Fixed issue with missing argument in MNIST example (#520)

Summary:
Pull Request resolved: #520

sr stands for sampling rate, which is now legacy code. Now, it's just sample_rate = 1 / len(data_loader). This has been fixed in the example, by setting batch size to be 60000 * 0.004 = 240 (thanks to https://github.com/ffuuugor for the clarification).

On another note, when running with DP, the following error is thrown
```
AttributeError: Can't pickle local object 'wrap_collate_with_empty.<locals>.collate'
```

For now, a temporary fix (based on IBM/Project_CodeNet#21 (comment)) is to make num_workers=0 in the dataset loaders. This commit does that.

Reviewed By: ffuuugor

Differential Revision: D40253037

fbshipit-source-id: 99984f8963a4efea6829d109bb81acff0e587c93

examples/mnist.py

@@ -224,7 +224,7 @@ def main():
             ),
         ),
         batch_size=args.batch_size,
-        num_workers=1,
+        num_workers=0,
         pin_memory=True,
     )
     test_loader = torch.utils.data.DataLoader(
@@ -240,7 +240,7 @@ def main():
         ),
         batch_size=args.test_batch_size,
         shuffle=True,
-        num_workers=1,
+        num_workers=0,
         pin_memory=True,
     )
     run_results = []

examples/mnist_README.md

@@ -1,7 +1,7 @@
 # First run
 To run a basic training script without differential privacy:
 ```shell
-python mnist.py --device=cpu --disable-dp --n=20 --lr=.1 -sr=0.004
+python mnist.py --device=cpu --disable-dp --n=20 --lr=.1 -b=240
 ```
 The first time the script runs, it attempts to download the MNIST dataset from http://yann.lecun.com and place it in `../mnist/MNIST/raw`. If you prefer a different location or your execution environment does not have access to the outside world, download and unpack the dataset yourself and pass the location as `--data-root=custom_dir_name`. The script will expect to find under `custom_dir_name/MNIST/processed` two files: `test.pt` (7.9 MB) and `training.pt` (47.5 MB).
 
@@ -21,7 +21,7 @@ Test set: Average loss: 0.0000, Accuracy: 9893/10000 (98.93%)
 
 To train a differentially private model, run the following command:
 ```shell
-python mnist.py --device=cpu -n=15 --lr=.25 --sigma=1.3 -c=1.5 -sr=0.004
+python mnist.py --device=cpu -n=15 --lr=.25 --sigma=1.3 -c=1.5 -b=240
 ```
 If the run is successful, expect to see
 ```
@@ -39,24 +39,24 @@ Test set: Average loss: 0.0004, Accuracy: 9486/10000 (94.86%)
 
 **Baseline: no differential privacy**
 
-Command: `--disable-dp --n=20 --lr=.1 -sr=0.004`
+Command: `--disable-dp --n=20 --lr=.1 -b=240`
 
 Result: accuracy averaged over 10 runs 98.94% ± 0.32%
 
 **(6.86, 10<sup>-5</sup>)-DP**
 
-Command: `-n=45 --lr=.25 --sigma=.7 -c=1.5 -sr=0.004`
+Command: `-n=45 --lr=.25 --sigma=.7 -c=1.5 -b=240`
 
 Result: accuracy averaged over 10 runs 97.09% ± 0.17%
 
 **(2.91, 10<sup>-5</sup>)-DP**
 
-Command: `-n 60 --lr=.15 --sigma=1.1 -c=1.0 -sr=0.004`
+Command: `-n 60 --lr=.15 --sigma=1.1 -c=1.0 -b=240`
 
 Result: accuracy averaged over 10 runs 96.78% ± 0.21%
 
 **(1.16, 10<sup>-5</sup>)-DP**
 
-Command: `-n=15 --lr=.25 --sigma=1.3 -c=1.5 -sr=0.004`
+Command: `-n=15 --lr=.25 --sigma=1.3 -c=1.5 -b=240`
 
 Result: accuracy averaged over 10 runs 94.63% ± 0.34%