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Add Dockerfile-conda FROM continuumio/miniconda3:latest (#4706)

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Glenn Jocher 2023-09-03 23:44:11 +02:00 committed by GitHub
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10 changed files with 152 additions and 28 deletions
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71
.github/workflows/ci.yaml vendored
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@ -128,9 +128,9 @@ jobs:
python --version
pip --version
pip list
# - name: Benchmark DetectionModel
# shell: bash
# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.318
# - name: Benchmark DetectionModel
# shell: bash
# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.318
- name: Benchmark SegmentationModel
shell: bash
run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.286
@ -144,6 +144,7 @@ jobs:
run: |
coverage xml -o coverage-benchmarks.xml
- name: Upload Coverage Reports to CodeCov
if: github.repository == 'ultralytics/ultralytics'
uses: codecov/codecov-action@v3
with:
flags: Benchmarks
@ -235,13 +236,73 @@ jobs:
env:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
Conda:
if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'workflow_dispatch' || github.event_name == 'schedule')
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest]
python-version: ['3.11']
defaults:
run:
shell: bash -el {0}
steps:
- uses: conda-incubator/setup-miniconda@v2
with:
python-version: ${{ matrix.python-version }}
mamba-version: "*"
channels: conda-forge,defaults
channel-priority: true
activate-environment: anaconda-client-env
- name: Install Libmamba
run: |
# conda install conda-libmamba-solver
conda config --set solver libmamba
- name: Install Ultralytics package from conda-forge
run: |
conda install -c conda-forge ultralytics
- name: Install pip packages
run: |
pip install pytest 'coremltools>=7.0.b1' # 'openvino-dev>=2023.0'
- name: Check environment
run: |
echo "RUNNER_OS is ${{ runner.os }}"
echo "GITHUB_EVENT_NAME is ${{ github.event_name }}"
echo "GITHUB_WORKFLOW is ${{ github.workflow }}"
echo "GITHUB_ACTOR is ${{ github.actor }}"
echo "GITHUB_REPOSITORY is ${{ github.repository }}"
echo "GITHUB_REPOSITORY_OWNER is ${{ github.repository_owner }}"
python --version
conda list
- name: Test CLI
run: |
yolo predict model=yolov8n.pt imgsz=320
yolo train model=yolov8n.pt data=coco8.yaml epochs=1 imgsz=32
yolo val model=yolov8n.pt data=coco8.yaml imgsz=32
yolo export model=yolov8n.pt format=torchscript imgsz=160
- name: Test Python
run: |
python -c "
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
results = model.train(data='coco8.yaml', epochs=3, imgsz=160)
results = model.val(imgsz=160)
results = model.predict(imgsz=160)
results = model.export(format='onnx', imgsz=160)
"
- name: PyTest
run: |
git clone https://github.com/ultralytics/ultralytics
pytest ultralytics/tests/test_cli.py # full tests fail due to openvino export failure
Summary:
runs-on: ubuntu-latest
needs: [HUB, Benchmarks, Tests, GPU] # Add job names that you want to check for failure
needs: [HUB, Benchmarks, Tests, GPU, Conda] # Add job names that you want to check for failure
if: always() # This ensures the job runs even if previous jobs fail
steps:
- name: Check for failure and notify
if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.GPU.result == 'failure') && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
if: (needs.HUB.result == 'failure' || needs.Benchmarks.result == 'failure' || needs.Tests.result == 'failure' || needs.GPU.result == 'failure' || needs.Conda.result == 'failure') && github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event_name == 'push')
uses: slackapi/slack-github-action@v1.24.0
with:
payload: |

