Downloading data in a temporary folder

The `/tmp` folder is available on most unix-flavored systems (inluding Ubuntu & Apple) and is a good place to put temporary data like MNIST. Good is subject to subjectivity :-)
jeshraghian · Oct 10, 2023 · 6d1c48e · 6d1c48e
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diff --git a/docs/quickstart.rst b/docs/quickstart.rst
@@ -48,7 +48,7 @@ Define variables for dataloading.
 ::
 
     batch_size = 128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")
 
 Load MNIST dataset.

diff --git a/docs/tutorials/legacy/tutorial_1_old.rst b/docs/tutorials/legacy/tutorial_1_old.rst
@@ -51,7 +51,7 @@ Let's define a few variables:
 
   # Training Parameters
   batch_size=128
-  data_path='/data/mnist'
+  data_path='/tmp/data/mnist'
   num_classes = 10
 
   # Temporal Dynamics

diff --git a/docs/tutorials/legacy/tutorial_3_old.rst b/docs/tutorials/legacy/tutorial_3_old.rst
@@ -161,7 +161,7 @@ Much of the following code has already been explained in the first two tutorials
 
   # Training Parameters
   batch_size=128
-  data_path='/data/mnist'
+  data_path='/tmp/data/mnist'
 
   # Temporal Dynamics
   num_steps = 25

diff --git a/docs/tutorials/tutorial_1.rst b/docs/tutorials/tutorial_1.rst
@@ -95,7 +95,7 @@ Install the latest PyPi distribution of snnTorch:
 
     # Training Parameters
     batch_size=128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     num_classes = 10  # MNIST has 10 output classes
     
     # Torch Variables

diff --git a/docs/tutorials/tutorial_5.rst b/docs/tutorials/tutorial_5.rst
@@ -388,7 +388,7 @@ training a fully-connected spiking neural net.
 
     # dataloader arguments
     batch_size = 128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     
     dtype = torch.float
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")

diff --git a/docs/tutorials/tutorial_6.rst b/docs/tutorials/tutorial_6.rst
@@ -191,7 +191,7 @@ here. <https://snntorch.readthedocs.io/en/latest/snntorch.surrogate.html>`__
 
     # dataloader arguments
     batch_size = 128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     
     dtype = torch.float
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")

diff --git a/docs/tutorials/tutorial_ipu_1.rst b/docs/tutorials/tutorial_ipu_1.rst
@@ -62,7 +62,7 @@ Load in the MNIST dataset.
     from torchvision import datasets, transforms
 
     batch_size = 128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     
     # Define a transform
     transform = transforms.Compose([

diff --git a/docs/tutorials/tutorial_pop.rst b/docs/tutorials/tutorial_pop.rst
@@ -62,7 +62,7 @@ Define variables for dataloading.
 ::
 
     batch_size = 128
-    data_path='/data/fmnist'
+    data_path='/tmp/data/fmnist'
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")
 
 Load FashionMNIST dataset.

diff --git a/docs/tutorials/tutorial_regression_2.rst b/docs/tutorials/tutorial_regression_2.rst
@@ -332,7 +332,7 @@ temporal data is an exercise left to the reader/coder.
 ::
 
     batch_size = 128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     
     # Define a transform
     transform = transforms.Compose([

diff --git a/docs/tutorials/tutorial_sae.rst b/docs/tutorials/tutorial_sae.rst
@@ -177,7 +177,7 @@
 
       # dataloader arguments
       batch_size = 250
-      data_path='/data/mnist'
+      data_path='/tmp/data/mnist'
 
       dtype = torch.float
       device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu")

diff --git a/docs/tutorials/zh-cn/tutorial_1_cn.rst b/docs/tutorials/zh-cn/tutorial_1_cn.rst
@@ -85,7 +85,7 @@ snnTorch 教程系列基于以下论文。如果您发现这些资源或代码
 
     # Training Parameters
     batch_size=128
-    data_path='/data/mnist'
+    data_path='/tmp/data/mnist'
     num_classes = 10  # MNIST has 10 output classes
     
