How to use compute method in avocado
Best Python code snippet using avocado_python
views.py
Source:views.py 
1import hashlib2import json3import subprocess4from django.core.exceptions import PermissionDenied5from django.shortcuts import get_object_or_4046from rest_framework import status7from rest_framework.decorators import detail_route8from rest_framework.generics import RetrieveUpdateAPIView, ListAPIView9from rest_framework.parsers import MultiPartParser10from rest_framework.permissions import IsAuthenticated11from rest_framework.response import Response12from rest_framework.views import APIView13from rest_framework.viewsets import ModelViewSet, ViewSet14from tasks import poweron_nodes, poweroff_nodes15from tasks import submit_computeset, cancel_computeset, attach_iso16import hostlist17from models import Cluster, Compute, ComputeSet, Frontend18from serializers import ComputeSerializer, ComputeSetSerializer, FullComputeSetSerializer19from serializers import ClusterSerializer, FrontendSerializer, ProjectSerializer20from serializers import UserDetailsSerializer21import re, os, random, string, datetime22from django.db.models import Q23from rest_framework.authentication import BasicAuthentication24from rest_framework.permissions import IsAuthenticated25# #################################################26# CLUSTER27# #################################################28class ClusterViewSet(ModelViewSet):29 lookup_field = 'cluster_name'30 serializer_class = ClusterSerializer31 def get_queryset(self):32 """Obtain details about all clusters."""33 clusters = Cluster.objects.filter(34 project__in=self.request.user.groups.all())35 return clusters36 def retrieve(self, request, cluster_name, format=None):37 """Obtain details about the named cluster."""38 clust = get_object_or_404(Cluster, name=cluster_name)39 if not clust.project in request.user.groups.all():40 raise PermissionDenied()41 serializer = ClusterSerializer(clust)42 return Response(serializer.data)43 def destroy(self, request, cluster_name, format=None):44 """Destroy the named cluster."""45 return Response("destroy todo")46# #################################################47# COMPUTE48# #################################################49class ComputeViewSet(ViewSet):50 lookup_field = 'compute_name'51 serializer_class = ClusterSerializer52 def retrieve(self, request, compute_name_cluster_name, compute_name, format=None):53 """Obtain the details of a named compute resource in a named cluster."""54 compute = get_object_or_404(55 Compute, name=compute_name, cluster__name=compute_name_cluster_name)56 if not compute.cluster.project in request.user.groups.all():57 raise PermissionDenied()58 serializer = ComputeSerializer(compute)59 return Response(serializer.data)60 def destroy(self, request, compute_name_cluster_name, compute_name, format=None):61 """Destroy the named compute resource in a named cluster."""62 return Response("todo")63 @detail_route(methods=['put'])64 def shutdown(self, request, compute_name_cluster_name, compute_name, format=None):65 """Shutdown the named compute resource in a named cluster.66 """67 compute = get_object_or_404(68 Compute, name=compute_name, cluster__name=compute_name_cluster_name)69 if not compute.cluster.project in request.user.groups.all():70 raise PermissionDenied()71 if(not compute.computeset.filter(state=ComputeSet.CSET_STATE_RUNNING).exists()):72 return Response("Compute is not a member of an active computeset",73 status=status.HTTP_400_BAD_REQUEST)74 poweroff_nodes.delay([compute.rocks_name], "shutdown")75 return Response(status=204)76 @detail_route(methods=['put'])77 def reboot(self, request, compute_name_cluster_name, compute_name, format=None):78 """Reboot the named compute resource in a named cluster.79 """80 compute = get_object_or_404(81 Compute, name=compute_name, cluster__name=compute_name_cluster_name)82 if not compute.cluster.project in request.user.groups.all():83 raise PermissionDenied()84 if(not compute.computeset.filter(state=ComputeSet.CSET_STATE_RUNNING).exists()):85 return Response("Compute is not a member of an active computeset",86 status=status.HTTP_400_BAD_REQUEST)87 poweroff_nodes.delay([compute.rocks_name], "reboot")88 return Response(status=204)89 @detail_route(methods=['put'])90 def reset(self, request, compute_name_cluster_name, compute_name, format=None):91 """Reset the named compute resource in a named cluster.92 """93 compute = get_object_or_404(94 Compute, name=compute_name, cluster__name=compute_name_cluster_name)95 if not compute.cluster.project in request.user.groups.all():96 raise PermissionDenied()97 if(not compute.computeset.filter(state=ComputeSet.CSET_STATE_RUNNING).exists()):98 return Response("Compute is not a member of an active computeset",99 status=status.HTTP_400_BAD_REQUEST)100 poweroff_nodes.delay([compute.rocks_name], "reset")101 return Response(status=204)102 @detail_route(methods=['put'])103 def poweroff(self, request, compute_name_cluster_name, compute_name, format=None):104 """Power off the named compute resource in a named cluster.105 """106 compute = get_object_or_404(107 Compute, name=compute_name, cluster__name=compute_name_cluster_name)108 if not compute.cluster.project in request.user.groups.all():109 raise PermissionDenied()110 if(not compute.computeset.filter(state=ComputeSet.CSET_STATE_RUNNING).exists()):111 return Response("Compute is not a member of an active computeset",112 status=status.HTTP_400_BAD_REQUEST)113 poweroff_nodes.delay([compute.rocks_name], "poweroff")114 return Response(status=204)115 @detail_route(methods=['put'])116 def poweron(self, request, compute_name_cluster_name, compute_name, format=None):117 """Power on the named compute resource in a named cluster.118 """119 compute = get_object_or_404(120 Compute, name=compute_name, cluster__name=compute_name_cluster_name)121 if not compute.cluster.project in request.user.groups.all():122 raise PermissionDenied()123 if(not