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deepflash2
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deepflash2 - pypi Package Compare versions
| Metadata-Version: 2.1 | ||
| Name: deepflash2 | ||
| Version: 0.1.6 | ||
| Version: 0.1.7 | ||
| Summary: A Deep learning pipeline for segmentation of fluorescent labels in microscopy images | ||
@@ -33,3 +33,3 @@ Home-page: https://github.com/matjesg/deepflash2 | ||
| [](https://pypistats.org/packages/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://github.com/matjesg/deepflash2) | ||
@@ -57,9 +57,19 @@ [](https://github.com/matjesg/deepflash2/) | ||
| ## Quick Start and Demo | ||
| > Get started in less than a minute. Watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html) for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory with free access to graphics processing units (GPUs). | ||
| > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory (Google account required). | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| <video src="https://user-images.githubusercontent.com/13711052/139751414-acf737db-2d8a-4203-8a34-7a38e5326b5e.mov" controls width="100%"></video> | ||
| To try the functionalities of *deepflash2*, open the *deepflash2* GUI in [Colab]((https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) or follow the installation instructions below. The GUI provides a build-in use for sample data. After starting the GUI, select the task (GT Estimation, Training, or Prediction) and click `Load Sample Data`. For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| #### Demo usage | ||
| The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| 1. Starting the GUI (in <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> or follow the installation instructions below) | ||
| 2. Select the task (GT Estimation, Training, or Prediction) | ||
| 3. Click the `Load Sample Data` button in the sidebar and continue to the next sidebar section. | ||
| For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| We provide an overview of the tasks below: | ||
@@ -74,7 +84,7 @@ | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). You can download the sample data [here](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). | ||
| ## Paper and Experiments | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](xxx). | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](https://drive.google.com/drive/folders/1r9AqP9qW9JThbMIvT0jhoA5mPxWEeIjs?usp=sharing). | ||
@@ -96,11 +106,11 @@ The preprint of our paper is available on [arXiv](https://arxiv.org/abs/2111.06693). Please cite | ||
| ## System requirements | ||
| > Works in the browser an on your local pc/server | ||
| > Works in the browser or on your local pc/server | ||
| *deepflash2* is designed to run on Windows, Linux, or Mac (x86-64) if [pytorch](https://pytorch.org/get-started/locally/) is installable. | ||
| We generally recommend using Google Colab as it only requires a Google Account and a device with a web browser. | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, NVIDIA Tesla K80 GPU or better). | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, 8 GB RAM, NVIDIA GPU with 8GB VRAM or better). | ||
| Software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on the simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose with commit hash `316927eff7ad2201391957909a2114c68baee309`. | ||
| *deepflash2* requires Python>3.6 and the software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose v0.6.6.dev13+g316927e. | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10 (tbd), MacOS 12.0.1 (tbd)). | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10, MacOS 12.0.1). | ||
@@ -111,36 +121,46 @@ ## Installation Guide | ||
| The GUI of *deepflash2* runs as a web application inside a Jupyter Notebook, the de-facto standard of computational notebooks in the scientific community. The GUI is built on top of the *deepflash2* Python API, which can be used independently (read the [docs](https://matjesg.github.io/deepflash2/)). | ||
| #### Google Colab | ||
| ### Google Colab | ||
| Excute the `Set up environment` cell or follow the `pip` instructions. | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| Open <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> and excute the `Set up environment` cell or follow the `pip` instructions. Colab provides free access to graphics processing units (GPUs) for fast model training and prediction (Google account required). | ||
| #### Other systems | ||
| ##### [conda](https://docs.conda.io/en/latest/) | ||
| ### Other systems | ||
| We recommend installation into a new, clean [environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). | ||
| We recommend installation into a clean Python environment (e.g., using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)). | ||
| #### [conda](https://docs.conda.io/en/latest/)/[mamba](https://github.com/mamba-org/mamba) | ||
| If you replace `conda` with `mamba` the install process will be much faster and more reliable (you need to install [mamba](https://github.com/mamba-org/mamba) first): | ||
| ```bash | ||
| conda install -c conda-forge -c fastchan -c matjesg deepflash2 | ||
| conda install -c fastchan -c conda-forge -c matjesg deepflash2 | ||
