Hayato M. Yamanouchi

Tag: インストール

How to Install SLEAP [Updated February 2024]
Here I’d like to walk through how to install SLEAP, a machine learning-based tool for tracking animal body parts.

DeepLabCut is the best-known tracking tool, and I’ve written about how to install it myself in the past, while many others have posted articles about it as well.

SLEAP, however, has relatively few articles written about it in Japanese.
SLEAP has many advantages over DeepLabCut, so I decided to write up the installation procedure to make it a viable option for more people.

SLEAP’s documentation covers everything from installation to usage very clearly, complete with videos, so anyone comfortable with English should find it easy to get started.

The SLEAP paper is available here

The official SLEAP website is here

The SLEAP GitHub repository is here

Introduction

First, a note about the approach described in the official SLEAP installation guide:
```
mamba create -y -n sleap -c conda-forge -c nvidia -c sleap -c anaconda sleap=1.3.3
```
the one-line command that is supposed to handle environment creation, package installation, and everything else at once. I tried it on several computers, but in every case it hung indefinitely during the environment creation step and never completed.

So in this post I’ll introduce the alternative installation method that the developers also suggest.

Installation

We’ll basically follow the installation instructions on the official SLEAP site.

Environment

・Windows 11 Pro
・Verified on an NVIDIA 3080
・SLEAP 1.3.3
・Python 3.7.12

Downloading the files

First, download the files from the SLEAP GitHub repository to your computer.

If you’re using Git, run the following command.
```
git clone https://github.com/talmolab/sleap
```
If the git command isn’t available, either install git or download the files directly from GitHub.

Installing the GPU driver

Install the NVIDIA driver.
(Skip this step if it’s already installed.)

Installing Anaconda

Next, download and install the Windows 10 64-bit version from the Anaconda website.
Click Free Download on the Anaconda site and scroll down; you’ll see a screen like the one below.
Install the Windows installer on the far left.

Once Anaconda is installed, you should find “Anaconda Prompt” in your Windows app list.
We’ll use it to run the commands below to create the virtual environment, install the packages, and finally launch SLEAP.

Creating the virtual environment and installing

Next, run the following commands to create the virtual environment and install SLEAP.
The environment will be named “sleap”.
```
cd sleap
conda env create -f environment.yml -n sleap
```
Incidentally, the commands above work on computers with a GPU;
on machines without one, use the following instead.
```
cd sleap
conda env create -f environment_no_cuda.yml -n sleap
```
That completes the installation.

Checking that the installation worked

Activate the virtual environment and launch SLEAP with sleap-label.
```
conda activate sleap
sleap-label
```
You’ll need to activate the virtual environment (the conda activate sleap command) every time you reopen Anaconda Prompt.

After activating the environment, the prompt should change from (base) to (sleap).

If it launches, the installation was successful.

Checking version information for reporting in a paper

To check the version, activate the sleap environment and then run the following command.
```
python -c "import sleap; sleap.versions()"
```
This outputs:
```
SLEAP: 1.3.3
TensorFlow: 2.7.0
Numpy: 1.21.5
Python: 3.7.12
OS: Windows-10-10.0.22621-SP0
```
which shows the SLEAP version and related information.

To check whether SLEAP can use the GPU, run:
```
python -c "import sleap; sleap.system_summary()"
```
If the GPU is available, you’ll see output like the following.
```
GPUs: 1/1 available
  Device: /physical_device:GPU:0
         Available: True
       Initialized: False
     Memory growth: None
```
In future posts, I hope to walk through how to actually use SLEAP.

Bonus

For those who aren’t sure which computer to buy, I’ve started offering PC purchase consultations on Coconala!
あなたの要望に合わせてパソコンを選び、提案しますパソコン選びに困っている方々へ！様々な目的に対応できます！
I often pick out computers and give advice about them, and many friends have told me I could make money doing PC consultations.
That inspired me to give it a try!

