* dsvm builder * dsvm builder util * dsvm builder update * dsvm update * dsvm.py update * rename create_dsvm.py * minor update * readme steps * readme steps * readmes * Update README.md * mv dsvm builder to contrib * update to dsvm builder so that it automatically installs the cv repo and starts a jupyter server
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Коммит
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3
.flake8
3
.flake8
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@ -10,6 +10,7 @@
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# E402 module level import not at top of file
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# E731 do not assign a lambda expression, use a def
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# F821 undefined name 'get_ipython' --> from generated python files using nbconvert
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# W605 invalid escape sequence '\W'
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ignore = E203, E266, E501, W503, F405, E402, E731, F821
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ignore = E203, E266, E501, W503, F405, E402, E731, F821, W605
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max-line-length = 79
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@ -33,6 +33,34 @@ We have also found that some browsers do not render Jupyter widgets correctly. I
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| [23_aci_aks_web_service_testing.ipynb](notebooks/23_aci_aks_web_service_testing.ipynb)| Tests the deployed models on either ACI or AKS. |
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| [24_exploring_hyperparameters_on_azureml.ipynb](notebooks/24_exploring_hyperparameters_on_azureml.ipynb)| Performs highly parallel parameter sweeping using AzureML's HyperDrive. |
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## Using a Virtual Machine
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You may want to use a virtual machine to run the notebooks. Doing so will give you a lot more flexibility -- whether it is using a GPU enabled machine or simply working in Linux.
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__Data Science Virtual Machine Builder__
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One easy way to create your VM is to use the 'create_dsvm.py' tool located inside of the 'tools' folder in the root directory of the repo. Simply run `python tools/create_dsvm.py` at the root level of the repo. This tool preconfigures your virtual machine with the appropriate settings for working with this repository.
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__Using the Azure Portal or CLI__
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You can also spin up a VM directly using the Azure portal. For this repository,
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you will want to create a Data Science Virtual Machine (DSVM). To do so, follow
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[this](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro)
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link that shows you how to provision your VM through the portal.
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You can alternatively use the Azure command line (CLI) as well. Follow
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[this](https://docs.microsoft.com/en-us/cli/azure/azure-cli-vm-tutorial?view=azure-cli-latest)
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link to learn more about the Azure CLI and how it can be used to provision
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resources.
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Once your virtual machine has been created, ssh and tunnel into the machine, then run the "Getting started" steps inside of it. The 'create_dsvm' tool will show you how to properly perform the tunneling too. If you created your virtual machine using the portal or the CLI, you can tunnel your jupyter notebook ports using the following command:
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```
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$ssh -L local_port:remote_address:remote_port username@server.com
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```
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## Azure-enhanced notebooks
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Azure products and services are used in certain notebooks to enhance the efficiency of developing classification systems at scale.
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|
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@ -7,4 +7,4 @@ Each project should live in its own subdirectory ```/contrib/<project>``` and co
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| Directory | Project description |
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|---|---|
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| | |
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| vm_builder | This script helps users easily create an Ubuntu Data Science Virtual Machine with a GPU with the Computer Vision repo installed and ready to be used. If you find the script to be out-dated or not working, you can create the VM using the Azure portal or the Azure CLI tool with a few more steps. |
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@ -0,0 +1,25 @@
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# VM Builder
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This mini project will help you set up a Virtual Machine with the Computer
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Vision repo installed on it.
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You can use this project simply by running:
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```bash
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python vm_builder.py
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```
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This will kick off an interactive bash session that will create your VM on
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Azure and install the repo on it.
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Once your VM has been setup, you can ssh tunnel to port 8899 and you'll
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find the Computer Vision repo setup and ready to be used.
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```bash
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ssh -L 8899:localhost:8899 <username>@<ip-address>
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```
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Visit localhost:8899 on your browser to start using the notebooks.>
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```
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Visit localhost:8899 on your browser to start using the notebooks.
