Aneesh Panoli aneeshpanoli

## Dockerfile
# pull the official image
FROM tensorflow/tensorflow:latest-gpu-jupyter

# make a dir inside the container to copy files
WORKDIR /tfjupyter

# install additional packages
COPY requirements.txt .
COPY entrypoint.sh .
RUN ["chmod", "+x", "entrypoint.sh"]

## docker-compose.yaml
version: "3"

services:
  tensorflow:
    container_name: TfJupyter
    build:
      context: .
      dockerfile: Dockerfile
    volumes:
      - .:/tf

## spak_conf.py
def set_conf():
    conf = SparkConf().setAppName("App")
    conf = (conf.setMaster('local[*]')
      .set('spark.executor.memory', '4G')
      .set('spark.driver.memory', '16G')
      .set('spark.driver.maxResultSize', '8G'))
    return conf

## download_sra.py
import os

#download SRA tool kit
def setup_sra_tool(url):
  !wget $url
  !gunzip sratoolkit.2.9.6-1-ubuntu64.tar.gz
  !tar -xf sratoolkit.2.9.6-1-ubuntu64.tar


#download SRA file and extract fastq

## setup_notebook.py
from subprocess import STDOUT, check_call, check_output

def install_java_scala():
          try:
          java_ver = check_output(['java', '-version'], stderr=STDOUT)
        except:
          java_ver = b''
        try:
          scala_ver = check_output(['scala', '-version'], stderr=STDOUT)
        except:

## understanding-word-vectors.ipynb

      
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                aneeshpanoli
                / understanding-word-vectors.ipynb
            
            
              Created
              October 5, 2019 21:33
                — forked from aparrish/understanding-word-vectors.ipynb
            
              
                Understanding word vectors: A tutorial for "Reading and Writing Electronic Text," a class I teach at ITP. (Python 2.7) Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
              
          
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## Django Startup
def startup():
  global matrix
  matrix = load_large_matrix()


# then you would
from yourapp import matrix
	# pull the official image
	FROM tensorflow/tensorflow:latest-gpu-jupyter

	# make a dir inside the container to copy files
	WORKDIR /tfjupyter

	# install additional packages
	COPY requirements.txt .
	COPY entrypoint.sh .
	RUN ["chmod", "+x", "entrypoint.sh"]
	version: "3"

	services:
	tensorflow:
	container_name: TfJupyter
	build:
	context: .
	dockerfile: Dockerfile
	volumes:
	- .:/tf
	def set_conf():
	conf = SparkConf().setAppName("App")
	conf = (conf.setMaster('local[*]')
	.set('spark.executor.memory', '4G')
	.set('spark.driver.memory', '16G')
	.set('spark.driver.maxResultSize', '8G'))
	return conf
	import os

	#download SRA tool kit
	def setup_sra_tool(url):
	!wget $url
	!gunzip sratoolkit.2.9.6-1-ubuntu64.tar.gz
	!tar -xf sratoolkit.2.9.6-1-ubuntu64.tar


	#download SRA file and extract fastq
	from subprocess import STDOUT, check_call, check_output

	def install_java_scala():
	try:
	java_ver = check_output(['java', '-version'], stderr=STDOUT)
	except:
	java_ver = b''
	try:
	scala_ver = check_output(['scala', '-version'], stderr=STDOUT)
	except:
	def startup():
	global matrix
	matrix = load_large_matrix()


	# then you would
	from yourapp import matrix