I’ll recommend a machine that fits both what you want to use it for and your budget.
In particular, I’ve chosen and used many computers intended for machine learning.

And if you’d like, I can also advise you on what to look for the next time you buy a computer.

Computers I’ve picked out so far include lab analysis machines, everyday-use machines, game streaming machines, machines for incoming university students, machines that can run CAD for architecture students, and simple entry-level machines.

I use both Mac and Windows, so I can discuss and recommend either one!

Please feel free to make use of it.
2024-01-21
[Updated December 2022] How to Install YOLOv5 [Object Detection Tool]
What is YOLOv5?

YOLO is an object detection algorithm. The name stands for You Only Look Once.
It differs somewhat from object tracking: it detects objects and identifies what they are.

YOLOのHPより引用(https://pjreddie.com/darknet/yolo/)

In applications such as autonomous driving, objects are detected and classified the way a human would judge them—as a person, a pet, or a car, for example.

The classification algorithm itself is complex, so I will skip the explanation here, but a quick search turns up a great many sites that describe it.

New versions of YOLO are currently released roughly every other year. We now have YOLO v1 through YOLOv5, and in August 2021 a new version, YOLO X, was announced.
The differences between versions involve substantial changes in the details, but generally speaking accuracy improves with each new release.

Here I will walk through the steps for installing YOLO v5—which offers ample accuracy and, thanks to the wealth of information now available, is easy to set up—and verifying that it works.

Installation environment

OS: Windows 10 (this also worked on macOS Monterey)
Python: 3.9.7 (3.6 or later)
CUDA: 11.3

How to install

Creating a virtual environment

Create a virtual environment using Anaconda.
If you do not have Anaconda installed, install it first.

Installing YOLOv5 pulls in quite a few packages, so I recommend installing it inside a virtual environment.
You can name the environment anything you like; here we will call it “Yolov5_env”.
Enter the following command in Anaconda Prompt.
```
conda create -n Yolov5_env python=3.9
```
This should create the virtual environment.
Activate it with the following command.
```
conda activate Yolov5_env
```
Installing CUDA

First, if this is your first time installing machine learning libraries and you plan to train YOLO on a GPU, install CUDA. (Training on a CPU takes an absurdly long time, so I do not recommend it.)
CUDA can be downloaded from the official site.

As for which CUDA version to use, I suggest checking which CUDA versions PyTorch supports and installing that version.
PyTorch official site

To install an older version, click Download now and then, on the resulting page, use Archive of Previous CUDA Releases under Resources further down the page.

The latest version may well work too, but for now, matching the version PyTorch expects should let you run everything without trouble.

To check whether it is installed on Windows, open “Edit the system environment variables,” go to Advanced > Environment Variables,
and look for a path beginning with CUDA_PATH.
The number after the V indicates the version.

Installing PyTorch

Before installing, update pip.
Activate the virtual environment in Anaconda Prompt and run the following command.
```
python -m pip install --upgrade pip
```
Go to the PyTorch official site and choose the installation options that match your environment.
One thing to watch out for is the Package field: be sure to select pip here.
Installing with conda will cause errors later on.

Copy and paste the command shown under “Run this Command” to install it into your virtual environment.

Once the installation finishes, use the following command to confirm that PyTorch is installed.
```
pip list
```
If torch appears in the list, you are good to go!
As a quick check, start Python and run the following command.
```
import torch
print(torch.cuda.is_available())
```
If the output is True, the installation succeeded and torch is ready to run on the GPU.
(On a Mac or a computer without a GPU you will see False, but as long as the import goes through, you are fine.)
If you get an error, go back and review the installation.

Installing YOLOv5

Download (clone) the YOLOv5 files from GitHub.
```
git clone https://github.com/ultralytics/yolov5
```
If you cannot use the git command, either install git or download the files from the YOLOv5 GitHub repository.