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@ -0,0 +1,624 @@
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import json
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import re
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import subprocess
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import textwrap
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from shutil import which
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from prompt_toolkit import prompt
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from prompt_toolkit import print_formatted_text, HTML
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# variables
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UBUNTU_DSVM_IMAGE = (
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"microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest"
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)
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vm_options = dict(
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gpu=dict(size="Standard_NC6s_v3", family="NCSv3", cores=6),
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cpu=dict(size="Standard_DS3_v2", family="DSv2", cores=4),
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)
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# list of cmds
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account_list_cmd = "az account list -o table"
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sub_id_list_cmd = 'az account list --query []."id" -o tsv'
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region_list_cmd = 'az account list-locations --query []."name" -o tsv'
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silent_login_cmd = 'az login --query "[?n]|[0]"'
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set_account_sub_cmd = "az account set -s {}"
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provision_rg_cmd = "az group create --name {} --location {}"
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provision_vm_cmd = (
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"az vm create --resource-group {} --name {} --size {} --image {} "
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"--admin-username {} --admin-password {} --authentication-type password"
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)
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vm_ip_cmd = (
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"az vm show -d --resource-group {}-rg --name {} "
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'--query "publicIps" -o json'
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)
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quota_cmd = (
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"az vm list-usage --location {} --query "
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"[?contains(localName,'{}')].{{max:limit,current:currentValue}}"
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)
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install_repo_cmd = (
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"az vm run-command invoke -g {}-rg -n {} "
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"--command-id RunShellScript --scripts"
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)
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# install repo invoke script
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install_repo_script = """<<<EOF
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ls
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EOF
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"""
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tmp = """<<<EOF
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rm -rf computervision
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conda remove -n cv --all
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git clone https://www.github.com/microsoft/computervision
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cd computervision
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conda env create -f environment.yml
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tmux
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jupyter notebook --port 8888
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EOF"""
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def is_installed(cli_app: str) -> bool:
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"""Check whether `name` is on PATH and marked as executable."""
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return which(cli_app) is not None
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|
||||
|
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def validate_password(password: str) -> bool:
|
||||
""" Checks that the password is valid.
|
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|
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Args:
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password: password string
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|
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Returns: True if valid, else false.
|
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"""
|
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|
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if len(password) < 12 or len(password) > 123:
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print_formatted_text(
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HTML(
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(
|
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"<ansired>Input must be between 12 and 123 characters. "
|
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"Please try again.</ansired>"
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)
|
||||
)
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)
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return False
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||||
|
||||
if (
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||||
len([c for c in password if c.islower()]) <= 0
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or len([c for c in password if c.isupper()]) <= 0
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):
|
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print_formatted_text(
|
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HTML(
|
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(
|
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"<ansired>Input must contain a upper and a lower case "
|
||||
"character. Please try again.</ansired>"
|
||||
)
|
||||
)
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)
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return False
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|
||||
if len([c for c in password if c.isdigit()]) <= 0:
|
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print_formatted_text(
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HTML(
|
||||
"<ansired>Input must contain a digit. Please try again.</ansired>"
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)
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)
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return False
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|
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if len(re.findall("[\W_]", password)) <= 0:
|
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print_formatted_text(
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HTML(
|
||||
(
|
||||
"<ansired>Input must contain a special character. "
|
||||
"Please try again.</ansired>"
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||||
)
|
||||
)
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||||
)
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||||
return False
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||||
|
||||
return True
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||||
|
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def validate_vm_name(vm_name) -> bool:
|
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""" Checks that the vm name is valid.
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Args:
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vm_name: the name of the vm to check
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Returns: True if valid, else false.