26
.github/workflows/docker.yaml vendored
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@ -24,6 +24,9 @@ on:
Dockerfile-python:
type: boolean
description: Use Dockerfile-python
Dockerfile-conda:
type: boolean
description: Use Dockerfile-conda
push:
type: boolean
description: Push images to Docker Hub
@ -36,9 +39,15 @@ jobs:
runs-on: ubuntu-latest
strategy:
fail-fast: false
max-parallel: 5
max-parallel: 6
matrix:
include:
- dockerfile: "Dockerfile"
tags: "latest"
platforms: "linux/amd64"
- dockerfile: "Dockerfile-cpu"
tags: "latest-cpu"
platforms: "linux/amd64"
- dockerfile: "Dockerfile-arm64"
tags: "latest-arm64"
platforms: "linux/arm64"
@ -48,12 +57,9 @@ jobs:
- dockerfile: "Dockerfile-python"
tags: "latest-python"
platforms: "linux/amd64"
- dockerfile: "Dockerfile-cpu"
tags: "latest-cpu"
platforms: "linux/amd64"
- dockerfile: "Dockerfile"
tags: "latest"
platforms: "linux/amd64"
# - dockerfile: "Dockerfile-conda"
# tags: "latest-conda"
# platforms: "linux/amd64"
steps:
- name: Checkout repo
uses: actions/checkout@v3
@ -109,16 +115,16 @@ jobs:
-t ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }} .
- name: Run Tests
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' # arm64 images not supported on GitHub CI runners
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
run: docker run ultralytics/ultralytics:${{ matrix.tags }} /bin/bash -c "pip install pytest && pytest tests"
- name: Run Benchmarks
# WARNING: Dockerfile (GPU) error on TF.js export 'module 'numpy' has no attribute 'object'.
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' # arm64 images not supported on GitHub CI runners
if: (github.event_name == 'push' || github.event.inputs[matrix.dockerfile] == 'true') && matrix.platforms == 'linux/amd64' && matrix.dockerfile != 'Dockerfile' && matrix.dockerfile != 'Dockerfile-conda' # arm64 images not supported on GitHub CI runners
run: docker run ultralytics/ultralytics:${{ matrix.tags }} yolo benchmark model=yolov8n.pt imgsz=160 verbose=0.26
- name: Push Docker Image with Ultralytics version tag
if: (github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false'
if: (github.event_name == 'push' || (github.event.inputs[matrix.dockerfile] == 'true' && github.event.inputs.push == 'true')) && steps.check_tag.outputs.exists == 'false' && matrix.dockerfile != 'Dockerfile-conda'
run: |
docker push ultralytics/ultralytics:${{ steps.get_version.outputs.version_tag }}

37
docker/Dockerfile-conda Normal file
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@ -0,0 +1,37 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
# Builds ultralytics/ultralytics:latest-conda image on DockerHub https://hub.docker.com/r/ultralytics/ultralytics
# Image is optimized for Ultralytics Anaconda (https://anaconda.org/conda-forge/ultralytics) installation and usage
# Start FROM miniconda3 image https://hub.docker.com/r/continuumio/miniconda3
FROM continuumio/miniconda3:latest
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
RUN apt update \
&& apt install --no-install-recommends -y libgl1-mesa-glx
# Copy contents
ADD https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt .
# Install conda packages
# mkl required to fix 'OSError: libmkl_intel_lp64.so.2: cannot open shared object file: No such file or directory'
RUN conda config --set solver libmamba && \
conda install -c conda-forge ultralytics mkl
# conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
# t=ultralytics/ultralytics:latest-conda && sudo docker build -f docker/Dockerfile-cpu -t $t . && sudo docker push $t
# Run
# t=ultralytics/ultralytics:latest-conda && sudo docker run -it --ipc=host $t
# Pull and Run
# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host $t
# Pull and Run with local volume mounted
# t=ultralytics/ultralytics:latest-conda && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t

1
docker/Dockerfile-cpu
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@ -37,6 +37,7 @@ RUN yolo export model=tmp/yolov8n.pt format=ncnn imgsz=32
# Remove exported models
RUN rm -rf tmp
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push

1
docker/Dockerfile-python
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@ -37,6 +37,7 @@ RUN pip install --no-cache paddlepaddle==2.4.2 x2paddle
# Remove exported models
RUN rm -rf tmp
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push

2
docs/integrations/index.md
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@ -12,7 +12,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
## Datasets Integrations
- [Roboflow](/integrations/roboflow/): Facilitate seamless dataset management for Ultralytics models, offering robust annotation, preprocessing, and augmentation capabilities.
- [Roboflow](roboflow.md): Facilitate seamless dataset management for Ultralytics models, offering robust annotation, preprocessing, and augmentation capabilities.
## Training Integrations