Next, run the following command in your virtual environment in Anaconda Prompt.
```
cd yolov5
pip install -r requirements.txt
```
The first command moves you into the yolov5 folder you downloaded from GitHub. If you installed it with the git command, this is all you need; if you downloaded it directly from the website, you will have to navigate to that directory yourself.

The downloaded yolov5 folder contains a file called “requirements.txt” that lists the required packages. The second command opens this file in read-only mode and installs the packages listed in it.

Once the installation is finished, let’s check which packages were installed.
```
pip list 
```
You should see that quite a few packages were installed.
That completes the installation of YOLOv5 for now.

Running It

To check that everything works, let’s try it with the data that comes with the yolov5 folder.
Open Anaconda Prompt, activate the virtual environment, and use the cd command to move to the yolov5 folder.

Then run the following command.
```
python detect.py --source ./data/images/ --weights yolov5s.pt --conf 0.4
```
detect.py is the program that performs object detection using YOLO.
With source, you specify the path to the folder containing the material you want analyzed.
Here I used the images included in yolov5.

With weights, you choose which model to use.
Here we use yolov5.pt.

conf sets the likelihood threshold at which an object is recognized.
This is a term you will run into often when studying machine learning.
If you are not sure, leaving it as is should be fine.

After running it, you should find the annotated images in runsdetectexp inside the yolov5 folder.
This is how object detection works in YOLOv5.

Errors I Ran Into

・ModuleNotFoundError: No module named ‘torch’

You may see this error when trying to run YOLO.
It means PyTorch is not available, i.e., the module cannot be found.

If you get this error, first check whether torch is installed in your virtual environment.
```
pip list
```
If torch is not listed, install PyTorch.
If it is there but things still do not work and you are not sure why, launch python, import PyTorch, and check whether it can actually be used.
```
import torch
torch.cuda.is_acailable()
```
If it is usable, True will be returned; if not, you will likely see the error above.

One possible cause of this error is that PyTorch was installed with the conda command.
If PyTorch is installed via conda while the other packages are installed via pip, the error above can occur.

In that case, uninstall torch with the conda command and reinstall it with pip.

Module-not-found problems often come from mixing up whether you installed something with pip or with conda.
Here we use pip throughout, but it is a good habit to always keep track of which command you used to install a package.

Links

Anaconda
CUDA
PyTorch
YOLOv5 GitHub
YOLO homepage

Bonus

For those who aren’t sure which computer to buy, I’ve started offering PC purchase consultations on Coconala!
あなたの要望に合わせてパソコンを選び、提案しますパソコン選びに困っている方々へ！様々な目的に対応できます！
I often pick out computers and give advice, and many friends have told me I could make money doing PC consultations.
That inspired me to give it a try!

I’ll recommend a machine based on what you plan to use it for and your budget.
In particular, I’ve chosen and used many computers for machine learning.

And if you’d like, I can also advise you on what to look for the next time you buy a computer.

Computers I’ve picked out so far include analysis machines for the lab, everyday machines, PCs for game streaming, PCs for incoming university students, CAD-capable PCs for architecture students, and simple all-purpose machines.

I use both Mac and Windows, so I can discuss and recommend either!

Please give it a try!
2022-02-23
How to Install DeepLabCut 2.3 [Updated December 2023]
In this post, I’ll walk through how to install DeepLabCut.

Reference sites
・Japanese-language sites
https://qiita.com/auditorycortex/items/1b3a55101cddf09553b2
↑Very clear. Following this should get you through just fine.

https://note.com/sakulab/n/n9caeb32d74d6
↑Includes screenshots

・DeepLabCut homepage
・GitHub

Installation

Environment
・Windows 10
・Verified on NVIDIA 2060 SUPER, NVIDIA 1080 Ti, and NVIDIA 3080
(did not work on NVIDIA 1060 SUPER)
・DeepLabCut 2.1
・NVIDIA Driver, latest version
・Anaconda3
・Python 3.8
・CUDA 11.8
・tensorflow-gpu 2.5

Steps
First, download the master files from the GitHub page.

