
     manager = Manager(
         pg=pg,
         min_replica_size=1,
         load_state_dict=load_state_dict,
         state_dict=state_dict,
         replica_id=f"train_ddp_{REPLICA_GROUP_ID}",
+        timeout=timedelta(seconds=10),
     )
 
     class Net(nn.Module):