đź”® Execution time predictions for deep neural network training iterations across different GPUs.

Overview

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training

DOI DOI

Habitat is a tool that predicts a deep neural network's training iteration execution time on a given GPU. It currently supports PyTorch. To learn more about how Habitat works, please see our research paper.

Running From Source

Currently, the only way to run Habitat is to build it from source. You should use the Docker image provided in this repository to make sure that you can compile the code.

  1. Download the Habitat pre-trained models.
  2. Run extract-models.sh under analyzer to extract and install the pre-trained models.
  3. Run setup.sh under docker/ to build the Habitat container image.
  4. Run start.sh to start a new container. By default, your home directory will be mounted inside the container under ~/home.
  5. Once inside the container, run install-dev.sh under analyzer/ to build and install the Habitat package.
  6. In your scripts, import habitat to get access to Habitat. See experiments/run_experiment.py for an example showing how to use Habitat.

License

The code in this repository is licensed under the Apache 2.0 license (see LICENSE and NOTICE), with the exception of the files mentioned below.

This software contains source code provided by NVIDIA Corporation. These files are:

  • The code under cpp/external/cupti_profilerhost_util/ (CUPTI sample code)
  • cpp/src/cuda/cuda_occupancy.h

The code mentioned above is licensed under the NVIDIA Software Development Kit End User License Agreement.

We include the implementations of several deep neural networks under experiments/ for our evaluation. These implementations are copyrighted by their original authors and carry their original licenses. Please see the corresponding README files and license files inside the subdirectories for more information.

Research Paper

Habitat began as a research project in the EcoSystem Group at the University of Toronto. The accompanying research paper will appear in the proceedings of USENIX ATC'21. If you are interested, you can read a preprint of the paper here.

If you use Habitat in your research, please consider citing our paper:

@inproceedings{habitat-yu21,
  author = {Yu, Geoffrey X. and Gao, Yubo and Golikov, Pavel and Pekhimenko,
    Gennady},
  title = {{Habitat: A Runtime-Based Computational Performance Predictor for
    Deep Neural Network Training}},
  booktitle = {{Proceedings of the 2021 USENIX Annual Technical Conference
    (USENIX ATC'21)}},
  year = {2021},
}
Comments
  • I wonder what the meaning of varing kernel is.

    I wonder what the meaning of varing kernel is.

    Hi I am reading Habitat research paper.

    I wonder what the meaning of varing kernel is. I thought the GPU kernel is a collection of instructions that run in parallel, is that right?

    Can you give me an example of this phrase? 'some DNN operations are implemented using different GPU kernels on different GPUs '

    Thank you for taking the time to read.

    question 
    opened by Baek-sohyeon 6
  •  error: function cuptiProfilerBeginSession(&begin_session_params) failed with error CUPTI_ERROR_UNKNOWN

    error: function cuptiProfilerBeginSession(&begin_session_params) failed with error CUPTI_ERROR_UNKNOWN

    Hi @geoffxy,

    Great work here. I am quite interested in your project and try to reproduce from my side. Hower hit the error in the titel, I suspect that it may be caused by incompetible between CUPTI and NVIDIA driver version, I am wondering if could share you experiment setup here, mostly the host side, are you still using 18.04, what the nvidia driver version, did you use nvidia-docker2 or nvidia-container-runtime? what is your docker version?

    As mine, I am using 18.04 as host, driver 470.103.01, nvidia-docker2, docker 20.10.12.

    Thanks, Liang

    opened by liayan 3
  • How Habitat measures the execution time associated with the operation’s backward pass?

    How Habitat measures the execution time associated with the operation’s backward pass?

    Hi! Thanks for your perfect job.

    It's easy to understand to measure the execution time in the forward pass. But in the backward pass, how Habitat does? I think it is an undoubtedly different processor, right?

    @geoffxy Hope for your reply soon!

    question 
    opened by xiyiyia 2
  • Large Prediction Errors

    Large Prediction Errors

    Hi, I am reproducing the experiments in Habitat now. This is an interesting work and it's very convenient to run Habitat and process the results using the following two scripts.

    bash habitat/experiments/gather_raw_data.sh  <target_device>
    bash habitat/experiments/process_raw_data.sh
    

    Due to the limitation of GPU resources, I can not access all GPU models listed in the paper and only test it on V100, P100 and T4. But the prediction error is quite large, compared to that shown in the paper. You can check the results here.

