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anaconda3

Anaconda3, Jupyter Notebook, OpenCV3, TensorFlow and Keras2 for Deep Learning

Available tags

Anaconda3, Jupyter, OpenCV3

Tag	Size / Layers	CUDA Toolkit (Linux x86_64 / Windows x86_64)	CUDNN
latest, cpu		-	-
10.0-cudnn7		CUDA 10.0 ( >= 410.48 / 411.31 )	7
9.2-cudnn7		CUDA 9.2 ( >= 396.26 / 397.44 )	7
9.1-cudnn7		CUDA 9.1 ( >= 387.26 / 388.19 )	7
9.0-cudnn7		CUDA 9.0 ( >= 384.81 / 385.54 )	7
8.0-cudnn6		CUDA 8.0 ( >= 375.26 / 376.51 )	6

Tensorflow

Tag	Size / Layers	Base	Tensorflow
tf-cpu, tf		cpu	1.12.0
tf-10.0-cudnn7		10.0-cudnn7	1.12.0
tf-9.2-cudnn7		9.2-cudnn7	1.12.0
tf-9.1-cudnn7		9.1-cudnn7	1.7.0
tf-9.0-cudnn7		9.0-cudnn7	1.7.0
tf-8.0-cudnn6		8.0-cudnn6	1.4.1

Keras (Tensorflow Backend)

Tag	Size / Layers	Base	Tensorflow	Keras
keras-cpu, keras		tf-cpu	1.12.0	2.2.4
keras-10.0-cudnn7		tf-10.0-cudnn7	1.12.0	2.2.4
keras-9.2-cudnn7		tf-9.2-cudnn7	1.12.0	2.2.4
keras-9.1-cudnn7		tf-9.1-cudnn7	1.7.0	2.1.5
keras-9.0-cudnn7		tf-9.0-cudnn7	1.7.0	2.1.5
keras-8.0-cudnn6		tf-8.0-cudnn6	1.4.1	2.1.3

Pytorch

Tag	Size / Layers	Base	Pytorch
pytorch-cpu, pytorch		cpu	1.0.0
pytorch-10.0-cudnn7		10.0-cudnn7	1.0.0
pytorch-9.2-cudnn7		9.2-cudnn7	1.0.0
pytorch-9.1-cudnn7		9.1-cudnn7	1.0.0
pytorch-9.0-cudnn7		9.0-cudnn7	1.0.0
pytorch-8.0-cudnn6		8.0-cudnn6	1.0.0

Mxnet

Tag	Size / Layers	Base	Mxnet
mxnet-cpu, mxnet		cpu	mxnet
mxnet-10.0-cudnn7		10.0-cudnn7	mxnet-cu100
mxnet-9.2-cudnn7		9.2-cudnn7	mxnet-cu92
mxnet-9.1-cudnn7		9.1-cudnn7	mxnet-cu91
mxnet-9.0-cudnn7		9.0-cudnn7	mxnet-cu90
mxnet-8.0-cudnn6		8.0-cudnn6	mxnet-cu80

How to Use

CPU

1. Run with docker (image: okwrtdsh/anaconda3:keras-cpu)

$ docker run -v $(pwd):/src/notebooks -p 8888:8888 -td okwrtdsh/anaconda3:keras-cpu

2. Open http://localhost:8888 in web browser

GPU

1. Run with nvidia-docker (image: okwrtdsh/anaconda3:keras-10.0-cudnn7)

$ nvidia-docker run -v $(pwd):/src/notebooks -p 8888:8888 -td okwrtdsh/anaconda3:keras-10.0-cudnn7

2. Open http://localhost:8888 in web browser

CPU (docker-compose)

1. docker-compose.yml (image: okwrtdsh/anaconda3:keras-cpu)

version: '3'
services:
  jupyter:
    image: okwrtdsh/anaconda3:keras-cpu
    ports:
      - '8888:8888'
    volumes:
      - ./notebooks:/src/notebooks

2. Run with docker-compose

$ docker-compose up -d

3. Open http://localhost:8888 in web browser

GPU (docker-compose)

1. docker-compose.yml (image: okwrtdsh/anaconda3:keras-10.0-cudnn7)

version: '3'
services:
  jupyter:
    image: okwrtdsh/anaconda3:keras-10.0-cudnn7
    ports:
      - '8888:8888'
    volumes:
      - ./notebooks:/src/notebooks

2. Run with nvidia-docker

# Run with nvidia-docker-compose (nvidia-docker v1)
$ nvidia-docker-compose up -d
# Run with docker-compose (nvidia-docker v2)
$ docker-compose up -d

3. Open http://localhost:8888 in web browser

PyTorch

Please note that PyTorch uses shared memory to share data between processes, so if torch multiprocessing is used (e.g. for multithreaded data loaders) the default shared memory segment size that container runs with is not enough, and you should increase shared memory size either with --ipc=host or --shm-size command line options to nvidia-docker run.

nvidia-docker

$ nvidia-docker run --ipc=host -v $(pwd):/src/notebooks -p 8888:8888 -td okwrtdsh/anaconda3:pytorch-10.0-cudnn7

docker-compose

version: '3'
services:
  jupyter:
    image: okwrtdsh/anaconda3:pytorch-10.0-cudnn7
    ipc: host
    ports:
      - '8888:8888'
    volumes:
      - ./notebooks:/src/notebooks

# Run with nvidia-docker-compose (nvidia-docker v1)
$ nvidia-docker-compose up -d
# Run with docker-compose (nvidia-docker v2)
$ docker-compose up -d