| ``` | ||
| ##### [pip](https://pip.pypa.io/en/stable/) | ||
| #### [pip](https://pip.pypa.io/en/stable/) | ||
| You should install PyTorch first by following the installation instructions of [pytorch](https://pytorch.org/get-started/locally/). | ||
| If you want to use your GPU and install with pip, we recommend installing PyTorch first by following the [installation instructions](https://pytorch.org/get-started/locally/). | ||
| ```bash | ||
| pip install deepflash2 | ||
| pip install -U deepflash2 | ||
| ``` | ||
| #### Using the GUI | ||
| If you want to use the GUI, make sure to download the GUI notebook and start a Jupyter server. | ||
| If you want to use the GUI, make sure to download the GUI notebook, e.g., using `curl` | ||
| ```bash | ||
| curl -o deepflash2_GUI.ipynb https://raw.githubusercontent.com/matjesg/deepflash2/master/deepflash2_GUI.ipynb | ||
| ``` | ||
| and start a Jupyter server. | ||
| ```bash | ||
| jupyter notebook | ||
| ``` | ||
| Then, open `deepflash2_GUI.ipynb` within Notebook environment. | ||
| ##### Docker | ||
| ### Docker | ||
@@ -147,0 +167,0 @@ Docker images for __deepflash2__ are built on top of [the latest pytorch image](https://hub.docker.com/r/pytorch/pytorch/). |
@@ -1,1 +0,1 @@ | ||
| __version__ = "0.1.6" | ||
| __version__ = "0.1.7" |
@@ -293,3 +293,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_data.ipynb (unless otherwise specified). | ||
| tile_shape=(512,512), padding=(0,0),preproc_dir=None, verbose=1, scale=1, pdf_reshape=512, use_preprocessed_labels=False, **kwargs): | ||
| store_attr('files, label_fn, instance_labels, n_classes, ignore, tile_shape, remove_connectivity, padding, normalize, scale, pdf_reshape, use_preprocessed_labels') | ||
| store_attr('files, label_fn, instance_labels, n_classes, ignore, tile_shape, remove_connectivity, padding, preproc_dir, normalize, scale, pdf_reshape, use_preprocessed_labels') | ||
| self.c = n_classes | ||
@@ -301,3 +301,3 @@ | ||
| if label_fn is not None: | ||
| self.preproc_dir = preproc_dir or zarr.storage.TempStore() | ||
| self.preproc_dir = self.preproc_dir or zarr.storage.TempStore() | ||
| root = zarr.group(store=self.preproc_dir, overwrite= not use_preprocessed_labels) | ||
@@ -304,0 +304,0 @@ self.labels, self.pdfs = root.require_groups('labels','pdfs') |
+43
-23
| Metadata-Version: 2.1 | ||
| Name: deepflash2 | ||
| Version: 0.1.6 | ||
| Version: 0.1.7 | ||
| Summary: A Deep learning pipeline for segmentation of fluorescent labels in microscopy images | ||
@@ -33,3 +33,3 @@ Home-page: https://github.com/matjesg/deepflash2 | ||
| [](https://pypistats.org/packages/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://github.com/matjesg/deepflash2) | ||
@@ -57,9 +57,19 @@ [](https://github.com/matjesg/deepflash2/) | ||
| ## Quick Start and Demo | ||
| > Get started in less than a minute. Watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html) for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory with free access to graphics processing units (GPUs). | ||
| > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory (Google account required). | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| <video src="https://user-images.githubusercontent.com/13711052/139751414-acf737db-2d8a-4203-8a34-7a38e5326b5e.mov" controls width="100%"></video> | ||
| To try the functionalities of *deepflash2*, open the *deepflash2* GUI in [Colab]((https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) or follow the installation instructions below. The GUI provides a build-in use for sample data. After starting the GUI, select the task (GT Estimation, Training, or Prediction) and click `Load Sample Data`. For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| #### Demo usage | ||
| The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| 1. Starting the GUI (in <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> or follow the installation instructions below) | ||
| 2. Select the task (GT Estimation, Training, or Prediction) | ||
| 3. Click the `Load Sample Data` button in the sidebar and continue to the next sidebar section. | ||
| For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| We provide an overview of the tasks below: | ||
@@ -74,7 +84,7 @@ | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). You can download the sample data [here](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). | ||
| ## Paper and Experiments | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](xxx). | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](https://drive.google.com/drive/folders/1r9AqP9qW9JThbMIvT0jhoA5mPxWEeIjs?usp=sharing). | ||
@@ -96,11 +106,11 @@ The preprint of our paper is available on [arXiv](https://arxiv.org/abs/2111.06693). Please cite | ||
| ## System requirements | ||
| > Works in the browser an on your local pc/server | ||
| > Works in the browser or on your local pc/server | ||
| *deepflash2* is designed to run on Windows, Linux, or Mac (x86-64) if [pytorch](https://pytorch.org/get-started/locally/) is installable. | ||
| We generally recommend using Google Colab as it only requires a Google Account and a device with a web browser. | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, NVIDIA Tesla K80 GPU or better). | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, 8 GB RAM, NVIDIA GPU with 8GB VRAM or better). | ||
