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主要是因为tensorflow与keras的版本不兼容
import tensorflow
print(tensorflow.__version__)
Below is the list of Deep Learning environments supported by FloydHub. Any of these can be specified in the floyd command using the --env option.
If no --env is provided, it uses the tensorflow-1.9 image by default, which comes with Python 3.6, Keras 2.2.0 and TensorFlow 1.9.0 pre-installed.
| Framework | Env name (--env parameter) | Description | Docker Image | Packages and Nvidia Settings |
|---|---|---|---|---|
| TensorFlow 2.1 | tensorflow-2.1 | TensorFlow 2.1.0 + Keras 2.3.1 on Python 3.6. | ||
| TensorFlow 2.0 | tensorflow-2.0 | TensorFlow 2.0.0 + Keras 2.3.1 on Python 3.6. | ||
| TensorFlow 1.15 | tensorflow-1.15 | TensorFlow 1.15.0 + Keras 2.3.1 on Python 3.6. | ||
| TensorFlow 1.14 | tensorflow-1.14 | TensorFlow 1.14.0 + Keras 2.2.5 on Python 3.6. | ||
| TensorFlow 1.13 | tensorflow-1.13 | TensorFlow 1.13.0 + Keras 2.2.4 on Python 3.6. | ||
| TensorFlow 1.12 | tensorflow-1.12 | TensorFlow 1.12.0 + Keras 2.2.4 on Python 3.6. | ||
| tensorflow-1.12:py2 | TensorFlow 1.12.0 + Keras 2.2.4 on Python 2. | |||
| TensorFlow 1.11 | tensorflow-1.11 | TensorFlow 1.11.0 + Keras 2.2.4 on Python 3.6. | ||
| tensorflow-1.11:py2 | TensorFlow 1.11.0 + Keras 2.2.4 on Python 2. | |||
| TensorFlow 1.10 | tensorflow-1.10 | TensorFlow 1.10.0 + Keras 2.2.0 on Python 3.6. | ||
| tensorflow-1.10:py2 | TensorFlow 1.10.0 + Keras 2.2.0 on Python 2. | |||
| TensorFlow 1.9 | tensorflow-1.9 | TensorFlow 1.9.0 + Keras 2.2.0 on Python 3.6. | ||
| tensorflow-1.9:py2 | TensorFlow 1.9.0 + Keras 2.2.0 on Python 2. | |||
| TensorFlow 1.8 | tensorflow-1.8 | TensorFlow 1.8.0 + Keras 2.1.6 on Python 3.6. | ||
| tensorflow-1.8:py2 | TensorFlow 1.8.0 + Keras 2.1.6 on Python 2. | |||
| TensorFlow 1.7 | tensorflow-1.7 | TensorFlow 1.7.0 + Keras 2.1.6 on Python 3.6. | ||
| tensorflow-1.7:py2 | TensorFlow 1.7.0 + Keras 2.1.6 on Python 2. | |||
| TensorFlow 1.5 | tensorflow-1.5 | TensorFlow 1.5.0 + Keras 2.1.6 on Python 3.6. | ||
| tensorflow-1.5:py2 | TensorFlow 1.5.0 + Keras 2.1.6 on Python 2. | |||
| TensorFlow 1.4 | tensorflow-1.4 | TensorFlow 1.4.0 + Keras 2.0.8 on Python 3.6. | ||
| tensorflow-1.4:py2 | TensorFlow 1.4.0 + Keras 2.0.8 on Python 2. | |||
| TensorFlow 1.3 | tensorflow-1.3 | TensorFlow 1.3.0 + Keras 2.0.6 on Python 3.6. | ||
| tensorflow-1.3:py2 | TensorFlow 1.3.0 + Keras 2.0.6 on Python 2. | |||
| TensorFlow 1.2 | tensorflow-1.2 | TensorFlow 1.2.0 + Keras 2.0.6 on Python 3.5. | ||
| tensorflow-1.2:py2 | TensorFlow 1.2.0 + Keras 2.0.6 on Python 2. | |||
