common command line argument and docs update

.gitignore

@@ -0,0 +1,5 @@
+data/*
+lightning_logs/*
+runs/*
+MNIST/*
+cifar-10-*

README.md

@@ -2,24 +2,95 @@
 
 Simple MNIST classifier written in PyTorch, PyTorch Lightning, and Keras.
 
-## Install Dependencies
+# Install Dependencies
 
 ```bash
+conda create --yes --name mnist python=3.8
+conda activate mnist
 pip install -r requirements.txt
+pip install lightning-grid
+grid login
 ```
 
-## PyTorch / Lightning
+# PyTorch
+
+Use the CLI commands below or click 
+[![PyTorch](https://img.shields.io/badge/rid_AI-run-78FF96.svg?labelColor=black&logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iNDgiIGhlaWdodD0iNDgiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0iTTEgMTR2MjBhMTQgMTQgMCAwMDE0IDE0aDlWMzYuOEgxMi42VjExaDIyLjV2N2gxMS4yVjE0QTE0IDE0IDAgMDAzMi40IDBIMTVBMTQgMTQgMCAwMDEgMTR6IiBmaWxsPSIjZmZmIi8+PHBhdGggZD0iTTM1LjIgNDhoMTEuMlYyNS41SDIzLjl2MTEuM2gxMS4zVjQ4eiIgZmlsbD0iI2ZmZiIvPjwvc3ZnPg==)](
+https://platform.grid.ai/#/runs?script=https://github.com/robert-s-lee/hello_mnists/blob/6cbeebc74035cc802ddcacb852e8c284e243e4cf/pytorch.py&cloud=grid&instance=t2.medium&accelerators=1&disk_size=200&framework=lightning 
+)
 
 ```bash
-# pytorch
+# Local Run
 python pytorch.py
 
-# lightning
+# Grid.ai Run
+grid run pytorch.py | tee /tmp/grid.run.log
+```
+
+# PyTorch Lightning
+
+Use the CLI commands below or click 
+[![Lightning](https://img.shields.io/badge/rid_AI-run-78FF96.svg?labelColor=black&logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iNDgiIGhlaWdodD0iNDgiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0iTTEgMTR2MjBhMTQgMTQgMCAwMDE0IDE0aDlWMzYuOEgxMi42VjExaDIyLjV2N2gxMS4yVjE0QTE0IDE0IDAgMDAzMi40IDBIMTVBMTQgMTQgMCAwMDEgMTR6IiBmaWxsPSIjZmZmIi8+PHBhdGggZD0iTTM1LjIgNDhoMTEuMlYyNS41SDIzLjl2MTEuM2gxMS4zVjQ4eiIgZmlsbD0iI2ZmZiIvPjwvc3ZnPg==)](
+https://platform.grid.ai/#/runs?script=https://github.com/robert-s-lee/hello_mnists/blob/6cbeebc74035cc802ddcacb852e8c284e243e4cf/pl_mnist.py&cloud=grid&instance=t2.medium&accelerators=1&disk_size=200&framework=lightning
+)
+
+```bash
+# Local Run
 python pl_mnist.py
+
+# Grid.ai Run
+grid run pl_mnist.py | tee /tmp/grid.run.log
 ```
 
