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src="https://opendatascience.com/wp-content/uploads/2019/06/1_OgeJ5Y2fFHhRi-PLVi0HlA-750x350.jpeg" class="mobile-post-thumb" alt="Using RAPIDS with PyTorch"/> <div> <div class="social"> <div class="post-image-sharrre"> <div class="shareme" data-image="https://opendatascience.com/wp-content/uploads/2019/06/1_OgeJ5Y2fFHhRi-PLVi0HlA.jpeg" data-url="https://opendatascience.com/using-rapids-with-pytorch/" data-text="Using RAPIDS with PyTorch"></div> </div></div> </div> </div> <div class="title"> <h1 id="intro"><a href="https://opendatascience.com/using-rapids-with-pytorch/" class="entry-title" itemprop="headline">Using RAPIDS with PyTorch</a></h1> <p> <a href="https://opendatascience.com/category/modeling/deep-learning/" title="Deep Learning" class="tag-default">Deep Learning</a><a href="https://opendatascience.com/category/modeling/machine-learning/" title="Machine Learning" class="tag-default">Machine Learning</a><a href="https://opendatascience.com/category/modeling/" title="Modeling" class="tag-default">Modeling</a><a href="https://opendatascience.com/category/tools-languages/" title="Tools & Languages" class="tag-default">Tools & Languages</a><a href="https://opendatascience.com/tag/deep-learning/" title="Tools & Languages" class="tag-default">Deep Learning</a><a href="https://opendatascience.com/tag/machine-learning/" title="Tools & Languages" class="tag-default">Machine Learning</a><a href="https://opendatascience.com/tag/rapids/" title="Tools & Languages" class="tag-default">rapids</a>posted by <a href="https://opendatascience.com/user/rapids/" title="Tools & Languages" class="tag-defaultic">RAPIDS</a> <span class="legend-default"> <i class="fa fa-clock-o"></i><span class="updated" >June 19, 2019</span> <i class="fa fa-user"></i><span ><a href="" class="author url fn">RAPIDS</a></span> </span> </p> <div class="tag-cloud tag-title"> <i class="fa fa-tags"></i> <a href="https://opendatascience.com/tag/deep-learning/" title="Deep Learning" class="tag-1"><span>Deep Learning</span><s>48</s></a> <a href="https://opendatascience.com/tag/machine-learning/" title="Machine Learning" class="tag-1"><span>Machine Learning</span><s>111</s></a> <a href="https://opendatascience.com/tag/rapids/" title="rapids" class="tag-1"><span>rapids</span><s>13</s></a> </div> </div> <span class="item-summary-hidden" itemprop="description">In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing...</span> <img class="item-image-hidden" itemprop="image" src="https://opendatascience.com/wp-content/uploads/2019/06/1_OgeJ5Y2fFHhRi-PLVi0HlA-640x300.jpeg" alt="Using RAPIDS with PyTorch" /> <div class="post" itemprop="articleBody"><div class="at-above-post addthis_tool" data-url="https://opendatascience.com/using-rapids-with-pytorch/"></div><section class="section section--body section--first"> <div class="section-content"> <div class="section-inner sectionLayout--insetColumn"> <p id="8af6" class="graf graf--p graf-after--figure">In this post we take a look at how to use <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/cudf" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/cudf">cuDF, the RAPIDS dataframe library</a>, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on tabular data and compare it to the xgboost method.</p> <p id="8f78" class="graf graf--p graf-after--p">While deep learning (DL) has been successfully adopted in many domains (Vision, NLP, Recommendation, etc.), there are still areas where the use of DL is not mainstream. Tabular data problems, which consists of columns of categorical and continuous variables, commonly make use of techniques like <a class="markup--anchor markup--p-anchor" href="https://github.com/dmlc/xgboost" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/dmlc/xgboost">xgboost</a>, <a class="markup--anchor markup--p-anchor" href="https://github.com/catboost" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/catboost">catboost</a>, <a class="markup--anchor markup--p-anchor" href="https://github.com/Microsoft/LightGBM" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/Microsoft/LightGBM">gbm</a>, or even linear models. The <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/cuml" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/cuml">RAPIDS