in Machine Learning” – http://arkitus.com/patterns-for-research-in-machine-learning/ – Always, always use version control. – Separate code from data. – Separate input data, working data and output data. – Modify input data with care. – Save everything to disk frequently. – Separate options from parameters. – Do not use global variables. – Record the options used to generate each run of the algorithm. – Make it easy to sweep options. – Make it easy to execute only portions of the code.
# ロードする def loadmat(filepath): with tb.open_file(filepath, 'r') as f: l = f.root.shape[1] n = f.root.shape[0] X = ss.csr_matrix( (f.root.data[:], f.root.indices[:], f.root.indptr[:]), shape=(l, n)) X = ss.csc_matrix(X.T) return X
AWS 上に作成する (c3.8xlarge を 5 USD で⼊札) $ docker-machine create \ --driver amazonec2 \ --amazonec2-vpc-id vpc-43537026 \ --amazonec2-subnet-id subnet-9cfbaeeb \ --amazonec2-region us-west-2 \ --amazonec2-zone a \ --amazonec2-instance-type c3.8xlarge \ --amazonec2-root-size 20 \ --amazonec2-ami ami-16b1a077 \ --amazonec2-security-group kaggle-srck \ --amazonec2-request-spot-instance \ --amazonec2-spot-price 5 aws01 Running pre-create checks... Creating machine... (aws01) Launching instance... (aws01) Waiting for spot instance... (aws01) Created spot instance request %v sir-0396hlmq Waiting for machine to be running, this may take a few minutes... Detecting operating system of created instance... Waiting for SSH to be available... (snip) Docker is up and running! To see how to connect your Docker Client to the Docker Engine running on this virtual machine, run: docker-machine env aws01