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
simple artificial intelligence utilities

Simple AI Project home: http://github.com/simpleai-team/simpleai This lib implements many of the artificial intelligence algorithms described on the b

921 Dec 08, 2022
Using PyTorch Perform intent classification using three different models to see which one is better for this task

Using PyTorch Perform intent classification using three different models to see which one is better for this task

Yoel Graumann 1 Feb 14, 2022
Code for "AutoMTL: A Programming Framework for Automated Multi-Task Learning"

AutoMTL: A Programming Framework for Automated Multi-Task Learning This is the website for our paper "AutoMTL: A Programming Framework for Automated M

Ivy Zhang 40 Dec 04, 2022
Cross Quality LFW: A database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments

Cross-Quality Labeled Faces in the Wild (XQLFW) Here, we release the database, evaluation protocol and code for the following paper: Cross Quality LFW

Martin Knoche 10 Dec 12, 2022
Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images"

GANInversion_with_ConsecutiveImgs Official code for our ICCV paper: "From Continuity to Editability: Inverting GANs with Consecutive Images" https://a

QingyangXu 38 Dec 07, 2022
PyTorch implementation of Barlow Twins.

Barlow Twins: Self-Supervised Learning via Redundancy Reduction PyTorch implementation of Barlow Twins. @article{zbontar2021barlow, title={Barlow Tw

Facebook Research 839 Dec 29, 2022
Using pytorch to implement unet network for liver image segmentation.

Using pytorch to implement unet network for liver image segmentation.

zxq 1 Dec 17, 2021
Shitty gaze mouse controller

demo.mp4 shitty_gaze_mouse_cotroller install tensofflow, cv2 run the main.py and as it starts it will collect data so first raise your left eyebrow(bo

16 Aug 30, 2022
Code for our paper 'Generalized Category Discovery'

Generalized Category Discovery This repo is a placeholder for code for our paper: Generalized Category Discovery Abstract: In this paper, we consider

107 Dec 28, 2022
Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

Code for EmBERT, a transformer model for embodied, language-guided visual task completion.

41 Jan 03, 2023
Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognition

Light-SERNet This is the Tensorflow 2.x implementation of our paper "Light-SERNet: A lightweight fully convolutional neural network for speech emotion

Arya Aftab 29 Nov 12, 2022
[ECCV2020] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

[ECCV20] Content-Consistent Matching for Domain Adaptive Semantic Segmentation This is a PyTorch implementation of CCM. News: GTA-4K list is available

Guangrui Li 88 Aug 25, 2022
An algorithm that handles large-scale aerial photo co-registration, based on SURF, RANSAC and PyTorch autograd.

An algorithm that handles large-scale aerial photo co-registration, based on SURF, RANSAC and PyTorch autograd.

Luna Yue Huang 41 Oct 29, 2022
Equivariant layers for RC-complement symmetry in DNA sequence data

Equi-RC Equivariant layers for RC-complement symmetry in DNA sequence data This is a repository that implements the layers as described in "Reverse-Co

7 May 19, 2022
Convert openmmlab (not only mmdetection) series model to tensorrt

MMDet to TensorRT This project aims to convert the mmdetection model to TensorRT model end2end. Focus on object detection for now. Mask support is exp

JinTian 4 Dec 17, 2021
An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

Zou 33 Jan 03, 2023
Framework to build and train RL algorithms

RayLink RayLink is a RL framework used to build and train RL algorithms. RayLink was used to build a RL framework, and tested in a large-scale multi-a

Bytedance Inc. 32 Oct 07, 2022
CoSMA: Convolutional Semi-Regular Mesh Autoencoder. From Paper "Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes"

Mesh Convolutional Autoencoder for Semi-Regular Meshes of Different Sizes Implementation of CoSMA: Convolutional Semi-Regular Mesh Autoencoder arXiv p

Fraunhofer SCAI 10 Oct 11, 2022
Code to accompany our paper "Continual Learning Through Synaptic Intelligence" ICML 2017

Continual Learning Through Synaptic Intelligence This repository contains code to reproduce the key findings of our path integral approach to prevent

Ganguli Lab 82 Nov 03, 2022
PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition

PyTorch implementation of CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition The unofficial code of CDistNet. Now, we ha

25 Jul 20, 2022