compute.computeset.filter(state=ComputeSet.CSET_STATE_RUNNING).exists()):124 return Response("Compute is not a member of an active computeset",125 status=status.HTTP_400_BAD_REQUEST)126 poweron_nodes.delay([compute.rocks_name])127 return Response(status=204)128 @detail_route(methods=['put'])129 def attach_iso(self, request, compute_name_cluster_name, compute_name, format=None):130 """Attach an ISO to the named compute resource in a named cluster.131 """132 compute = get_object_or_404(133 Compute, name=compute_name, cluster__name=compute_name_cluster_name)134 if not compute.cluster.project in request.user.groups.all():135 raise PermissionDenied()136 if not "iso_name" in request.GET:137 return Response("Please provide the iso_name", status=400)138 attach_iso.delay([compute.rocks_name], request.GET["iso_name"])139 return Response(status=204)140 @detail_route(methods=['post'])141 def rename(self, request, compute_name_cluster_name, compute_name, format=None):142 """Rename the named compute resource in a named cluster.143 """144 compute = get_object_or_404(145 Compute, name=compute_name, cluster__name=compute_name_cluster_name)146 if not compute.cluster.project in request.user.groups.all():147 raise PermissionDenied()148 new_name = request.data.get("name")149 if(not re.match('^[a-zA-Z0-9_-]+$',new_name)):150 return Response("New name can opnly contain alphanumeric symbols, digits and '-_'.",151 status=status.HTTP_400_BAD_REQUEST)152 compute.name = new_name153 compute.save()154 return Response(status=204)155# #################################################156# CONSOLE157##################################################158def get_console(request, console_compute_name, nucleus_name=None, is_frontend=False):159 """Open VNC console to named resource."""160 resp = "Success"161 sleep_time = 15162 from xml.dom.minidom import parse, parseString163 import libvirt164 if(is_frontend):165 compute = Frontend.objects.get(rocks_name=console_compute_name)166 else:167 compute = Compute.objects.get(rocks_name=console_compute_name)168 physical_host = compute.physical_host169 if(not physical_host):170 return Response("The VM is not running",171 status=status.HTTP_400_BAD_REQUEST)172 hypervisor = libvirt.open("qemu+tls://%s.comet/system?pkipath=/var/secrets/cometvc" %173 physical_host)174 domU = hypervisor.lookupByName(compute.name)175 # Grab the current XML definition of the domain...176 flags = libvirt.VIR_DOMAIN_XML_SECURE177 domU_xml = parseString(domU.XMLDesc(flags))178 # Parse out the <graphics>...</graphics> device node...179 for gd in domU_xml.getElementsByTagName('graphics'):180 xml = gd.toxml()181 duration = 3600182 password = ''.join(183 random.SystemRandom().choice(184 string.ascii_uppercase +185 string.ascii_lowercase +186 string.digits) for _ in range(16))187 # Generate a new passwdValidTo string...188 dt1 = datetime.datetime.utcnow()189 dt2 = dt1 + datetime.timedelta(0, int(duration))190 timestr = dt2.strftime("%Y-%m-%dT%H:%M:%S")191 # Modify the passwd and passwdValidUntil fields...192 gd.setAttribute('passwd', password)193 gd.setAttribute('passwdValidTo', timestr)194 port = gd.getAttribute("port")195 # Apply the change to the domain...196 flags = libvirt.VIR_DOMAIN_DEVICE_MODIFY_FORCE | \197 libvirt.VIR_DOMAIN_DEVICE_MODIFY_LIVE198 retval = domU.updateDeviceFlags(gd.toxml(), flags)199 cmd = ['/usr/bin/sudo',200 '-u',201 'nucleus_comet',202 '/opt/nucleus-scripts/bin/open_tunnel.py',203 '-H',204 '{hostname}'.format(hostname=physical_host),205 '-p',206 '{hostport}'.format(hostport=port),207 '-s',208 '{duration}'.format(duration=sleep_time)]209 try:210 proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)211 except OSError as e:212 resp = "Execution failed: %s" % (e)213 return Response(resp)214 tun_port = ''215 tun_port = proc.stdout.readline().strip()216 url_base = "/nucleus-guacamole-0.9.8/index.html?hostname=localhost"217 url = request.build_absolute_uri("%s&port=%s&token=%s&host=%s" % (url_base, tun_port, password, nucleus_name))218 response = Response(219 url,220 status=303,221 headers={'Location': url})222 return response223class ConsoleViewSet(ViewSet):224 """Open VNC console to named compute resource."""225 authentication_classes = (BasicAuthentication,)226 permission_classes = (IsAuthenticated,)227 def retrieve(self, request, compute_name_cluster_name, console_compute_name, format=None):228 compute = get_object_or_404(229 Compute, name=console_compute_name, cluster__name=compute_name_cluster_name)230 if not compute.cluster.project in request.user.groups.all():231 raise PermissionDenied()232 return get_console(request, compute.rocks_name, console_compute_name)233class FrontendConsoleViewSet(ViewSet):234 """Open VNC console to name frontend resource."""235 authentication_classes = (BasicAuthentication,)236 permission_classes = (IsAuthenticated,)237 def retrieve(self, request, console_cluster_name, format=None):238 clust = get_object_or_404(Cluster, name=console_cluster_name)239 if not clust.project in request.user.groups.all():240 raise PermissionDenied()241 return get_console(request, clust.frontend.rocks_name, console_cluster_name, True)242##################################################243# COMPUTESET244# #################################################245class ComputeSetViewSet(ModelViewSet):246 lookup_field = 'computeset_id'247 serializer_class = ComputeSetSerializer248 def get_queryset(self):249 """Obtain the details of all ComputeSets."""250 cset = ComputeSet.objects.filter(251 cluster__project__in=self.request.user.groups.all())252 states = self.request.GET.getlist('state')253 #state = self.request.query_params.get('state', None)254 if states:255 cset = cset.filter(state__in=states)256 return cset257 def retrieve(self, request, computeset_id, format=None):258 """Obtain the details of the identified ComputeSet."""259 cset = get_object_or_404(ComputeSet, pk=computeset_id)260 if not cset.cluster.project in request.user.groups.all():261 raise PermissionDenied()262 