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"""
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# we minus 3 for max length because we generate the rg using: "{vm_name}-rg"
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if len(vm_name) < 1 or len(vm_name) > (64 - 3):
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print_formatted_text(
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HTML(
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(
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f"<ansired>Input must be between 1 and {64-3} characters. "
|
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"Please try again.</ansired>"
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||||
)
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)
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)
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return False
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||||
|
||||
if not bool(re.match("^[A-Za-z0-9-]*$", vm_name)):
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print_formatted_text(
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HTML(
|
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(
|
||||
"<ansired>You can only use alphanumeric characters and "
|
||||
"hyphens. Please try again.</ansired>"
|
||||
)
|
||||
)
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||||
)
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return False
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||||
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||||
return True
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||||
|
||||
|
||||
def check_valid_yes_no_response(input: str) -> bool:
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if input in ("Y", "y", "N", "n"):
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return True
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else:
|
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print_formatted_text(
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HTML("<ansired>Enter 'y' or 'n'. Please try again.</ansired>")
|
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)
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return False
|
||||
|
||||
|
||||
def yes_no_prompter(msg: str) -> bool:
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cond = None
|
||||
valid_response = False
|
||||
while not valid_response:
|
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cond = prompt(msg)
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valid_response = check_valid_yes_no_response(cond)
|
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return True if cond in ("Y", "y") else False
|
||||
|
||||
|
||||
def prompt_subscription_id() -> str:
|
||||
""" Prompt for subscription id. """
|
||||
subscription_id = None
|
||||
subscription_is_valid = False
|
||||
results = subprocess.run(
|
||||
sub_id_list_cmd.split(" "), stdout=subprocess.PIPE
|
||||
)
|
||||
subscription_ids = results.stdout.decode("utf-8").strip().split("\n")
|
||||
while not subscription_is_valid:
|
||||
subscription_id = prompt(
|
||||
("Enter your subscription id " "(copy & paste it from above): ")
|
||||
)
|
||||
if subscription_id in subscription_ids:
|
||||
subscription_is_valid = True
|
||||
else:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansired>The subscription id you entered is not "
|
||||
"valid. Please try again.</ansired>"
|
||||
)
|
||||
)
|
||||
)
|
||||
return subscription_id
|
||||
|
||||
|
||||
def prompt_vm_name() -> str:
|
||||
""" Prompt for VM name. """
|
||||
vm_name = None
|
||||
vm_name_is_valid = False
|
||||
while not vm_name_is_valid:
|
||||
vm_name = prompt(
|
||||
f"Enter a name for your vm (ex. 'cv-datascience-vm'): "
|
||||
)
|
||||
vm_name_is_valid = validate_vm_name(vm_name)
|
||||
return vm_name
|
||||
|
||||
|
||||
def prompt_region() -> str:
|
||||
""" Prompt for region. """
|
||||
region = None
|
||||
region_is_valid = False
|
||||
results = subprocess.run(
|
||||
region_list_cmd.split(" "), stdout=subprocess.PIPE
|
||||
)
|
||||
valid_regions = results.stdout.decode("utf-8").strip().split("\n")
|
||||
while not region_is_valid:
|
||||
region = prompt(f"Enter a region for your vm (ex. 'eastus'): ")
|
||||
if region in valid_regions:
|
||||
region_is_valid = True
|
||||
else:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
textwrap.dedent(
|
||||
"""\
|
||||
<ansired>The region you entered is invalid. You can run
|
||||
`az account list-locations` to see a list of the valid
|
||||
regions. Please try again.</ansired>\
|
||||
"""
|
||||
)
|
||||
)
|
||||
)
|
||||
return region
|
||||
|
||||
|
||||
def prompt_username() -> str:
|
||||
""" Prompt username. """
|
||||
username = None
|
||||
username_is_valid = False
|
||||
while not username_is_valid:
|
||||
username = prompt("Enter a username: ")
|
||||
if len(username) > 0:
|
||||
username_is_valid = True
|
||||
else:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansired>Username cannot be empty. "
|
||||
"Please try again.</ansired>"
|
||||
)
|
||||
)
|
||||
)
|
||||
return username
|
||||
|
||||
|
||||
def prompt_password() -> str:
|
||||
""" Prompt for password. """
|
||||
password = None
|
||||
|
||||
password_is_valid = False
|
||||
while not password_is_valid:
|
||||
password = prompt("Enter a password: ", is_password=True)
|
||||
if not validate_password(password):
|
||||
continue
|
||||
|
||||
password_match = prompt(
|
||||
"Enter your password again: ", is_password=True
|
||||
)
|
||||
if password == password_match:
|
||||
password_is_valid = True
|
||||
else:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansired>Your passwords do not match. Please try "
|
||||
"again.</ansired>"
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
return password
|
||||
|
||||
|
||||
def prompt_use_gpu() -> str:
|
||||
""" Prompt for GPU or CPU. """
|
||||
return yes_no_prompter(
|
||||
(
|
||||
"Do you want to use a GPU-enabled VM (It will incur a "
|
||||
"higher cost) [y/n]: "
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def prompt_use_cpu_instead() -> str:
|
||||
""" Prompt switch to using CPU. """
|
||||
return yes_no_prompter(
|
||||
(
|
||||
"Do you want to switch to using a CPU instead? (This will "
|
||||
"likely solve your out-of-quota problem) [y/n]: "
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def get_available_quota(region: str, vm_family: str) -> int:
|
||||
""" Get available quota of the subscription in the specified region.