cuML library</a> is focused on building out those capabilities on GPUs, and in the <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/notebooks" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/notebooks">first notebook released by the RAPIDS team</a> we used a <a class="markup--anchor markup--p-anchor" href="https://xgboost.readthedocs.io/en/latest/gpu/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://xgboost.readthedocs.io/en/latest/gpu/">GPU-accelerated version of xgboost</a> to train a model as quickly as possible at scale on the <a class="markup--anchor markup--p-anchor" href="http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html" target="_blank" rel="nofollow noopener noreferrer" data-href="http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html">Fannie Mae mortgage dataset</a>.</p> <p><em>[Related Article: <a href="https://opendatascience.com/how-to-choose-machine-learning-or-deep-learning-for-your-business/" target="_blank" rel="noopener noreferrer">How to Choose Machine Learning or Deep Learning for Your Business</a>]</em></p> <p id="f2c6" class="graf graf--p graf-after--p">Deep learning is, however, making inroads into tabular data problems. Recent Kaggle competition winners of the <a class="markup--anchor markup--p-anchor" href="https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89003#latest-515385" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.kaggle.com/c/santander-customer-transaction-prediction/discussion/89003#latest-515385">Santander</a>, <a class="markup--anchor markup--p-anchor" href="https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/discussion/44629#250927" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.kaggle.com/c/porto-seguro-safe-driver-prediction/discussion/44629#250927">Porto Seguro</a>, and <a class="markup--anchor markup--p-anchor" href="http://blog.kaggle.com/2015/07/27/taxi-trajectory-winners-interview-1st-place-team-%F0%9F%9A%95/" target="_blank" rel="nofollow noopener noreferrer" data-href="http://blog.kaggle.com/2015/07/27/taxi-trajectory-winners-interview-1st-place-team-%F0%9F%9A%95/">Taxi Trajectory</a> competitions used deep learning as a significant part of their solution, and the Rossman store sales (<a class="markup--anchor markup--p-anchor" href="http://blog.kaggle.com/2016/01/22/rossmann-store-sales-winners-interview-3rd-place-cheng-gui/" target="_blank" rel="nofollow noopener noreferrer" data-href="http://blog.kaggle.com/2016/01/22/rossmann-store-sales-winners-interview-3rd-place-cheng-gui/">#3 solution</a>) and Petfinder competitions (<a class="markup--anchor markup--p-anchor" href="https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/88690#latest-515029" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/88690#latest-515029">#6</a> and <a class="markup--anchor markup--p-anchor" href="https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/88740#latest-515210" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.kaggle.com/c/petfinder-adoption-prediction/discussion/88740#latest-515210">#9 solution</a>) both had interesting architectures and methodologies that used DNNs to achieve top ten rankings. The Petfinder solutions are of particular interest because they combine tabular, image and text data in a single model. Many companies have come forward stating that they use deep learning in these or similar contexts, including <a class="markup--anchor markup--p-anchor" href="https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://twimlai.com/twiml-talk-124-systems-software-machine-learning-scale-jeff-dean/">Google</a>, <a class="markup--anchor markup--p-anchor" href="https://medium.com/the-graph/applying-deep-learning-to-related-pins-a6fee3c92f5e" target="_blank" rel="noopener noreferrer" data-href="https://medium.com/the-graph/applying-deep-learning-to-related-pins-a6fee3c92f5e">Pinterest</a>, <a class="markup--anchor markup--p-anchor" href="https://blog.twitter.com/engineering/en_us/topics/insights/2018/embeddingsattwitter.html" target="_blank" rel="nofollow noopener noreferrer" data-href="https://blog.twitter.com/engineering/en_us/topics/insights/2018/embeddingsattwitter.html">Twitter</a>, <a