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

.github/workflows/ci.yaml

@@ -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: |

.github/workflows/docker.yaml

@@ -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 }}
 

docker/Dockerfile-conda

@@ -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

docker/Dockerfile-cpu

@@ -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

docker/Dockerfile-python

@@ -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

docs/integrations/index.md

@@ -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
 

docs/quickstart.md

@@ -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
 

ultralytics/engine/exporter.py

@@ -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]

ultralytics/engine/validator.py

@@ -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

ultralytics/models/utils/ops.py

@@ -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.
     """