serializer = ComputeSetSerializer(cset)263 return Response(serializer.data)264 @detail_route(methods=['put'])265 def poweroff(self, request, computeset_id, format=None):266 """Power off the identified ComputeSet."""267 cset = ComputeSet.objects.get(pk=computeset_id)268 if not cset.cluster.project in request.user.groups.all():269 raise PermissionDenied()270 computes = []271 for compute in cset.computes.all():272 computes.append(compute.rocks_name)273 poweroff_nodes.delay(computes, "poweroff")274 cancel_computeset.delay(FullComputeSetSerializer(cset).data)275 return Response(status=204)276 def poweron(self, request, format=None):277 """ Power on a set of computes creating a ComputeSet."""278 clust = get_object_or_404(Cluster, name=request.data["cluster"])279 if not clust.project in request.user.groups.all():280 raise PermissionDenied()281 walltime_mins = request.data.get("walltime_mins")282 if not walltime_mins:283 walltime_mins = 2880284 nodes = []285 hosts = []286 if request.data.get("computes"):287 if request.data["computes"] is list:288 for obj in request.data["computes"]:289 nodes.append(obj["name"])290 hosts.append(obj["host"])291 else:292 nodes = hostlist.expand_hostlist("%s" % request.data["computes"])293 if request.data.get("hosts"):294 hosts = hostlist.expand_hostlist("%s" % request.data["hosts"])295 elif request.data.get("count"):296 computes_selected = Compute.objects.filter(cluster=clust).exclude(297 computeset__state__in=[298 ComputeSet.CSET_STATE_CREATED,299 ComputeSet.CSET_STATE_SUBMITTED,300 ComputeSet.CSET_STATE_RUNNING,301 ComputeSet.CSET_STATE_ENDING]302 ).exclude(state="active").filter(Q(image_state="unmapped") | Q(image_state__isnull=True)).exclude(image_locked=True)[:int(request.data["count"])]303 nodes.extend([comp.name for comp in computes_selected])304 if(len(nodes) < int(request.data["count"]) or int(request.data["count"]) == 0):305 return Response("There are %i nodes available for starting. Requested number should be greater than zero."%len(nodes),306 status=status.HTTP_400_BAD_REQUEST)307 if hosts and len(nodes) != len(hosts):308 return Response("The length of hosts should be equal to length of nodes",309 status=status.HTTP_400_BAD_REQUEST)310 cset = ComputeSet()311 cset.cluster = clust312 cset.user = clust.username313 if(request.data.get("allocation")):314 cset.account = request.data["allocation"]315 elif(clust.allocations.count() == 1):316 cset.account = clust.allocations.get().allocation317 else:318 return Response("Please specify the allocation",319 status=status.HTTP_400_BAD_REQUEST)320 if(not clust.allocations.filter(allocation=cset.account).exists()):321 return Response("Allocation %s does not belong to the cluster."%cset.account,322 status=status.HTTP_400_BAD_REQUEST)323 cset.walltime_mins = walltime_mins324 cset.jobid = None325 cset.name = None326 cset.nodelist = ""327 cset.reservation = request.data.get("reservation")328 cset.state = ComputeSet.CSET_STATE_CREATED329 cset.node_count = len(nodes)330 cset.save()331 for node in nodes:332 compute = Compute.objects.get(name=node, cluster=clust)333 other_cs_query = ComputeSet.objects.filter(computes__id__exact=compute.id).exclude(334 state__in=[ComputeSet.CSET_STATE_COMPLETED, ComputeSet.CSET_STATE_FAILED, ComputeSet.CSET_STATE_CANCELLED])335 if other_cs_query.exists():336 cset.delete()337 err_cs = other_cs_query.get()338 return Response("The compute %s belongs to computeset %s which is in %s state" % (node, err_cs.id, err_cs.state), status=status.HTTP_400_BAD_REQUEST)339 if (compute.image_state not in ["unmapped", None]) or compute.image_locked:340 cset.delete()341 return Response("The node %s's image is in %s state and image locked status is %s. Please contact the user support if the VM is not running." %(node, compute.image_state, compute.image_locked), status=status.HTTP_400_BAD_REQUEST)342 if compute.cluster.name != request.data["cluster"]:343 cset.delete()344 return Response("The node %s does not belong to the cluster %s, belongs to %s" % (node, request.data["cluster"], compute.cluster.name), status=status.HTTP_400_BAD_REQUEST)345 cset.computes.add(compute)346 submit_computeset.delay(FullComputeSetSerializer(cset).data)347 # We should only poweron computes after entering jobscript and348 # finishing the PROLOG on all allocated nodes. At that point the349 # nodelist will be returned and we can call poweron_nodeset()350 #poweron_nodeset.delay(nodes, hosts)351 location = "/nucleus/v1/computeset/%s" % (cset.id)352 serializer = ComputeSetSerializer(cset)353 response = Response(354 serializer.data,355 status=201,356 headers={'Location': request.build_absolute_uri(location)})357 return response358 @detail_route(methods=['put'])359 def shutdown(self, request, computeset_id, format=None):360 """Shutdown the nodes in the identified ComputeSet."""361 cset = ComputeSet.objects.get(pk=computeset_id)362 if not cset.cluster.project in request.user.groups.all():363 raise PermissionDenied()364 computes = []365 for compute in cset.computes.all():366 computes.append(compute.rocks_name)367 if compute.cluster.name != request.data["cluster"]:368 cset.delete()369 return Response("The node %s does not belong to the cluster %s, belongs to %s" % (node, request.data["cluster"], compute.cluster.name), status=status.HTTP_400_BAD_REQUEST)370 cset.computes.add(compute)371 submit_computeset.delay(FullComputeSetSerializer(cset).data)372 # We should only poweron computes after entering jobscript and373 # finishing the PROLOG on all allocated nodes. At that point the374 # nodelist will be returned and we can call poweron_nodeset()375 #poweron_nodeset.delay(nodes, hosts)376 location = "/nucleus/v1/computeset/%s" % (cset.id)377 serializer = ComputeSetSerializer(cset)378 response = Response(379 serializer.data,380 status=201,381 headers={'Location': request.build_absolute_uri(location)})382 return response383 @detail_route(methods=['put'])384 def shutdown(self, request, computeset_id, format=None):385 """Shutdown the nodes in the identified ComputeSet."""386 cset = ComputeSet.objects.get(pk=computeset_id)387 if not cset.cluster.project in request.user.groups.all():388 raise PermissionDenied()389 computes = []390 for compute in cset.computes.all():391 computes.append(compute.rocks_name)392 if compute.cluster.name != request.data["cluster"]:393 cset.delete()394 return Response("The node %s does not belong to the cluster %s, belongs to %s" % (node, request.data["cluster"], compute.cluster.name), status=status.HTTP_400_BAD_REQUEST)395 cset.computes.add(compute)396 submit_computeset.delay(FullComputeSetSerializer(cset).data)397 # We should only poweron computes after entering jobscript and398 # finishing the PROLOG on all allocated nodes. At that point the399 # nodelist will be returned and we can call poweron_nodeset()400 #poweron_nodeset.delay(nodes, hosts)401 location = "/nucleus/v1/computeset/%s" % (cset.id)402 serializer = ComputeSetSerializer(cset)403 response = Response(404 serializer.data,405 status=201,406 headers={'Location': request.build_absolute_uri(location)})407 return response408 @detail_route(methods=['put'])409 def shutdown(self, request, computeset_id, format=None):410 """Shutdown the nodes in the identified ComputeSet."""411 cset = ComputeSet.objects.get(pk=computeset_id)412 if not cset.cluster.project in request.user.groups.all():413 raise PermissionDenied()414 computes = []415 for compute in cset.computes.all():416 computes.append(compute.rocks_name)417 poweroff_nodes.delay(computes, "shutdown")418 cancel_computeset.delay(FullComputeSetSerializer(cset).data)419 return Response(status=204)420 @detail_route(methods=['put'])421 def reboot(self, request, computeset_id, format=None):422 """Reboot the nodes in the identified ComputeSet."""423 cset = ComputeSet.objects.get(pk=computeset_id)424 if not cset.cluster.project in request.user.groups.all():425 raise PermissionDenied()426 computes = []427 for compute in cset.computes.all():428 computes.append(compute.rocks_name)429 poweroff_nodes.delay(computes, "reboot")430 return Response(status=204)431 @detail_route(methods=['put'])432 def reset(self, request, computeset_id, format=None):433 """Reset the nodes in the identified ComputeSet."""434 cset = ComputeSet.objects.get(pk=computeset_id)435 if not cset.cluster.project in request.user.groups.all():436 raise PermissionDenied()437 computes = []438 for compute in cset.computes.all():439 computes.append(compute.rocks_name)440 poweroff_nodes.delay(computes, "reset")441 return Response(status=204)442# #################################################443# FRONTEND444# #################################################445class FrontendViewSet(ViewSet):446 def retrieve(self, request, frontend_cluster_name, format=None):447 """Obtain the details of a frontend resource in a named cluster."""448 clust = get_object_or_404(Cluster, name=frontend_cluster_name)449 if not clust.project in request.user.groups.all():450 raise PermissionDenied()451 serializer = FrontendSerializer(clust.frontend)452 return Response(serializer.data)453 @detail_route(methods=['put'])454 def shutdown(self, request, frontend_cluster_name, format=None):455 """Shutdown the frontend of a named cluster."""456 clust = get_object_or_404(Cluster, name=frontend_cluster_name)457 if not clust.project in request.user.groups.all():458 raise PermissionDenied()459 poweroff_nodes.delay([clust.frontend.rocks_name], "shutdown")460 return Response(status=204)461 @detail_route(methods=['put'])462 def reboot(self, request, frontend_cluster_name, format=None):463 """Reboot the frontend of a named cluster."""464 clust = get_object_or_404(Cluster, name=frontend_cluster_name)465 if not clust.project in request.user.groups.all():466 raise PermissionDenied()467 poweroff_nodes.delay([clust.frontend.rocks_name], "reboot")468 return Response(status=204)469 @detail_route(methods=['put'])470 def reset(self, request, frontend_cluster_name, format=None):471 """Reset the frontend of a named cluster."""472 clust = get_object_or_404(Cluster, name=frontend_cluster_name)473 if not clust.project in request.user.groups.all():474 raise PermissionDenied()475 poweroff_nodes.delay([clust.frontend.rocks_name], "reset")476 return Response(status=204)477 @detail_route(methods=['put'])478 def poweron(self, request, frontend_cluster_name, format=None):479 """Power on the frontend of a named cluster."""480 clust = get_object_or_404(Cluster, name=frontend_cluster_name)481 if not clust.project in request.user.groups.all():482 raise PermissionDenied()483 poweron_nodes.delay([clust.frontend.rocks_name])484 return Response(status=204)485 @detail_route(methods=['put'])486 def poweroff(self, request, frontend_cluster_name, format=None):487 """Power off the frontend of a named cluster."""488 clust = get_object_or_404(Cluster, name=frontend_cluster_name)489 if not clust.project in request.user.groups.all():490 raise PermissionDenied()491 poweroff_nodes.delay([clust.frontend.rocks_name], "poweroff")492 return Response(status=204)493 @detail_route(methods=['put'])494 def attach_iso(self, request, frontend_cluster_name, format=None):495 """Attach an ISO to the frontendresource in a named cluster.496 """497 clust = get_object_or_404(Cluster, name=frontend_cluster_name)498 if not clust.project in request.user.groups.all():499 raise PermissionDenied()500 if not "iso_name" in request.GET:501 return Response("Please provide the iso_name", status=400)502 attach_iso.delay([clust.frontend.rocks_name], request.GET["iso_name"])503 return Response(status=204)504# #################################################505# USER506# #################################################507class UserDetailsView(RetrieveUpdateAPIView):508 """509 Returns User's details in JSON format.510 Accepts the following GET parameters: token511 Accepts the following POST parameters:512 Required: token513 Optional: email, first_name, last_name and UserProfile