Hacktoberfest 2026: le issue che i maintainer hanno segnato per ottobre, aperte e adatte ai principianti. Sfoglia le issue Hacktoberfest

ModelBuilder with source_code + DJL LMI: /opt/ml/model becomes read-only, breaking HF Hub model downloads

Aperta
  • Qualcosa non va?
#5,698 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 2 giorni

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
48/100
Tipo di issue
Bug
Chiarezza
Abbastanza chiara
Stato di attività
Tranquilla
Stack tecnologico
aws, python
Ambito
cloud, machine-learning

Direzione di ricerca

Inizia da ModelBuilder.build() e deploy(), insieme al packaging di SourceCode, e riproduci il problema con la DJL LMI image e la configurazione del modello fornita. Traccia come viene pacchettizzato source_code e come vengono applicati HF_MODEL_ID ed env_vars. Il lavoro è completato quando i download del modello in runtime possono scrivere in una posizione scrivibile e un HF_MODEL_ID fornito dall’utente non viene sostituito inaspettatamente.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Description

When using ModelBuilder (SDK v3) with a pre-built DJL LMI container image and source_code (via SourceCode) to provide custom requirements.txt, the model directory /opt/ml/model/ becomes read-only at runtime. This prevents the DJL container from downloading models from HuggingFace Hub, which tries to write cache files to /opt/ml/model/.

Additionally, ModelBuilder overrides user-provided HF_MODEL_ID environment variable with the value from the model= parameter, making it impossible to point the container to the local model path (/opt/ml/model) when S3 model artifacts are also provided via s3_model_data_url.

How to Reproduce

from sagemaker.serve import ModelBuilder, ModelServer
from sagemaker.serve.builder.schema_builder import SchemaBuilder
from sagemaker.serve.mode.function_pointers import Mode
from sagemaker.serve.model_builder import SourceCode

source_code = SourceCode(
    source_dir="./model_code",
    requirements="requirements.txt",  # e.g. transformers>=4.55.0
)

mb = ModelBuilder(
    model="chromadb/context-1",  # HF Hub model ID
    role_arn=ROLE,
    image_uri="763104351884.dkr.ecr.us-east-1.amazonaws.com/djl-inference:0.36.0-lmi22.0.0-cu129",
    model_server=ModelServer.DJL_SERVING,
    schema_builder=SchemaBuilder(
        {"inputs": "Hello", "parameters": {"max_new_tokens": 64}},
        [{"generated_text": "Hi"}],
    ),
    source_code=source_code,
    env_vars={"OPTION_TENSOR_PARALLEL_DEGREE": "4", ...},
    instance_type="ml.g6e.12xlarge",
    mode=Mode.SAGEMAKER_ENDPOINT,
)

model = mb.build()
endpoint = mb.deploy(endpoint_name="test", wait=True)
# FAILS: OSError: [Errno 30] Read-only file system: /opt/ml/model/models--chromadb--context-1

Observed Behavior

  1. ModelBuilder.build() packages the source_code directory into a model.tar.gz and uploads to S3
  2. At deploy time, SageMaker mounts this tar.gz at /opt/ml/model/ — which becomes read-only
  3. ModelBuilder sets HF_MODEL_ID=chromadb/context-1 (from model=), overriding any user-provided value
  4. DJL LMI container sees HF_MODEL_ID=chromadb/context-1 and tries to download from HF Hub
  5. HF Hub download tries to write cache to /opt/ml/model/models--chromadb--context-1/
  6. Fails with OSError: [Errno 30] Read-only file system

CloudWatch logs confirm:

OSError: [Errno 30] Read-only file system: /opt/ml/model/models--chromadb--context-1

Expected Behavior

Users should be able to use ModelBuilder with:

  • A pre-built container image (e.g. DJL LMI)
  • source_code with a custom requirements.txt to install additional dependencies at container startup
  • A HuggingFace Hub model ID that the container downloads at runtime

The requirements.txt installation should not make /opt/ml/model/ read-only, or the HF Hub cache should be redirected to a writable location (e.g. /tmp).

Workaround Attempted

Setting HF_HOME=/tmp/hf_home and HUGGINGFACE_HUB_CACHE=/tmp/hf_home/hub in env_vars — these appear in the container environment but the DJL container still writes to /opt/ml/model/.

Use Case

This is a common pattern for deploying newer models (e.g. OpenAI GPT-OSS based models like chromadb/context-1) that require a newer transformers version than what is bundled in the DJL LMI container. The source_code with requirements.txt is the natural SDK v3 mechanism for this, but it is incompatible with HF Hub model downloads.

Environment

  • SageMaker Python SDK: 3.6.0
  • Container: djl-inference:0.36.0-lmi22.0.0-cu129
  • Instance: ml.g6e.12xlarge
  • Region: us-east-1
Lingua principale
Python
Stelle
2.3k
Fork
1.3k
Merge medio
3g 2h
PR unite (30g)
70

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di aws/sagemaker-python-sdk

Tutte le issue di aws/sagemaker-python-sdk

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.