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Anda dapat mengonfigurasi tugas pelatihan kustom untuk memasang bagian Network File System (NFS) ke container tempat kode Anda berjalan. Hal ini memungkinkan tugas Anda mengakses file jarak jauh seolah-olah file tersebut bersifat lokal, sehingga memungkinkan throughput tinggi dan latensi rendah.
Panduan ini menunjukkan cara memasang berbagi Network File System saat menjalankan tugas pelatihan kustom.
Sebelum memulai
Buat berbagi secara NFS di Virtual Private Cloud (VPC). Konten yang Anda bagikan harus dapat diakses tanpa autentikasi.
Anda dapat menggunakan instance Filestore sebagai berbagi NFS.
Jika Anda menggunakan Filestore dan berencana menggunakan peering VPC untuk Vertex AI pada langkah berikutnya, pilih akses layanan pribadi sebagai mode koneksi saat Anda membuat instance. Sebagai contoh, lihat Membuat instance dalam dokumentasi Filestore.
Informasi Network File System untuk pelatihan kustom
Saat membuat tugas pelatihan kustom yang memasang berbagi secara NFS, Anda harus menentukan hal berikut:
Nama jaringan yang akan diakses oleh Vertex AI. Cara Anda
menentukan nama jaringan akan berbeda-beda, bergantung pada jenis tugas pelatihan
kustom. Untuk mengetahui detailnya, lihat Melakukan pelatihan kustom.
Alamat IP server NFS Anda. Alamat ini harus berupa alamat pribadi di VPC Anda.
nfsMounts.path
Jalur berbagi secara NFS. Jalur ini harus berupa jalur absolut yang dimulai dengan /.
nfsMounts.mountPoint
Lokasi titik pemasangan lokal. Titik ini harus berupa nama direktori UNIX yang valid. Misalnya, jika direktori pemasangan lokal adalah sourceData, tentukan jalur /mnt/nfs/sourceData dari instance VM pelatihan Anda.
Buat file bernama config.yaml yang mendeskripsikan setelan pemasangan konfigurasi antarmuka PSA atau Private Service Connect untuk tugas pelatihan Anda. Gunakan salah satu format berikut:
Antarmuka Private Service Connect
Untuk menggunakan antarmuka Private Service Connect:
NETWORK_ATTACHMENT_NAME: Nama lampiran jaringan Anda.
MACHINE_TYPE: ID jenis mesin virtual Anda.
PYTHON_PACKAGE_EXECUTOR_IMAGE_URI atau PRE_BUILT_CONTAINER_IMAGE_URI:
URI image container di Artifact Registry yang akan menjalankan paket Python
yang disediakan. Vertex AI menyediakan
berbagai image eksekutor dengan paket yang sudah terinstal
untuk memenuhi berbagai kasus penggunaan pengguna.
PYTHON_PACKAGE_URIS: Daftar URI Cloud Storage yang dipisahkan koma yang menentukan file paket Python yang membentuk program pelatihan dan paket dependennya. Jumlah
maksimum URI paket adalah 100.
PYTHON_MODULE: Nama modul Python yang akan dijalankan setelah menginstal
paket.
NFS_SERVER_IP: Alamat IP server NFS Anda.
NFS_SHARE_NAME: Jalur berbagi NFS, yang merupakan
jalur absolut yang dimulai dengan /.
LOCAL_FOLDER: Titik pemasangan lokal (nama direktori UNIX).
Pastikan nama jaringan diformat dengan benar dan file NFS ada di jaringan yang ditentukan.
Buat tugas kustom dan teruskan file config.yaml Anda ke parameter --config.
PROJECT_NUMBER: Project ID Google Cloud project Anda.
NETWORK_NAME: Nama VPC pribadi atau VPC Bersama Anda.
MACHINE_TYPE: ID jenis mesin virtual Anda.
