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Removing references to installing PyTorch via Conda #3349

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e5e95fc
Removing Conda install references.
AlannaBurke May 1, 2025
3bd2b22
Removing Conda install references.
AlannaBurke May 1, 2025
1257ab1
Removing Conda install references.
AlannaBurke May 1, 2025
9d49d11
Removing Conda install references.
AlannaBurke May 1, 2025
1d59f85
Removing Conda install references.
AlannaBurke May 1, 2025
1588c5e
Removing Conda install references.
AlannaBurke May 1, 2025
78f9670
Removing Conda install references.
AlannaBurke May 1, 2025
19319ae
Merge branch 'main' into remove-conda
svekars May 2, 2025
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10 changes: 5 additions & 5 deletions advanced_source/sharding.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,10 @@ Requirements: - python >= 3.7
We highly recommend CUDA when using torchRec. If using CUDA: - cuda >=
11.0

.. Should these be updated?
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Does this need to be updated?

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lines 26-29 need to be removed.

.. 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
Expand Down Expand Up @@ -213,7 +214,7 @@ 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),
Expand All @@ -234,7 +235,7 @@ ranks.
.. code:: python

import multiprocess

def spmd_sharing_simulation(
sharding_type: ShardingType = ShardingType.TABLE_WISE,
world_size = 2,
Expand All @@ -254,7 +255,7 @@ ranks.
)
p.start()
processes.append(p)

for p in processes:
p.join()
assert 0 == p.exitcode
Expand Down Expand Up @@ -333,4 +334,3 @@ 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)}}

2 changes: 2 additions & 0 deletions advanced_source/torch_script_custom_ops.rst
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Expand Up @@ -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
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Needs an update

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change to pip install


conda install -c pytorch pytorch
conda install opencv

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