class="markup--anchor markup--p-anchor" href="https://tech.instacart.com/deep-learning-with-emojis-not-math-660ba1ad6cdc" target="_blank" rel="nofollow noopener noreferrer" data-href="https://tech.instacart.com/deep-learning-with-emojis-not-math-660ba1ad6cdc">Instacart</a>, and <a class="markup--anchor markup--p-anchor" href="https://ai.google/research/pubs/pub45530" target="_blank" rel="nofollow noopener noreferrer" data-href="https://ai.google/research/pubs/pub45530">Youtube</a>. Deep learning library<a class="markup--anchor markup--p-anchor" href="https://docs.fast.ai/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://docs.fast.ai/"> fast.ai</a> has even added support for<a class="markup--anchor markup--p-anchor" href="https://docs.fast.ai/tabular.html" target="_blank" rel="nofollow noopener noreferrer" data-href="https://docs.fast.ai/tabular.html"> tabular data</a> directly into the library in the form of <a class="markup--anchor markup--p-anchor" href="https://www.fast.ai/2018/04/29/categorical-embeddings/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.fast.ai/2018/04/29/categorical-embeddings/">categorical embeddings</a> and a range of preprocessing options required for tabular data.</p> <p id="85c6" class="graf graf--p graf-after--p graf--trailing">Please check out the <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e_deep_learning.ipynb" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e_deep_learning.ipynb">accompanying notebook</a> in the <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/">RAPIDS.AI repo</a> for the implementation details. The notebook and experiments were developed and run by <a class="markup--anchor markup--p-anchor" href="https://twitter.com/cmgreen210" target="_blank" rel="nofollow noopener noreferrer" data-href="https://twitter.com/cmgreen210">Christopher Green</a> and we’re grateful for his work.</p> </div> </div> </section> <section class="section section--body"> <div class="section-divider"> <hr class="section-divider" /> </div> <div class="section-content"> <div class="section-inner sectionLayout--insetColumn"> <h4 id="ec9f" class="graf graf--h4 graf--leading">Establishing a baseline</h4> <p id="7383" class="graf graf--p graf-after--h4">As our starting point we’ll take <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e.ipynb" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e.ipynb">the original mortgage notebook from the RAPIDS repo</a>. Feature engineering is identical in order to prevent performance improvements unrelated to deep learning.</p> <p id="3682" class="graf graf--p graf-after--p">Here, when we say performance, we’re talking about how well the algorithm is able to classify loans, which we’ll measure as the <a class="markup--anchor markup--p-anchor" href="http://www.chioka.in/differences-between-roc-auc-and-pr-auc/" target="_blank" rel="nofollow noopener noreferrer" data-href="http://www.chioka.in/differences-between-roc-auc-and-pr-auc/">Area Under the Precision Recall Curve</a>, or PR-AUC for short. We chose PR-AUC over cross entropy, accuracy and ROC-AUC because we think it provides a better representation of the performance of the algorithm. Lowest cross entropy loss often doesn’t correspond to the most accurate model when examining the model’s ability to predict whether a loan will go delinquent. On the other hand, using accuracy as the only metric hides precision/recall tradeoffs necessary when evaluating a model in the real world. Finally, <a class="markup--anchor markup--p-anchor" href="http://pages.cs.wisc.edu/~jdavis/davisgoadrichcamera2.pdf" target="_blank" rel="nofollow noopener noreferrer" data-href="http://pages.cs.wisc.edu/~jdavis/davisgoadrichcamera2.pdf">ROC-AUC is less able to distinguish between model performance when there is a class imbalance</a>, which we see in the mortgage dataset.</p> <p id="0e50" class="graf graf--p graf-after--p">We use a time-based split of the data, creating validation and test sets such that they occur after the training data in order to prevent data leakage. Since we’re predicting 12-month loan delinquency we need to ensure that the validation and test data start at least 12 months after the latest date in the training dataset. Similarly, we establish a test set from a later time window than the validation set to ensure that we don’t overfit the hyperparameters.