From 148cb86dc4c762cea99a80095daca5b6c3471eca Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Wed, 26 Jun 2024 17:26:27 -0700 Subject: [PATCH 01/13] Add pytorch article --- docs/articles/2024/20240702-pytorch.md | 76 ++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 docs/articles/2024/20240702-pytorch.md diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md new file mode 100644 index 000000000..f65c91404 --- /dev/null +++ b/docs/articles/2024/20240702-pytorch.md @@ -0,0 +1,76 @@ +# First stable iteration of Census (SOMA) PyTorch loaders + +*Published:* *July 2nd, 2024* + +*By:* *[Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com), [Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com)* + +The Census team is excited to share the release of PyTorch loaders that work out-of-the-box for memory-efficient training across any slice of the >70M cells in Census. + +In 2023, we released a beta version of the loaders and we have observed interest from users to utilize them with Census or their own data. For example [Wolf et al.](https://lamin.ai/blog/arrayloader-benchmarks) performed comparisons across different training apporaches and found our loaders to be ideal for _uncached_ training of Census data, albeit with some caveats. + +We have continued the development of the loaders in collaboration with our partners at TileDB, and we are happy to announce this release as the first stable iteration. We hope the loaders can accelerate the development of large-scale models of single-cell data by leveraging the following main features: + +- **Efficient memory usage with out-of-core training.** +- **Calibrated shuffling of observations (cells).** +- **Out-of-the-box training on all or any slice of Census data.** +- **Cloud-based or local data access.** +- **Custom data encoders.** +- **Incresed training speed.** + +Keep on reading for more usage and benchmark details. + +## Census PyTorch loaders usage + +The loaders are ready to use for PyTorch modelling via the specilized Data Pipe [`ExperimentDataPipe`](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html#cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe), which takes advantage of the out-of-core data access TileDB-SOMA offers. + +Please follow our [Training a PyTorch Model](https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html) tutorial for a full reproducible example to train a Logistic Regression. + +Shortly, you can intilize `ExperimentDataPipe` to train a model on tongue cells as follows: + +\#TODO verify with ebezzi that this code is up to date + +```python +import cellxgene_census.experimental.ml as census_ml +import cellxgene_census +import tiledbsoma as soma + +experiment = census["census_data"]["homo_sapiens"] + +experiment_datapipe = census_ml.ExperimentDataPipe( + experiment, + measurement_name="RNA", + X_name="raw", + obs_query=soma.AxisQuery(value_filter="tissue_general == 'tongue' and is_primary_data == True"), + obs_column_names=["cell_type"], + batch_size=128, + shuffle=True, + soma_chunk_size=10_000, +) +``` + +Then you can perform any PyTorch operations and training. + +```python +# Splitting training and test sets +train_datapipe, test_datapipe = experiment_datapipe.random_split(weights={"train": 0.8, "test": 0.2}, seed=1) + +# Creating data loader +experiment_dataloader = census_ml.experiment_dataloader(train_datapipe) + +# Training a PyTorch model +device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") +model = MODEL().to(device) +model.train() +``` + +## Census PyTorch loaders main features + +### Efficient memory usage with out-of-core training + +Thanks to the underlying backend of Census — TileDB-SOMA — the PyTorch loaders take advantage of incremental data materilization of fix size to keep memory usage constant throughout training. + +In addition, data is eagerly fetched while training batches are processed so that compute is never idle and waiting for data to be loaded, this feature is particularly useful when fetching Census data directly from the cloud. + +Memory usage is defined by the parameters #TODO ask ebezzi to fill out. + +### Calibrated shuffling of observations (cells) From af077a1f49b30049d867d003897982ed8348e2f7 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Thu, 27 Jun 2024 11:24:35 -0700 Subject: [PATCH 02/13] update article --- docs/articles/2024/20240702-pytorch.