fields514 Returns the updated UserProfile and/or User object.515 """516 serializer_class = UserDetailsSerializer517 permission_classes = (IsAuthenticated,)518 def get_object(self):519 return self.request.user520# #################################################521# PROJECT522# #################################################523class ProjectListView(ListAPIView):524 """Returns project details."""525 serializer_class = ProjectSerializer526 def get_queryset(self):527 return self.request.user.groups.all()528class ImageUploadView(APIView):529 parser_classes = (MultiPartParser,)530 def get(self, request, format=None):531 filepath = '/mnt/images/public'532 return Response(["public/%s"%dir for dir in os.listdir(filepath)])533 def post(self, request, format=None):534 file_obj = request.FILES['file']535 try:536 filepath = '/mnt/images/public/%s' % (file_obj.name)537 if not request.META.get('HTTP_MD5'):538 return Response("md5 was not provided", status=400)539 if request.META['HTTP_MD5'] != md5_for_file(file_obj.chunks()):540 return Response("md5 does not match the file", status=400)541 with open(filepath, 'wb+') as destination:542 for chunk in file_obj.chunks():543 destination.write(chunk)544 return Response(status=204)545 finally:546 if(file_obj):547 file_obj.close()548def md5_for_file(chunks):549 md5 = hashlib.md5()550 for data in chunks:551 md5.update(data)...
fake_policy.py
Source:fake_policy.py
1# Copyright (c) 2012 OpenStack Foundation2#3# Licensed under the Apache License, Version 2.0 (the "License"); you may4# not use this file except in compliance with the License. You may obtain5# a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT11# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the12# License for the specific language governing permissions and limitations13# under the License.14policy_data = """15{16 "admin_api": "is_admin:True",17 "cells_scheduler_filter:TargetCellFilter": "is_admin:True",18 "context_is_admin": "role:admin or role:administrator",19 "compute:create": "",20 "compute:create:attach_network": "",21 "compute:create:attach_volume": "",22 "compute:get": "",23 "compute:get_all": "",24 "compute:get_all_tenants": "",25 "compute:update": "",26 "compute:get_instance_metadata": "",27 "compute:get_all_instance_metadata": "",28 "compute:get_all_instance_system_metadata": "",29 "compute:update_instance_metadata": "",30 "compute:delete_instance_metadata": "",31 "compute:get_instance_faults": "",32 "compute:get_diagnostics": "",33 "compute:get_instance_diagnostics": "",34 "compute:get_lock": "",35 "compute:lock": "",36 "compute:unlock": "",37 "compute:unlock_override": "is_admin:True",38 "compute:get_vnc_console": "",39 "compute:get_spice_console": "",40 "compute:get_rdp_console": "",41 "compute:get_serial_console": "",42 "compute:get_console_output": "",43 "compute:associate_floating_ip": "",44 "compute:reset_network": "",45 "compute:inject_network_info": "",46 "compute:add_fixed_ip": "",47 "compute:remove_fixed_ip": "",48 "compute:attach_volume": "",49 "compute:detach_volume": "",50 "compute:attach_interface": "",51 "compute:detach_interface": "",52 "compute:set_admin_password": "",53 "compute:rescue": "",54 "compute:unrescue": "",55 "compute:suspend": "",56 "compute:resume": "",57 "compute:pause": "",58 "compute:unpause": "",59 "compute:start": "",60 "compute:stop": "",61 "compute:resize": "",62 "compute:confirm_resize": "",63 "compute:revert_resize": "",64 "compute:rebuild": "",65 "compute:reboot": "",66 "compute:snapshot": "",67 "compute:backup": "",68 "compute:shelve": "",69 "compute:shelve_offload": "",70 "compute:unshelve": "",71 "compute:security_groups:add_to_instance": "",72 "compute:security_groups:remove_from_instance": 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"compute_extension:baremetal_nodes": "",114 "compute_extension:cells": "",115 "compute_extension:cells:create": "rule:admin_api",116 "compute_extension:cells:delete": "rule:admin_api",117 "compute_extension:cells:update": "rule:admin_api",118 "compute_extension:cells:sync_instances": "rule:admin_api",119 "compute_extension:v3:os-cells": "",120 "compute_extension:v3:os-cells:create": "rule:admin_api",121 "compute_extension:v3:os-cells:delete": "rule:admin_api",122 "compute_extension:v3:os-cells:update": "rule:admin_api",123 "compute_extension:v3:os-cells:sync_instances": "rule:admin_api",124 "compute_extension:certificates": "",125 "compute_extension:v3:os-certificates:create": "",126 "compute_extension:v3:os-certificates:show": "",127 "compute_extension:cloudpipe": "",128 "compute_extension:cloudpipe_update": "",129 "compute_extension:config_drive": "",130 "compute_extension:v3:os-config-drive": "",131 "compute_extension:console_output": "",132 "compute_extension:v3:os-console-output": 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"compute_extension:hide_server_addresses": "",192 "compute_extension:v3:os-hide-server-addresses": "",193 "compute_extension:hosts": "rule:admin_api",194 "compute_extension:v3:os-hosts": "rule:admin_api",195 "compute_extension:hypervisors": "rule:admin_api",196 "compute_extension:v3:os-hypervisors": "rule:admin_api",197 "compute_extension:image_size": "",198 "compute_extension:v3:image-size": "",199 "compute_extension:instance_actions": "",200 "compute_extension:v3:os-instance-actions": "",201 "compute_extension:instance_actions:events": "is_admin:True",202 "compute_extension:v3:os-instance-actions:events": "is_admin:True",203 "compute_extension:instance_usage_audit_log": "",204 "compute_extension:keypairs": "",205 "compute_extension:keypairs:index": "",206 "compute_extension:keypairs:show": "",207 "compute_extension:keypairs:create": "",208 "compute_extension:keypairs:delete": "",209 "compute_extension:v3:os-keypairs": "",210 "compute_extension:v3:os-keypairs:index": "",211 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"compute_extension:volume_attachments:delete": "",274 "compute_extension:v3:os-volumes": "",275 "compute_extension:volumetypes": "",276 "compute_extension:zones": "",277 "compute_extension:availability_zone:list": "",278 "compute_extension:v3:os-availability-zone:list": "",279 "compute_extension:availability_zone:detail": "is_admin:True",280 "compute_extension:v3:os-availability-zone:detail": "is_admin:True",281 "compute_extension:used_limits_for_admin": "is_admin:True",282 "compute_extension:v3:os-used-limits": "is_admin:True",283 "compute_extension:migrations:index": "is_admin:True",284 "compute_extension:v3:os-migrations:index": "is_admin:True",285 "compute_extension:os-assisted-volume-snapshots:create": "",286 "compute_extension:os-assisted-volume-snapshots:delete": "",287 "compute_extension:console_auth_tokens": "is_admin:True",288 "compute_extension:v3:os-console-auth-tokens": "is_admin:True",289 "compute_extension:os-server-external-events:create": "rule:admin_api",290 "compute_extension:v3:os-server-external-events:create": "rule:admin_api",291 "volume:create": "",292 "volume:get": "",293 "volume:get_all": "",294 "volume:get_volume_metadata": "",295 "volume:delete": "",296 "volume:update": "",297 "volume:delete_volume_metadata": "",298 "volume:update_volume_metadata": "",299 "volume:attach": "",300 "volume:detach": "",301 "volume:reserve_volume": "",302 "volume:unreserve_volume": "",303 "volume:begin_detaching": "",304 "volume:roll_detaching": "",305 "volume:check_attach": "",306 "volume:check_detach": "",307 "volume:initialize_connection": "",308 "volume:terminate_connection": "",309 "volume:create_snapshot": "",310 "volume:delete_snapshot": "",311 "volume:get_snapshot": "",312 "volume:get_all_snapshots": "",313 "volume_extension:volume_admin_actions:reset_status": "rule:admin_api",314 "volume_extension:snapshot_admin_actions:reset_status": "rule:admin_api",315 "volume_extension:volume_admin_actions:force_delete": "rule:admin_api",316 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implementations.py
Source:implementations.py
1""" Module containing all implementations of ML techniques required for the project """2import numpy as np3from helpers import (batch_iter, compute_gradient_hinge,4 compute_gradient_mse, compute_gradient_nll,5 compute_hessian_nll, compute_loss, compute_loss_hinge,6 compute_loss_nll, compute_subgradient_mae,7 map_target_classes_to_boolean)8def least_squares_GD(y, x, initial_w, max_iters, gamma, mae=False, threshold=1e-5):9 """10 Implementation of the Gradient Descent optimization algorithm for linear regression11 Can be run with both MSE and MAE loss12 :param x: data matrix, numpy ndarray with shape with shape (N, D),13 where N is the number of samples and D is the number of features14 :param y: vector of target values, numpy array with dimensions (N, 1)15 :param initial_w: vector of initial weights, numpy array with dimensions (D, 1)16 :param max_iters: how many iterations to run the algorithm, integer17 :param gamma: learning rate, positive float value18 :param mae: whether to use MAE loss, boolean, optional, the default value is False19 :param threshold: convergence threshold, positive float value20 :returns: (final weights, final loss value), tuple21 """22 # Set the initial values for the weights23 w = initial_w24 # Compute the initial loss value25 prev_loss = compute_loss(y, x, initial_w, mae)26 for n_iter in range(max_iters):27 # Compute the total gradient (or subgradient if MAE loss is used)28 grd = compute_subgradient_mae(y, x, w) if mae else compute_gradient_mse(y, x, w)29 # Update the weights using the gradient and learning rate30 w -= gamma * grd31 # Compute the current loss and test convergence32 loss = compute_loss(y, x, w, mae)33 if abs(loss - prev_loss) < threshold:34 print(f'converged at iter : {n_iter}')35 break36 prev_loss = loss.copy()37 # Compute the final loss value38 loss = compute_loss(y, x, w, mae)39 return w, loss40def least_squares_SGD(y, x, initial_w, max_iters, gamma, mae=False, threshold=1e-5):41 """42 Implementation of the Stochastic Gradient Descent optimization algorithm for linear regression43 Can be run with both MSE and MAE loss44 :param x: data matrix, numpy ndarray with shape with shape (N, D),45 where N is the number of samples and D is the number of features46 :param y: vector of target values, numpy array with dimensions (N, 1)47 :param initial_w: vector of initial weights, numpy array with dimensions (D, 1)48 :param max_iters: how many iterations to run the algorithm, integer49 :param gamma: learning rate, positive float value50 :param mae: whether to use MAE loss, boolean, optional, the default value is False51 :param threshold: convergence threshold, positive float value52 :returns: (final weights, final loss value), tuple53 """54 # Set the initial values for the weights55 w = initial_w56 # Compute the initial loss value57 prev_loss = compute_loss(y, x, initial_w, mae)58 # Use the helper function batch_iter from Exercise 2,59 # to get a random sample from the data in the form (y_n, x_n) for each iteration60 for n_iter in range(max_iters):61 for y_n, x_n in batch_iter(y, x, batch_size=1, num_batches=1):62 # Compute the gradient for only one sample (or subgradient if MAE loss is used)63 grd = compute_subgradient_mae(y_n, x_n, w) if mae else compute_gradient_mse(y_n, x_n, w)64 # Update the weights using the gradient and learning rate65 w = w - gamma * grd66 # Compute the current loss and test convergence67 loss = compute_loss(y, x, w, mae) 68 if abs(loss - prev_loss) < threshold:69 print(f'converged at iter : {n_iter}')70 break 71 prev_loss = loss.copy()72 # Compute the final loss value73 loss = compute_loss(y, x, w, mae)74 return w, loss75def least_squares(y, x):76 """77 Calculate the least squares solution explicitly using the normal equations78 :param x: data matrix, numpy ndarray with shape with shape (N, D),79 where N is the number of samples and D is the number of features80 :param y: vector of target values, numpy array with dimensions (N, 1)81 :raises