PYTHON_PACKAGE_EXECUTOR_IMAGE_URI or PRE_BUILT_CONTAINER_IMAGE_URI:
URI image container di Artifact Registry yang akan menjalankan paket Python
yang disediakan. Vertex AI menyediakan
berbagai image eksekutor dengan paket yang sudah terinstall
untuk memenuhi berbagai kasus penggunaan pengguna.
PYTHON_PACKAGE_URIS: Daftar URI Cloud Storage yang dipisahkan koma yang menentukan file paket Python yang membentuk program pelatihan dan paket dependennya. Jumlah
maksimum URI paket adalah 100.
PYTHON_MODULE: Nama modul Python yang akan dijalankan setelah menginstal
paket.
NFS_SERVER_IP: Alamat IP server NFS Anda.
NFS_SHARE_NAME: Jalur berbagi NFS, yang merupakan
jalur absolut yang dimulai dengan /.
LOCAL_FOLDER: Titik pemasangan lokal (nama direktori UNIX).
Pastikan nama jaringan diformat dengan benar dan file NFS ada di jaringan yang ditentukan.
Buat tugas kustom dan teruskan file config.yaml Anda ke parameter --config.
Anda harus memasang berbagi secara NFS menggunakan alamat IP yang bersifat internal untuk VPC Anda; dilarang menggunakan URL publik.
Tugas pelatihan memasang fitur berbagi secara NFS tanpa autentikasi, dan akan gagal jika nama pengguna dan sandi diperlukan.
Untuk mengamankan data Anda, tetapkan izin akses pada NFS. Jika Anda menggunakan Filestore, lihat kontrol akses dalam dokumentasi Filestore.
Anda tidak dapat menjalankan dua tugas pelatihan yang memasang berbagi secara NFS dari jaringan VPC yang berbeda secara bersamaan. Hal ini disebabkan oleh pembatasan peering jaringan.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-09-02 UTC."],[],[],null,["# Mount a Network File System share\n\nYou can configure your custom training jobs to mount Network File System (NFS)\nshares to the container where your code is running. This lets your jobs\naccess remote files as if they were local, enabling high throughput and\nlow latency.\n\nThis guide shows how to mount a Network File System share when running a\ncustom training job.\n\nBefore you begin\n----------------\n\n1. Create an NFS share in a\n [Virtual Private Cloud (VPC)](/vpc/docs/vpc-peering). Your share must be\n accessible without authentication.\n\n You can use a Filestore instance as your NFS share.\n If you are using [Filestore](/filestore) and plan to use VPC\n peering for Vertex AI in the next step, select **private service\n access** as the connect mode when you create an instance. For an example, see\n [Create instances](/filestore/docs/creating-instances)\n in the Filestore documentation.\n2. To connect Vertex AI with the VPC that hosts your NFS share,\n follow the instructions in [Use Private Service Connect interface for Vertex AI](/vertex-ai/docs/training/psc-i-egress) (recommended), or [Set up VPC Network Peering](/vertex-ai/docs/general/vpc-peering).\n\nNetwork File System information for custom training\n---------------------------------------------------\n\nWhen you create a custom training job that mounts an NFS share, you must\nspecify the following:\n\n- The name of the network for Vertex AI to access. The way that you\n specify the network name differs depending on the type of custom training\n job. For details, see [Perform custom training](/vertex-ai/docs/training/using-private-ip#perform-custom-training).\n\n- Your NFS configuration in the [WorkerPoolSpec field](/vertex-ai/docs/reference/rest/v1/CustomJobSpec#workerpoolspec).\n Include the following fields:\n\n For more information, see [Where to specify compute resources](/vertex-ai/docs/training/configure-compute#where_to_specify_compute_resources).\n\nExample: create a custom job using the gcloud CLI\n-------------------------------------------------\n\n1. Follow the steps in\n [Create a Python training application for a prebuilt container](/vertex-ai/docs/training/create-python-pre-built-container)\n to build a training application to run on Vertex AI.