Машинное обучение и Большие данные: различия между версиями
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* Check all possible TensorFlow and Cuda versions here: https://www.tensorflow.org/install/source#gpu | * Check all possible TensorFlow and Cuda versions here: https://www.tensorflow.org/install/source#gpu | ||
* For me - '''tenorflow-2.3.0''', '''cuda 10.2''', '''nvidia-440.33.0''', '''cuDNN 7.6''', '''Bazel 3.1.0''', '''GCC 7.3.1''' | * For me - '''tenorflow-2.3.0''', '''cuda 10.2''', '''nvidia-440.33.0''', '''cuDNN 7.6''', '''Bazel 3.1.0''', '''GCC 7.3.1''' | ||
** PS cuda 10.1 won't install due 418 driver | ** PS cuda 10.1 won't install due 418 driver is not comportable with new kernel 5.4.0-70-generic | ||
<pre> | <pre> | ||
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin | wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin |
Версия от 15:52, 30 марта 2021
Hardware
- Lenovo x230 + eGPU
- Expresscard V8.0 EXP GDC Beast PCIe PCI-E
- Блок питания на 350-600 ватт
- Nvidia GeForce 760 4gb
- Similar setup
- Troubleshooting
- 16 GB of mem will produce lags. Remove 1 stick of mem to 8 GB
- Be sure that you GPU conneted to power fully(8+6 or 8+8) - it can produce 43 error
- eGPU setup Lenovo Thinkpad x230 with GTX 760 Part 1 ( setup )
- eGPU setup Lenovo Thinkpad x230 with GTX 760 Part 2 ( Fixing Error 12)
- eGPU setup Lenovo Thinkpad x230 with GTX 760 Part 3 ( Gameplay )
- In Windows go to Control panel, Hardware setup, Nvidia settings, 3d graphics, There you can select default video adapter
- But it won't help to use GPU in games - you need to connect external screen to GPU and disable laptop screen. Then games will run on eGPU.
Software
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600 wget https://developer.download.nvidia.com/compute/cuda/11.2.2/local_installers/cuda-repo-ubuntu2004-11-2-local_11.2.2-460.32.03-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu2004-11-2-local_11.2.2-460.32.03-1_amd64.deb sudo apt-key add /var/cuda-repo-ubuntu2004-11-2-local/7fa2af80.pub sudo apt-get update sudo apt-get -y install cuda nvidia-cuda-toolkit
- TensorFlow for GPU
- cuDNN SDK
- TensorRT
- Installing TensorFlow GPU in Ubuntu 20.04
- https://developer.nvidia.com/cuda-gpus
Change Default Python
sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 1 sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 2 sudo update-alternatives --config python
Define Your Cuda Version
- Nvidia GeForce GTX 760 4gb -> Nvidia Kepler
- Nvidia Kepler -> CUDA SDK 10.0 – 10.2 support for compute capability 3.0 – 7.5 (Kepler, Maxwell, Pascal, Volta, Turing). Last version with support for compute capability 3.x (Kepler). 10.2 is the last official release for macOS, as support will not be available for macOS in newer releases.
Define Your Tensorflow Version
- Check all possible TensorFlow and Cuda versions here: https://www.tensorflow.org/install/source#gpu
- For me - tenorflow-2.3.0, cuda 10.2, nvidia-440.33.0, cuDNN 7.6, Bazel 3.1.0, GCC 7.3.1
- PS cuda 10.1 won't install due 418 driver is not comportable with new kernel 5.4.0-70-generic
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600 wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub sudo apt-get update sudo apt-get -y install cuda
>>> import tensorflow as tf >>> tf.__version__ '2.3.0' >>> tf.test.is_built_with_cuda() True
$ nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Sun_Jul_28_19:07:16_PDT_2019 Cuda compilation tools, release 10.1, V10.1.243
Python 3.8.5 (default, Jan 27 2021, 15:41:15) [GCC 9.3.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> tf.config.list_physical_devices("GPU") 2021-03-29 00:19:23.520023: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 2021-03-29 00:19:23.575281: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-03-29 00:19:23.575801: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties: pciBusID: 0000:04:00.0 name: GeForce GTX 760 computeCapability: 3.0 coreClock: 1.15GHz coreCount: 6 deviceMemorySize: 3.94GiB deviceMemoryBandwidth: 179.05GiB/s 2021-03-29 00:19:23.576789: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1 2021-03-29 00:19:23.581937: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 2021-03-29 00:19:23.583502: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 2021-03-29 00:19:23.585776: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 2021-03-29 00:19:23.591336: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 2021-03-29 00:19:23.593271: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 2021-03-29 00:19:23.701034: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 2021-03-29 00:19:23.701468: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-03-29 00:19:23.702637: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2021-03-29 00:19:23.703486: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1657] Ignoring visible gpu device (device: 0, name: GeForce GTX 760, pci bus id: 0000:04:00.0, compute capability: 3.0) with Cuda compute capability 3.0. The minimum required Cuda capability is 3.5. []
