October 2018 - Candle-aesp - mailman3.cels.anl.gov

notes from today
by Stevens, Rick L. 22 Oct '18

22 Oct '18

Here are some next steps we discussed in the meeting today. Please add additional items and feel free to add details and questions, etc. Could also tag things that you are working on so we know who is working on what. Thanks —Rick 1. Combine the SAVI generated synthetically accessible structures with the generated structures from Songhao’s generator to construct a balanced training set that includes roughly half that are synthetically accessible and half where we have no information on synthetic accessibility and see if we can train a model to discriminate between them. 2. Run the SAVI structures through the synethsizability rating tool that Fangfang found to see if the tools estimates are consistent with the SAVI expectations. 3. Write a script to sweep through features in both Gene Expression and Drug Descriptors to see which columns can be predicted from the remaining columns. 4. Look into adding dropout based UQ to UnoMT pytorch implementation 5. Look into a quantized inference optimized version of UnoMT to accelerate inference use case and assuming progress on #4 construct a inference optimized version of UnoMT with UQ support. 6. Gather in one place a large-scale chemical compound library of SMILE strings from the various sets to use for training and testing. 7. Get a pipeline working to go from SMILES to Mordred descriptors (2D and 3D) so that we can generate descriptors for the library in #6 8. Construct a variety of training sets for the experiment to see if we can learn a model to invert the SMILES —> Descriptor function. 9. Get everyone accounts on Theta and Washington. 10. Gather relevant papers together in one Repo

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Fwd: NVIDIA RAPIDS Announcement
by Stevens, Rick L. 10 Oct '18

10 Oct '18

Sent from my iPad Begin forwarded message: From: Steven Rohm <srohm(a)nvidia.com<mailto:[email protected]>> Date: October 10, 2018 at 08:20:00 CDT To: "Venkat Vishwanath (venkat(a)anl.gov<mailto:[email protected]>)" <venkat(a)anl.gov<mailto:[email protected]>>, "Balaprakash, Prasanna" <pbalapra(a)mcs.anl.gov<mailto:[email protected]>> Cc: "Rick Stevens (stevens(a)anl.gov<mailto:[email protected]>)" <stevens(a)anl.gov<mailto:[email protected]>>, Joshua Patterson <joshuap(a)nvidia.com<mailto:[email protected]>>, Louis Capps <lcapps(a)nvidia.com<mailto:[email protected]>> Subject: NVIDIA RAPIDS Announcement Venkat, Prasanna, Per our discussion the other day, Jensen announced the RAPIDS GPU-Accelerated Data Science & Machine Learning platform at GTC-Europe this morning. Press Release; https://nvidianews.nvidia.com/news/nvidia-introduces-rapids-open-source-gpu… NVIDIA Data Science Web page; https://www.nvidia.com/en-us/deep-learning-ai/solutions/data-science/ RAPIDS Open Source Web Page; http://rapids.ai/ This is just the beginning and there will be more added as the ecosystem matures, but please download what’s there and give it a try (on a DGX system if you can.) We’d be interested to get your feedback. Thanks. Steve Rohm HPC Business Development (513) 235-7560 srohm(a)nvidia.com<mailto:[email protected]> ________________________________ This email message is for the sole use of the intended recipient(s) and may contain confidential information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. ________________________________

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plaidml/plaidml: PlaidML is a framework for making deep learning work everywhere.
by Stevens, Rick L. 06 Oct '18