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index f65c91404..f9c55d3fc 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -69,8 +69,16 @@ model.train() Thanks to the underlying backend of Census — TileDB-SOMA — the PyTorch loaders take advantage of incremental data materilization of fix size to keep memory usage constant throughout training. -In addition, data is eagerly fetched while training batches are processed so that compute is never idle and waiting for data to be loaded, this feature is particularly useful when fetching Census data directly from the cloud. +In addition, data is eagerly fetched while batches go through training so that compute is never idle or waiting for data to be loaded. This feature is particularly useful when fetching Census data directly from the cloud. Memory usage is defined by the parameters #TODO ask ebezzi to fill out. ### Calibrated shuffling of observations (cells) + +Shuffling along efficient out-of-core data fetching is a challenge. Increasing randomness of shuffling leads to slower data fetching. + +In the first iteration of the loaders, shuffling was done per large blocks of data with size defined by the user. This shuffling strategy led to non-random distribution of observations per training batch, and since Census has very established data structure (observations from the same datasets are adjacent) the training loss was unstable. + +Now we have implemented a scatter-gather approach, whereby multiple blocks of data are fetched randomly from Census, then an N number of blocks are concatenated and all observations within the concatenation are randomly shuffled. Adjusting the size and number of blocks per gather, leads to well-calibrated shuffling with stable training loss while maintaining efficient data fetching. + + From 632543994b03fd1094073dee132aa72f72527aa4 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Fri, 28 Jun 2024 13:12:54 -0700 Subject: [PATCH 03/13] add article --- .../2024/20240702-pytorch-fig-benchmark.png | Bin 0 -> 80456 bytes .../2024/20240702-pytorch-fig-loss-after.png | Bin 0 -> 234996 bytes .../2024/20240702-pytorch-fig-loss-before.png | Bin 0 -> 188948 bytes .../2024/20240702-pytorch-fig-scvi.png | Bin 0 -> 44502 bytes docs/articles/2024/20240702-pytorch.md | 82 +++++++++++++++--- 5 files changed, 69 insertions(+), 13 deletions(-) create mode 100644 docs/articles/2024/20240702-pytorch-fig-benchmark.png create mode 100644 docs/articles/2024/20240702-pytorch-fig-loss-after.png create mode 100644 docs/articles/2024/20240702-pytorch-fig-loss-before.png create mode 100644 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b/docs/articles/2024/20240702-pytorch.md index f9c55d3fc..54cb75641 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -2,30 +2,30 @@ *Published:* *July 2nd, 2024* -*By:* *[Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com), [Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com)* +*By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com), [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com)* -The Census team is excited to share the release of PyTorch loaders that work out-of-the-box for memory-efficient training across any slice of the >70M cells in Census. +The Census team is excited to share the release of Census PyTorch loaders that work out-of-the-box for memory-efficient training across any slice of the >70M cells in Census. -In 2023, we released a beta version of the loaders and we have observed interest from users to utilize them with Census or their own data. For example [Wolf et al.](https://lamin.ai/blog/arrayloader-benchmarks) performed comparisons across different training apporaches and found our loaders to be ideal for _uncached_ training of Census data, albeit with some caveats. +In 2023, we released a beta version of the loaders and we have observed interest from users to utilize them with Census or their own data. For example [Wolf et al.](https://lamin.ai/blog/arrayloader-benchmarks) performed comparisons across different training approaches and found our loaders to be ideal for _uncached_ training of Census data, albeit with some caveats. We have continued the development of the loaders in collaboration with our partners at TileDB, and we are happy to announce this release as the first stable iteration. We hope the loaders can accelerate the development of large-scale models of single-cell data by leveraging the following main features: +- **Out-of-the-box training on all or any slice of Census data.** - **Efficient memory usage with out-of-core training.** - **Calibrated shuffling of observations (cells).** -- **Out-of-the-box training on all or any slice of Census data.** - **Cloud-based or local data access.** +- **Increased training speed.** - **Custom data encoders.** -- **Incresed training speed.