\n\n2. Create a file named `config.yaml` that describes the PSA or Private Service Connect interface config\n mount settings for your training job. Use one of the following formats:\n\n### Private Service Connect interface\n\n\n| **Preview\n| --- Private Service Connect interface**\n|\n|\n| This feature is subject to the \"Pre-GA Offerings Terms\" in the General Service Terms section\n| of the [Service Specific Terms](/terms/service-terms#1).\n|\n| Pre-GA features are available \"as is\" and might have limited support.\n|\n| For more information, see the\n| [launch stage descriptions](/products#product-launch-stages).\n\n\u003cbr /\u003e\n\n1. To use Private Service Connect interface:\n\n pscInterfaceConfig:\n network_attachment: \u003cvar translate=\"no\"\u003eNETWORK_ATTACHMENT_NAME\u003c/var\u003e\n workerPoolSpecs:\n - machineSpec:\n machineType: \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e\n replicaCount: 1\n pythonPackageSpec:\n executorImageUri: \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_EXECUTOR_IMAGE_URI\u003c/var\u003e or \u003cvar translate=\"no\"\u003ePRE_BUILT_CONTAINER_IMAGE_URI\u003c/var\u003e\n packageUris:\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_URIS\u003c/var\u003e\n pythonModule: PYTHON_MODULE\n nfsMounts:\n - server: \u003cvar translate=\"no\"\u003eNFS_SERVER_IP\u003c/var\u003e\n path: \u003cvar translate=\"no\"\u003eNFS_SHARE_NAME\u003c/var\u003e\n mountPoint: LOCAL_FOLDER\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eNETWORK_ATTACHMENT_NAME\u003c/var\u003e: The name of your network attachment.\n\n - \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e: The identifier of your virtual machine type.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_EXECUTOR_IMAGE_URI\u003c/var\u003e or \u003cvar translate=\"no\"\u003ePRE_BUILT_CONTAINER_IMAGE_URI\u003c/var\u003e:\n The URI of a container image in Artifact Registry that will run the provided\n Python package. Vertex AI provides a\n [wide range of executor images with pre-installed packages](/vertex-ai/docs/training/pre-built-containers)\n to meet users' various use cases.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_URIS\u003c/var\u003e: A comma-separated list of\n Cloud Storage URIs that specify the Python package files that\n make up the training program and its dependent packages. The maximum\n number of package URIs is 100.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_MODULE\u003c/var\u003e: The Python module name to run after installing\n the packages.\n\n - \u003cvar translate=\"no\"\u003eNFS_SERVER_IP\u003c/var\u003e: The IP address of your NFS server.\n\n - \u003cvar translate=\"no\"\u003eNFS_SHARE_NAME\u003c/var\u003e: The NFS share path, which is an\n absolute path that begins with `/`.\n\n - \u003cvar translate=\"no\"\u003eLOCAL_FOLDER\u003c/var\u003e: The local mount point (UNIX directory name).\n\n Make sure that your network name is formatted correctly and that your NFS\n share exists in the specified network.\n2. Create your custom job and pass your `config.yaml` file to the `--config`\n parameter.\n\n gcloud ai custom-jobs create \\\n --region=\u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e \\\n --display-name=\u003cvar translate=\"no\"\u003eJOB_NAME\u003c/var\u003e \\\n --config=config.yaml\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: Specify the region to create the job in.\n\n - \u003cvar translate=\"no\"\u003eJOB_NAME\u003c/var\u003e: A name for the custom job.\n\n### VPC peering\n\n1. Use VPC Peering if you want the job to use VPC Peering/PSA on the job\n or not.