    Basically, the setting I used follows habitat/docker/Dockerfile. Here are some of my experiment settings that may be different from yours:

    • CUDA driver version: 455.32.00,
    • I do not mount the user account on the host machine into the container

    So,

    1. Is there any hyper-parameter I need to tune to get a better prediction error ? 2.Can you share the cross-GPU prediction error between each pair of GPUs or just the output of habitat/experiments/process_raw_data.sh? Fig 3 in the paper only shows the results "averaged across all other “origin” GPUs".
    2. Will the setting differences listed above affect the prediction error ? Or any other possible reasons ?

    Thanks.

    question 
    opened by joapolarbear 2
  • CMake Error at CMakeLists.txt:22 (pybind11_add_module):

    CMake Error at CMakeLists.txt:22 (pybind11_add_module):

    when running "install-dev.sh", hit below error:

    CMake Error at CMakeLists.txt:22 (pybind11_add_module): Unknown CMake command "pybind11_add_module".

    -- Configuring incomplete, errors occurred!

    opened by liayan 1
  • CUPTI_ERROR_INSUFFICIENT_PRIVILEGES in container

    CUPTI_ERROR_INSUFFICIENT_PRIVILEGES in container

    The default configuration on my OS and current directions in README may lead to a CUPTI_ERROR_INSUFFICIENT_PRIVILEGES when using CUPTI inside the container.

    The example log is attached below:

    /home/ubuntu/home/habitat/cpp/src/cuda/cupti_tracer.cpp:120: error: function cuptiActivityRegisterCallbacks(cuptiBufferRequested, cuptiBufferCompleted) failed with error CUPTI_ERROR_INSUFFICIENT_PRIVILEGES.
    Traceback (most recent call last):
      File "run_experiment.py", line 246, in <module>
        main()
      File "run_experiment.py", line 238, in main
        run_dcgan_experiments(context)
      File "run_experiment.py", line 155, in run_dcgan_experiments
        context,
      File "run_experiment.py", line 85, in run_experiment_config
        threshold = compute_threshold(runnable, context)
      File "run_experiment.py", line 66, in compute_threshold
        runnable()
      File "run_experiment.py", line 150, in runnable
        iteration(*inputs)
      File "/home/ubuntu/home/habitat/experiments/dcgan/entry_point.py", line 41, in iteration
        netD.zero_grad()
      File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 1098, in zero_grad
        p.grad.detach_()
      File "/home/ubuntu/home/habitat/analyzer/habitat/tracking/operation.py", line 62, in hook
        kwargs,
      File "/home/ubuntu/home/habitat/analyzer/habitat/profiling/operation.py", line 45, in measure_operation
        record_kernels,
      File "/home/ubuntu/home/habitat/analyzer/habitat/profiling/operation.py", line 164, in _to_run_time_measurement
        if record_kernels else []
      File "/home/ubuntu/home/habitat/analyzer/habitat/profiling/kernel.py", line 34, in measure_kernels
        self._measure_kernels_raw(runnable, fname)
      File "/home/ubuntu/home/habitat/analyzer/habitat/profiling/kernel.py", line 48, in _measure_kernels_raw
        time_kernels = hc.profile(runnable)
    RuntimeError: CUPTI_ERROR_INSUFFICIENT_PRIVILEGES
    

    My solution: Adding options nvidia "NVreg_RestrictProfilingToAdminUsers=0" to /etc/modprobe.d/nvidia-kernel-common.conf and reboot.

