WIP Remove Conda instructions

README.md

@@ -37,7 +37,7 @@ The tutorial build is very large and requires a GPU. If your machine does not ha
 
 1. Install required dependencies by running: `pip install -r requirements.txt`.
 
-> Typically, you would run either in `conda` or `virtualenv`. If you want to use `virtualenv`, in the root of the repo, run: `virtualenv venv`, then `source venv/bin/activate`.
+> To use `virtualenv`, in the root of the repo, run: `virtualenv venv`, then `source venv/bin/activate`.
 
 - If you have a GPU-powered laptop, you can build using `make docs`. This will download the data, execute the tutorials and build the documentation to `docs/` directory. This might take about 60-120 min for systems with GPUs. If you do not have a GPU installed on your system, then see next step.
 - You can skip the computationally intensive graph generation by running `make html-noplot` to build basic html documentation to `_build/html`. This way, you can quickly preview your tutorial.

_templates/layout.html

@@ -22,7 +22,7 @@
 <script>
 if((window.location.href.indexOf("/prototype/")!= -1) && (window.location.href.indexOf("/prototype/prototype_index")< 1))
   {
-    var div = '<div class="admonition note"><p class="admonition-title">Note</p><p><i class="fa fa-flask" aria-hidden="true">&nbsp</i> This tutorial describes a prototype feature. Prototype features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing.</p></div>'
+    var div = '<div class="admonition note"><p class="admonition-title">Note</p><p><i class="fa fa-flask" aria-hidden="true">&nbsp</i> This tutorial describes a prototype feature. Prototype features are typically not available as part of binary distributions like PyPI, except sometimes behind run-time flags, and are at an early stage for feedback and testing.</p></div>'
     document.getElementById("pytorch-article").insertAdjacentHTML('afterBegin', div)
   }
 </script>

advanced_source/sharding.rst

@@ -9,52 +9,52 @@ tables by explicitly configuring them.
 Installation
 ------------
 
-Requirements: - python >= 3.7
+Requirements: - Python >= 3.7
 
 We highly recommend CUDA when using torchRec. If using CUDA: - cuda >=
 11.0
 
 .. code:: python
-
-    # install conda to make installying pytorch with cudatoolkit 11.3 easier. 
+    # TODO: replace these
+    # install Conda to make installing 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
+    # install PyTorch with cudatoolkit 11.3
     !sudo conda install pytorch cudatoolkit=11.3 -c pytorch-nightly -y
 
-Installing torchRec will also install
+Installing TorchRec will also install
 `FBGEMM <https://github.com/pytorch/fbgemm>`__, a collection of CUDA
-kernels and GPU enabled operations to run
+kernels and GPU enabled operations to run.
 
 .. code:: python
 
     # install torchrec
     !pip3 install torchrec-nightly
 
-Install multiprocess which works with ipython to for multi-processing
-programming within colab
+Install `multiprocess`` which works with `iPython` for multi-processing
+programming within `Colab``:
 
 .. code:: python
 
     !pip3 install multiprocess
 
 The following steps are needed for the Colab runtime to detect the added
-shared libraries. The runtime searches for shared libraries in /usr/lib,
-so we copy over the libraries which were installed in /usr/local/lib/.
-**This is a very necessary step, only in the colab runtime**.
+shared libraries. The runtime searches for shared libraries is in `/usr/lib`,
+so we copy over the libraries which were installed in `/usr/local/lib/`.
+**This is a very necessary step, only in the Colab runtime**.
 
 .. code:: python
 
     !sudo cp /usr/local/lib/lib* /usr/lib/
 
 **Restart your runtime at this point for the newly installed packages
 to be seen.** Run the step below immediately after restarting so that
-python knows where to look for packages. **Always run this step after
+Python knows where to look for packages. **Always run this step after
 restarting the runtime.**
 
 .. code:: python
@@ -71,7 +71,7 @@ Due to the notebook enviroment, we cannot run
 can do multiprocessing inside the notebook to mimic the setup. Users
 should be responsible for setting up their own
 `SPMD <https://en.wikipedia.org/wiki/SPMD>`_ launcher when using
-Torchrec. We setup our environment so that torch distributed based
+TorchRec. We setup our environment so that torch distributed based
 communication backend can work.
 
 .. code:: python
@@ -213,7 +213,7 @@ 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 +234,7 @@ ranks.
 .. code:: python
 
     import multiprocess
-       
+
     def spmd_sharing_simulation(
         sharding_type: ShardingType = ShardingType.TABLE_WISE,
         world_size = 2,
@@ -254,7 +254,7 @@ ranks.
           )
           p.start()
           processes.append(p)
-    
+
       for p in processes:
           p.join()
           assert 0 == p.exitcode
@@ -333,4 +333,3 @@ 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

@@ -189,6 +189,7 @@ Environment setup
 
 We need an installation of PyTorch and OpenCV.  The easiest and most platform
 independent way to get both is to via Conda::
+.. # TODO: replace these
 
   conda install -c pytorch pytorch
   conda install opencv

beginner_source/hta_intro_tutorial.rst

@@ -9,7 +9,6 @@ below.
 
 Installing HTA
 ~~~~~~~~~~~~~~
-
 We recommend using a Conda environment to install HTA. To install Anaconda, see
 `the official Anaconda documentation <https://docs.anaconda.com/anaconda/install/index.html>`_.
 
@@ -130,12 +129,12 @@ on each rank.
 
 .. image:: ../_static/img/hta/idle_time_summary.png
    :scale: 100%
-   
+
 .. tip::
 
    By default, the idle time breakdown presents the percentage of each of the
    idle time categories. Setting the ``visualize_pctg`` argument to ``False``,
-   the function renders with absolute time on the y-axis. 
+   the function renders with absolute time on the y-axis.
 
 
 Kernel Breakdown