Renamed references to optim/optimization to sparse (neuralmagic#68)

Author: kevinaer

README.md

@@ -53,9 +53,9 @@ limitations under the License.
 ## Overview
 
 SparseZoo is a constantly-growing repository of highly sparse and sparse-quantized models with matching sparsification recipes for neural networks. 
-It simplifies and accelerates your time-to-value in building performant deep learning models with a collection of inference-optimized models and recipes to prototype from.
+It simplifies and accelerates your time-to-value in building performant deep learning models with a collection of inference-sparsified models and recipes to prototype from.
 
-Available via API and hosted in the cloud, the SparseZoo contains both baseline and models optimized to different degrees of inference performance vs. baseline loss recovery. 
+Available via API and hosted in the cloud, the SparseZoo contains both baseline and models sparsified to different degrees of inference performance vs. baseline loss recovery. 
 Recipe-driven approaches built around sparsification algorithms allow you to take the models as given, transfer-learn from the models onto private datasets, or transfer the recipes to your architectures.
 
 This repository contains the Python API code to handle the connection and authentication to the cloud.
@@ -148,9 +148,9 @@ from sparsezoo import Zoo
 from sparsezoo.models.classification import resnet_50
 
 search_model = resnet_50()
-optimized_models = Zoo.search_optimized_models(search_model)
+sparse_models = Zoo.search_sparse_models(search_model)
 
-print(optimized_models)
+print(sparse_models)
 ```
 
 ### Console Scripts
@@ -200,7 +200,7 @@ sparsezoo download -h
 sparsezoo download --domain cv --sub-domain classification \
     --architecture resnet_v1 --sub-architecture 50 \
     --framework pytorch --repo sparseml --dataset imagenet \
-    --optim-name base --optim-category none
+    --sparse-name base --sparse-category none
 ```
 
 <br>Download pruned and quantized ResNet-50 Model
@@ -210,7 +210,7 @@ sparsezoo download --domain cv --sub-domain classification \
     --architecture resnet_v1 --sub-architecture 50 \
     --framework pytorch --repo sparseml \
     --dataset imagenet --training-scheme augmented \
-    --optim-name pruned_quant --optim-category aggressive
+    --sparse-name pruned_quant --sparse-category aggressive
 ```
 
 For a more in-depth read, check out [SparseZoo documentation](https://docs.neuralmagic.com/sparsezoo/).

docs/conf.py

@@ -177,7 +177,7 @@
         "sparsezoo",
         (
             "Neural network model repository for highly sparse "
-            "models and optimization recipes"
+            "models and recipes"
         ),
         "Miscellaneous",
     ),

docs/source/quicktour.md

@@ -75,16 +75,16 @@ model.download()
 print(model.onnx_file.downloaded_path())
 ```
 
-#### Searching Optimized Versions
+#### Searching Sparse Versions
 
 ```python
 from sparsezoo import Zoo
 from sparsezoo.models.classification import resnet_50
 
 search_model = resnet_50()
-optimized_models = Zoo.search_optimized_models(search_model)
+sparse_models = Zoo.search_sparse_models(search_model)
 
-print(optimized_models)
+print(sparse_models)
 ```
 
 ### Console Scripts
@@ -134,7 +134,7 @@ sparsezoo download -h
 sparsezoo download --domain cv --sub-domain classification \
     --architecture resnet_v1 --sub-architecture 50 \
     --framework pytorch --repo sparseml --dataset imagenet \
-    --optim-name base --optim-category none
+    --sparse-name base --sparse-category none
 ```
 
 <br>Download pruned and quantized ResNet-50 Model
@@ -144,5 +144,5 @@ sparsezoo download --domain cv --sub-domain classification \
     --architecture resnet_v1 --sub-architecture 50 \
     --framework pytorch --repo sparseml \
     --dataset imagenet --training-scheme augmented \
-    --optim-name pruned_quant --optim-category aggressive
+    --sparse-name pruned_quant --sparse-category aggressive
 ```