    Ref:

    • https://developer.nvidia.com/nvidia-development-tools-solutions-err_nvgpuctrperm-permission-issue-performance-counters
    • https://github.com/tensorflow/tensorflow/issues/35860#issuecomment-585436324
    opened by yzs981130 1
  • Fail to build the image

    Fail to build the image

    Hi, I am following the steps here to reproduce habitat. When running setup.sh to build the image, the following error occurs

    Step 14/19 : RUN gpg --keyserver ha.pool.sks-keyservers.net --recv-keys B42F6819007F00F88E364FD4036A9C25BF357DD4
    ---> Running in d42ae3b13a05
    gpg: WARNING: unsafe permissions on homedir '/root/.gnupg'
    gpg: keybox '/root/.gnupg/pubring.kbx' created
    gpg: keyserver receive failed: No name
    The command '/bin/sh -c gpg --keyserver ha.pool.sks-keyservers.net --recv-keys B42F6819007F00F88E364FD4036A9C25BF357DD4' returned a non-zero code: 2
    

    Does it mean the keyserver ha.pool.sks-keyservers.net is not accessible now?

    I wonder whether it is necessary to duplicate the user account on the host machine into the container. With a root account in the container, I can access everything mounted from the host machine. What problem does it cause?

    Looking forward to your reply. Thanks.

    opened by joapolarbear 1
  • Fix format specifier for size_t

    Fix format specifier for size_t

    https://stackoverflow.com/questions/2524611/how-can-one-print-a-size-t-variable-portably-using-the-printf-family

    Signed-off-by: Kiruya Momochi [email protected]

    opened by KiruyaMomochi 0
  • Broken pillow for torchvision in Dockerfile causes docker build failed

    Broken pillow for torchvision in Dockerfile causes docker build failed

    Currently, pip3 install torchvision==0.5.0 should fail due to the broken dependency of pillow, shown in the following CI building process:

    https://github.com/yzs-lab/habitat/runs/4311964953?check_suite_focus=true#step:3:915

    Corresponding logs are attached below:

    The headers or library files could not be found for zlib,
        a required dependency when compiling Pillow from source.
        
        Please see the install instructions at:
           https://pillow.readthedocs.io/en/latest/installation.html
        
        Traceback (most recent call last):
          File "/tmp/pip-build-c0iq5ua_/pillow/setup.py", line 1024, in <module>
            zip_safe=not (debug_build() or PLATFORM_MINGW),
          File "/usr/lib/python3/dist-packages/setuptools/__init__.py", line 129, in setup
            return distutils.core.setup(**attrs)
          File "/usr/lib/python3.6/distutils/core.py", line 148, in setup
            dist.run_commands()
          File "/usr/lib/python3.6/distutils/dist.py", line 955, in run_commands
            self.run_command(cmd)
          File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
            cmd_obj.run()
          File "/usr/lib/python3/dist-packages/setuptools/command/install.py", line 61, in run
            return orig.install.run(self)
          File "/usr/lib/python3.6/distutils/command/install.py", line 589, in run
            self.run_command('build')
          File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
            self.distribution.run_command(command)
          File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
            cmd_obj.run()
          File "/usr/lib/python3.6/distutils/command/build.py", line 135, in run
            self.run_command(cmd_name)
          File "/usr/lib/python3.6/distutils/cmd.py", line 313, in run_command
            self.distribution.run_command(command)
          File "/usr/lib/python3.6/distutils/dist.py", line 974, in run_command
            cmd_obj.run()
          File "/usr/lib/python3/dist-packages/setuptools/command/build_ext.py", line 78, in run
            _build_ext.run(self)
          File "/usr/lib/python3.6/distutils/command/build_ext.py", line 339, in run
            self.build_extensions()
          File "/tmp/pip-build-c0iq5ua_/pillow/setup.py", line 790, in build_extensions
            raise RequiredDependencyException(f)
        __main__.RequiredDependencyException: zlib
        
        During handling of the above exception, another exception occurred:
        
        Traceback (most recent call last):
          File "<string>", line 1, in <module>
          File "/tmp/pip-build-c0iq5ua_/pillow/setup.py", line 1037, in <module>
            raise RequiredDependencyException(msg)
        __main__.RequiredDependencyException:
        
        The headers or library files could not be found for zlib,
        a required dependency when compiling Pillow from source.
        
        Please see the install instructions at:
           https://pillow.readthedocs.io/en/latest/installation.html
        
        
        
        ----------------------------------------
    Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-c0iq5ua_/pillow/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-8eakyb7g-record/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-build-c0iq5ua_/pillow/
    The command '/bin/sh -c pip3 install   torch==1.4.0   torchvision==0.5.0   pandas==1.1.2   tqdm==4.49.0' returned a non-zero code: 1
    
    opened by yzs981130 0
Releases(v1.0.0)
  • v1.0.0(Jun 1, 2021)

    This release is the first feature release of Habitat.