</p> <p id="eeec" class="graf graf--p graf-after--p">Because of the size of the dataset (1.75 billion rows!) and the nature of xgboost on GPU which requires the entire training dataset to be in memory, we are only able to train on 17% with a single<a class="markup--anchor markup--p-anchor" href="https://www.nvidia.com/en-us/data-center/dgx-1/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.nvidia.com/en-us/data-center/dgx-1/"> DGX-1</a> server, which has eight<a class="markup--anchor markup--p-anchor" href="https://www.nvidia.com/en-us/data-center/tesla-v100/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.nvidia.com/en-us/data-center/tesla-v100/">Tesla-V100 32GB GPUs</a>. A single V100 can handle ~2% of the dataset. If you were to try to run this on commodity hardware like a GTX 1080-ti you’d be limited to ~0.7% of the dataset. By taking advantage of the GPU’s massive parallelism <a class="markup--anchor markup--p-anchor" href="https://github.com/dmlc/xgboost/pull/4283" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/dmlc/xgboost/pull/4283">along with recent optimizations</a>, we’re able to train the multi-gpu xgboost model in an astonishing 60 seconds. The full run time including ETL and preprocessing is 178 seconds. Scaling resources to a six-node cluster allows training across the entire dataset with similar timing, but that isn’t the focus of this post.</p> <p id="0ced" class="graf graf--p graf-after--p graf--trailing">After running a hyperparameter search we achieved a best PR-AUC of 0.814104 on the validation set, which corresponds to 0.8103 on the test set. This is the benchmark we try to reach or beat using a deep learning model.</p> </div> </div> </section> <section class="section section--body"> <div class="section-divider"> <hr class="section-divider" /> </div> <div class="section-content"> <div class="section-inner sectionLayout--insetColumn"> <h4 id="5391" class="graf graf--h4 graf--leading">Deep learning specific data transforms</h4> <p id="2915" class="graf graf--p graf-after--h4">There are many ways of encoding tabular data into a format that a deep neural network (DNN) can understand. One common approach is to <a class="markup--anchor markup--p-anchor" href="https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-to-use-it-e3c6186d008f" target="_blank" rel="nofollow noopener noreferrer" data-href="https://hackernoon.com/what-is-one-hot-encoding-why-and-when-do-you-have-to-use-it-e3c6186d008f">one-hot encode</a> categorical variables, and to normalize continuous variables before feeding them directly into the first feed-forward layer. However, in our experience this is not very effective. In our own previous testing across multiple datasets, DNNs that use one-hot encoding weren’t competitive with gradient boosting methods.</p> <p id="2d2d" class="graf graf--p graf-after--p">More recently, <a class="markup--anchor markup--p-anchor" href="https://www.fast.ai/2018/04/29/categorical-embeddings/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.fast.ai/2018/04/29/categorical-embeddings/">categorical embeddings</a> — sometimes referred to as <a class="markup--anchor markup--p-anchor" href="https://arxiv.org/pdf/1604.06737.pdf" target="_blank" rel="nofollow noopener noreferrer" data-href="https://arxiv.org/pdf/1604.06737.pdf">entity embeddings</a> — have become popular for dealing with categorical variables. Categorical embeddings assign a learnable feature vector, or embedding, to each category, generally with a size correlated with the number of unique values in that category. Each unique category value has its own associated vector, allowing for a rich representation of the data where the values are represented as multiple floating-point values, rather than a single binary. The categorical embedding outputs and normalized continuous variables are then concatenated together as the input to the model.