notebooks/model_download.ipynb

@@ -23,7 +23,7 @@
     "- 15 minutes\n",
     "\n",
     "# Background\n",
-    "Neural networks can take a long time to train. Model optimization techniques such as model pruning may be necessary to achieve both performance and optimization goals. However, these model optimizations can involve many trials and errors due to a large number of hyperparameters. Fortunately, in the computer vision and natural language space, pruned (sparsified) neural networks transfer learn.\n",
+    "Neural networks can take a long time to train. Model sparsification techniques such as model pruning may be necessary to achieve both performance and sparsification goals. However, the sparsification of models can involve many trials and errors due to a large number of hyperparameters. Fortunately, in the computer vision and natural language space, pruned (sparsified) neural networks transfer learn.\n",
     "\n",
     "To make it easier to use pruned models, Neural Magic is actively:\n",
     "- Creating pruned versions of popular models and datasets\n",

scripts/sparsezoo_script.py

@@ -36,9 +36,9 @@
                                 [--framework FRAMEWORK] [--repo REPO]
                                 [--dataset DATASET]
                                 [--training-scheme TRAINING_SCHEME]
-                                [--optim-name OPTIM_NAME]
-                                [--optim-category OPTIM_CATEGORY]
-                                [--optim-target OPTIM_TARGET]
+                                [--sparse-name OPTIM_NAME]
+                                [--sparse-category OPTIM_CATEGORY]
+                                [--sparse-target OPTIM_TARGET]
                                 [--release-version RELEASE_VERSION]
                                 [--page PAGE] [--page-length PAGE_LENGTH]
 
@@ -67,16 +67,16 @@
   --training-scheme TRAINING_SCHEME
                         The training scheme used on the model the object
                         belongs to if any; e.g. augmented
-  --optim-name OPTIM_NAME
-                        The name describing the optimization of the model the
+  --sparse-name OPTIM_NAME
+                        The name describing the sparsification of the model the
                         object belongs to, e.g. base, pruned, pruned_quant
-  --optim-category OPTIM_CATEGORY
-                        The degree of optimization of the model the object
+  --sparse-category OPTIM_CATEGORY
+                        The degree of sparsification of the model the object
                         belongs to; e.g. none, conservative (~100 baseline
                         metric), moderate (>=99 baseline metric), aggressive
                         (<99 baseline metric)
-  --optim-target OPTIM_TARGET
-                        The deployment target of optimization of the model the
+  --sparse-target OPTIM_TARGET
+                        The deployment target of sparsification of the model the
                         object belongs to; e.g. edge, deepsparse,
                         deepsparse_throughput, gpu
   --release-version RELEASE_VERSION
@@ -93,8 +93,8 @@
                                   [--sub-architecture SUB_ARCHITECTURE]
                                   --framework FRAMEWORK --repo REPO --dataset
                                   DATASET [--training-scheme TRAINING_SCHEME]
-                                  --optim-name OPTIM_NAME --optim-category
-                                  OPTIM_CATEGORY [--optim-target OPTIM_TARGET]
+                                  --sparse-name OPTIM_NAME --sparse-category
+                                  OPTIM_CATEGORY [--sparse-target OPTIM_TARGET]
                                   [--release-version RELEASE_VERSION]
                                   [--save-dir SAVE_DIR]
 