You can download them via “Download ZIP” under Code.

Next, download and install the Windows 10 64-bit version from the Anaconda website.

Install the NVIDIA driver. (Skip this step if it is already installed.)

Scroll down on the DeepLabCut website, click “DOWNLOAS CONDA FILE” on the right,
and download DEEPLABCUT.yaml.

Create a DeepLabCut folder somewhere on your PC (the Desktop or a directory on the C drive is recommended), and make an “environment” folder inside it.
Then place the DEEPLABCUT.yaml file you just downloaded into that folder.
This is just personal preference, but I like to keep environment files in one fixed location, so this is how I do it.

Launch Anaconda Prompt as an administrator. (A terminal-like window full of white text on black, similar to Command Prompt, will appear.)
Anaconda Prompt is installed together with Anaconda, so you should be able to find it in your list of installed programs.

Now build a virtual environment using the DEEPLABCUT.yaml file you downloaded.
In Anaconda Prompt, enter
```
conda env create -f C:(DLC-GPU.yamlファイルの場所)DEEPLABCUT.yaml
```
You can check the location of the DEEPLABCUT.yaml file from the file’s properties, so look it up there and enter it.

When you run this command, various components will be downloaded.
After that, activate the virtual environment you created.
```
conda activate DEEPLABCUT
```
The prompt should change from (base) to (DEEPLABCUT).
If you get that far, you’re good for now.

Next, install CUDA and cuDNN.
```
conda install -c conda-forge cudnn
```
This will download suitable versions of the CUDA toolkit and cuDNN for you.

Then enter the following four commands to complete the installation.
```
pip install numpy
pip install deeplabcut
pip install imgaug
pip install torch
```
Update DeepLabCut, and the installation is complete.
```
pip install --upgrade deeplabcut
```
Once you’ve made it this far, try launching it.
```
conda activate DEEPLABCUT
python -m deeplabcut
```
Entering this will launch the DeepLabCut GUI.

What to do when DeepLabCut won’t run on the GPU

Sometimes you start training and think, “Huh? Isn’t this slow?”
That’s because DeepLabCut is running on the CPU when you intended it to run on the GPU.

A common cause is a version mismatch among CUDA, cuDNN, and tensorflow.

Packages like tensorflow keep getting updated to newer versions,
so things occasionally stop working.

For the latest version information, please check DeepLabCut’s GitHub.

That covers the installation process.
In upcoming posts, I plan to explain how to use it.

Bonus

For those who aren’t sure which computer to buy, I’ve started offering PC purchase consultations on Coconala!
あなたの要望に合わせてパソコンを選び、提案しますパソコン選びに困っている方々へ！様々な目的に対応できます！
I often pick out computers and give advice on them, and many friends have told me I could make money doing PC consultations.
That inspired me to give it a try!

I’ll recommend a purchase that fits how you plan to use the computer and your budget.
In particular, I’ve chosen and used a lot of computers for machine learning.

And if you’d like, I can also advise you on what to look for the next time you buy a computer.

Computers I’ve helped select include analysis machines for research labs, everyday-use PCs, PCs for game streaming, PCs for incoming university students, CAD-capable PCs for architecture students, and simple stopgap machines.

I use both Mac and Windows, so I can discuss and recommend either one!

Please feel free to make use of it.

Addendum 1

I’ve also written an article on how to install “SLEAP,” another behavior tracking tool like DeepLabCut.

SLEAP is every bit as capable as DeepLabCut, so please give it a try.

SLEAPのインストール方法[2024年2月更新]
2021-03-26

Hayato M. Yamanouchi

PhD student, Kamikouchi Laboratory, Nagoya University / JSPS Research Fellow (DC1)
Systems Ethology (Neuroethology × Informatics)

© 2026 Hayato M. Yamanouchi

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