WIP Removing references to installing PyTorch via Conda

advanced_source/sharding.rst

@@ -14,18 +14,15 @@ Requirements: - python >= 3.7
 We highly recommend CUDA when using torchRec. If using CUDA: - cuda >=
 11.0
 
+.. Should these be updated?
 .. code:: python
 
-    # install conda to make installying pytorch with cudatoolkit 11.3 easier. 
+    # install conda to make installying pytorch with cudatoolkit 11.3 easier.
     !sudo rm Miniconda3-py37_4.9.2-Linux-x86_64.sh Miniconda3-py37_4.9.2-Linux-x86_64.sh.*
     !sudo wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.9.2-Linux-x86_64.sh
     !sudo chmod +x Miniconda3-py37_4.9.2-Linux-x86_64.sh
     !sudo bash ./Miniconda3-py37_4.9.2-Linux-x86_64.sh -b -f -p /usr/local
 
-.. code:: python
-
-    # install pytorch with cudatoolkit 11.3
-    !sudo conda install pytorch cudatoolkit=11.3 -c pytorch-nightly -y
 
 Installing torchRec will also install
 `FBGEMM <https://github.com/pytorch/fbgemm>`__, a collection of CUDA
@@ -213,7 +210,7 @@ embedding table placement using planner and generate sharded model using
         )
         sharders = [cast(ModuleSharder[torch.nn.Module], EmbeddingBagCollectionSharder())]
         plan: ShardingPlan = planner.collective_plan(module, sharders, pg)
-    
+
         sharded_model = DistributedModelParallel(
             module,
             env=ShardingEnv.from_process_group(pg),
@@ -234,7 +231,7 @@ ranks.
 .. code:: python
 
     import multiprocess
-       
+
     def spmd_sharing_simulation(
         sharding_type: ShardingType = ShardingType.TABLE_WISE,
         world_size = 2,
@@ -254,7 +251,7 @@ ranks.
           )
           p.start()
           processes.append(p)
-    
+
       for p in processes:
           p.join()
           assert 0 == p.exitcode
@@ -333,4 +330,3 @@ With data parallel, we will repeat the tables for all devices.
 
     rank:0,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'large_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None)}}
     rank:1,sharding plan: {'': {'large_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'large_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_0': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None), 'small_table_1': ParameterSharding(sharding_type='data_parallel', compute_kernel='batched_dense', ranks=[0, 1], sharding_spec=None)}}
-

advanced_source/torch_script_custom_ops.rst

@@ -190,6 +190,8 @@ Environment setup
 We need an installation of PyTorch and OpenCV.  The easiest and most platform
 independent way to get both is to via Conda::
 
+.. these need to be updated
+
   conda install -c pytorch pytorch
   conda install opencv
 

beginner_source/colab.rst

@@ -11,7 +11,7 @@ PyTorch Version in Google Colab
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Wen you are running a tutorial that requires a version of PyTorch that has
-just been released, that version might not be yet available in Google Colab.
+jst been released, that version might not be yet available in Google Colab.
 To check that you have the required ``torch`` and compatible domain libraries
 installed, run ``!pip list``.
 
@@ -27,7 +27,7 @@ Using Tutorial Data from Google Drive in Colab
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 We've added a new feature to tutorials that allows users to open the
-notebook associated with a tutorial in Google Colab. You may need to
+ntebook associated with a tutorial in Google Colab. You may need to
 copy data to your Google drive account to get the more complex tutorials
 to work.