    Habitat is a tool that predicts a deep neural network's training iteration execution time on a given GPU. To learn more about how Habitat works, please see our research paper.

    Source code(tar.gz)
    Source code(zip)
Owner
Geoffrey Yu
Computer Science PhD Student at MIT | Software Engineering '18 @uWaterloo
Geoffrey Yu
The repository includes the code for training cell counting applications. (Keras + Tensorflow)

cell_counting_v2 The repository includes the code for training cell counting applications. (Keras + Tensorflow) Dataset can be downloaded here : http:

Weidi 113 Oct 06, 2022
An implementation of the proximal policy optimization algorithm

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

If you are using this code in your own project, please cite our paper: @inproceedings{awiszus2020toadgan, title={TOAD-GAN: Coherent Style Level Gene

Maren A. 13 Dec 14, 2022
A command line simple note taking app

Why yet another note taking program? note was designed with a very specific target in mind: me, and my 2354 scraps of paper. It runs from the command

64 Nov 20, 2022
Clustergram - Visualization and diagnostics for cluster analysis in Python

Clustergram Visualization and diagnostics for cluster analysis Clustergram is a diagram proposed by Matthias Schonlau in his paper The clustergram: A

Martin Fleischmann 96 Dec 26, 2022
Codes for our IJCAI21 paper: Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

DDAMS This is the pytorch code for our IJCAI 2021 paper Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization [Arxiv Pr

xcfeng 55 Dec 27, 2022
Pytorch implementation for our ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering".

TRAnsformer Routing Networks (TRAR) This is an official implementation for ICCV 2021 paper "TRAR: Routing the Attention Spans in Transformers for Visu

Ren Tianhe 49 Nov 10, 2022
Constraint-based geometry sketcher for blender

Constraint-based sketcher addon for Blender that allows to create precise 2d shapes by defining a set of geometric constraints like tangent, distance,

1.7k Dec 31, 2022
Text Summarization - WCN — Weighted Contextual N-gram method for evaluation of Text Summarization

Text Summarization WCN — Weighted Contextual N-gram method for evaluation of Text Summarization In this project, I fine tune T5 model on Extreme Summa

Aditya Shah 1 Jan 03, 2022
A system used to detect whether a person is wearing a medical mask or not.

Mask_Detection_System A system used to detect whether a person is wearing a medical mask or not. To open the program, please follow these steps: Make

Mohamed Emad 0 Nov 17, 2022
Tackling Obstacle Tower Challenge using PPO & A2C combined with ICM.

Obstacle Tower Challenge using Deep Reinforcement Learning Unity Obstacle Tower is a challenging realistic 3D, third person perspective and procedural

Zhuoyu Feng 5 Feb 10, 2022
PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

0 Mar 01, 2022
Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks"

LUNAR Official Implementation of "LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks" Adam Goodge, Bryan Hooi, Ng See Kiong and

Adam Goodge 25 Dec 28, 2022
Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection Main requirements torch = 1.0 torchvision = 0.2.0 Python 3 Environm

15 Apr 04, 2022
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps[AAAI2021]

Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps Here is the code for ssbassline model. We also provide OCR results/features/mode

ZephyrZhuQi 51 Nov 18, 2022
[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

CPT: Efficient Deep Neural Network Training via Cyclic Precision Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accep

26 Oct 25, 2022
[NeurIPS 2021] Code for Unsupervised Learning of Compositional Energy Concepts

Unsupervised Learning of Compositional Energy Concepts This is the pytorch code for the paper Unsupervised Learning of Compositional Energy Concepts.

45 Nov 30, 2022
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems

The SLIDE package contains the source code for reproducing the main experiments in this paper. Dataset The Datasets can be downloaded in Amazon-

Intel Labs 72 Dec 16, 2022
PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.

Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp

22 Dec 02, 2022
Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing"

One-Shot Free-View Neural Talking Head Synthesis Unofficial pytorch implementation of paper "One-Shot Free-View Neural Talking-Head Synthesis for Vide

ZLH 406 Dec 23, 2022