diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..2321c30
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,13 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+torch = "*"
+numpy = "*"
+
+[requires]
+python_version = "3.6"
diff --git a/Pipfile.lock b/Pipfile.lock
new file mode 100644
index 0000000..aafabe0
--- /dev/null
+++ b/Pipfile.lock
@@ -0,0 +1,70 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "3be011e0d66a2db2fc86c9454891ca8da0bb70018ae828e6a08d97fc6e744a14"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.6"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "numpy": {
+            "hashes": [
+                "sha256:0df89ca13c25eaa1621a3f09af4c8ba20da849692dcae184cb55e80952c453fb",
+                "sha256:154c35f195fd3e1fad2569930ca51907057ae35e03938f89a8aedae91dd1b7c7",
+                "sha256:18e84323cdb8de3325e741a7a8dd4a82db74fde363dce32b625324c7b32aa6d7",
+                "sha256:1e8956c37fc138d65ded2d96ab3949bd49038cc6e8a4494b1515b0ba88c91565",
+                "sha256:23557bdbca3ccbde3abaa12a6e82299bc92d2b9139011f8c16ca1bb8c75d1e95",
+                "sha256:24fd645a5e5d224aa6e39d93e4a722fafa9160154f296fd5ef9580191c755053",
+                "sha256:36e36b6868e4440760d4b9b44587ea1dc1f06532858d10abba98e851e154ca70",
+                "sha256:3d734559db35aa3697dadcea492a423118c5c55d176da2f3be9c98d4803fc2a7",
+                "sha256:416a2070acf3a2b5d586f9a6507bb97e33574df5bd7508ea970bbf4fc563fa52",
+                "sha256:4a22dc3f5221a644dfe4a63bf990052cc674ef12a157b1056969079985c92816",
+                "sha256:4d8d3e5aa6087490912c14a3c10fbdd380b40b421c13920ff468163bc50e016f",
+                "sha256:4f41fd159fba1245e1958a99d349df49c616b133636e0cf668f169bce2aeac2d",
+                "sha256:561ef098c50f91fbac2cc9305b68c915e9eb915a74d9038ecf8af274d748f76f",
+                "sha256:56994e14b386b5c0a9b875a76d22d707b315fa037affc7819cda08b6d0489756",
+                "sha256:73a1f2a529604c50c262179fcca59c87a05ff4614fe8a15c186934d84d09d9a5",
+                "sha256:7da99445fd890206bfcc7419f79871ba8e73d9d9e6b82fe09980bc5bb4efc35f",
+                "sha256:99d59e0bcadac4aa3280616591fb7bcd560e2218f5e31d5223a2e12a1425d495",
+                "sha256:a4cc09489843c70b22e8373ca3dfa52b3fab778b57cf81462f1203b0852e95e3",
+                "sha256:a61dc29cfca9831a03442a21d4b5fd77e3067beca4b5f81f1a89a04a71cf93fa",
+                "sha256:b1853df739b32fa913cc59ad9137caa9cc3d97ff871e2bbd89c2a2a1d4a69451",
+                "sha256:b1f44c335532c0581b77491b7715a871d0dd72e97487ac0f57337ccf3ab3469b",
+                "sha256:b261e0cb0d6faa8fd6863af26d30351fd2ffdb15b82e51e81e96b9e9e2e7ba16",
+                "sha256:c857ae5dba375ea26a6228f98c195fec0898a0fd91bcf0e8a0cae6d9faf3eca7",
+                "sha256:cf5bb4a7d53a71bb6a0144d31df784a973b36d8687d615ef6a7e9b1809917a9b",
+                "sha256:db9814ff0457b46f2e1d494c1efa4111ca089e08c8b983635ebffb9c1573361f",
+                "sha256:df04f4bad8a359daa2ff74f8108ea051670cafbca533bb2636c58b16e962989e",
+                "sha256:ecf81720934a0e18526177e645cbd6a8a21bb0ddc887ff9738de07a1df5c6b61",
+                "sha256:edfa6fba9157e0e3be0f40168eb142511012683ac3dc82420bee4a3f3981b30e"
+            ],
+            "index": "pypi",
+            "version": "==1.15.4"
+        },
+        "torch": {
+            "hashes": [
+                "sha256:012a9c7efce86c7a0ce78cd7945fe7c798049537fc3e85af9f14e8789d13c17f",
+                "sha256:4aadc7124afc431ac6a227a05dc8eff417b6fd8f90fc6c44d514ddfca9a6b474",
+                "sha256:53e12607830ccb1e5fc4076aafe19bdbbc380799793fbaad696714b72859bde6",
+                "sha256:7e73a141bf817c0a914131dec51ea24a2f1946b96749b003af664230a9b95197",
+                "sha256:cb92ac65fcc7685fa6c5920b24101182dcb706d841fc6154ada604f749b615e3",
+                "sha256:cedbc382a0e992a169c73d2c469887c2e5ce0c6fa88b1dabe8f9021e1acb564f",
+                "sha256:ded9e2e59c086127423c23e902e2bec42b3b443a0e458fae76c013f62a7e0748",
+                "sha256:df005dff3e3f12911630e48e0e75d3594a424a317d785b49426c23d0810a4682",
+                "sha256:f4196ce8ba17797f3c2d13c0d53cf461a8e32f6130a08e6e4ce917637afccdc6"
+            ],
+            "index": "pypi",
+            "version": "==1.0.0"
+        }
+    },
+    "develop": {}
+}
diff --git a/README.md b/README.md
index ddccc6b..0999d91 100644
--- a/README.md
+++ b/README.md
@@ -8,9 +8,10 @@ The model is based on deep AutoEncoders.
 