| TensorFlow 1.1 | tensorflow | TensorFlow 1.1.0 + Keras 2.0.6 on Python 3.5. | ||
| tensorflow:py2 | TensorFlow 1.1.0 + Keras 2.0.6 on Python 2. | |||
| TensorFlow 1.0 | tensorflow-1.0 | TensorFlow 1.0.0 + Keras 2.0.6 on Python 3.5. | ||
| tensorflow-1.0:py2 | TensorFlow 1.0.0 + Keras 2.0.6 on Python 2. | |||
| TensorFlow 0.12 | tensorflow-0.12 | TensorFlow 0.12.1 + Keras 1.2.2 on Python 3.5. | ||
| tensorflow-0.12:py2 | TensorFlow 0.12.1 + Keras 1.2.2 on Python 2. | |||
| PyTorch 1.4 | pytorch-1.4 | PyTorch 1.4.0 + fastai 1.0.60 on Python 3.6. | ||
| PyTorch 1.3 | pytorch-1.3 | PyTorch 1.3.0 + fastai 1.0.60 on Python 3.6. | ||
| PyTorch 1.2 | pytorch-1.2 | PyTorch 1.2.0 + fastai 1.0.60 on Python 3.6. | ||
| PyTorch 1.1 | pytorch-1.1 | PyTorch 1.1.0 + fastai 1.0.57 on Python 3.6. | ||
| PyTorch 1.0 | pytorch-1.0 | PyTorch 1.0.0 + fastai 1.0.51 on Python 3.6. | ||
| pytorch-1.0:py2 | PyTorch 1.0.0 on Python 2. | |||
| PyTorch 0.4 | pytorch-0.4 | PyTorch 0.4.1 on Python 3.6. | ||
| pytorch-0.4:py2 | PyTorch 0.4.1 on Python 2. | |||
| PyTorch 0.3 | pytorch-0.3 | PyTorch 0.3.1 on Python 3.6. | ||
| pytorch-0.3:py2 | PyTorch 0.3.1 on Python 2. | |||
| PyTorch 0.2 | pytorch-0.2 | PyTorch 0.2.0 on Python 3.5 | ||
| pytorch-0.2:py2 | PyTorch 0.2.0 on Python 2. | |||
| PyTorch 0.1 | pytorch-0.1 | PyTorch 0.1.12 on Python 3. | ||
| pytorch-0.1:py2 | PyTorch 0.1.12 on Python 2. | |||
| Theano 0.9 | theano-0.9 | Theano rel-0.8.2 + Keras 2.0.3 on Python3.5. | ||
| theano-0.9:py2 | Theano rel-0.8.2 + Keras 2.0.3 on Python2. | |||
| Caffe | caffe | Caffe rc4 on Python3.5. | ||
| caffe:py2 | Caffe rc4 on Python2. | |||
| Torch | torch | Torch 7 with Python 3 env. | ||
| torch:py2 | Torch 7 with Python 2 env. | |||
| Chainer 1.23 | chainer-1.23 | Chainer 1.23.0 on Python 3. | ||
| chainer-1.23:py2 | Chainer 1.23.0 on Python 2. | |||
| Chainer 2.0 | chainer-2.0 | Chainer 1.23.0 on Python 3. | ||
| chainer-2.0:py2 | Chainer 1.23.0 on Python 2. | |||
| MxNet 1.0 | mxnet | MxNet 1.0.0 on Python 3.6. | ||
| mxnet:py2 | MxNet 1.0.0 on Python 2. |
All environments are available for both CPU and GPU execution. For example,
To run a Python2 Tensorflow job on CPU
$ floyd run --env tensorflow:py2 "python mnist_cnn.py"
To run a Python2 Tensorflow job on GPU (CUDA, cuDNN, etc. installed)
$ floyd run --env tensorflow:py2 --gpu "python mnist_cnn.py"
The following software packages (in addition to many other common libraries) are available in all the environments:
h5py, iPython, Jupyter, matplotlib, numpy, OpenCV, Pandas, Pillow, scikit-learn, scipy, sklearn
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