06 Oct '18

Keras seems to work well on top of this.. and they have the benchmarks working solidly. https://github.com/plaidml/plaidml <https://github.com/plaidml> plaidml/plaidml PlaidML is a framework for making deep learning work everywhere. http://vertex.ai README.md [X]</plaidml/plaidml/blob/master/docs/images/plaid-final.png> A platform for making deep learning work everywhere. Vertex.AI (the creators of PlaidML) is excited to join Intel's Artificial Intelligence Products Group. PlaidML will soon be re-licensed under Apache 2. Read the announcement here!<http://vertex.ai> [Build Status]<https://travis-ci.org/plaidml/plaidml> PlaidML is the easiest, fastest way to learn and deploy deep learning on any device, especially those running macOS or Windows: * Fastest: PlaidML is often 10x faster (or more) than popular platforms (like TensorFlow CPU) because it supports all GPUs, independent of make and model. * PlaidML accelerates deep learning on AMD, Intel, NVIDIA, ARM, and embedded GPUs. * Easiest: PlaidML is simple to install</plaidml/plaidml/blob/master/docs/installing.md> and supports multiple frontends (Keras and ONNX currently) * Free: PlaidML is completely open source and doesn't rely on any vendor libraries with proprietary and restrictive licenses. For most platforms, getting started with accelerated deep learning is as easy as running a few commands (assuming you have Python (v2 or v3) installed (if this doesn't work, see the installation instructions</plaidml/plaidml/blob/master/docs/installing.md>): virtualenv plaidml source plaidml/bin/activate pip install plaidml-keras plaidbench Choose which accelerator you'd like to use (many computers, especially laptops, have multiple): plaidml-setup Next, try benchmarking MobileNet inference performance: plaidbench keras mobilenet Or, try training MobileNet: plaidbench --batch-size 16 keras --train mobilenet About PlaidML PlaidML is a multi-language acceleration platform that: * Enables practitioners to deploy high-performance neural nets on any device * Allows hardware developers to quickly integrate with high-level frameworks * Allows framework developers to easily add support for many kinds of hardware * Works on all major platforms — Linux, macOS<http://vertex.ai/blog/plaidml-mac-preview>, Windows<http://vertex.ai/blog/deep-learning-for-everyone-plaidml-for-windows> * Allows developers to create hardware accelerated, novel, performance portable research kernels. For examples and benchmarks, see our blog<http://vertex.ai/blog>. * Documentation<https://vertexai-plaidml.readthedocs-hosted.com/> * Installation Instructions</plaidml/plaidml/blob/master/docs/installing.md> * Building PlaidML</plaidml/plaidml/blob/master/docs/building.md> * Contributing</plaidml/plaidml/blob/master/docs/contributing.rst> * Reporting Issues Recent Release Notes * PlaidML 0.3.3 - 0.3.5 * Support Keras 2.2.0 - 2.2.2 * Support ONNX 1.2.1 * Upgrade kernel scheduling * Revise documentation * Add HALs for CUDA and Metal * Various bugfixes and improvements * PlaidML 0.3.2 * Now supports ONNX 1.1.0 as a backend through onnx-plaidml<https://github.com/plaidml/onnx-plaidml> * Preliminary support for LLVM. Currently only supports CPUs, and only on Linux and macOS. More soon. * Support for LSTMs & RNNs with static loop sizes, such as examples/imdb_lstm.py (from Keras) * Training networks with embeddings is especially slow (#96) * RNNs are only staticly sized if the input's sequence length is explicitly specified (#97) * Fixes bug related to embeddings (#92) * Adds a shared generic op library in python to make creating frontends easier * plaidml-keras now uses this library * Uses plaidml/toolchain<https://github.com/plaidml/toolchain> for builds * Building for ARM is now simple (–-config=linux_arm_32v7) * Various fixes for bugs (#89) Validated Hardware Vertex.AI runs a comprehensive set of tests for each release against these hardware targets: * AMD * R9 Nano * RX 480 * Vega 10 * NVIDIA * K80, GTX 780, GT 640M * GTX 1070, 1050 * Intel * HD4000 * HD Graphics 505 Validated Networks We support all of the Keras application networks from current versions of 2.x. Validated networks are tested for performance and correctness as part of our continuous integration system. * CNNs * Inception v3 * ResNet50 * VGG19 * Xception * MobileNet * DenseNet * ShuffleNet * LSTM * examples/imdb_lstm.py (from keras) Installation Instructions See detailed per platform instructions here</plaidml/plaidml/blob/master/docs/installing.md>. Plaidvision and Plaidbench We've developed two open source projects: * plaidvision<https://github.com/plaidml/plaidvision> provides a simple shell for developing vision applications using your webcam * plaidbench<https://github.com/plaidml/plaidbench> is a performance testing suite designed to help users compare the performance of different cards and different frameworks Hello VGG One of the great things about Keras is how easy it is to play with state of the art networks. Here's all the code you need to run VGG-19: #!/usr/bin/env python import numpy as np import os import time os.environ["KERAS_BACKEND"] = "plaidml.keras.backend" import keras import keras.applications as kapp from keras.datasets import cifar10 (x_train, y_train_cats), (x_test, y_test_cats) = cifar10.load_data() batch_size = 8 x_train = x_train[:batch_size] x_train = np.repeat(np.repeat(x_train, 7, axis=1), 7, axis=2) model = kapp.VGG19() model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) print("Running initial batch (compiling tile program)") y = model.predict(x=x_train, batch_size=batch_size) # Now start the clock and run 10 batches print("Timing inference...") start = time.time() for i in range(10): y = model.predict(x=x_train, batch_size=batch_size) print("Ran in {} seconds".format(time.time() - start)) License PlaidML is licensed under the AGPLv3<https://www.gnu.org/licenses/agpl-3.0.txt>. Our open source goals include 1) helping students get started with deep learning as easily as possible and 2) helping researchers develop new methods more quickly than is possible with other tools. PlaidML is unique in being fully open source and free of dependence on libraries like cuDNN that carry revocable and redistribution-prohibiting licenses. For situations where an alternate license is preferable please contact solutions(a)vertex.ai<mailto:[email protected]>. Reporting Issues Either open a ticket on GitHub<https://github.com/plaidml/plaidml/issues> or post to plaidml-dev<https://groups.google.com/forum/#!forum/plaidml-dev>.

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