** -Keep on reading for more usage and benchmark details. +Keep on reading for usage and more details on the main loader features. ## Census PyTorch loaders usage -The loaders are ready to use for PyTorch modelling via the specilized Data Pipe [`ExperimentDataPipe`](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html#cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe), which takes advantage of the out-of-core data access TileDB-SOMA offers. +The loaders are ready to use for PyTorch modeling via the specialized Data Pipe [`ExperimentDataPipe`](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html#cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe), which takes advantage of the out-of-core data access TileDB-SOMA offers. -Please follow our [Training a PyTorch Model](https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html) tutorial for a full reproducible example to train a Logistic Regression. +Please follow the [Training a PyTorch Model](https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html) tutorial for a full reproducible example to train a logistic regression on cell type labels. -Shortly, you can intilize `ExperimentDataPipe` to train a model on tongue cells as follows: +In short, the following shows you how to initialize the loader to train a model on a small subset of cells. First, you can initialize a `ExperimentDataPipe` to train a model on tongue cells as follows: \#TODO verify with ebezzi that this code is up to date @@ -65,9 +65,15 @@ model.train() ## Census PyTorch loaders main features +### Out-of-the-box training on all or any slice of Census data + +Since the `ExperimentDataPipe` inherits from the [PyTorch Iterable-style DataPipe](https://pytorch.org/data/main/torchdata.datapipes.iter.html) it can be readily used with PyTorch models. + +The single-cell expression data is encoded in numerical tensors, and for supervised training the cell metadata can be automatically transformed with a default encoder, or with custom user-defined encoders (see below). + ### Efficient memory usage with out-of-core training -Thanks to the underlying backend of Census — TileDB-SOMA — the PyTorch loaders take advantage of incremental data materilization of fix size to keep memory usage constant throughout training. +Thanks to the underlying backend of Census — TileDB-SOMA — the PyTorch loaders take advantage of incremental data materialization of fixed and small size to keep memory usage constant throughout training. In addition, data is eagerly fetched while batches go through training so that compute is never idle or waiting for data to be loaded. This feature is particularly useful when fetching Census data directly from the cloud. @@ -75,10 +81,60 @@ Memory usage is defined by the parameters #TODO ask ebezzi to fill out. ### Calibrated shuffling of observations (cells) -Shuffling along efficient out-of-core data fetching is a challenge. Increasing randomness of shuffling leads to slower data fetching. +Shuffling along efficient out-of-core data fetching is a challenge. In general, increasing randomness of shuffling leads to slower data fetching. + +In the first iteration of the loaders, shuffling was done through large blocks of data of user-defined size. This shuffling strategy led to non-random distribution of observations per training batch, becasue Census has a non-random data structure (observations from the same datasets are adjacent to one another) thus training loss was unstable (Figure 1). + +**Now we have implemented a scatter-gather approach**, whereby multiple chunks of data are fetched randomly from Census, then a number of chunks are concatenated into a block and all observations within the block are randomly shuffled. Adjusting the size and number of chunks per block leads to well-calibrated shuffling with stable training loss (Figure 2) while maintaining efficient data fetching (Figure 3). -In the first iteration of the loaders, shuffling was done per large blocks of data with size defined by the user. This shuffling strategy led to non-random distribution of observations per training batch, and since Census has very established data structure (observations from the same datasets are adjacent) the training loss was unstable. +The balance between memory usage, efficiency, and level of randomness can be adjusted with the parameters #TODO ask ebezzi to fill in + +```{figure} ./20240702-pytorch-fig-loss-before.png +:alt: Census PyTorch loaders shuffling +:align: center +:figwidth: 80% + +**Figure 1. Training loss was unstable with the previous shuffling strategy**. Based on a trial scVI run on 64K Census cells. +``` + +```{figure} ./20240702-pytorch-fig-loss-after.png +:alt: Census PyTorch loaders callibrated shuffling +:align: center +:figwidth: 80% + +**Figure 2. Training loss is well-calibrated with the current scatter-gather shuffling strategy.