git clone https://github.com/tensorflow/tensorflow.git cd ./tensorflow git checkout r2.2 sudo apt install apt-transport-https curl gnupg curl -fsSL https://bazel.build/bazel-release.pub.gpg | gpg --dearmor > bazel.gpg sudo mv bazel.gpg /etc/apt/trusted.gpg.d/ echo "deb [arch=amd64] https://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list sudo apt update && sudo apt install bazel-2.0.0
Курсы
- https://medium.com/nuances-of-programming/%D1%82%D0%BE%D0%BF-10-%D0%BA%D1%83%D1%80%D1%81%D0%BE%D0%B2-%D0%BF%D0%BE-%D0%BC%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%BC%D1%83-%D0%B8-%D0%B3%D0%BB%D1%83%D0%B1%D0%BE%D0%BA%D0%BE%D0%BC%D1%83-%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D1%8E-%D0%B2-2020-1e1d870a24b7
- https://skillbox.ru/course/profession-data-scientist/
- https://new.geekbrains.ru/machine-learning
- https://praktikum.yandex.ru/data-scientist/
- https://www.coursera.org/learn/machine-learning
- https://www.coursera.org/specializations/deep-learning
- https://ru.coursera.org/specializations/machine-learning-data-analysis
- https://www.udacity.com/course/machine-learning--ud262
- https://www.udacity.com/course/intro-to-machine-learning--ud120
Большие данные
- Лучшие в своём деле: Артур Хачуян | Большие данные — Big Data | ЛСД #7 https://www.youtube.com/watch?v=frLydE1UCvA
- «На чём корпорации вертели вашу приватность», Артур Хачуян (Tazeros Global) https://www.youtube.com/watch?v=8IJxO44kq24
Методы
- Теорема Байеса
- Функции ошибки и регуляризация
- Расстояние Кульбака-Лейблера и перекрестная энтропия
- Градиентный спуск: основы
- Граф вычислений и дифференцирование на нем
- Перцептрон
- Глубокие нейронные сети
- Классификация
- Кластеризация
- Регрессия
- Машинное зрение
- Метод к-средних
- word2vec
Библиотеки
- NumPy - https://www.numpy.org/
- SciPy - https://www.scipy.org/
- Pandas - https://pandas.pydata.org/
- Scikit-learn - https://scikit-learn.org/stable/
- Matplotlib - https://matplotlib.org/
- Gensim - https://radimrehurek.com/gensim/
- H2O - https://github.com/h2oai/h2o-tutorials
- XGBoost - https://xgboost.readthedocs.io/en/latest/
- CatBoost - https://yandex.ru/dev/catboost/
- Theano - http://deeplearning.net/software/theano/
- TensorFlow - https://www.tensorflow.org/
- sknn - https://scikit-neuralnetwork.readthedocs.io/en/latest/
- Theanets - https://github.com/lmjohns3/theanets
- Keras - https://keras.io/
- OpenCV - https://opencv.org/
Датасеты
- http://yann.lecun.com/exdb/mnist/
- http://www.image-net.org/
- https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- https://storage.googleapis.com/openimages/web/index.html
- https://github.com/philipperemy/timit
- https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc
Железо и драйверы
- https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1604&target_type=deblocal
- https://www.tensorflow.org/install/gpu
- https://cloud.google.com/tpu/
Темы
- https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence#Branches_of_artificial_intelligence
Face Recognition
Speech Recognition
Image Object Recognition
Anomaly Detection
Prediction
StereoVision
Некоторые полезные ресурсы
- https://ods.ai/
- https://en.wikipedia.org/wiki/Outline_of_artificial_intelligence
- https://en.wikipedia.org/wiki/List_of_artificial_intelligence_projects
- https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
- http://wordnet.princeton.edu/
- http://project.phil.spbu.ru/RussNet/index_ru.shtml
- http://wordnet.ru/
- http://www.vision.caltech.edu/Image_Datasets/Caltech101/
- https://www.scipy.org/
- http://www.numpy.org/
- http://leenissen.dk/fann/html/files/fann_train-h.html
- https://archive.ics.uci.edu/ml/datasets/mushroom
- https://archive.ics.uci.edu/ml/index.php
- https://www.kaggle.com/
- https://keras.io/
- https://xgboost.readthedocs.io/en/latest/
- https://lasagne.readthedocs.io/en/latest/
- http://deeplearning.net/software/theano/index.html
- https://catboost.yandex/
- https://arxiv.org/
- https://arxiv.org/list/cs.CV/recent
- https://yandex.ru/dev/catboost/