|
||||
|
||||
Args:
|
||||
region: the region to check
|
||||
vm_family: the vm family to check
|
||||
|
||||
Returns: the available quota
|
||||
"""
|
||||
results = subprocess.run(
|
||||
quota_cmd.format(region, vm_family).split(" "), stdout=subprocess.PIPE
|
||||
)
|
||||
quota = json.loads("".join(results.stdout.decode("utf-8")))
|
||||
return int(quota[0]["max"]) - int(quota[0]["current"])
|
||||
|
||||
|
||||
def print_intro_dialogue():
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
textwrap.dedent(
|
||||
"""
|
||||
Azure Data Science Virtual Machine Builder
|
||||
|
||||
This utility will help you create an Azure Data Science Ubuntu Virtual
|
||||
Machine that you will be able to run your notebooks in. The VM will
|
||||
be based on the Ubuntu DSVM image.
|
||||
|
||||
For more information about Ubuntu DSVMs, see here:
|
||||
https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro
|
||||
|
||||
This utility will let you select a GPU machine or a CPU machine.
|
||||
|
||||
The GPU machine specs:
|
||||
- size: Standard_NC6s_v3 (NVIDIA Tesla V100 GPUs)
|
||||
- family: NC6s
|
||||
- cores: 6
|
||||
|
||||
The CPU machine specs:
|
||||
- size: Standard_DS3_v2 (Intel Xeon® E5-2673 v3 2.4 GHz (Haswell))
|
||||
- family: DSv2
|
||||
- cores: 4
|
||||
|
||||
Pricing information on the SKUs can be found here:
|
||||
https://azure.microsoft.com/en-us/pricing/details/virtual-machines
|
||||
|
||||
To use this utility, you must have an Azure subscription which you can
|
||||
get from azure.microsoft.com.
|
||||
|
||||
Answer the questions below to setup your machine.
|
||||
|
||||
------------------------------------------
|
||||
"""
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def check_az_cli_installed():
|
||||
if not is_installed("az"):
|
||||
print(
|
||||
textwrap.dedent(
|
||||
"""\
|
||||
You must have the Azure CLI installed. For more information on
|
||||
installing the Azure CLI, see here:
|
||||
https://docs.microsoft.com/en-us/cli/azure/?view=azure-cli-latest
|
||||
"""
|
||||
)
|
||||
)
|
||||
exit(0)
|
||||
|
||||
|
||||
def check_logged_in() -> bool:
|
||||
print("Checking to see if you are logged in...")
|
||||
results = subprocess.run(
|
||||
account_list_cmd.split(" "),
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
)
|
||||
return False if "az login" in str(results.stderr) else True
|
||||
|
||||
|
||||
def log_in(logged_in: bool):
|
||||
if not logged_in:
|
||||
subprocess.run(silent_login_cmd.split(" "))
|
||||
print("\n")
|
||||
else:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansigreen>Looks like you're already logged "
|
||||
"in.</ansigreen>\n"
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def show_accounts():
|
||||
print("Here is a list of your subscriptions:")
|
||||
results = subprocess.run(
|
||||
account_list_cmd.split(" "), stdout=subprocess.PIPE
|
||||
)
|
||||
print_formatted_text(
|
||||
HTML(f"<ansigreen>{results.stdout.decode('utf-8')}</ansigreen>")
|
||||
)
|
||||
|
||||
|
||||
def check_quota(region: str, vm: dict, subscription_id: str) -> dict:
|
||||
if get_available_quota(region, vm["family"]) < vm["cores"]:
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
textwrap.dedent(
|
||||
f"""\
|
||||
<ansired>
|
||||
The subscription '{subscription_id}' does not have enough
|
||||
cores of {vm['family']} in the region: {region}.