** Based on a trial scVI run on 250K Census cells. +``` + +### Increased training speed + +We have made improvements to the loaders to reduce the amount of data transformations required from data fetching to model training. One such important change is to encode the expression data as a dense matrix immediately after the data is retrieved from disk/cloud. + +In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the #TODO ask ebezzi to include name of parameter. + +\#TODO ask Ryan to review + +```{figure} ./20240702-pytorch-fig-benchmark.png +:alt: Census PyTorch loaders benchmark +:align: center +:figwidth: 80% + +**Figure 3. Benchmark of memory usage and speed of data processing during modeling, default parameters lead to 3K+ samples/sec with 27GB of memory.** The benchmark was done processing 4M cells out of a 10M-cell Census, data was fetched from the cloud (S3). "Method" indicates the expression matrix encoding, circles are dense (np.array) and squares are sparse (scipy.csr). Size indicates the total number of cells per processing block (max cells materialized at any given time) and color is the number of individual randomly grabbed chunks composing a processing block, higher chunks per block lead to better shuffling. Data was fetched until modeling step, but no model was trained. +``` + +We repeated the benchmark in Figure 3 in different conditions encompassing varying number of total cells and multiple epochs, please [follow this link for the full benchmark report and code.](https://github.com/ryan-williams/arrayloader-benchmarks). + +When comparing dense vs sparse processing in an end-to-end training exercise with scVI, we also observed slight increased speed with the dense approach and comparable memory usage to sparse processing (Figure 4). However in this full training example the differences were less substantial, highlighting that other model-specific factors during the training phase will contribute to memory and speed performance. + +```{figure} ./20240702-pytorch-fig-scvi.png +:alt: Census scVI PyTorch run +:align: center +:figwidth: 80% + +**Figure 4. Trial scVI training run with default parameters of the Census Pytorch loaders, highlighting increased speed of dense vs sparse data processing.** Training was done on X mouse cells for 1 epoch in X EC2 instance #TODO ask ebezzi for further details. +``` -Now we have implemented a scatter-gather approach, whereby multiple blocks of data are fetched randomly from Census, then an N number of blocks are concatenated and all observations within the concatenation are randomly shuffled. Adjusting the size and number of blocks per gather, leads to well-calibrated shuffling with stable training loss while maintaining efficient data fetching. +### Custom data encoders +For maximum flexibility, users can provide custom encoders for the cell metadata enabling custom transformations or interactions between different metadata variables. +To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperiementDataPipe` \ No newline at end of file From a87b65bb20c46917ed6b025620059513137acc57 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Fri, 28 Jun 2024 13:44:09 -0700 Subject: [PATCH 04/13] add author --- docs/articles/2024/20240702-pytorch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index 54cb75641..8a1f65405 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -2,7 +2,7 @@ *Published:* *July 2nd, 2024* -*By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com), [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com)* +*By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com), [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Prathap Sridharan](mailto:psridharan@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com)* The Census team is excited to share the release of Census PyTorch loaders that work out-of-the-box for memory-efficient training across any slice of the >70M cells in Census. From cadef7a81e7ddd97f0d711eea3446eca08c50246 Mon Sep 17 00:00:00 2001 From: pablo-gar Date: Fri, 28 Jun 2024 13:48:09 -0700 Subject: [PATCH 05/13] Editorial Co-authored-by: Emanuele Bezzi --- docs/articles/2024/20240702-pytorch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index 8a1f65405..fc50de443 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -137,4 +137,4 @@ When comparing dense vs sparse processing in an end-to-end training exercise wit For maximum flexibility, users can provide custom encoders for the cell metadata enabling custom transformations or interactions between different metadata variables. -To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperiementDataPipe` \ No newline at end of file +To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe` \ No newline at end of file From e7d398cc588b80452bf8a073426f12166ed62331 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Fri, 28 Jun 2024 13:57:03 -0700 Subject: [PATCH 06/13] lint --- docs/articles/2024/20240702-pytorch.