| Software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on the simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose with commit hash `316927eff7ad2201391957909a2114c68baee309`. | ||
| *deepflash2* requires Python>3.6 and the software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose v0.6.6.dev13+g316927e. | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10 (tbd), MacOS 12.0.1 (tbd)). | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10, MacOS 12.0.1). | ||
@@ -111,36 +121,46 @@ ## Installation Guide | ||
| The GUI of *deepflash2* runs as a web application inside a Jupyter Notebook, the de-facto standard of computational notebooks in the scientific community. The GUI is built on top of the *deepflash2* Python API, which can be used independently (read the [docs](https://matjesg.github.io/deepflash2/)). | ||
| #### Google Colab | ||
| ### Google Colab | ||
| Excute the `Set up environment` cell or follow the `pip` instructions. | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| Open <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> and excute the `Set up environment` cell or follow the `pip` instructions. Colab provides free access to graphics processing units (GPUs) for fast model training and prediction (Google account required). | ||
| #### Other systems | ||
| ##### [conda](https://docs.conda.io/en/latest/) | ||
| ### Other systems | ||
| We recommend installation into a new, clean [environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). | ||
| We recommend installation into a clean Python environment (e.g., using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)). | ||
| #### [conda](https://docs.conda.io/en/latest/)/[mamba](https://github.com/mamba-org/mamba) | ||
| If you replace `conda` with `mamba` the install process will be much faster and more reliable (you need to install [mamba](https://github.com/mamba-org/mamba) first): | ||
| ```bash | ||
| conda install -c conda-forge -c fastchan -c matjesg deepflash2 | ||
| conda install -c fastchan -c conda-forge -c matjesg deepflash2 | ||
| ``` | ||
| ##### [pip](https://pip.pypa.io/en/stable/) | ||
| #### [pip](https://pip.pypa.io/en/stable/) | ||
| You should install PyTorch first by following the installation instructions of [pytorch](https://pytorch.org/get-started/locally/). | ||
| If you want to use your GPU and install with pip, we recommend installing PyTorch first by following the [installation instructions](https://pytorch.org/get-started/locally/). | ||
| ```bash | ||
| pip install deepflash2 | ||
| pip install -U deepflash2 | ||
| ``` | ||
| #### Using the GUI | ||
| If you want to use the GUI, make sure to download the GUI notebook and start a Jupyter server. | ||
| If you want to use the GUI, make sure to download the GUI notebook, e.g., using `curl` | ||
| ```bash | ||
| curl -o deepflash2_GUI.ipynb https://raw.githubusercontent.com/matjesg/deepflash2/master/deepflash2_GUI.ipynb | ||
| ``` | ||
| and start a Jupyter server. | ||
| ```bash | ||
| jupyter notebook | ||
| ``` | ||
| Then, open `deepflash2_GUI.ipynb` within Notebook environment. | ||
| ##### Docker | ||
| ### Docker | ||
@@ -147,0 +167,0 @@ Docker images for __deepflash2__ are built on top of [the latest pytorch image](https://hub.docker.com/r/pytorch/pytorch/). |
+42
-22
@@ -12,3 +12,3 @@ # Title | ||
| [](https://pypistats.org/packages/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://anaconda.org/matjesg/deepflash2) | ||
| [](https://github.com/matjesg/deepflash2) | ||
@@ -36,9 +36,19 @@ [](https://github.com/matjesg/deepflash2/) | ||
| ## Quick Start and Demo | ||
| > Get started in less than a minute. Watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html) for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory with free access to graphics processing units (GPUs). | ||
| > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help. | ||
| For a quick start, run *deepflash2* in Google Colaboratory (Google account required). | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| <video src="https://user-images.githubusercontent.com/13711052/139751414-acf737db-2d8a-4203-8a34-7a38e5326b5e.mov" controls width="100%"></video> | ||
| To try the functionalities of *deepflash2*, open the *deepflash2* GUI in [Colab]((https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) or follow the installation instructions below. The GUI provides a build-in use for sample data. After starting the GUI, select the task (GT Estimation, Training, or Prediction) and click `Load Sample Data`. For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| #### Demo usage | ||
| The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| 1. Starting the GUI (in <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> or follow the installation instructions below) | ||
| 2. Select the task (GT Estimation, Training, or Prediction) | ||
| 3. Click the `Load Sample Data` button in the sidebar and continue to the next sidebar section. | ||
| For futher instructions watch the [tutorials](https://matjesg.github.io/deepflash2/tutorial.html). | ||
| We provide an overview of the tasks below: | ||
