Führe groß angelegte parallele Batch-Jobs und High-Performance Computing mit Pools, Jobs, Tasks und Compute Nodes in Azure aus.
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- 11. Nov. 2026
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Was macht der azure-compute-batch-java Skill?
azure-compute-batch-java ist ein Agent Skill. Führe groß angelegte parallele Batch-Jobs und High-Performance Computing mit Pools, Jobs, Tasks und Compute Nodes in Azure aus.
Zusammenfassung erzeugt aus der SKILL.md des Skills.
Über den azure-compute-batch-java skill
Entwickler, die High-Performance Computing Workloads verwalten, können dieses Java SDK nutzen, um groß angelegte parallele Batch-Jobs innerhalb von Azure zu orchestrieren. Der Skill bietet die notwendigen Tools, um den gesamten Lebenszyklus von Compute-Ressourcen zu verwalten – von der Erstellung von Pools aus Virtual-Machine-Nodes bis hin zur Planung wiederkehrender Jobs. Er ist für Engineers konzipiert, die komplexe Rechenaufgaben automatisieren müssen, indem sie diese in logische Jobs gruppieren und als einzelne Arbeitseinheiten, sogenannte Tasks, ausführen.
Ein Agent sollte diesen Skill laden, wenn ein User Compute-Infrastruktur bereitstellen, bestehende Pools skalieren oder den Fortschritt verteilter Workloads überwachen muss. Die Library unterstützt sowohl synchrone als auch asynchrone Clients, was eine flexible Integration in verschiedene Applikationsarchitekturen ermöglicht. User können mit spezifischen Komponenten wie Pools, Jobs, Tasks und Nodes interagieren und dabei Features wie Auto-Scaling-Formeln und Job Schedules nutzen, um die Ressourcennutzung und Kosten zu optimieren. Die Authentifizierung kann über Microsoft Entra ID oder Shared Key Credentials erfolgen, sofern die notwendigen Umgebungsvariablen wie AZURE_BATCH_ENDPOINT konfiguriert sind.
Was ein agent damit machen kann
- 1Compute-Infrastruktur bereitstellenErstelle einen neuen Pool von Compute Nodes unter Verwendung eines spezifischen Ubuntu-Images und einer festgelegten Anzahl an Nodes.
- 2Ressourcen dynamisch skalierenPasse die Anzahl der Dedicated und Low-Priority Nodes in einem bestehenden Pool an, um die Nachfrage zu decken.
- 3Parallele Workloads ausführenÜbermittle eine große Sammlung einzelner Rechen-Tasks an einen spezifischen Job zur Ausführung.
- 4Wiederkehrende Berechnungen automatisierenRichte einen Job Schedule ein, der einen spezifischen Job in regelmäßigen Intervallen triggert.
- 5Job-Fortschritt überwachenRufe die Anzahl der aktiven, laufenden und abgeschlossenen Tasks innerhalb eines spezifischen Jobs ab.
Geschrieben basierend auf dem SKILL.md des skills. Jeder use case wurde gegen ein zitat daraus geprüft.
So installierst du den azure-compute-batch-java Skill
Jeder Befehl kopiert den Skill-Ordner in das Skills-Verzeichnis des Clients. Starte den Client neu oder beginne eine neue Session, um ihn zu laden.Den azure-compute-batch-java Skill in Claude Code installieren
~/.claude/skills/azure-compute-batch-java/SKILL.md
$ npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java ~/.claude/skills/azure-compute-batch-javaDen azure-compute-batch-java Skill in Claude Code (project) installieren
.claude/skills/azure-compute-batch-java/SKILL.md
$ npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java .claude/skills/azure-compute-batch-javaDen azure-compute-batch-java Skill in Codex installieren
~/.agents/skills/azure-compute-batch-java/SKILL.md
$ npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java ~/.agents/skills/azure-compute-batch-javaDen azure-compute-batch-java Skill in Gemini CLI installieren
~/.gemini/skills/azure-compute-batch-java/SKILL.md
$ npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java ~/.gemini/skills/azure-compute-batch-javaDen azure-compute-batch-java Skill in Claude.ai installieren
npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java azure-compute-batch-java && zip -r azure-compute-batch-java.zip azure-compute-batch-java # claude.ai → Customize → Skills → + → Create skill → Upload a skill → azure-compute-batch-java.zip
Automatisierte Checks
Geluften am 14. Nov. 2026Vollständiger Bericht →Hat unsere automatischen Prüfungen bestanden. Das ist kein Sicherheitsaudit: Lesen Sie die Skripte, bevor ein Agent sie ausführt. Wie die Checks funktionieren
- Source Repository verknüpft und erreichbarmicrosoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java is public and reachablegithub.com/microsoft/skills/tree/HEAD/.github/plugins/azure- ↗Bestanden
- Open Source Lizenz (OSI)MITspdx.org/licenses/MIT.html ↗Bestanden
- Commit oder Release im letzten JahrLast commit 2026-11-11github.com/microsoft/skills/commits ↗Bestanden
- Keine Download-und-Ausführen-Muster oder Zugangsdatendiebstahl in SkriptenNo scripts in the skill folderBestanden
- Keine versteckten Anweisungen in SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/microsoft/skills/blob/HEAD/.github/plugins/azure- ↗Bestanden
- Vorab erlaubte Tools sind eingeschränktNo pre-approved tools; the agent asks before using onegithub.com/microsoft/skills/blob/HEAD/.github/plugins/azure- ↗Bestanden
SKILL.md
Auf GitHub ansehenAzure Batch SDK for Java
Client library for running large-scale parallel and high-performance computing (HPC) batch jobs in Azure.
