Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "07ca7101",
+   "id": "15908dbe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5c80c64e",
+   "id": "70b8ab1b",
    "metadata": {},
    "source": [
     "\n",

_downloads/4065a985b933a4377d3c7d93557e2282/reinforcement_ppo.ipynb

@@ -836,7 +836,7 @@
    "outputs": [],
    "source": [
     "advantage_module = GAE(\n",
-    "    gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True\n",
+    "    gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True, device=device,\n",
     ")\n",
     "\n",
     "loss_module = ClipPPOLoss(\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "65cd1d93",
+   "id": "a167c59c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "355262c2",
+   "id": "43ebf05d",
    "metadata": {},
    "source": [
     "\n",

_downloads/5508d56677e1ebd9b3560596e7e41f57/semi_structured_sparse.py

@@ -210,6 +210,8 @@
 SparseSemiStructuredTensor._FORCE_CUTLASS = True
 torch.manual_seed(100)
 
+# Set default device to "cuda:0"
+torch.set_default_device(torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
 
 ######################################################################
 # We’ll also need to define some helper functions that are specific to the

_downloads/592c87306513ba042d759e142b59fea4/coding_ddpg.py

@@ -1040,7 +1040,7 @@ def ceil_div(x, y):
 
 ###############################################################################
 # let's use the TD(lambda) estimator!
-loss_module.make_value_estimator(ValueEstimators.TDLambda, gamma=gamma, lmbda=lmbda)
+loss_module.make_value_estimator(ValueEstimators.TDLambda, gamma=gamma, lmbda=lmbda, device=device)
 
 ###############################################################################
 # .. note::

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c15d3934",
+   "id": "a45d7099",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edc64c5d",
+   "id": "2732eb55",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "416301d9",
+   "id": "1073f678",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8d96351",
+   "id": "27e3c66b",
    "metadata": {},
    "source": [
     "\n",

_downloads/7ed508ed54ec36ee5c1d3fa1e8ceede0/reinforcement_ppo.py

@@ -551,7 +551,7 @@
 #
 
 advantage_module = GAE(
-    gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True
+    gamma=gamma, lmbda=lmbda, value_network=value_module, average_gae=True, device=device,
 )
 
 loss_module = ClipPPOLoss(

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1f4610df",
+   "id": "2a24e8c1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73c65f06",
+   "id": "1b79833c",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd2b8139",
+   "id": "99cb8ba2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4fc6d23a",
+   "id": "f54e4f7b",
    "metadata": {},
    "source": [
     "\n",

_downloads/e6af5f45662e344f06f9caca32c8752f/semi_structured_sparse.ipynb

@@ -250,7 +250,10 @@
     "\n",
     "# force CUTLASS use if ``cuSPARSELt`` is not available\n",
     "SparseSemiStructuredTensor._FORCE_CUTLASS = True\n",
-    "torch.manual_seed(100)"
+    "torch.manual_seed(100)\n",
+    "\n",
+    "# Set default device to \"cuda:0\"\n",
+    "torch.set_default_device(torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"))"
    ]
   },
   {

_downloads/e9ea08c1c8bbc809d69c69a6043d56ee/coding_ddpg.ipynb

@@ -1479,7 +1479,7 @@
    },
    "outputs": [],
    "source": [
-    "loss_module.make_value_estimator(ValueEstimators.TDLambda, gamma=gamma, lmbda=lmbda)"
+    "loss_module.make_value_estimator(ValueEstimators.TDLambda, gamma=gamma, lmbda=lmbda, device=device)"
    ]
   },
   {

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4fdbbc06",
+   "id": "ec2b0c14",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59a4103a",
+   "id": "160ae8e6",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f252b13b",
+   "id": "612b5862",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f07beb5",
+   "id": "8b01b0b8",
    "metadata": {},
    "source": [
     "\n",