 ## Requirements
 * Python 3.6
-* [Pytorch](http://pytorch.org/)
+* [Pytorch](http://pytorch.org/): `pipenv install`
 * CUDA (recommended version >= 8.0)
 
+
 ## Training using mixed precision with Tensor Cores
 * You would need NVIDIA Volta-based GPU
 * Checkout [mixed precision branch](https://github.com/NVIDIA/DeepRecommender/tree/mp_branch)
diff --git a/run.py b/run.py
index 471b673..052bc65 100644
--- a/run.py
+++ b/run.py
@@ -71,8 +71,8 @@ def do_eval(encoder, evaluation_data_layer):
     targets = Variable(eval.cuda().to_dense() if use_gpu else eval.to_dense())
     outputs = encoder(inputs)
     loss, num_ratings = model.MSEloss(outputs, targets)
-    total_epoch_loss += loss.data[0]
-    denom += num_ratings.data[0]
+    total_epoch_loss += loss.item()
+    denom += num_ratings.item()
   return sqrt(total_epoch_loss / denom)
 
 def log_var_and_grad_summaries(logger, layers, global_step, prefix, log_histograms=False):
@@ -195,7 +195,7 @@ def main():
       loss.backward()
       optimizer.step()
       global_step += 1
-      t_loss += loss.data[0]
+      t_loss += loss.item()
       t_loss_denom += 1
 
       if i % args.summary_frequency == 0:
@@ -209,7 +209,7 @@ def main():
           log_var_and_grad_summaries(logger, rencoder.decode_w, global_step, "Decode_W")
         log_var_and_grad_summaries(logger, rencoder.decode_b, global_step, "Decode_b")
 
-      total_epoch_loss += loss.data[0]
+      total_epoch_loss += loss.item()
       denom += 1
 
       #if args.aug_step > 0 and i % args.aug_step == 0 and i > 0:
diff --git a/test/test_model.py b/test/test_model.py
index 6f54f5f..ab72385 100644
--- a/test/test_model.py
+++ b/test/test_model.py
@@ -31,7 +31,7 @@ def test_CPU(self):
         loss = loss / num_ratings
         loss.backward()
         optimizer.step()
-        print('[%d, %5d] loss: %.7f' % (epoch, i, loss.data[0]))
+        print('[%d, %5d] loss: %.7f' % (epoch, i, loss.item()))
 
   def test_GPU(self):
     print("iRecAutoEncoderTest Test on GPU started")
@@ -56,7 +56,7 @@ def test_GPU(self):
         loss = loss / num_ratings
         loss.backward()
         optimizer.step()
-        total_epoch_loss += loss.data[0]
+        total_epoch_loss += loss.item()
         denom += 1
       print("Total epoch {} loss: {}".format(epoch, total_epoch_loss/denom))
 
@@ -81,7 +81,7 @@ def test_CPU(self):
         loss = loss / num_ratings
         loss.backward()
         optimizer.step()
-        print('[%d, %5d] loss: %.7f' % (epoch, i, loss.data[0]))
+        print('[%d, %5d] loss: %.7f' % (epoch, i, loss.item()))
         if i == 5: # too much compute for CPU
           break
 
@@ -108,7 +108,7 @@ def test_GPU(self):
         loss = loss / num_ratings
         loss.backward()
         optimizer.step()
-        total_epoch_loss += loss.data[0]
+        total_epoch_loss += loss.item()
         denom += 1
       print("Total epoch {} loss: {}".format(epoch, total_epoch_loss / denom))