Installation
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-compute-batch</artifactId>
<version>1.0.0-beta.5</version>
</dependency>
Prerequisites
- Azure Batch account
- Pool configured with compute nodes
- Azure subscription
Environment Variables
AZURE_BATCH_ENDPOINT=https://<account>.<region>.batch.azure.com # Required for all auth methods
AZURE_BATCH_ACCOUNT=<account-name> # Only required for shared key auth
AZURE_BATCH_ACCESS_KEY=<account-key> # Only required for shared key auth
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
Client Creation
With Microsoft Entra ID (Recommended)
import com.azure.compute.batch.BatchClient;
import com.azure.compute.batch.BatchClientBuilder;
import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
TokenCredential credential = new DefaultAzureCredentialBuilder()
.requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
.build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
BatchClient batchClient = new BatchClientBuilder()
.credential(credential)
.endpoint(System.getenv("AZURE_BATCH_ENDPOINT"))
.buildClient();
Async Client
import com.azure.compute.batch.BatchAsyncClient;
BatchAsyncClient batchAsyncClient = new BatchClientBuilder()
.credential(credential)
.endpoint(System.getenv("AZURE_BATCH_ENDPOINT"))
.buildAsyncClient();
With Shared Key Credentials
import com.azure.core.credential.AzureNamedKeyCredential;
String accountName = System.getenv("AZURE_BATCH_ACCOUNT");
String accountKey = System.getenv("AZURE_BATCH_ACCESS_KEY");
AzureNamedKeyCredential sharedKeyCreds = new AzureNamedKeyCredential(accountName, accountKey);
BatchClient batchClient = new BatchClientBuilder()
.credential(sharedKeyCreds)
.endpoint(System.getenv("AZURE_BATCH_ENDPOINT"))
.buildClient();
Key Concepts
| Concept | Description |
|---|---|
| Pool | Collection of compute nodes that run tasks |
| Job | Logical grouping of tasks |
| Task | Unit of computation (command/script) |
| Node | VM that executes tasks |
| Job Schedule | Recurring job creation |
Pool Operations
Create Pool
import com.azure.compute.batch.models.*;
batchClient.createPool(new BatchPoolCreateParameters("myPoolId", "STANDARD_DC2s_V2")
.setVirtualMachineConfiguration(
new VirtualMachineConfiguration(
new BatchVmImageReference()
.setPublisher("Canonical")
.setOffer("UbuntuServer")
.setSku("22_04-lts")
.setVersion("latest"),
"batch.node.ubuntu 22.04"))
.setTargetDedicatedNodes(2)
.setTargetLowPriorityNodes(0), null);
Get Pool
BatchPool pool = batchClient.getPool("myPoolId");
System.out.println("Pool state: " + pool.getState());
System.out.println("Current dedicated nodes: " + pool.getCurrentDedicatedNodes());
List Pools
import com.azure.core.http.rest.PagedIterable;
PagedIterable<BatchPool> pools = batchClient.listPools();
for (BatchPool pool : pools) {
System.out.println("Pool: " + pool.getId() + ", State: " + pool.getState());
}
Resize Pool
import com.azure.core.util.polling.SyncPoller;
BatchPoolResizeParameters resizeParams = new BatchPoolResizeParameters()
.setTargetDedicatedNodes(4)
.setTargetLowPriorityNodes(2);
SyncPoller<BatchPool, BatchPool> poller = batchClient.beginResizePool("myPoolId", resizeParams);
poller.waitForCompletion();
BatchPool resizedPool = poller.getFinalResult();
Enable AutoScale
BatchPoolEnableAutoScaleParameters autoScaleParams = new BatchPoolEnableAutoScaleParameters()
.setAutoScaleEvaluationInterval(Duration.ofMinutes(5))
.setAutoScaleFormula("$TargetDedicatedNodes = min(10, $PendingTasks.GetSample(TimeInterval_Minute * 5));");
batchClient.enablePoolAutoScale("myPoolId", autoScaleParams);