-## Keras
+# Keras
+
+Use the CLI commands below or click 
+[![Keras](https://img.shields.io/badge/rid_AI-run-78FF96.svg?labelColor=black&logo=data:image/svg%2bxml;base64,PHN2ZyB3aWR0aD0iNDgiIGhlaWdodD0iNDgiIGZpbGw9Im5vbmUiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+PHBhdGggZD0iTTEgMTR2MjBhMTQgMTQgMCAwMDE0IDE0aDlWMzYuOEgxMi42VjExaDIyLjV2N2gxMS4yVjE0QTE0IDE0IDAgMDAzMi40IDBIMTVBMTQgMTQgMCAwMDEgMTR6IiBmaWxsPSIjZmZmIi8+PHBhdGggZD0iTTM1LjIgNDhoMTEuMlYyNS41SDIzLjl2MTEuM2gxMS4zVjQ4eiIgZmlsbD0iI2ZmZiIvPjwvc3ZnPg==)](
+https://platform.grid.ai/#/runs?script=https://github.com/robert-s-lee/hello_mnists/blob/6cbeebc74035cc802ddcacb852e8c284e243e4cf/keras.py&cloud=grid&instance=t2.medium&accelerators=1&disk_size=200&framework=lightning&script_args=keras.py  
+)
 
 ```bash
+# Local Run
 python keras.py
+
+# Grid.ai Run
+grid run keras.py | tee /tmp/grid.run.log
+```
+
+# CIFAR-10 Bonus
+
+
+```bash
+# Local Run
+python pl_cifar10.py
+
+# Grid.ai Run
+grid run pl_cifar10.py | tee /tmp/grid.run.log
+```
+
+# Default Command Line Argument Values per Script
+
+| Argument Name  | keras.py | pl_cifar10.py | pl_mnist.py | pytorch.py| 
+|  --:| :--: | :--: | :--: | :--: | 
+| --max_epochs | 5 | 10 | 10 | 14| 
+| --lr | 1.00E-03 | 1.00E-03 | 1.00E-03 | 1 | 
+| --batch_size | 32 | 32 | 32 | 64 | 
+| --data_dir | ./data/ | os.getcwd | os.getcwd |  | 
+| --num_workers |   | 8 | 8 |  | 
+| --gpus |   | 0 | 0 |  | 
+| --test_batch_size |   |   |   | 1000| 
+| --seed |   |   |   | 1| 
+| --save_model |   |   |   | FALSE| 
+| --log_interval |   |   |   | 10| 
+| --gamma |   |   |   | 0.7| 
+| --dry_run |   |   |   | FALSE| 
+| --cuda |   |   |   | FALSE | 
+
+# Grid.ai Run Monitor
+
+```bash
+RUN_NAME=$(grep grid_name /tmp/grid.run.log | cut -d':' -f 2 | sed -e 's/^[[:space:]]*//')
+watch grid status --details $RUN_NAME
+grid history | grep $RUN_NAME
+grid logs ${RUN_NAME}-exp0 | tee /tmp/grid.exp0.log
 ```

keras.py

@@ -1,14 +1,15 @@
-from argparse import ArgumentParser
+from configargparse import ArgumentParser
 from pathlib import Path
 
 from tensorflow import keras
 
 # Define this script's flags
 parser = ArgumentParser()
-parser.add_argument('--lr', type=float, default=1e-3)
-parser.add_argument('--batch_size', type=int, default=32)
-parser.add_argument('--max_epochs', type=int, default=5)
+parser.add_argument('--lr', type=float, default=1e-3, env_var="MNIST_LR")
+parser.add_argument('--batch_size', type=int, default=32, env_var="MNIST_BATCH_SIZE")
+parser.add_argument('--max_epochs', type=int, default=5, env_var="MNIST_MAX_EPOCHS")
 parser.add_argument('--data_dir', type=str, default="./data/")
+parser.add_argument('--num_workers', type=int, default=8)
 args = parser.parse_args()
 
 # Make sure data_dir is absolute + create it if it doesn't exist

pl_cifar10.py

@@ -37,18 +37,20 @@ def training_step(self, batch, batch_idx):
         return loss
 
 if __name__ == '__main__':
-    from argparse import ArgumentParser
+    from configargparse import ArgumentParser
 
     parser = ArgumentParser()
-    parser.add_argument('--gpus', type=int, default=None)
-    parser.add_argument('--lr', type=float, default=1e-3)
-    parser.add_argument('--batch_size', type=int, default=32)
-    parser.add_argument('--max_epochs', type=int, default=10)
+    parser.add_argument('--gpus', type=int, default=0)
+    parser.add_argument('--lr', type=float, default=1e-3, env_var="CIFAR_LR")
+    parser.add_argument('--batch_size', type=int, default=32, env_var="CIFAR_BATCH_SIZE")
+    parser.add_argument('--max_epochs', type=int, default=10, env_var="CIFAR_MAX_EPOCHS")
     parser.add_argument('--data_dir', type=str, default=os.getcwd())
+    parser.add_argument('--num_workers', type=int, default=8)
+
     args = parser.parse_args()
 
     dataset = CIFAR10(args.data_dir, download=True, transform=transforms.ToTensor())
-    train_loader = DataLoader(dataset, batch_size=args.batch_size)
+    train_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers)
 