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index fc50de443..0e3cbdad9 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -6,7 +6,7 @@ The Census team is excited to share the release of Census PyTorch loaders that work out-of-the-box for memory-efficient training across any slice of the >70M cells in Census. -In 2023, we released a beta version of the loaders and we have observed interest from users to utilize them with Census or their own data. For example [Wolf et al.](https://lamin.ai/blog/arrayloader-benchmarks) performed comparisons across different training approaches and found our loaders to be ideal for _uncached_ training of Census data, albeit with some caveats. +In 2023, we released a beta version of the loaders and we have observed interest from users to utilize them with Census or their own data. For example [Wolf et al.](https://lamin.ai/blog/arrayloader-benchmarks) performed comparisons across different training approaches and found our loaders to be ideal for *uncached* training of Census data, albeit with some caveats. We have continued the development of the loaders in collaboration with our partners at TileDB, and we are happy to announce this release as the first stable iteration. We hope the loaders can accelerate the development of large-scale models of single-cell data by leveraging the following main features: @@ -21,9 +21,9 @@ Keep on reading for usage and more details on the main loader features. ## Census PyTorch loaders usage -The loaders are ready to use for PyTorch modeling via the specialized Data Pipe [`ExperimentDataPipe`](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html#cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe), which takes advantage of the out-of-core data access TileDB-SOMA offers. +The loaders are ready to use for PyTorch modeling via the specialized Data Pipe [`ExperimentDataPipe`](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe.html#cellxgene_census.experimental.ml.pytorch.ExperimentDataPipe), which takes advantage of the out-of-core data access TileDB-SOMA offers. -Please follow the [Training a PyTorch Model](https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html) tutorial for a full reproducible example to train a logistic regression on cell type labels. +Please follow the [Training a PyTorch Model](https://chanzuckerberg.github.io/cellxgene-census/notebooks/experimental/pytorch.html) tutorial for a full reproducible example to train a logistic regression on cell type labels. In short, the following shows you how to initialize the loader to train a model on a small subset of cells. First, you can initialize a `ExperimentDataPipe` to train a model on tongue cells as follows: @@ -67,7 +67,7 @@ model.train() ### Out-of-the-box training on all or any slice of Census data -Since the `ExperimentDataPipe` inherits from the [PyTorch Iterable-style DataPipe](https://pytorch.org/data/main/torchdata.datapipes.iter.html) it can be readily used with PyTorch models. +Since the `ExperimentDataPipe` inherits from the [PyTorch Iterable-style DataPipe](https://pytorch.org/data/main/torchdata.datapipes.iter.html) it can be readily used with PyTorch models. The single-cell expression data is encoded in numerical tensors, and for supervised training the cell metadata can be automatically transformed with a default encoder, or with custom user-defined encoders (see below). @@ -105,11 +105,11 @@ The balance between memory usage, efficiency, and level of randomness can be adj **Figure 2. Training loss is well-calibrated with the current scatter-gather shuffling strategy.** Based on a trial scVI run on 250K Census cells. ``` -### Increased training speed +### Increased training speed -We have made improvements to the loaders to reduce the amount of data transformations required from data fetching to model training. One such important change is to encode the expression data as a dense matrix immediately after the data is retrieved from disk/cloud. +We have made improvements to the loaders to reduce the amount of data transformations required from data fetching to model training. One such important change is to encode the expression data as a dense matrix immediately after the data is retrieved from disk/cloud. -In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the #TODO ask ebezzi to include name of parameter. +In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the #TODO ask ebezzi to include name of parameter. \#TODO ask Ryan to review @@ -121,7 +121,7 @@ In our benchmarks, we found that densifying data increases training speed ~3X wh **Figure 3. Benchmark of memory usage and speed of data processing during modeling, default parameters lead to 3K+ samples/sec with 27GB of memory.