azure-blob-tui 0.1.2

changeme

azure-blob-tui

Terminal TUI for browsing Azure Blob Storage, plus Python helpers to read/write blobs directly from your training code (no local files required).

First-time setup

Run the TUI once to configure account/container/prefix and (optionally) store SAS in an encrypted local file. On later runs, if SAS is stored, you only need the passphrase (no need to re-enter SAS).

azure-blob-tui --configure

During first run you will be prompted for:

• account name / container name / default prefix
• whether to store SAS in an encrypted local file
• (if yes) the SAS token and a passphrase to encrypt it

Later runs:

• If SAS is stored, you will only be prompted for the passphrase.
• If you set AZURE_BLOB_TUI_PASSPHRASE, no prompt is needed.

Use the TUI

azure-blob-tui

Reconfigure

azure-blob-tui --configure

Python API (no local files)

All helpers use the same config (account/container/default prefix) and SAS token stored by azure-blob-tui --configure. If AZURE_BLOB_TUI_PASSPHRASE is set, no prompt is needed.

Default prefix behavior:

• Default prefix is not applied automatically.
• Pass use_default_prefix=True to opt in per call.

blob_open (file-like stream)

import torch
from azure_blob import blob_open

# Save directly to Blob
with blob_open("checkpoints/step-100/model.pt", "wb") as f:
    torch.save(model.state_dict(), f)

# Load directly from Blob
with blob_open("checkpoints/step-100/model.pt", "rb") as f:
    state = torch.load(f, weights_only=False)

Save JSON/YAML/text to Blob

import io
import json
from azure_blob import blob_open

with blob_open("artifacts/config.json", "wb") as raw:
    with io.TextIOWrapper(raw, encoding="utf-8") as f:
        json.dump({"lr": 1e-4}, f)

blob_url (signed URL helper)

from azure_blob import blob_url

url = blob_url("images/cat.png")

Storage helpers

from azure_blob import (
    download_dir,
    download_file,
    list_blobs,
    upload_dir,
    upload_file,
)

upload_file("local.txt", "artifacts/local.txt")
download_file("artifacts/local.txt", "local_copy.txt")

for name in list_blobs(prefix="artifacts/"):
    print(name)

BlobContext (high-level workflow helper)

BlobContext wraps a configured ContainerClient and adds convenience helpers for prefix resolution, listing, and in-memory read/write.

import os
from azure_blob import BlobContext

os.environ["AZURE_BLOB_TUI_PASSPHRASE"] = "your-passphrase"

ctx = BlobContext.from_config()

for prefix in ctx.iter_prefixes(prefix="overview/among/"):
    print(prefix)

data = ctx.read_bytes("overview/among/123/topdown.png")
ctx.write_bytes(
    "overview/among/123/processed.png",
    data,
    content_type="image/png",
)

Choosing the right helper

• BlobContext: best for workflows that need listing, grouping, and in-memory read/write without touching disk.
• blob_open: best for file-like streaming APIs (e.g., torch.save/load, TextIOWrapper, or very large files).
• read_bytes/write_bytes: best for small-to-medium blobs where a full in-memory buffer is fine (images, JSON, etc.).
• list_blobs/iter_prefixes: best for enumeration and directory-like traversal.
• download_*/upload_*: local file paths (avoid these if you want no local storage).

Notes

• SAS tokens are not stored in the config file, but in an encrypted local file.
• blob_open works for any file type (torch checkpoints, JSON, YAML, text, etc.).