</p> <p id="16b0" class="graf graf--p graf-after--p">Bag-of-features type approaches have also been used, where all of the features are embedded in the same size embedding and the input to the model is composed of the sum of its feature embeddings. This technique is modelled after <a class="markup--anchor markup--p-anchor" href="https://en.wikipedia.org/wiki/Bag-of-words_model" target="_blank" rel="nofollow noopener noreferrer" data-href="https://en.wikipedia.org/wiki/Bag-of-words_model">bag-of-words</a> techniques from natural language processing, the most famous of which is <a class="markup--anchor markup--p-anchor" href="https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-cbow.html" target="_blank" rel="nofollow noopener noreferrer" data-href="https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-cbow.html">word2vec</a>.</p> <p><img fetchpriority="high" decoding="async" class="alignnone size-full wp-image-27077" src="https://opendatascience.com/wp-content/uploads/2019/06/1_LEGTisA6yHNJCcBFZtSnEg.png" alt="" width="456" height="217" srcset="https://opendatascience.com/wp-content/uploads/2019/06/1_LEGTisA6yHNJCcBFZtSnEg.png 456w, https://opendatascience.com/wp-content/uploads/2019/06/1_LEGTisA6yHNJCcBFZtSnEg-300x143.png 300w" sizes="(max-width: 456px) 100vw, 456px" /></p> <p id="ddef" class="graf graf--p graf-after--figure">This first <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/notebooks/blob/b2ff2b82af22b66ff9f90351bef8aa4560e57ac0/mortgage/E2E_Deep_Learning.ipynb" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/notebooks/blob/b2ff2b82af22b66ff9f90351bef8aa4560e57ac0/mortgage/E2E_Deep_Learning.ipynb">RAPIDS+Deep Learning notebook</a> uses the bag-of-features approach, mapping all categorical and continuous variables into a single latent space, and uses an <a class="markup--anchor markup--p-anchor" href="https://pytorch.org/docs/stable/nn.html#embeddingbag" target="_blank" rel="nofollow noopener noreferrer" data-href="https://pytorch.org/docs/stable/nn.html#embeddingbag">EmbeddingBag layer</a> to take the average of all of the feature embeddings. These aggregated features are then fed into a sequence of four linear layers of width 600. Future posts will explore a wider range of options, and introduce some new ways of processing tabular data and new architectures for tabular data, an area of research that we are actively exploring.</p> <h4 id="9054" class="graf graf--h4 graf-after--p">Continuous Input Variables</h4> <p id="a6ad" class="graf graf--p graf-after--h4">To map continuous variables to an embedding space they must be discretized. <a class="markup--anchor markup--p-anchor" href="https://rapidsai.github.io/projects/cudf/en/0.7.0/api.html#cudf.dataframe.DataFrame.quantile" target="_blank" rel="nofollow noopener noreferrer" data-href="https://rapidsai.github.io/projects/cudf/en/0.7.0/api.html#cudf.dataframe.DataFrame.quantile">We’ve added functionality to the cuDF Series</a> to map a continuous input into buckets based on<a class="markup--anchor markup--p-anchor" href="https://en.wikipedia.org/wiki/Quantile" target="_blank" rel="nofollow noopener noreferrer" data-href="https://en.wikipedia.org/wiki/Quantile"> quantiles</a> which split a distribution into equal parts. In this context, we’re converting continuous variables to categoricals in order to take advantage of the power of embeddings. For our quantization in this notebook we use a quantile of 20 regions, each with 5% of the input distribution. A quantile dividing into four regions is shown below for a normal distribution to demonstrate how the split is applied when the data isn’t uniform.</p> <p><img decoding="async" class="alignnone size-full wp-image-27076" src="https://opendatascience.com/wp-content/uploads/2019/06/1_FqgYdLXf2nKSpjdD0ov7iQ.png" alt="" width="432" height="246" srcset="https://opendatascience.com/wp-content/uploads/2019/06/1_FqgYdLXf2nKSpjdD0ov7iQ.png 432w, https://opendatascience.com/wp-content/uploads/2019/06/1_FqgYdLXf2nKSpjdD0ov7iQ-300x171.png 300w" sizes="(max-width: 432px) 100vw, 432px" /></p> <h4 id="b9be" class="graf graf--h4 graf-after--figure">Sharing an Embedding Space</h4> <p id="3da0" class="graf graf--p graf-after--h4">Because we’re mapping all of our categorical and continuous variables into the same embedding space when using the bag of features model, we also need to make sure that the same value in different categories is handled correctly and