|
||||
|
||||
To request more cores:
|
||||
https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request
|
||||
|
||||
(If you selected GPU, you may try using CPU instead.)
|
||||
</ansired>\
|
||||
"""
|
||||
)
|
||||
)
|
||||
)
|
||||
if prompt_use_cpu_instead():
|
||||
vm = vm_options["cpu"]
|
||||
else:
|
||||
print_formatted_text(HTML("Exiting.."))
|
||||
exit()
|
||||
return vm
|
||||
|
||||
|
||||
def create_rg(vm_name: str, region: str):
|
||||
print_formatted_text(
|
||||
HTML(("\n<ansiyellow>Creating the resource group.</ansiyellow>"))
|
||||
)
|
||||
results = subprocess.run(
|
||||
provision_rg_cmd.format(f"{vm_name}-rg", region).split(" "),
|
||||
stdout=subprocess.PIPE,
|
||||
)
|
||||
if "Succeeded" in results.stdout.decode("utf-8"):
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansigreen>Your resource group was "
|
||||
"successfully created.</ansigreen>\n"
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def create_vm(vm_name: str, vm: dict, username: str, password: str):
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansiyellow>Creating the Data Science VM. "
|
||||
"This may take up a few minutes...</ansiyellow>"
|
||||
)
|
||||
)
|
||||
)
|
||||
subprocess.run(
|
||||
provision_vm_cmd.format(
|
||||
f"{vm_name}-rg",
|
||||
vm_name,
|
||||
vm["size"],
|
||||
UBUNTU_DSVM_IMAGE,
|
||||
username,
|
||||
password,
|
||||
).split(" "),
|
||||
stdout=subprocess.PIPE,
|
||||
)
|
||||
|
||||
|
||||
def get_vm_ip(vm_name: str) -> str:
|
||||
results = subprocess.run(
|
||||
vm_ip_cmd.format(vm_name, vm_name).split(" "),
|
||||
stdout=subprocess.PIPE,
|
||||
stderr=subprocess.PIPE,
|
||||
)
|
||||
|
||||
vm_ip = results.stdout.decode("utf-8").strip().strip('"')
|
||||
if len(vm_ip) > 0:
|
||||
print_formatted_text(
|
||||
HTML("<ansigreen>VM creation succeeded.</ansigreen>\n")
|
||||
)
|
||||
return vm_ip
|
||||
|
||||
|
||||
def install_repo(username: str, password: str, vm_ip: str, vm_name: str):
|
||||
print_formatted_text(
|
||||
HTML("<ansiyellow>Setting up your machine...</ansiyellow>")
|
||||
)
|
||||
invoke_cmd = install_repo_cmd.format(vm_name, vm_name)
|
||||
cmds = invoke_cmd.split(" ")
|
||||
cmds.append(
|
||||
f"""<<<EOF
|
||||
export PATH=/anaconda/bin:$PATH
|
||||
conda remove -n cv --all
|
||||
cd /home/{username}
|
||||
rm -rf computervision
|
||||
git clone https://www.github.com/microsoft/computervision
|
||||
chmod 777 computervision
|
||||
cd computervision
|
||||
conda env create -f environment.yml
|
||||
source activate cv
|
||||
python -m ipykernel install --user --name cv --display-name "Python (cv)"
|
||||
jupyter notebook --port 8899 --allow-root --NotebookApp.token='' --NotebookApp.password='' &
|
||||
EOF"""
|
||||
)
|
||||
subprocess.run(cmds, stdout=subprocess.PIPE)
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
(
|
||||
"<ansigreen>Successfully installed the repo "
|
||||
"on the machine.</ansigreen>\n"
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def print_exit_dialogue(
|
||||
vm_name: str, vm_ip: str, region: str, username: str, subscription_id: str
|
||||
):
|
||||
print_formatted_text(
|
||||
HTML(
|
||||
textwrap.dedent(
|
||||
f"""
|
||||
DSVM creation is complete. We recommend saving the details below.