     # Torch Variables

diff --git a/examples/legacy/Alpha_neuron_training_example.ipynb b/examples/legacy/Alpha_neuron_training_example.ipynb
@@ -93,7 +93,7 @@
         "\n",
         "# Training Parameters\n",
         "batch_size=128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "# Temporal Dynamics\n",
         "num_steps = 25\n",

diff --git a/examples/legacy/CIFAR_temp.ipynb b/examples/legacy/CIFAR_temp.ipynb
@@ -145,7 +145,7 @@
         "\n",
         "# Training Parameters\n",
         "batch_size=128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "# Temporal Dynamics\n",
         "num_steps = 25\n",

diff --git a/examples/legacy/FCN_truncatedfromscratch.ipynb b/examples/legacy/FCN_truncatedfromscratch.ipynb
@@ -107,7 +107,7 @@
     "\n",
     "# Training Parameters\n",
     "batch_size=128\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "\n",
     "# Temporal Dynamics\n",
     "num_steps = 25\n",

diff --git a/examples/legacy/TBPTT.ipynb b/examples/legacy/TBPTT.ipynb
@@ -121,7 +121,7 @@
     "\n",
     "# Training Parameters\n",
     "batch_size=128\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "\n",
     "# Temporal Dynamics\n",
     "num_steps = 25\n",

diff --git a/examples/legacy/tutorial_3_FCN.ipynb b/examples/legacy/tutorial_3_FCN.ipynb
@@ -239,7 +239,7 @@
         "\n",
         "# Training Parameters\n",
         "batch_size=128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "# Temporal Dynamics\n",
         "num_steps = 25\n",

diff --git a/examples/legacy/tutorial_4_CNN.ipynb b/examples/legacy/tutorial_4_CNN.ipynb
@@ -99,7 +99,7 @@
     "\n",
     "# Training Parameters\n",
     "batch_size=128\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "\n",
     "# Temporal Dynamics\n",
     "num_steps = 25\n",

diff --git a/examples/quickstart.ipynb b/examples/quickstart.ipynb
@@ -88,7 +88,7 @@
       "outputs": [],
       "source": [
         "batch_size = 128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device(\"cpu\")"
       ]
     },

diff --git a/examples/tutorial_1_spikegen.ipynb b/examples/tutorial_1_spikegen.ipynb
@@ -151,7 +151,7 @@
    "source": [
     "# Training Parameters\n",
     "batch_size=128\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "num_classes = 10  # MNIST has 10 output classes\n",
     "\n",
     "# Torch Variables\n",

diff --git a/examples/tutorial_5_FCN.ipynb b/examples/tutorial_5_FCN.ipynb
@@ -398,7 +398,7 @@
       "source": [
         "# dataloader arguments\n",
         "batch_size = 128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "dtype = torch.float\n",
         "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device(\"cpu\")"

diff --git a/examples/tutorial_6_CNN.ipynb b/examples/tutorial_6_CNN.ipynb
@@ -261,7 +261,7 @@
       "source": [
         "# dataloader arguments\n",
         "batch_size = 128\n",
-        "data_path='/data/mnist'\n",
+        "data_path='/tmp/data/mnist'\n",
         "\n",
         "dtype = torch.float\n",
         "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device(\"cpu\")"

diff --git a/examples/tutorial_pop.ipynb b/examples/tutorial_pop.ipynb
@@ -113,8 +113,8 @@
       "outputs": [],
       "source": [
         "batch_size = 128\n",
-        "data_path='/data/fmnist'\n",
-        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device(\"cpu\")"
+        "data_path='/tmp/data/fmnist'\n",
+        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device('mps') if torch.backends.mps.is_available() else torch.device(\"cpu\")"
       ]
     },
     {

diff --git a/examples/tutorial_sae.ipynb b/examples/tutorial_sae.ipynb
@@ -199,7 +199,7 @@
    "source": [
     "# dataloader arguments\n",
     "batch_size = 250\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "\n",
     "dtype = torch.float\n",
     "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device("mps") if torch.backends.mps.is_available() else torch.device(\"cpu\")"

diff --git a/examples/zh-cn/tutorial_1_spikegen_cn.ipynb b/examples/zh-cn/tutorial_1_spikegen_cn.ipynb
@@ -152,7 +152,7 @@
    "source": [
     "# Training Parameters\n",
     "batch_size=128\n",
-    "data_path='/data/mnist'\n",
+    "data_path='/tmp/data/mnist'\n",
     "num_classes = 10  # MNIST has 10 output classes\n",
     "\n",
     "# Torch Variables\n",