Repository contains plugins for ImJoy
Plugin repository for ImJoy.IO, with a special focus on deep learning applications. For more details on ImJoy, see https://github.com/oeway/ImJoy.
These example plugins are described in our publication Ouyang et al., arXiv:1905.13105, ImJoy: an open-source computational platform for the deep learning era.
The provided version are frozen at the time of publication. For more recent version, please also visit the plugin repositories
The provided deep learning plugins are built on open-source Python libraries
with specific dependencies. For these plugins we therefore provide different
tags, that allow to control which libraries are installed.
Deep learning methods require large computational resources, e.g. training is often performed on GPUs rather than CPUs. For a quick primer on 'CPU vs GPU computing' see the dedicated section below.
For the ImJoy plugins, we hence provide tags to determine if a plugin will be running on a GPU or CPU. Below we detail the operating systems and hardware configurations where the plugins were tested.
Some plugins, such as the DPNUnet are based on implementations that
only permit GPU computations. Here, no CPU tag is provided.
Additional tags can be added for other configurations. For this, please file an issue here.
Table 1. Tested environments for the DPNUnet plugin
| Operating System | CPU | GPU | Hardware specification |
|---|---|---|---|
| Ubuntu 16.04 | [X] | [X] | Application pod running on a NVIDIA DGX-1 node, with 4 Tesla V100 GPUs |
| MacOS ( Mojave 10.14.4 ) | [X] | [ ] | iMac (Retina 5K, 27-inch, Late 2014), 3.5 GHz Intel Core i5,32 GB 1600 MHz DDR3 |
| Window 10 | [ ] | [ ] | Not tested yet |
Table 2. Tested environments for the CARE plugin
| Operating System | CPU | GPU | Hardware specification |
|---|---|---|---|
| Ubuntu 16.04 | [X] | [X] | Application pod running on a NVIDIA DGX-1 node, with 4 Tesla V100 GPUs |
| MacOS ( Mojave 10.14.4 ) | [X] | [ ] | iMac (Retina 5K, 27-inch, Late 2014), 3.5 GHz Intel Core i5,32 GB 1600 MHz DDR3 |
| Window 10 | [X] | [ ] | Windows 10 (Core i7 16GB DDR4) |
Table 3. Tested environments for the ANNA-PALM plugin
| Operating System | CPU | GPU | Hardware specification |
|---|---|---|---|
| Ubuntu 16.04 | [X] | [X] | Application pod running on a NVIDIA DGX-1 node, with 4 Tesla V100 GPUs |
| MacOS ( Mojave 10.14.4 ) | [X] | [ ] | iMac (Retina 5K, 27-inch, Late 2014), 3.5 GHz Intel Core i5,32 GB 1600 MHz DDR3 |
| Window 10 | [ ] | [ ] | Not tested yet |
Below we only a provide very brief overview over these two computational modes. More details can be found in many online tutorials, for instance here
- Training of large neural networks is frequently performed on so-called GPUs (Graphics processing unit). These are specialised devices, which are ideally suited for training and prediction in deep learning applications. Many of the open-source libraries require GPUs of Nvidia. Such a device will not be available on every computer. Libraries might further depend on which specific GPU will be used.
- As an alternative computations can be performed on the CPU (central processing units), which is the general purpose processing unit of every computer. Here, training is often slow and in extreme cases not even possible. However, frequently trained networks can be be used with a CPU. While every computer will have a CPU, libraries will depend on the precise hardware specification and also the operating system.
- Installations can depend on the operating systems, e.g. to account for
differences between Windows and Linux. This could be accounted for by
operating system specific tags, such as
CPU_Windowsfor a plugin that will be using CPUs on Windows.