Add acpt-pytorch-2.8-cuda12.6 env

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/asset.yaml

@@ -0,0 +1,11 @@
+name: acpt-pytorch-2.8-cuda12.6
+version: auto
+type: environment
+spec: spec.yaml
+extra_config: environment.yaml
+test:
+  pytest:
+    enabled: true 
+    pip_requirements: tests/requirements.txt
+    tests_dir: tests
+categories: ["PyTorch", "Training"]

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/context/Dockerfile

@@ -0,0 +1,33 @@
+FROM mcr.microsoft.com/aifx/acpt/stable-ubuntu2204-cu126-py310-torch280:{{latest-image-tag:biweekly\.\d{6}\.\d{1}.*}}
+
+# Install pip dependencies
+COPY requirements.txt .
+RUN pip install -r requirements.txt --no-cache-dir
+
+# Inference requirements
+COPY --from=mcr.microsoft.com/azureml/o16n-base/python-assets:20230419.v1 /artifacts /var/
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends \
+        libcurl4 \
+        liblttng-ust1 \
+        libunwind8 \
+        libxml++2.6-2v5 \
+        nginx-light \
+        psmisc \
+        rsyslog \
+        runit \
+        unzip && \
+    apt-get clean && rm -rf /var/lib/apt/lists/* && \
+    cp /var/configuration/rsyslog.conf /etc/rsyslog.conf && \
+    cp /var/configuration/nginx.conf /etc/nginx/sites-available/app && \
+    ln -sf /etc/nginx/sites-available/app /etc/nginx/sites-enabled/app && \
+    rm -f /etc/nginx/sites-enabled/default
+ENV SVDIR=/var/runit
+ENV WORKER_TIMEOUT=400
+EXPOSE 5001 8883 8888
+
+# support Deepspeed launcher requirement of passwordless ssh login
+RUN apt-get update
+RUN apt-get install -y openssh-server openssh-client
+
+RUN pip list

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/context/requirements.txt

@@ -0,0 +1,25 @@
+azureml-core=={{latest-pypi-version}}
+azureml-dataset-runtime=={{latest-pypi-version}}
+azureml-defaults=={{latest-pypi-version}}
+azure-ml-component=={{latest-pypi-version}}
+azureml-mlflow=={{latest-pypi-version}}
+azureml-contrib-services=={{latest-pypi-version}}
+azureml-inference-server-http
+inference-schema
+MarkupSafe
+regex
+pybind11
+urllib3
+requests
+pillow
+transformers
+aiohttp>=3.12.14
+py-spy
+debugpy
+ipykernel
+tensorboard
+psutil
+matplotlib
+tqdm
+py-cpuinfo
+torch-tb-profiler

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/environment.yaml

@@ -0,0 +1,12 @@
+image:
+  name: azureml/curated/acpt-pytorch-2.8-cuda12.6
+  os: linux
+  context:
+    dir: context
+    dockerfile: Dockerfile
+    template_files:
+    - Dockerfile
+    - requirements.txt
+  publish:
+    location: mcr
+    visibility: public

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/spec.yaml

@@ -0,0 +1,26 @@
+$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json
+
+description: >-
+  Recommended environment for Deep Learning in public preview with PyTorch on Azure containing the Azure ML SDK with the latest compatible versions of Ubuntu, Python, PyTorch, CUDA\RocM, combined with optimizers like ORT Training,+DeepSpeed+MSCCL+ORT MoE and more. The image introduces newly released PyTorch 2.1 for early testing, and preview of new fastcheckpointing capability called Nebula. 
+  Azure Container Registry:acptdev.azurecr.io/test/public/aifx/acpt/stable-ubuntu2004-cu121-py310-torch212
+
+name: "{{asset.name}}"
+version: "{{asset.version}}"
+
+build:
+  path: "{{image.context.path}}"
+  dockerfile_path: "{{image.dockerfile.path}}"
+
+os_type: linux
+
+tags:
+  PyTorch: "2.8"
+  GPU: Cuda12
+  OS: Ubuntu22.04
+  Training: ""
+  Preview: ""
+  Python: "3.10"
+  DeepSpeed: "0.13.1"
+  ONNXRuntime: "1.17.1"
+  torch_ORT: "1.17.0"
+  Checkpointing:Nebula: "0.16.10"

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/tests/pytorch2_8_sample_test.py

