added issue_4169 file and other modified files

Signed-off-by: Jules Damji <[email protected]>

Author: dmatrix

py/mlflow/apis/models/pytorch/autolog.py

@@ -5,7 +5,7 @@
 from torch.nn import functional as F
 from torch.utils.data import DataLoader
 from torchvision import transforms
-from torchvision.datasets import MNIST
+from torchvision.datasets import FashionMNIST
 from pytorch_lightning.metrics.functional import accuracy
 
 import mlflow.pytorch
@@ -14,7 +14,7 @@
 # Here's the simplest most minimal example with just a training loop
 # (no validation, no testing). It illustrates how you can use MLflow
 # to auto log parameters, metrics, and models.
-# Explore the PyTorch MNIST example for an expansive example with all the
+# Explore the PyTorch FashionMNIST example for an expansive example with all the
 # lightening hooks as the next step.
 
 
@@ -57,7 +57,7 @@ def print_auto_logged_info(r):
     mnist_model = MNISTModel()
 
     # Init DataLoader from MNIST Dataset
-    train_ds = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
+    train_ds = FashionMNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())
     train_loader = DataLoader(train_ds, batch_size=32)
 
     # Initialize a trainer

py/mlflow/mlflow_issues/issue_4169.py

@@ -0,0 +1,8 @@
+import nltk
+import mlflow
+
+if __name__ == '__main__':
+    nltk.download('punkt')
+    # Write all files in downloaded in the destination directory to root artifact_uri/
+    with mlflow.start_run():
+        mlflow.log_artifacts("/Users/julesdamji/nltk_data", artifact_path="nltk")

pytorch/numpy/autograd_nn.ipynb

@@ -115,7 +115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

pytorch/numpy/custom_two_layer_nn.ipynb

@@ -110,7 +110,7 @@
     "    # Zero gradients, perform a backward pass, and update the weights.\n",
     "    optimizer.zero_grad()\n",
     "    loss.backward()\n",
-    "    optimizer.set()"
+    "    optimizer.step()"
    ]
   }
  ],
@@ -130,7 +130,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

pytorch/numpy/numpy_nn.ipynb

@@ -649,7 +649,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

pytorch/numpy/tensor_nn.ipynb

@@ -143,7 +143,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

pytorch/numpy/two_layer_nn.ipynb

@@ -105,7 +105,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

pytorch/tutorials/18_custom_dataset.ipynb

@@ -904,7 +904,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,