\n\n network: projects/\u003cvar translate=\"no\"\u003ePROJECT_NUMBER\u003c/var\u003e/global/networks/\u003cvar translate=\"no\"\u003eNETWORK_NAME\u003c/var\u003e\n workerPoolSpecs:\n - machineSpec:\n machineType: \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e\n replicaCount: 1\n pythonPackageSpec:\n executorImageUri: \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_EXECUTOR_IMAGE_URI\u003cspan class=\"devsite-syntax-w\"\u003e \u003c/span\u003eor\u003cspan class=\"devsite-syntax-w\"\u003e \u003c/span\u003e\n \u003cspan class=\"devsite-syntax-w\"\u003e \u003c/span\u003ePRE_BUILT_CONTAINER_IMAGE_URI\u003c/var\u003e\n packageUris:\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_URIS\u003c/var\u003e\n pythonModule: \u003cvar translate=\"no\"\u003ePYTHON_MODULE\u003c/var\u003e\n nfsMounts:\n - server: \u003cvar translate=\"no\"\u003eNFS_SERVER_IP\u003c/var\u003e\n path: \u003cvar translate=\"no\"\u003eNFS_SHARE_NAME\u003c/var\u003e\n mountPoint: \u003cvar translate=\"no\"\u003eLOCAL_FOLDER\u003c/var\u003e\n\n Replace the following:\n - \u003cvar translate=\"no\"\u003ePROJECT_NUMBER\u003c/var\u003e: The project ID of your Google Cloud project.\n\n - \u003cvar translate=\"no\"\u003eNETWORK_NAME\u003c/var\u003e: The name of your private or Shared VPC.\n\n - \u003cvar translate=\"no\"\u003eMACHINE_TYPE\u003c/var\u003e: The identifier of your virtual machine type.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_EXECUTOR_IMAGE_URI or PRE_BUILT_CONTAINER_IMAGE_URI\u003c/var\u003e:\n The URI of a container image in Artifact Registry that will run the provided\n Python package. Vertex AI provides a\n [wide range of executor images with pre-installed packages](/vertex-ai/docs/training/pre-built-containers)\n to meet users' various use cases.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_PACKAGE_URIS\u003c/var\u003e: A comma-separated list of\n Cloud Storage URIs that specify the Python package files that\n make up the training program and its dependent packages. The maximum\n number of package URIs is 100.\n\n - \u003cvar translate=\"no\"\u003ePYTHON_MODULE\u003c/var\u003e: The Python module name to run after installing\n the packages.\n\n - \u003cvar translate=\"no\"\u003eNFS_SERVER_IP\u003c/var\u003e: The IP address of your NFS server.\n\n - \u003cvar translate=\"no\"\u003eNFS_SHARE_NAME\u003c/var\u003e: The NFS share path, which is an\n absolute path that begins with `/`.\n\n - \u003cvar translate=\"no\"\u003eLOCAL_FOLDER\u003c/var\u003e: The local mount point (UNIX directory name).\n\n Make sure that your network name is formatted correctly and that your NFS\n share exists in the specified network.\n2. Create your custom job and pass your `config.yaml` file to the `--config`\n parameter.\n\n gcloud ai custom-jobs create \\\n --region=\u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e \\\n --display-name=\u003cvar translate=\"no\"\u003eJOB_NAME\u003c/var\u003e \\\n --config=config.yaml\n\nReplace the following:\n\n- \u003cvar translate=\"no\"\u003eLOCATION\u003c/var\u003e: Specify the region to create the job in.\n\n- \u003cvar translate=\"no\"\u003eJOB_NAME\u003c/var\u003e: A name for the custom job.\n\nLimitations\n-----------\n\n- You must mount your NFS share using an IP address that is internal to your\n VPC; using public URLs isn't allowed.\n\n- Training jobs mount NFS shares without authentication, and will fail\n if a username and password are required.\n\n To secure your data, set permissions\n on your NFS share. If you are using Filestore, see\n [access control](/filestore/docs/access-control) in the Filestore\n documentation.\n- You can't run two training jobs that mount NFS shares from different\n VPC networks at the same time. This is due to the\n [network peering restriction](/vertex-ai/docs/training/using-private-ip#run_jobs_on_different_networks)."]]