isn’t mapped to the same id. To ensure this happens we hash the name of the input column with the value to create a unique id representing the value in that specific column. These are then mapped to an embedding bag so that all embeddings for an entry can be looked up and combined in a single pass. <a class="markup--anchor markup--p-anchor" href="https://rapidsai.github.io/projects/cudf/en/0.7.0/api.html#cudf.dataframe.DataFrame.hash_columns" target="_blank" rel="nofollow noopener noreferrer" data-href="https://rapidsai.github.io/projects/cudf/en/0.7.0/api.html#cudf.dataframe.DataFrame.hash_columns">Hashing of a series was also added to cuDF</a> to make this step easy.</p> <h4 id="70ca" class="graf graf--h4 graf-after--p">Implementation Details</h4> <p id="4b28" class="graf graf--p graf-after--h4">Our final model is an <a class="markup--anchor markup--p-anchor" href="https://pytorch.org/docs/stable/nn.html#embeddingbag" target="_blank" rel="nofollow noopener noreferrer" data-href="https://pytorch.org/docs/stable/nn.html#embeddingbag">EmbeddingBag layer</a> followed by several feedforward layers as seen below. We perform a small hyperparameter search exploring learning rate [log_uniform(1e-4..1e-2)], weight decay [uniform(1e-6..1e-4)], batch size [512, 1024, 4096, 8192], embedding size [8, 16, 24, 32, 64], and activation type [relu, selu]. 200 different combinations were randomly sampled and evaluated using the validation set.</p> <p><img decoding="async" class="alignnone size-full wp-image-27074" src="https://opendatascience.com/wp-content/uploads/2019/06/0_yRDTcgUlGU9vk5WY.png" alt="" width="529" height="398" srcset="https://opendatascience.com/wp-content/uploads/2019/06/0_yRDTcgUlGU9vk5WY.png 529w, https://opendatascience.com/wp-content/uploads/2019/06/0_yRDTcgUlGU9vk5WY-300x226.png 300w" sizes="(max-width: 529px) 100vw, 529px" /></p> <h4 id="2a71" class="graf graf--h4 graf--leading">Results and Key Takeaways</h4> <p id="cea2" class="graf graf--p graf-after--h4">The best performing DNN model on the validation set (PR-AUC of 0.8146) is able to achieve a PR-AUC of 0.8105 on our test set, slightly better than that of the xgboost model. The final hyperparameters are as follows: [lr = 2.005e-3, wd = 8e-6, batch size = 8192, embedding size = 32] <p><img loading="lazy" decoding="async" class="alignnone size-full wp-image-27075" src="https://opendatascience.com/wp-content/uploads/2019/06/1_ExFwigidY6VNJSdn7BsHgw.png" alt="" width="656" height="136" srcset="https://opendatascience.com/wp-content/uploads/2019/06/1_ExFwigidY6VNJSdn7BsHgw.png 656w, https://opendatascience.com/wp-content/uploads/2019/06/1_ExFwigidY6VNJSdn7BsHgw-300x62.png 300w" sizes="auto, (max-width: 656px) 100vw, 656px" /></p> <p id="354b" class="graf graf--p graf-after--figure">Key Takeaways:</p> <ul class="postList"> <li id="5ad7" class="graf graf--li graf-after--p">Even with a simple architecture we’re able to achieve similar performance to xgboost with deep learning.</li> <li id="4c93" class="graf graf--li graf-after--li">The deep learning model processes 208K examples/s during training, taking 6–8 hours including preprocessing and ETL on a single V100. That’s much slower than the 4.95M examples/s xgboost model that ran in 60 seconds on a DGX-1 server; but it is roughly comparable considering it equates to 620K examples/s per GPU. In future posts we’ll explore<a class="markup--anchor markup--li-anchor" href="https://github.com/NVIDIA/apex" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/NVIDIA/apex"> mixed precision</a>, multi-gpu and other techniques to speed up the training of our DNN model. Our CPU benchmark processes only 2100 examples/s on a 40 core machine, which clearly demonstrates why we’re doing deep learning on GPUs. The CPU system would take over 12 days to complete a single run!</li> <li id="6804" class="graf graf--li graf-after--li">While slower, using data loaders, we are able to train across the entire dataset using a single GPU, which is an advantage in many applications. Currently this process involves batchwise transfer of data between the CPU and GPU. In future posts we’ll be exploring in-GPU-memory data loaders to dramatically speed up training.