24
docs/quickstart.md
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@ -20,7 +20,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
pip install ultralytics
```
You can also install the `ultralytics` package directly from the GitHub repository. This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed alltogether to default to `main` branch.
You can also install the `ultralytics` package directly from the GitHub [repository](https://github.com/ultralytics/ultralytics). This might be useful if you want the latest development version. Make sure to have the Git command-line tool installed on your system. The `@main` command installs the `main` branch and may be modified to another branch, i.e. `@my-branch`, or removed alltogether to default to `main` branch.
```bash
# Install the ultralytics package from GitHub
@ -44,9 +44,25 @@ Ultralytics provides various installation methods including pip, conda, and Dock
If you are installing in a CUDA environment best practice is to install `ultralytics`, `pytorch` and `pytorch-cuda` in the same command to allow the conda package manager to resolve any conflicts, or else to install `pytorch-cuda` last to allow it override the CPU-specific `pytorch` package if necessary.
```bash
# Install all packages together using conda
conda install -c conda-forge -c pytorch -c nvidia ultralytics pytorch torchvision pytorch-cuda=11.8
conda install -c pytorch -c nvidia -c conda-forge pytorch torchvision pytorch-cuda=11.8 ultralytics
```
### Conda Docker Image
Ultralytics Conda Docker images are also available from [DockerHub](https://hub.docker.com/r/ultralytics/ultralytics). These images are based on [Miniconda3](https://docs.conda.io/projects/miniconda/en/latest/) and are an simple way to start using `ultralytics` in a Conda environment.
```bash
# Set image name as a variable
t=ultralytics/ultralytics:latest-conda
# Pull the latest ultralytics image from Docker Hub
sudo docker pull $t
# Run the ultralytics image in a container with GPU support
sudo docker run -it --ipc=host --gpus all $t # all GPUs
sudo docker run -it --ipc=host --gpus '"device=2,3"' $t # specify GPUs
```
=== "Git clone"
Clone the `ultralytics` repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package in editable mode `-e` using pip.
```bash
@ -71,6 +87,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
- **Dockerfile-cpu:** Ubuntu-based CPU-only version suitable for inference and environments without GPUs.
- **Dockerfile-jetson:** Tailored for NVIDIA Jetson devices, integrating GPU support optimized for these platforms.
- **Dockerfile-python:** Minimal image with just Python and necessary dependencies, ideal for lightweight applications and development.
- **Dockerfile-conda:** Based on Miniconda3 with conda installation of ultralytics package.
Below are the commands to get the latest image and execute it:
@ -109,8 +126,7 @@ See the `ultralytics` [requirements.txt](https://github.com/ultralytics/ultralyt
## Use Ultralytics with CLI
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment.
CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
The Ultralytics command line interface (CLI) allows for simple single-line commands without the need for a Python environment. CLI requires no customization or Python code. You can simply run all tasks from the terminal with the `yolo` command. Check out the [CLI Guide](usage/cli.md) to learn more about using YOLOv8 from the command line.
!!! example

3
ultralytics/engine/exporter.py
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@ -154,7 +154,8 @@ class Exporter:
format = self.args.format.lower() # to lowercase
if format in ('tensorrt', 'trt'): # 'engine' aliases
format = 'engine'
if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios'): # 'coreml' aliases
if format in ('mlmodel', 'mlpackage', 'mlprogram', 'apple', 'ios', 'coreml'): # 'coreml' aliases
os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' # fix attempt for protobuf<3.20.x errors
format = 'coreml'
fmts = tuple(export_formats()['Argument'][1:]) # available export formats
flags = [x == format for x in fmts]

5
ultralytics/engine/validator.py
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@ -24,7 +24,6 @@ from pathlib import Path
import numpy as np
import torch
from scipy.optimize import linear_sum_assignment
from ultralytics.cfg import get_cfg, get_save_dir
from ultralytics.data.utils import check_cls_dataset, check_det_dataset
@ -226,9 +225,11 @@ class BaseValidator:
iou = iou.cpu().numpy()
for i, threshold in enumerate(self.iouv.cpu().tolist()):
if use_scipy:
# WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708
import scipy # scope import to avoid importing for all commands
cost_matrix = iou * (iou >= threshold)
if cost_matrix.any():
labels_idx, detections_idx = linear_sum_assignment(cost_matrix, maximize=True)
labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True)
valid = cost_matrix[labels_idx, detections_idx] > 0
if valid.any():
correct[detections_idx[valid], i] = True

10
ultralytics/models/utils/ops.py
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@ -14,11 +14,11 @@ class HungarianMatcher(nn.Module):
A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in
an end-to-end fashion.
HungarianMatcher performs optimal assignment over predicted and ground truth bounding boxes using a cost function
that considers classification scores, bounding box coordinates, and optionally, mask predictions.
HungarianMatcher performs optimal assignment over the predicted and ground truth bounding boxes using a cost
function that considers classification scores, bounding box coordinates, and optionally, mask predictions.
Attributes:
cost_gain (dict): Dictionary of cost coefficients for different components: 'class', 'bbox', 'giou', 'mask', and 'dice'.
cost_gain (dict): Dictionary of cost coefficients: 'class', 'bbox', 'giou', 'mask', and 'dice'.
use_fl (bool): Indicates whether to use Focal Loss for the classification cost calculation.
with_mask (bool): Indicates whether the model makes mask predictions.
num_sample_points (int): The number of sample points used in mask cost calculation.
@ -26,8 +26,8 @@ class HungarianMatcher(nn.Module):
gamma (float): The gamma factor in Focal Loss calculation.
Methods:
forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None): Computes the assignment
between predictions and ground truths for a batch.
forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None): Computes the
assignment between predictions and ground truths for a batch.
_cost_mask(bs, num_gts, masks=None, gt_mask=None): Computes the mask cost and dice cost if masks are predicted.
"""