@@ -53,7 +63,7 @@ | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). You can download the sample data [here](https://github.com/matjesg/deepflash2/releases/tag/sample_data). | ||
| Times are estimated for Google Colab (with free NVIDIA Tesla K80 GPU). | ||
| ## Paper and Experiments | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](xxx). | ||
| We provide a complete guide to reproduce our experiments using the *deepflash2 Python API* [here](https://github.com/matjesg/deepflash2/tree/master/paper). The data is currently available on [Google Drive](https://drive.google.com/drive/folders/1r9AqP9qW9JThbMIvT0jhoA5mPxWEeIjs?usp=sharing). | ||
@@ -75,11 +85,11 @@ The preprint of our paper is available on [arXiv](https://arxiv.org/abs/2111.06693). Please cite | ||
| ## System requirements | ||
| > Works in the browser an on your local pc/server | ||
| > Works in the browser or on your local pc/server | ||
| *deepflash2* is designed to run on Windows, Linux, or Mac (x86-64) if [pytorch](https://pytorch.org/get-started/locally/) is installable. | ||
| We generally recommend using Google Colab as it only requires a Google Account and a device with a web browser. | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, NVIDIA Tesla K80 GPU or better). | ||
| To run *deepflash2* locally, we recommend using a system with a GPU (e.g., 2 CPUs, 8 GB RAM, NVIDIA GPU with 8GB VRAM or better). | ||
| Software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on the simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose with commit hash `316927eff7ad2201391957909a2114c68baee309`. | ||
| *deepflash2* requires Python>3.6 and the software dependencies are defined in the [settings.ini](https://github.com/matjesg/deepflash2/blob/master/settings.ini) file. Additionally, the ground truth estimation functionalities are based on simpleITK>=2.0 and the instance segmentation capabilities are complemented using cellpose v0.6.6.dev13+g316927e. | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10 (tbd), MacOS 12.0.1 (tbd)). | ||
| *deepflash2* is tested on Google Colab (Ubuntu 18.04.5 LTS) and locally (Ubuntu 20.04 LTS, Windows 10, MacOS 12.0.1). | ||
@@ -90,36 +100,46 @@ ## Installation Guide | ||
| The GUI of *deepflash2* runs as a web application inside a Jupyter Notebook, the de-facto standard of computational notebooks in the scientific community. The GUI is built on top of the *deepflash2* Python API, which can be used independently (read the [docs](https://matjesg.github.io/deepflash2/)). | ||
| #### Google Colab | ||
| ### Google Colab | ||
| Excute the `Set up environment` cell or follow the `pip` instructions. | ||
| [](https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb) | ||
| Open <a href="https://colab.research.google.com/github/matjesg/deepflash2/blob/master/deepflash2_GUI.ipynb" target="_blank">Colab</a> and excute the `Set up environment` cell or follow the `pip` instructions. Colab provides free access to graphics processing units (GPUs) for fast model training and prediction (Google account required). | ||
| #### Other systems | ||
| ##### [conda](https://docs.conda.io/en/latest/) | ||
| ### Other systems | ||
| We recommend installation into a new, clean [environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). | ||
| We recommend installation into a clean Python environment (e.g., using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)). | ||
| #### [conda](https://docs.conda.io/en/latest/)/[mamba](https://github.com/mamba-org/mamba) | ||
| If you replace `conda` with `mamba` the install process will be much faster and more reliable (you need to install [mamba](https://github.com/mamba-org/mamba) first): | ||
| ```bash | ||
| conda install -c conda-forge -c fastchan -c matjesg deepflash2 | ||
| conda install -c fastchan -c conda-forge -c matjesg deepflash2 | ||
| ``` | ||
| ##### [pip](https://pip.pypa.io/en/stable/) | ||
| #### [pip](https://pip.pypa.io/en/stable/) | ||
| You should install PyTorch first by following the installation instructions of [pytorch](https://pytorch.org/get-started/locally/). | ||
| If you want to use your GPU and install with pip, we recommend installing PyTorch first by following the [installation instructions](https://pytorch.org/get-started/locally/). | ||
| ```bash | ||
| pip install deepflash2 | ||
| pip install -U deepflash2 | ||
| ``` | ||
| #### Using the GUI | ||
| If you want to use the GUI, make sure to download the GUI notebook and start a Jupyter server. | ||
| If you want to use the GUI, make sure to download the GUI notebook, e.g., using `curl` | ||
| ```bash | ||
| curl -o deepflash2_GUI.ipynb https://raw.githubusercontent.com/matjesg/deepflash2/master/deepflash2_GUI.ipynb | ||
| ``` | ||
| and start a Jupyter server. | ||
| ```bash | ||
| jupyter notebook | ||
| ``` | ||
| Then, open `deepflash2_GUI.ipynb` within Notebook environment. | ||
| ##### Docker | ||
| ### Docker | ||
@@ -126,0 +146,0 @@ Docker images for __deepflash2__ are built on top of [the latest pytorch image](https://hub.docker.com/r/pytorch/pytorch/). |
+1
-1
@@ -11,3 +11,3 @@ [DEFAULT] | ||
| branch = master | ||
| version = 0.1.6 | ||
| version = 0.1.7 | ||
| min_python = 3.6 | ||
@@ -14,0 +14,0 @@ audience = Developers |
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