</li> </ul> <p id="2408" class="graf graf--p graf-after--li">This first foray into deep learning for <a class="markup--anchor markup--p-anchor" href="http://rapids.ai/" target="_blank" rel="nofollow noopener noreferrer" data-href="http://rapids.ai">RAPIDS.AI</a> is a significant step, demonstrating that we can achieve similar performance to xgboost with DNNS with a reasonably simple model and motivating further exploration.</p> <h4 id="28d9" class="graf graf--h4 graf-after--p">Next Steps</h4> <p id="b8e0" class="graf graf--p graf-after--h4">In future posts we’ll undertake the following explorations.</p> <ul class="postList"> <li id="5145" class="graf graf--li graf-after--p">Simplify preprocessing of tabular data on GPUs and explore more direct ETL pipelines for getting data directly into a format that PyTorch and other deep learning frameworks can work with.</li> <li id="68a2" class="graf graf--li graf-after--li">Introduce some of the trappings of modern deep learning like dropout, batchnorm, learning rate schedules, concatenated embeddings, etc., and explore different architectures for tabular deep learning.</li> <li id="0d9a" class="graf graf--li graf-after--li">Evaluate the performance of deep learning for tabular data on a wider range of datasets.</li> <li id="3007" class="graf graf--li graf-after--li">Explore multi-GPU and multi-node scaling of DNN models.</li> </ul> <p id="438c" class="graf graf--p graf-after--li">In the meantime, please take a deeper look at <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e_deep_learning.ipynb" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai/notebooks-extended/blob/master/advanced/E2E/mortgage/mortgage_e2e_deep_learning.ipynb">the notebook</a>, adapt it to your favourite dataset, and let us know your experience.</p> <p><em>[Related Article: <a href="https://opendatascience.com/how-to-use-deep-learning-to-write-shakespeare/" target="_blank" rel="noopener noreferrer">How to Use Deep Learning to Write Shakespheare</a>]</em></p> <h4 id="acb0" class="graf graf--h4 graf-after--p">Get in touch and shape the future of RAPIDS</h4> <p id="bb74" class="graf graf--p graf-after--h4">RAPIDS relies on your feedback to make it a great platform for data science on GPUs. We’re excited to blur the lines between data analytics and deep learning. RAPIDS is committed to providing a first-class user experience, so thank you for being a part of this community.</p> <p id="cf86" class="graf graf--p graf-after--p graf--trailing">You can install the latest release with <a class="markup--anchor markup--p-anchor" href="https://anaconda.org/rapidsai/repo" target="_blank" rel="nofollow noopener noreferrer" data-href="https://anaconda.org/rapidsai/repo">Conda</a> or use the container found on <a class="markup--anchor markup--p-anchor" href="https://ngc.nvidia.com/catalog/containers/nvidia%2Frapidsai%2Frapidsai" target="_blank" rel="nofollow noopener noreferrer" data-href="https://ngc.nvidia.com/catalog/containers/nvidia%2Frapidsai%2Frapidsai">NVIDIA GPU Cloud</a> or <a class="markup--anchor markup--p-anchor" href="https://hub.docker.com/u/rapidsai/" target="_blank" rel="nofollow noopener noreferrer" data-href="https://hub.docker.com/u/rapidsai/">Docker Hub</a>. Let us know what you think via <a class="markup--anchor markup--p-anchor" href="https://github.com/rapidsai" target="_blank" rel="nofollow noopener noreferrer" data-href="https://github.com/rapidsai">GitHub stars and issues</a>.</p> <p><em>[Originally posted <a href="https://medium.com/rapids-ai/using-rapids-with-pytorch-e602da018285" target="_blank" rel="noopener noreferrer">here</a> by <a href="https://twitter.com/Even_Oldridge" target="_blank" rel="noopener noreferrer">@even_oldridge]</a></em></p> </div> </div> </section> <div class="at-below-post addthis_tool" data-url="https://opendatascience.com/using-rapids-with-pytorch/"></div></div> </div> <div class="post-sharrre"> <div class="sharrre-twitter" data-url="https://opendatascience.com/using-rapids-with-pytorch/" data-text="Using RAPIDS with PyTorch" data-title="Tweet"></div> <div class="sharrre-facebook" data-url="https://opendatascience.com/using-rapids-with-pytorch/" data-text="Using RAPIDS with PyTorch" 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