@@ -123,16 +123,16 @@
   --training-scheme TRAINING_SCHEME
                         The training scheme used on the model the object
                         belongs to if any; e.g. augmented
-  --optim-name OPTIM_NAME
-                        The name describing the optimization of the model the
+  --sparse-name OPTIM_NAME
+                        The name describing the sparsification of the model the
                         object belongs to, e.g. base, pruned, pruned_quant
-  --optim-category OPTIM_CATEGORY
-                        The degree of optimization of the model the object
+  --sparse-category OPTIM_CATEGORY
+                        The degree of sparsification of the model the object
                         belongs to; e.g. none, conservative (~100 baseline
                         metric), moderate (>=99 baseline metric), aggressive
                         (<99 baseline metric)
-  --optim-target OPTIM_TARGET
-                        The deployment target of optimization of the model the
+  --sparse-target OPTIM_TARGET
+                        The deployment target of sparsification of the model the
                         object belongs to; e.g. edge, deepsparse,
                         deepsparse_throughput, gpu
   --release-version RELEASE_VERSION
@@ -157,13 +157,13 @@
 Example download MobileNetV1:
 sparsezoo download --domain cv --sub-domain classification --architecture mobilenet_v1 \
     --sub-architecture 1.0 --framework pytorch --repo torchvision --dataset imagenet \
-    --optim-name base --optim-category none
+    --sparse-name base --sparse-category none
 
 #########
 Example download MobileNetV1 with maximum release version:
 sparsezoo download --domain cv --sub-domain classification --architecture mobilenet_v1 \
     --sub-architecture 1.0 --framework pytorch --repo torchvision --dataset imagenet \
-    --optim-name base --optim-category none --release-version 0.1.0
+    --sparse-name base --sparse-category none --release-version 0.1.0
 
 
 """

src/sparsezoo/main.py

@@ -36,9 +36,9 @@
                                 [--framework FRAMEWORK] [--repo REPO]
                                 [--dataset DATASET]
                                 [--training-scheme TRAINING_SCHEME]
-                                [--optim-name OPTIM_NAME]
-                                [--optim-category OPTIM_CATEGORY]
-                                [--optim-target OPTIM_TARGET]
+                                [--sparse-name OPTIM_NAME]
+                                [--sparse-category OPTIM_CATEGORY]
+                                [--sparse-target OPTIM_TARGET]
                                 [--release-version RELEASE_VERSION]
                                 [--page PAGE] [--page-length PAGE_LENGTH]
 
@@ -67,16 +67,16 @@
   --training-scheme TRAINING_SCHEME
                         The training scheme used on the model the object
                         belongs to if any; e.g. augmented
-  --optim-name OPTIM_NAME
-                        The name describing the optimization of the model the
+  --sparse-name OPTIM_NAME
+                        The name describing the sparsification of the model the
                         object belongs to, e.g. base, pruned, pruned_quant
-  --optim-category OPTIM_CATEGORY
-                        The degree of optimization of the model the object
+  --sparse-category OPTIM_CATEGORY
+                        The degree of sparsification of the model the object
                         belongs to; e.g. none, conservative (~100 baseline
                         metric), moderate (>=99 baseline metric), aggressive
                         (<99 baseline metric)
-  --optim-target OPTIM_TARGET
-                        The deployment target of optimization of the model the
+  --sparse-target OPTIM_TARGET
+                        The deployment target of sparsification of the model the
                         object belongs to; e.g. edge, deepsparse,
                         deepsparse_throughput, gpu
   --release-version RELEASE_VERSION
@@ -93,8 +93,8 @@
                                   [--sub-architecture SUB_ARCHITECTURE]
                                   --framework FRAMEWORK --repo REPO --dataset
                                   DATASET [--training-scheme TRAINING_SCHEME]
-                                  --optim-name OPTIM_NAME --optim-category
-                                  OPTIM_CATEGORY [--optim-target OPTIM_TARGET]
+                                  --sparse-name OPTIM_NAME --sparse-category
+                                  OPTIM_CATEGORY [--sparse-target OPTIM_TARGET]
                                   [--release-version RELEASE_VERSION]
                                   [--save-dir SAVE_DIR]
 