** The benchmark was done processing 4M cells out of a 10M-cell Census, data was fetched from the cloud (S3). "Method" indicates the expression matrix encoding, circles are dense (np.array) and squares are sparse (scipy.csr). Size indicates the total number of cells per processing block (max cells materialized at any given time) and color is the number of individual randomly grabbed chunks composing a processing block, higher chunks per block lead to better shuffling. Data was fetched until modeling step, but no model was trained. ``` -We repeated the benchmark in Figure 3 in different conditions encompassing varying number of total cells and multiple epochs, please [follow this link for the full benchmark report and code.](https://github.com/ryan-williams/arrayloader-benchmarks). +We repeated the benchmark in Figure 3 in different conditions encompassing varying number of total cells and multiple epochs, please [follow this link for the full benchmark report and code.](https://github.com/ryan-williams/arrayloader-benchmarks). When comparing dense vs sparse processing in an end-to-end training exercise with scVI, we also observed slight increased speed with the dense approach and comparable memory usage to sparse processing (Figure 4). However in this full training example the differences were less substantial, highlighting that other model-specific factors during the training phase will contribute to memory and speed performance. @@ -137,4 +137,5 @@ When comparing dense vs sparse processing in an end-to-end training exercise wit For maximum flexibility, users can provide custom encoders for the cell metadata enabling custom transformations or interactions between different metadata variables. -To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe` \ No newline at end of file +To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`. + From cbacf9e72d0ae19f9805168a2c249cee1b460fcd Mon Sep 17 00:00:00 2001 From: pablo-gar Date: Fri, 28 Jun 2024 14:13:53 -0700 Subject: [PATCH 07/13] editorial Co-authored-by: Emanuele Bezzi --- docs/articles/2024/20240702-pytorch.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index 0e3cbdad9..afb26a2b2 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -77,7 +77,7 @@ Thanks to the underlying backend of Census — TileDB-SOMA — the PyTorch loade In addition, data is eagerly fetched while batches go through training so that compute is never idle or waiting for data to be loaded. This feature is particularly useful when fetching Census data directly from the cloud. -Memory usage is defined by the parameters #TODO ask ebezzi to fill out. +Memory usage is defined by the parameters `soma_chunk_size` and `shuffle_chunk_count` - see below for a full description on how these should be tuned. ### Calibrated shuffling of observations (cells) @@ -87,7 +87,7 @@ In the first iteration of the loaders, shuffling was done through large blocks o **Now we have implemented a scatter-gather approach**, whereby multiple chunks of data are fetched randomly from Census, then a number of chunks are concatenated into a block and all observations within the block are randomly shuffled. Adjusting the size and number of chunks per block leads to well-calibrated shuffling with stable training loss (Figure 2) while maintaining efficient data fetching (Figure 3). -The balance between memory usage, efficiency, and level of randomness can be adjusted with the parameters #TODO ask ebezzi to fill in +The balance between memory usage, efficiency, and level of randomness can be adjusted with the parameters `soma_chunk_size` and `shuffle_chunk_count`. Increasing `shuffle_chunk_count` will improve randomness, as more scattered chunks will be collected before the pool is randomized. Increasing `soma_chunk_size` will improve I/O efficiency while decreasing it will improve memory usage. We recommend a default of `soma_chunk_size=64, shuffle_chunk_count=2000` as we determined this configuration yields a good balance. ```{figure} ./20240702-pytorch-fig-loss-before.png :alt: Census PyTorch loaders shuffling From 5c7e5bf7f5693d1efdd3f812809631a9bf1f3c5d Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Fri, 28 Jun 2024 14:15:32 -0700 Subject: [PATCH 08/13] editorial --- docs/articles/2024/20240702-pytorch.