Delete Pool
SyncPoller<BatchPool, Void> deletePoller = batchClient.beginDeletePool("myPoolId");
deletePoller.waitForCompletion();
Job Operations
Create Job
batchClient.createJob(
new BatchJobCreateParameters("myJobId", new BatchPoolInfo().setPoolId("myPoolId"))
.setPriority(100)
.setConstraints(new BatchJobConstraints()
.setMaxWallClockTime(Duration.ofHours(24))
.setMaxTaskRetryCount(3)),
null);
Get Job
BatchJob job = batchClient.getJob("myJobId", null, null);
System.out.println("Job state: " + job.getState());
List Jobs
PagedIterable<BatchJob> jobs = batchClient.listJobs(new BatchJobsListOptions());
for (BatchJob job : jobs) {
System.out.println("Job: " + job.getId() + ", State: " + job.getState());
}
Get Task Counts
BatchTaskCountsResult counts = batchClient.getJobTaskCounts("myJobId");
System.out.println("Active: " + counts.getTaskCounts().getActive());
System.out.println("Running: " + counts.getTaskCounts().getRunning());
System.out.println("Completed: " + counts.getTaskCounts().getCompleted());
Terminate Job
BatchJobTerminateParameters terminateParams = new BatchJobTerminateParameters()
.setTerminationReason("Manual termination");
BatchJobTerminateOptions options = new BatchJobTerminateOptions().setParameters(terminateParams);
SyncPoller<BatchJob, BatchJob> poller = batchClient.beginTerminateJob("myJobId", options, null);
poller.waitForCompletion();
Delete Job
SyncPoller<BatchJob, Void> deletePoller = batchClient.beginDeleteJob("myJobId");
deletePoller.waitForCompletion();
Task Operations
Create Single Task
BatchTaskCreateParameters task = new BatchTaskCreateParameters("task1", "echo 'Hello World'");
batchClient.createTask("myJobId", task);
Create Task with Exit Conditions
batchClient.createTask("myJobId", new BatchTaskCreateParameters("task2", "cmd /c exit 3")
.setExitConditions(new ExitConditions()
.setExitCodeRanges(Arrays.asList(
new ExitCodeRangeMapping(2, 4,
new ExitOptions().setJobAction(BatchJobActionKind.TERMINATE)))))
.setUserIdentity(new UserIdentity()
.setAutoUser(new AutoUserSpecification()
.setScope(AutoUserScope.TASK)
.setElevationLevel(ElevationLevel.NON_ADMIN))),
null);
Create Task Collection (up to 100)
List<BatchTaskCreateParameters> taskList = Arrays.asList(
new BatchTaskCreateParameters("task1", "echo Task 1"),
new BatchTaskCreateParameters("task2", "echo Task 2"),
new BatchTaskCreateParameters("task3", "echo Task 3")
);
BatchTaskGroup taskGroup = new BatchTaskGroup(taskList);
BatchCreateTaskCollectionResult result = batchClient.createTaskCollection("myJobId", taskGroup);
Create Many Tasks (no limit)
List<BatchTaskCreateParameters> tasks = new ArrayList<>();
for (int i = 0; i < 1000; i++) {
tasks.add(new BatchTaskCreateParameters("task" + i, "echo Task " + i));
}
batchClient.createTasks("myJobId", tasks);
Get Task
BatchTask task = batchClient.getTask("myJobId", "task1");
System.out.println("Task state: " + task.getState());
System.out.println("Exit code: " + task.getExecutionInfo().getExitCode());
List Tasks
PagedIterable<BatchTask> tasks = batchClient.listTasks("myJobId");
for (BatchTask task : tasks) {
System.out.println("Task: " + task.getId() + ", State: " + task.getState());
}
Get Task Output
import com.azure.core.util.BinaryData;
import java.nio.charset.StandardCharsets;
BinaryData stdout = batchClient.getTaskFile("myJobId", "task1", "stdout.txt");
System.out.println(new String(stdout.toBytes(), StandardCharsets.UTF_8));
Terminate Task
batchClient.terminateTask("myJobId", "task1", null, null);
Node Operations
List Nodes
