Merge branch 'main' of github.com:ebrahimpichka/examples into gat

Author: ebrahimpichka

distributed/minGPT-ddp/mingpt/main.py

@@ -1,3 +1,4 @@
+import os
 import torch
 from torch.utils.data import random_split
 from torch.distributed import init_process_group, destroy_process_group

distributed/rpc/batch/parameter_server.py

@@ -54,7 +54,7 @@ def update_and_fetch_model(ps_rref, grads):
                     p.grad /= self.batch_update_size
                 self.curr_update_size = 0
                 self.optimizer.step()
-                self.optimizer.zero_grad()
+                self.optimizer.zero_grad(set_to_none=False)
                 fut.set_result(self.model)
                 timed_log("PS updated model")
                 self.future_model = torch.futures.Future()

legacy/snli/README.md

@@ -0,0 +1,42 @@
+# PyTorch-based NLI Training with SNLI
+
+## 📝 Overview
+
+This repository contains Python scripts to train a Natural Language Inference (NLI) model, specifically the `SNLIClassifier`, using the Stanford Natural Language Inference (SNLI) corpus. The trained model predicts textual entailment, identifying if a statement is entailed, contradicted, or neither by another statement.
+
+## ⚙️ Dependencies
+
+Install the necessary Python libraries with:
+
+```bash
+pip install -r requirements.txt
+```
+
+The `requirements.txt` file includes:
+
+```
+torch
+torchtext
+spacy
+```
+
+## 💻 Usage
+
+Start the training process with:
+
+```bash
+python train.py --lower --word-vectors [PATH_TO_WORD_VECTORS] --vector-cache [PATH_TO_VECTOR_CACHE] --epochs [NUMBER_OF_EPOCHS] --batch-size [BATCH_SIZE] --save-path [PATH_TO_SAVE_MODEL] --gpu [GPU_NUMBER]
+```
+
+## 🏋️‍♀️ Training
+
+The script trains the model on mini-batches of data across a specified number of epochs. It saves the best-performing model on the validation set as a `.pt` file in the specified directory.
+
+## 📚 Scripts
+
+- `model.py`: Defines the `SNLIClassifier` model and auxiliary classes.
+- `util.py`: Contains utility functions for directory creation and command-line argument parsing.
+
+## 📣 Note
+
+Ensure the `model.py` and `util.py` scripts are available in your working directory.