     # init model
     model = LitModel(lr=args.lr)

pl_mnist.py

@@ -36,18 +36,19 @@ def training_step(self, batch, batch_idx):
         return loss
 
 if __name__ == '__main__':
-    from argparse import ArgumentParser
+    from configargparse import ArgumentParser
 
     parser = ArgumentParser()
-    parser.add_argument('--gpus', type=int, default=None)
-    parser.add_argument('--lr', type=float, default=1e-3)
-    parser.add_argument('--batch_size', type=int, default=32)
-    parser.add_argument('--max_epochs', type=int, default=10)
+    parser.add_argument('--gpus', type=int, default=0)
+    parser.add_argument('--lr', type=float, default=1e-3, env_var="MNIST_LR")
+    parser.add_argument('--batch_size', type=int, default=32, env_var="MNIST_BATCH_SIZE")
+    parser.add_argument('--max_epochs', type=int, default=10, env_var="MNIST_MAX_EPOCHS")
     parser.add_argument('--data_dir', type=str, default=os.getcwd())
+    parser.add_argument('--num_workers', type=int, default=8)
     args = parser.parse_args()
 
     dataset = MNIST(args.data_dir, download=True, transform=transforms.ToTensor())
-    train_loader = DataLoader(dataset, batch_size=args.batch_size)
+    train_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers)
 
     # init model
     model = LitModel(lr=args.lr)

pytorch.py

@@ -4,7 +4,7 @@
 
 """
 from __future__ import print_function
-import argparse
+from configargparse import ArgumentParser
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -87,26 +87,26 @@ def test(model, device, test_loader, epoch):
 
 def main():
     # Training settings
-    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
-    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
-                        help='input batch size for training (default: 64)')
-    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
+    parser = ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument('--batch_size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)', env_var="MNIST_BATCH_SIZE")
+    parser.add_argument('--test_batch_size', type=int, default=1000, metavar='N',
                         help='input batch size for testing (default: 1000)')
-    parser.add_argument('--epochs', type=int, default=14, metavar='N',
-                        help='number of epochs to train (default: 14)')
+    parser.add_argument('--max_epochs', type=int, default=14, metavar='N',
+                        help='number of epochs to train (default: 14)', env_var="MNIST_MAX_EPOCHS")
     parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
-                        help='learning rate (default: 1.0)')
+                        help='learning rate (default: 1.0)', env_var="MNIST_LR")
     parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
                         help='Learning rate step gamma (default: 0.7)')
     parser.add_argument('--cuda', action='store_true', default=False,
                         help='disables CUDA training')
-    parser.add_argument('--dry-run', action='store_true', default=False,
+    parser.add_argument('--dry_run', action='store_true', default=False,
                         help='quickly check a single pass')
     parser.add_argument('--seed', type=int, default=1, metavar='S',
                         help='random seed (default: 1)')
-    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
+    parser.add_argument('--log_interval', type=int, default=10, metavar='N',
                         help='how many batches to wait before logging training status')
-    parser.add_argument('--save-model', action='store_true', default=False,
+    parser.add_argument('--save_model', action='store_true', default=False,
                         help='For Saving the current Model')
     args = parser.parse_args()
     use_cuda = not args.cuda and torch.cuda.is_available()
@@ -137,7 +137,7 @@ def main():
     optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
 
     scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
-    for epoch in range(1, args.epochs + 1):
+    for epoch in range(1, args.max_epochs + 1):
         train(args, model, device, train_loader, optimizer, epoch)
         test(model, device, test_loader, epoch)
         scheduler.step()

requirements.txt

@@ -1,3 +1,10 @@
-torch==1.7.1
-pytorch-lightning==1.1.2
-torchvision==0.8.2
+# tensorflow version matches Grid.ai grid run --framework tensorflow 2.2.0
+tensorflow
+# lightning version matches Grid.ai grid run --framework lightning 1.2.1
+#    raise ValueError("The `preds` should be probabilities, but values were detected outside of [0,1] range.")
+# ValueError: The `preds` should be probabilities, but values were detected outside of [0,1] range.
+# https://github.com/PyTorchLightning/lightning-bolts/issues/551
+pytorch-lightning<1.2
+torch
+torchvision
+configargparser