|
||||
<ansiyellow>
|
||||
VM information:
|
||||
- vm_name: {vm_name}
|
||||
- ip: {vm_ip}
|
||||
- region: {region}
|
||||
- username: {username}
|
||||
- password: ****
|
||||
- resource_group: {vm_name}-rg
|
||||
- subscription_id: {subscription_id}
|
||||
</ansiyellow>
|
||||
To start/stop VM:
|
||||
<ansiyellow>
|
||||
$az vm stop -g {vm_name}-rg -n {vm_name}
|
||||
$az vm start -g {vm_name}-rg -n {vm_name}
|
||||
</ansiyellow>
|
||||
To connect via ssh and tunnel:
|
||||
<ansiyellow>
|
||||
$ssh -L 8899:localhost:8899 {username}@{vm_ip}
|
||||
</ansiyellow>
|
||||
To delete the VM (this command is unrecoverable):
|
||||
<ansiyellow>
|
||||
$az group delete -n {vm_name}-rg
|
||||
</ansiyellow>
|
||||
Please remember that virtual machines will incur a cost on your
|
||||
Azure subscription. Remember to stop your machine if you are not
|
||||
using it to minimize the cost.\
|
||||
"""
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def vm_builder() -> None:
|
||||
""" Interaction session to create a data science vm. """
|
||||
|
||||
# print intro dialogue
|
||||
print_intro_dialogue()
|
||||
|
||||
# validate active user
|
||||
prompt("Press enter to continue...\n")
|
||||
|
||||
# check that az cli is installed
|
||||
check_az_cli_installed()
|
||||
|
||||
# check if we are logged in
|
||||
logged_in = check_logged_in()
|
||||
|
||||
# login to the az cli and suppress output
|
||||
log_in(logged_in)
|
||||
|
||||
# show account sub list
|
||||
show_accounts()
|
||||
|
||||
# prompt fields
|
||||
subscription_id = prompt_subscription_id()
|
||||
vm_name = prompt_vm_name()
|
||||
region = prompt_region()
|
||||
use_gpu = prompt_use_gpu()
|
||||
username = prompt_username()
|
||||
password = prompt_password()
|
||||
|
||||
# set GPU
|
||||
vm = vm_options["gpu"] if use_gpu else vm_options["cpu"]
|
||||
|
||||
# check quota
|
||||
vm = check_quota(region, vm, subscription_id)
|
||||
|
||||
# set sub id
|
||||
subprocess.run(set_account_sub_cmd.format(subscription_id).split(" "))
|
||||
|
||||
# provision rg
|
||||
create_rg(vm_name, region)
|
||||
|
||||
# create vm
|
||||
create_vm(vm_name, vm, username, password)
|
||||
|
||||
# get vm ip
|
||||
vm_ip = get_vm_ip(vm_name)
|
||||
|
||||
# install cvbp on dsvm
|
||||
install_repo(username, password, vm_ip, vm_name)
|
||||
|
||||
# exit message
|
||||
print_exit_dialogue(vm_name, vm_ip, region, username, subscription_id)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
vm_builder()
|
||||
|
|
@ -0,0 +1,9 @@
|
|||
# Tools
|
||||
|
||||
This folder contains tools that the repo uses to perform tasks that are not
|
||||
built into the library itself.
|
||||
|
||||
| Folder/File | Purpose |
|
||||
|---|---|
|
||||
| /repo_metrics | The purpose of `repo_metrics` is to allow us to track this repo's usage over time. This will help us understand which notebooks are popular, and help us tailor our content to better fit our audience. |
|
||||
| /create_dsvm.py | This script helps users easily create an Ubuntu Data Science Virtual Machine with a GPU. If you find the script to be out-dated or not working, you can create the VM using the Azure portal or the Azure CLI tool with a few more steps. |
|
||||
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