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index afb26a2b2..eb2a70894 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -27,8 +27,6 @@ Please follow the [Training a PyTorch Model](https://chanzuckerberg.github.io/ce In short, the following shows you how to initialize the loader to train a model on a small subset of cells. First, you can initialize a `ExperimentDataPipe` to train a model on tongue cells as follows: -\#TODO verify with ebezzi that this code is up to date - ```python import cellxgene_census.experimental.ml as census_ml import cellxgene_census @@ -44,7 +42,6 @@ experiment_datapipe = census_ml.ExperimentDataPipe( obs_column_names=["cell_type"], batch_size=128, shuffle=True, - soma_chunk_size=10_000, ) ``` From a33479aed443a1d4aa34020cae2e9c2faacedc3f Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Fri, 28 Jun 2024 15:23:58 -0700 Subject: [PATCH 09/13] lint --- docs/articles/2024/20240702-pytorch.md | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index eb2a70894..fef233248 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -135,4 +135,3 @@ When comparing dense vs sparse processing in an end-to-end training exercise wit For maximum flexibility, users can provide custom encoders for the cell metadata enabling custom transformations or interactions between different metadata variables. To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`. - From db019c4d8ef50baf81297ee41bcc3e550d92fabd Mon Sep 17 00:00:00 2001 From: pablo-gar Date: Mon, 8 Jul 2024 16:44:09 -0700 Subject: [PATCH 10/13] editorial Co-authored-by: Emanuele Bezzi --- docs/articles/2024/20240702-pytorch.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index fef233248..ce8543f0d 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -127,11 +127,11 @@ When comparing dense vs sparse processing in an end-to-end training exercise wit :align: center :figwidth: 80% -**Figure 4. Trial scVI training run with default parameters of the Census Pytorch loaders, highlighting increased speed of dense vs sparse data processing.** Training was done on X mouse cells for 1 epoch in X EC2 instance #TODO ask ebezzi for further details. +**Figure 4. Trial scVI training run with default parameters of the Census Pytorch loaders, highlighting increased speed of dense vs sparse data processing.** Training was done on 5684805 mouse cells for 1 epoch on a g4dn.16xlarge EC2 machine. ``` ### Custom data encoders For maximum flexibility, users can provide custom encoders for the cell metadata enabling custom transformations or interactions between different metadata variables. -To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`. +To use custom encoders you need to instantiate the desired encoder via the [Encoder](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.encoders.Encoder.html#cellxgene_census.experimental.ml.encoders.Encoder) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`. From bdbdff89cdde81298cda26a9029a9ee899ac54cd Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Mon, 8 Jul 2024 17:02:14 -0700 Subject: [PATCH 11/13] final edits --- docs/articles/2024/20240702-pytorch.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240702-pytorch.md index ce8543f0d..d84750e60 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240702-pytorch.md @@ -1,6 +1,6 @@ # First stable iteration of Census (SOMA) PyTorch loaders -*Published:* *July 2nd, 2024* +*Published:* *July 8th, 2024* *By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com), [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Prathap Sridharan](mailto:psridharan@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com)* @@ -106,9 +106,7 @@ The balance between memory usage, efficiency, and level of randomness can be adj We have made improvements to the loaders to reduce the amount of data transformations required from data fetching to model training. One such important change is to encode the expression data as a dense matrix immediately after the data is retrieved from disk/cloud. -In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the #TODO ask ebezzi to include name of parameter. - -\#TODO ask Ryan to review +In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). For this reason, we have disable the intermediate data processing in sparse format unless Torch Sparse Tensors are requested via the `ExperimentDataPipe` parameter `return_sparse_X`. ```{figure} ./20240702-pytorch-fig-benchmark.png :alt: Census PyTorch loaders benchmark From cce23374005f2b211bf8f30b0e1a0d5e9e430fe6 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Mon, 8 Jul 2024 17:06:03 -0700 Subject: [PATCH 12/13] update date --- docs/articles/2024/{20240702-pytorch.md => 20240709-pytorch.md} | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename docs/articles/2024/{20240702-pytorch.md => 20240709-pytorch.md} (99%) diff --git a/docs/articles/2024/20240702-pytorch.md b/docs/articles/2024/20240709-pytorch.md similarity index 99% rename from docs/articles/2024/20240702-pytorch.md rename to docs/articles/2024/20240709-pytorch.md index d84750e60..9eeabf6d4 100644 --- a/docs/articles/2024/20240702-pytorch.md +++ b/docs/articles/2024/20240709-pytorch.md @@ -1,6 +1,6 @@ # First stable iteration of Census (SOMA) PyTorch loaders -*Published:* *July 8th, 2024* +*Published:* *July 9th, 2024* *By:* *[Emanuele Bezzi](mailto:ebezzi@chanzuckerberg.com), [Pablo