@@ -123,16 +123,16 @@
   --training-scheme TRAINING_SCHEME
                         The training scheme used on the model the object
                         belongs to if any; e.g. augmented
-  --optim-name OPTIM_NAME
-                        The name describing the optimization of the model the
+  --sparse-name OPTIM_NAME
+                        The name describing the sparsification of the model the
                         object belongs to, e.g. base, pruned, pruned_quant
-  --optim-category OPTIM_CATEGORY
-                        The degree of optimization of the model the object
+  --sparse-category OPTIM_CATEGORY
+                        The degree of sparsification of the model the object
                         belongs to; e.g. none, conservative (~100 baseline
                         metric), moderate (>=99 baseline metric), aggressive
                         (<99 baseline metric)
-  --optim-target OPTIM_TARGET
-                        The deployment target of optimization of the model the
+  --sparse-target OPTIM_TARGET
+                        The deployment target of sparsification of the model the
                         object belongs to; e.g. edge, deepsparse,
                         deepsparse_throughput, gpu
   --release-version RELEASE_VERSION
@@ -157,13 +157,13 @@
 Example download MobileNetV1:
 sparsezoo download --domain cv --sub-domain classification --architecture mobilenet_v1 \
     --sub-architecture 1.0 --framework pytorch --repo torchvision --dataset imagenet \
-    --optim-name base --optim-category none
+    --sparse-name base --sparse-category none
 
 #########
 Example download MobileNetV1 with maximum release version:
 sparsezoo download --domain cv --sub-domain classification --architecture mobilenet_v1 \
     --sub-architecture 1.0 --framework pytorch --repo torchvision --dataset imagenet \
-    --optim-name base --optim-category none --release-version 0.1.0
+    --sparse-name base --sparse-category none --release-version 0.1.0
 
 
 """
@@ -239,25 +239,25 @@ def add_model_arguments(parser, download_required=False):
         "belongs to if any; e.g. augmented",
     )
     parser.add_argument(
-        "--optim-name",
+        "--sparse-name",
         type=str,
         required=download_required,
-        help="The name describing the optimization of the model "
+        help="The name describing the sparsification of the model "
         "the object belongs to, e.g. base, pruned, pruned_quant",
     )
     parser.add_argument(
-        "--optim-category",
+        "--sparse-category",
         type=str,
         required=download_required,
-        help="The degree of optimization of the model the object "
+        help="The degree of sparsification of the model the object "
         "belongs to; e.g. none, conservative (~100 baseline metric), "
         "moderate (>=99 baseline metric), aggressive (<99 baseline metric)",
     )
     parser.add_argument(
-        "--optim-target",
+        "--sparse-target",
         type=str,
         default=None,
-        help="The deployment target of optimization of the model "
+        help="The deployment target of sparsification of the model "
         "the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu",
     )
     parser.add_argument(
@@ -323,9 +323,9 @@ def _get_command_from_model(model: Model):
         "repo",
         "dataset",
         "training_scheme",
-        "optim_name",
-        "optim_category",
-        "optim_target",
+        "sparse_name",
+        "sparse_category",
+        "sparse_target",
     ]
 
     command_strings = [
@@ -349,9 +349,9 @@ def search(args):
         repo=args.repo,
         dataset=args.dataset,
         training_scheme=args.training_scheme,
-        optim_name=args.optim_name,
-        optim_category=args.optim_category,
-        optim_target=args.optim_target,
+        sparse_name=args.sparse_name,
+        sparse_category=args.sparse_category,
+        sparse_target=args.sparse_target,
         release_version=args.release_version,
         page=args.page,
         page_length=args.page_length,
@@ -398,9 +398,9 @@ def main():
             repo=args.repo,
             dataset=args.dataset,
             training_scheme=args.training_scheme,
-            optim_name=args.optim_name,
-            optim_category=args.optim_category,
-            optim_target=args.optim_target,
+            sparse_name=args.sparse_name,
+            sparse_category=args.sparse_category,
+            sparse_target=args.sparse_target,
             release_version=args.release_version,
             override_parent_path=args.save_dir,
             overwrite=args.overwrite,