PagedIterable<BatchNode> nodes = batchClient.listNodes("myPoolId", new BatchNodesListOptions());
for (BatchNode node : nodes) {
System.out.println("Node: " + node.getId() + ", State: " + node.getState());
}
Reboot Node
SyncPoller<BatchNode, BatchNode> rebootPoller = batchClient.beginRebootNode("myPoolId", "nodeId");
rebootPoller.waitForCompletion();
Get Remote Login Settings
BatchNodeRemoteLoginSettings settings = batchClient.getNodeRemoteLoginSettings("myPoolId", "nodeId");
System.out.println("IP: " + settings.getRemoteLoginIpAddress());
System.out.println("Port: " + settings.getRemoteLoginPort());
Job Schedule Operations
Create Job Schedule
batchClient.createJobSchedule(new BatchJobScheduleCreateParameters("myScheduleId",
new BatchJobScheduleConfiguration()
.setRecurrenceInterval(Duration.ofHours(6))
.setDoNotRunUntil(OffsetDateTime.now().plusDays(1)),
new BatchJobSpecification(new BatchPoolInfo().setPoolId("myPoolId"))
.setPriority(50)),
null);
Get Job Schedule
BatchJobSchedule schedule = batchClient.getJobSchedule("myScheduleId");
System.out.println("Schedule state: " + schedule.getState());
Error Handling
import com.azure.compute.batch.models.BatchErrorException;
import com.azure.compute.batch.models.BatchError;
try {
batchClient.getPool("nonexistent-pool");
} catch (BatchErrorException e) {
BatchError error = e.getValue();
System.err.println("Error code: " + error.getCode());
System.err.println("Message: " + error.getMessage().getValue());
if ("PoolNotFound".equals(error.getCode())) {
System.err.println("The specified pool does not exist.");
}
}
Best Practices
- Use Entra ID — Preferred over shared key for authentication
- Use management SDK for pools —
azure-resourcemanager-batchsupports managed identities - Batch task creation — Use
createTaskCollectionorcreateTasksfor multiple tasks - Handle LRO properly — Pool resize, delete operations are long-running
- Monitor task counts — Use
getJobTaskCountsto track progress - Set constraints — Configure
maxWallClockTimeandmaxTaskRetryCount - Use low-priority nodes — Cost savings for fault-tolerant workloads
- Enable autoscale — Dynamically adjust pool size based on workload
Reference Links
| Resource | URL |
|---|---|
| Maven Package | https://central.sonatype.com/artifact/com.azure/azure-compute-batch |
| GitHub | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/batch/azure-compute-batch |
| API Documentation | https://learn.microsoft.com/java/api/com.azure.compute.batch |
| Product Docs | https://learn.microsoft.com/azure/batch/ |
| REST API | https://learn.microsoft.com/rest/api/batchservice/ |
| Samples | https://github.com/azure/azure-batch-samples |
Dateien in diesem Skill
2azure-compute-batch-java Skill-Fragen
Wie installiere ich den azure-compute-batch-java Skill in Claude Code?
Führe npx degit microsoft/skills/.github/plugins/azure-sdk-java/skills/azure-compute-batch-java ~/.claude/skills/azure-compute-batch-java aus. Claude Code lädt Skills aus ~/.claude/skills (alle Projekte) oder .claude/skills (ein Projekt) und nutzt einen Skill, wenn seine Beschreibung zur Aufgabe passt.
Welche Clients können den azure-compute-batch-java Skill nutzen?
Jeder Client, der das Agent Skills-Format liest: Claude Code, Codex, Gemini CLI und claude.ai (als ZIP hochladen). Skripte im Skill laufen nur dort, wo der Client Codeausführung erlaubt.
Was sind die Kernkomponenten von Azure Batch?
Die wichtigsten Konzepte sind Pools (Sammlungen von Compute Nodes), Jobs (logische Gruppierungen von Tasks), Tasks (Recheneinheiten), Nodes (VMs, die Tasks ausführen) und Job Schedules.