Garcia-Nieto](mailto:pgarcia-nieto@chanzuckerberg.com), [Prathap Sridharan](mailto:psridharan@chanzuckerberg.com), [Ryan Williams](mailto:pgarcia-nieto@chanzuckerberg.com)* From 273ffc34d5fb21e1dfcae766ea6f4daa9de45881 Mon Sep 17 00:00:00 2001 From: Pablo Garcia-Nieto Date: Mon, 8 Jul 2024 17:10:03 -0700 Subject: [PATCH 13/13] update date --- ...hmark.png => 20240709-pytorch-fig-benchmark.png} | Bin ...fter.png => 20240709-pytorch-fig-loss-after.png} | Bin ...ore.png => 20240709-pytorch-fig-loss-before.png} | Bin ...h-fig-scvi.png => 20240709-pytorch-fig-scvi.png} | Bin docs/articles/2024/20240709-pytorch.md | 8 ++++---- 5 files changed, 4 insertions(+), 4 deletions(-) rename docs/articles/2024/{20240702-pytorch-fig-benchmark.png => 20240709-pytorch-fig-benchmark.png} (100%) rename docs/articles/2024/{20240702-pytorch-fig-loss-after.png => 20240709-pytorch-fig-loss-after.png} (100%) rename docs/articles/2024/{20240702-pytorch-fig-loss-before.png => 20240709-pytorch-fig-loss-before.png} (100%) rename docs/articles/2024/{20240702-pytorch-fig-scvi.png => 20240709-pytorch-fig-scvi.png} (100%) diff --git a/docs/articles/2024/20240702-pytorch-fig-benchmark.png b/docs/articles/2024/20240709-pytorch-fig-benchmark.png similarity index 100% rename from docs/articles/2024/20240702-pytorch-fig-benchmark.png rename to docs/articles/2024/20240709-pytorch-fig-benchmark.png diff --git a/docs/articles/2024/20240702-pytorch-fig-loss-after.png b/docs/articles/2024/20240709-pytorch-fig-loss-after.png similarity index 100% rename from docs/articles/2024/20240702-pytorch-fig-loss-after.png rename to docs/articles/2024/20240709-pytorch-fig-loss-after.png diff --git a/docs/articles/2024/20240702-pytorch-fig-loss-before.png b/docs/articles/2024/20240709-pytorch-fig-loss-before.png similarity index 100% rename from docs/articles/2024/20240702-pytorch-fig-loss-before.png rename to docs/articles/2024/20240709-pytorch-fig-loss-before.png diff --git a/docs/articles/2024/20240702-pytorch-fig-scvi.png b/docs/articles/2024/20240709-pytorch-fig-scvi.png similarity index 100% rename from docs/articles/2024/20240702-pytorch-fig-scvi.png rename to docs/articles/2024/20240709-pytorch-fig-scvi.png diff --git a/docs/articles/2024/20240709-pytorch.md b/docs/articles/2024/20240709-pytorch.md index 9eeabf6d4..8515c9f21 100644 --- a/docs/articles/2024/20240709-pytorch.md +++ b/docs/articles/2024/20240709-pytorch.md @@ -86,7 +86,7 @@ In the first iteration of the loaders, shuffling was done through large blocks o The balance between memory usage, efficiency, and level of randomness can be adjusted with the parameters `soma_chunk_size` and `shuffle_chunk_count`. Increasing `shuffle_chunk_count` will improve randomness, as more scattered chunks will be collected before the pool is randomized. Increasing `soma_chunk_size` will improve I/O efficiency while decreasing it will improve memory usage. We recommend a default of `soma_chunk_size=64, shuffle_chunk_count=2000` as we determined this configuration yields a good balance. -```{figure} ./20240702-pytorch-fig-loss-before.png +```{figure} ./20240709-pytorch-fig-loss-before.png :alt: Census PyTorch loaders shuffling :align: center :figwidth: 80% @@ -94,7 +94,7 @@ The balance between memory usage, efficiency, and level of randomness can be adj **Figure 1. Training loss was unstable with the previous shuffling strategy**. Based on a trial scVI run on 64K Census cells. ``` -```{figure} ./20240702-pytorch-fig-loss-after.png +```{figure} ./20240709-pytorch-fig-loss-after.png :alt: Census PyTorch loaders callibrated shuffling :align: center :figwidth: 80% @@ -108,7 +108,7 @@ We have made improvements to the loaders to reduce the amount of data transforma In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). For this reason, we have disable the intermediate data processing in sparse format unless Torch Sparse Tensors are requested via the `ExperimentDataPipe` parameter `return_sparse_X`. -```{figure} ./20240702-pytorch-fig-benchmark.png +```{figure} ./20240709-pytorch-fig-benchmark.png :alt: Census PyTorch loaders benchmark :align: center :figwidth: 80% @@ -120,7 +120,7 @@ We repeated the benchmark in Figure 3 in different conditions encompassing varyi When comparing dense vs sparse processing in an end-to-end training exercise with scVI, we also observed slight increased speed with the dense approach and comparable memory usage to sparse processing (Figure 4). However in this full training example the differences were less substantial, highlighting that other model-specific factors during the training phase will contribute to memory and speed performance. -```{figure} ./20240702-pytorch-fig-scvi.png +```{figure} ./20240709-pytorch-fig-scvi.png :alt: Census scVI PyTorch run :align: center :figwidth: 80%