@memetzgz, ok. Thanks for your response.
Where are results stored after running the course1 notebook in crestle environment? I like to see the output. (predicted labels for images under ‘test’ directory).
So I’m running into this:
--------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <ipython-input-12-d4fe8b8bb391> in <module>() 3 # Setting requires_grad=False indicates that we do not need to compute gradients 4 # with respect to these Variables. ----> 5 x = Variable(x.type(dtype), requires_grad=False) 6 y = Variable(y.type(dtype), requires_grad=False) /home/ubuntu/anaconda3/lib/python3.5/site-packages/torch/_utils.py in _type(self, new_type, async) 33 if new_type.is_sparse: 34 raise RuntimeError("Cannot cast dense tensor to sparse tensor") ---> 35 return new_type(self.size()).copy_(self, async) 36 37 /home/ubuntu/anaconda3/lib/python3.5/site-packages/torch/cuda/__init__.py in _lazy_new(cls, *args, **kwargs) 264 @staticmethod 265 def _lazy_new(cls, *args, **kwargs): --> 266 _lazy_init() 267 # We need this method only for lazy init, so we can remove it 268 del _CudaBase.__new__ /home/ubuntu/anaconda3/lib/python3.5/site-packages/torch/cuda/__init__.py in _lazy_init() 82 raise RuntimeError( 83 "Cannot re-initialize CUDA in forked subprocess. " + msg) ---> 84 _check_driver() 85 torch._C._cuda_init() 86 torch._C._cuda_sparse_init() /home/ubuntu/anaconda3/lib/python3.5/site-packages/torch/cuda/__init__.py in _check_driver() 56 Found no NVIDIA driver on your system. Please check that you 57 have an NVIDIA GPU and installed a driver from ---> 58 http://www.nvidia.com/Download/index.aspx""") 59 else: 60 # TODO: directly link to the alternative bin that needs install AssertionError: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx
I’m running on a p2 instance on AWS, so very much have a GPU! a bit stumped on what to do
For how many lessons will Crestle be an acceptable environment?
If not optimum for all lessons, then will another AWS AMI be setup?
I am asking since the current AWS AMI requires a lot of setup adjustment as you are already aware. I do believe that this activity is a great learning step. But I also need to spend effort on lesson1 to start the deep learning mindfulness. The Crestle environment serves this purpose right now for me.
It worked for a while and after installing labextension I got this error
Crestle will be fine for the whole course. I’m setting up an AMI right now, which will not require any extra setup to use.
Thank you Mr. @jeremy for this AMI, i am waiting for this.
As we are running a fastai environment under Anaconda, I am confused about adding missing libraries like OpenCV. Do we add them after activating the fastai environment, or exit to our main Python 3.6 and install them there?
You can install them after you activate the environment or
conda install -n <env_name> <package>
If you are in the environment you can also use pip.
Thanks for that advice! If I think it would be a good idea to have those libraries anyway in my core Python system, can I install them back there and they become available whenever I load my fast.ai environment? Or do I have to install twice, in the core system AND the environment?
When loading a particular environment, you can only access the packages installed in that particular environment.
One clarification, here we are using already trained model resnet which was trained on another image dataset and now we are using it to adapt it according to our dog cat dataset: Is this called Transfer Learning or something else
My version of crestle environment only has the old version of the course.