LinAlgError: if the Gram matrix has no inverse82 :returns: (weights, loss value), tuple83 """84 # Compute the Gram matrix85 gram = x.T @ x86 # Use the normal equations to find the best weights87 w = np.linalg.solve(gram, x.T @ y)88 # Compute the loss89 loss = compute_loss(y, x, w)90 return w, loss91def ridge_regression(y, x, lambda_):92 """93 Calculate the least squares solution with L2 regularization explicitly using the normal equations94 :param x: data matrix, numpy ndarray with shape with shape (N, D),95 where N is the number of samples and D is the number of features96 :param y: vector of target values, numpy array with dimensions (N, 1)97 :param lambda_: regularization coefficient, positive float value98 :returns: (weights, loss value), tuple99 """100 # Compute the Gram matrix and update it with the regularization term101 gram = x.T @ x102 gram += 2 * x.shape[0] * lambda_ * np.identity(gram.shape[0])103 # Use the normal equations to find the best weights104 w = np.linalg.solve(gram, x.T @ y)105 # Compute the loss106 loss = compute_loss(y, x, w)107 return w, loss108def logistic_regression(y, x, initial_w, max_iters, gamma, threshold=1e-2):109 """110 Implementation of the Newton optimization algorithm for logistic regression111 :param x: data matrix, numpy ndarray with shape with shape (N, D),112 where N is the number of samples and D is the number of features113 :param y: vector of target values, numpy array with dimensions (N, 1)114 :param initial_w: vector of initial weights, numpy array with dimensions (D, 1)115 :param max_iters: how many iterations to run the algorithm, integer116 :param gamma: learning rate, positive float value117 :param threshold: convergence threshold, positive float value118 :returns: (final weights, final loss value), tuple119 120 :raises: LinAlgError if the Hessian matrix becomes singular121 """122 # Map the {-1, 1} classes to {0, 1}123 y = map_target_classes_to_boolean(y)124 # Set the initial values for the weights125 w = initial_w126 # Compute the initial loss value127 prev_loss = compute_loss_nll(y, x, initial_w)128 for n_iter in range(max_iters):129 # Compute the gradient and Hessian of the loss function130 grd = compute_gradient_nll(y, x, w)131 hess = compute_hessian_nll(y, x, w)132 # Update the weights using the gradient, Hessian and learning rate133 w -= gamma * np.linalg.solve(hess, grd)134 # Compute the current loss and test convergence135 loss = compute_loss_nll(y, x, w)136 if abs(loss - prev_loss) < threshold:137 print(f'converged at iter : {n_iter}')138 break139 prev_loss = loss.copy()140 # Compute the final loss value141 loss = compute_loss_nll(y, x, w)142 return w, loss143def reg_logistic_regression(y, x, lambda_, initial_w, max_iters, gamma, threshold=1e-2):144 """145 Implementation of the Newton optimization algorithm for logistic regression with L2 regularization146 :param x: data matrix, numpy ndarray with shape with shape (N, D),147 where N is the number of samples and D is the number of features148 :param y: vector of target values, numpy array with dimensions (N, 1)149 :param lambda_: regularization coefficient, positive float value150 :param initial_w: vector of initial weights, numpy array with dimensions (D, 1)151 :param max_iters: how many iterations to run the algorithm, integer152 :param gamma: learning rate, positive float value153 :param threshold: convergence threshold, positive float value154 :returns: (final weights, final loss value), tuple155 """156 # Map the {-1, 1} classes to {0, 1}157 y = map_target_classes_to_boolean(y)158 # Set the initial values for the weights159 w = initial_w160 # Compute the initial loss value161 prev_loss = compute_loss_nll(y, x, initial_w, lambda_)162 for n_iter in range(max_iters):163 # Compute the gradient and Hessian of the loss function164 grd = compute_gradient_nll(y, x, w, lambda_)165 hess = compute_hessian_nll(y, x, w, lambda_)166 # Update the weights using the gradient, Hessian and learning rate167 w -= gamma / np.sqrt(n_iter+1) * np.linalg.solve(hess, grd)168 # Compute the current loss and test convergence169 loss = compute_loss_nll(y, x, w, lambda_)170 if loss == np.inf:171 raise ArithmeticError('Training diverges')172 if abs(loss - prev_loss) < threshold:173 print(f'converged at iter : {n_iter}')174 break175 prev_loss = loss.copy()176 # Compute the final loss value177 loss = compute_loss_nll(y, x, w, lambda_)178 return w, loss179def svm(y, x, lambda_, initial_w, max_iters, gamma, threshold=1e-5):180 """181 Implementation of the linear SVM classification algorithm with L2 regularization182 The SVM is simulated through optimization of the Hinge loss function with gradient descent183 :param x: data matrix, numpy ndarray with shape with shape (N, D),184 where N is the number of samples and D is the number of features185 :param y: vector of target values, numpy array with length N186 :param lambda_: regularization coefficient, positive float value187 :param initial_w: vector of initial weights, numpy array with length D188 :param max_iters: how many iterations to run the algorithm, integer189 :param gamma: learning rate, positive float value190 :param threshold: convergence threshold, positive float value191 :returns: (final weights, final loss value), tuple192 """193 # Set the initial values for the weights194 w = initial_w195 196 # Compute the initial loss value197 prev_loss = compute_loss_hinge(y, x, w, lambda_)198 199 for n_iter in range(max_iters):200 # Compute the gradient of the loss function201 grd = compute_gradient_hinge(y, x, w, lambda_)202 # Update the weights using the gradient, Hessian and learning rate203 w -= gamma / (1 + 1e-2*n_iter) * grd204 # Compute the current loss and test convergence205 loss = compute_loss_hinge(y, x, w, lambda_)206 if abs(loss - prev_loss) < threshold:207 print(f'converged at iter : {n_iter}')208 break209 prev_loss = loss210 # Compute the final loss value211 loss = compute_loss_hinge(y, x, w, lambda_)...