@@ -0,0 +1,94 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+"""Test running a sample job in the pytorch 2.8 environment."""
+import os
+import time
+from pathlib import Path
+from azure.ai.ml import command, Output, MLClient, PyTorchDistribution
+from azure.ai.ml.entities import Environment, BuildContext, JobResourceConfiguration
+from azure.identity import AzureCliCredential
+import subprocess
+
+BUILD_CONTEXT = Path("../context")
+JOB_SOURCE_CODE = "../../acpt-tests/src"
+TIMEOUT_MINUTES = os.environ.get("timeout_minutes", 60)
+STD_LOG = Path("artifacts/user_logs/std_log.txt")
+
+
+def test_pytorch_2_8():
+    """Tests a sample job using pytorch 2.8 as the environment."""
+    this_dir = Path(__file__).parent
+
+    subscription_id = os.environ.get("subscription_id")
+    resource_group = os.environ.get("resource_group")
+    workspace_name = os.environ.get("workspace")
+
+    ml_client = MLClient(
+        AzureCliCredential(), subscription_id, resource_group, workspace_name
+    )
+
+    env_name = "acpt-pytorch-2_8-cuda12_6"
+
+    env_docker_context = Environment(
+        build=BuildContext(path=this_dir / BUILD_CONTEXT),
+        name=env_name,
+        description="Pytorch 2.8 environment created from a Docker context.",
+    )
+    ml_client.environments.create_or_update(env_docker_context)
+
+    # create the command
+    job = command(
+        code=this_dir / JOB_SOURCE_CODE,  # local path where the code is stored
+        command="pip install -r requirements.txt"
+                " && python pretrain_glue.py --tensorboard_log_dir \"/outputs/runs/\""
+                " --deepspeed ds_config.json --num_train_epochs 5 --output_dir outputs --disable_tqdm 1"
+                " --local_rank $RANK --logging_strategy \"epoch\""
+                " --per_device_train_batch_size 93 --gradient_accumulation_steps 1"
+                " --per_device_eval_batch_size 93 --learning_rate 3e-05 --adam_beta1 0.8 --adam_beta2 0.999"
+                " --weight_decay 3e-07 --warmup_steps 500 --fp16 --logging_steps 1000"
+                " --model_checkpoint \"bert-large-uncased\"",
+        outputs={
+            "output": Output(
+                type="uri_folder",
+                mode="rw_mount",
+                path="azureml://datastores/workspaceblobstore/paths/outputs"
+            )
+        },
+        environment=f"{env_name}@latest",
+        compute=os.environ.get("gpu_v100_cluster"),
+        display_name="bert-pretrain-GLUE",
+        description="Pretrain the BERT model on the GLUE dataset.",
+        experiment_name="pytorch27_Cuda126_py310_Experiment",
+        distribution=PyTorchDistribution(process_count_per_instance=1),
+        resources=JobResourceConfiguration(instance_count=2, shm_size='3100m'),
+    )
+
+    returned_job = ml_client.create_or_update(job)
+    assert returned_job is not None
+
+    # Poll until final status is reached or timed out
+    timeout = time.time() + (TIMEOUT_MINUTES * 60)
+    while time.time() <= timeout:
+        current_status = ml_client.jobs.get(returned_job.name).status
+        if current_status in ["Completed", "Failed"]:
+            break
+        time.sleep(30)  # sleep 30 seconds
+
+    bashCommand = "ls"
+    process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE)
+    output, error = process.communicate()
+    print(output)
+    print(error)
+
+    if current_status == "Failed" or current_status == "Cancelled":
+        ml_client.jobs.download(returned_job.name)
+        if STD_LOG.exists():
+            print(f"*** BEGIN {STD_LOG} ***")
+            with open(STD_LOG, "r") as f:
+                print(f.read(), end="")
+            print(f"*** END {STD_LOG} ***")
+        else:
+            ml_client.jobs.stream(returned_job.name)
+
+    assert current_status == "Completed"

assets/training/general/environments/acpt-pytorch-2.8-cuda12.6/tests/requirements.txt

@@ -0,0 +1,2 @@
+azure-ai-ml==1.27.1
+azure.identity==1.10.0

assets/training/general/environments/acpt-tests/src/requirements.txt

@@ -1,7 +1,6 @@
 transformers
 datasets
-evaluate
 accelerate
 scikit-learn
-apache_beam
+apache_beam~=2.69.0
 evaluate

scripts/setup/create_azure_resources_daily.sh

@@ -57,7 +57,7 @@ else
     echo "Checking ${resource_name}"
     if ! az ml compute show --name $gpu_v100_cluster --output none >/dev/null 2>&1; then
         echo "Creating ${resource_name}"
-        az ml compute create --name $gpu_v100_cluster --size Standard_NC6s_v3 --min-instances 0 --max-instances 2 --type AmlCompute --idle-time-before-scale-down 120
+        az ml compute create --name $gpu_v100_cluster --size Standard_NC4as_T4_v3 --min-instances 0 --max-instances 2 --type AmlCompute --idle-time-before-scale-down 120
     fi
 fi