gcp_compute_operator.py
Source:gcp_compute_operator.py
1#2# Licensed to the Apache Software Foundation (ASF) under one3# or more contributor license agreements. See the NOTICE file4# distributed with this work for additional information5# regarding copyright ownership. The ASF licenses this file6# to you under the Apache License, Version 2.0 (the7# "License"); you may not use this file except in compliance8# with the License. You may obtain a copy of the License at9#10# http://www.apache.org/licenses/LICENSE-2.011#12# Unless required by applicable law or agreed to in writing,13# software distributed under the License is distributed on an14# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY15# KIND, either express or implied. See the License for the16# specific language governing permissions and limitations17# under the License.18"""This module is deprecated. Please use `airflow.providers.google.cloud.operators.compute`."""19import warnings20from airflow.providers.google.cloud.operators.compute import (21 ComputeEngineBaseOperator,22 ComputeEngineCopyInstanceTemplateOperator,23 ComputeEngineInstanceGroupUpdateManagerTemplateOperator,24 ComputeEngineSetMachineTypeOperator,25 ComputeEngineStartInstanceOperator,26 ComputeEngineStopInstanceOperator,27)28warnings.warn(29 "This module is deprecated. Please use `airflow.providers.google.cloud.operators.compute`.",30 DeprecationWarning,31 stacklevel=2,32)33class GceBaseOperator(ComputeEngineBaseOperator):34 """35 This class is deprecated.36 Please use `airflow.providers.google.cloud.operators.compute.ComputeEngineBaseOperator`.37 """38 def __init__(self, *args, **kwargs):39 warnings.warn(40 """This class is deprecated.41 Please use `airflow.providers.google.cloud.operators.compute.ComputeEngineBaseOperator`.""",42 DeprecationWarning,43 stacklevel=3,44 )45 super().__init__(*args, **kwargs)46class GceInstanceGroupManagerUpdateTemplateOperator(ComputeEngineInstanceGroupUpdateManagerTemplateOperator):47 """48 This class is deprecated.49 Please use `airflow.providers.google.cloud.operators.compute50 .ComputeEngineInstanceGroupUpdateManagerTemplateOperator`.51 """52 def __init__(self, *args, **kwargs):53 warnings.warn(54 """This class is deprecated. Please use55 `airflow.providers.google.cloud.operators.compute56 .ComputeEngineInstanceGroupUpdateManagerTemplateOperator`.""",57 DeprecationWarning,58 stacklevel=3,59 )60 super().__init__(*args, **kwargs)61class GceInstanceStartOperator(ComputeEngineStartInstanceOperator):62 """63 This class is deprecated.64 Please use `airflow.providers.google.cloud.operators65 .compute.ComputeEngineStartInstanceOperator`.66 """67 def __init__(self, *args, **kwargs):68 warnings.warn(69 """This class is deprecated.70 Please use `airflow.providers.google.cloud.operators.compute71 .ComputeEngineStartInstanceOperator`.""",72 DeprecationWarning,73 stacklevel=3,74 )75 super().__init__(*args, **kwargs)76class GceInstanceStopOperator(ComputeEngineStopInstanceOperator):77 """78 This class is deprecated.79 Please use `airflow.providers.google.cloud.operators.compute.ComputeEngineStopInstanceOperator`.80 """81 def __init__(self, *args, **kwargs):82 warnings.warn(83 """This class is deprecated.84 Please use `airflow.providers.google.cloud.operators.compute85 .ComputeEngineStopInstanceOperator`.""",86 DeprecationWarning,87 stacklevel=3,88 )89 super().__init__(*args, **kwargs)90class GceInstanceTemplateCopyOperator(ComputeEngineCopyInstanceTemplateOperator):91 """92 This class is deprecated.93 Please use `airflow.providers.google.cloud.operators.compute.ComputeEngineCopyInstanceTemplateOperator`.94 """95 def __init__(self, *args, **kwargs):96 warnings.warn(97 """"This class is deprecated.98 Please use `airflow.providers.google.cloud.operators.compute99 .ComputeEngineCopyInstanceTemplateOperator`.""",100 DeprecationWarning,101 stacklevel=3,102 )103 super().__init__(*args, **kwargs)104class GceSetMachineTypeOperator(ComputeEngineSetMachineTypeOperator):105 """106 This class is deprecated.107 Please use `airflow.providers.google.cloud.operators.compute.ComputeEngineSetMachineTypeOperator`.108 """109 def __init__(self, *args, **kwargs):110 warnings.warn(111 """This class is deprecated.112 Please use `airflow.providers.google.cloud.operators.compute113 .ComputeEngineSetMachineTypeOperator`.""",114 DeprecationWarning,115 stacklevel=3,116 )...
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