microsoft/qlib · error · FileNotFoundError

Invalid mount path: {mount_path}! Please mount manually: {'

Error message

Invalid mount path: {mount_path}! Please mount manually: {' '.join(mount_command)} or Set init parameter `auto_mount=True`

What it means

Raised by the module-level file-reading helper in scripts/dump_bin.py (used by DumpDataBase._get_source_data and friends). It dispatches on the file suffix and only knows how to read .csv (via pd.read_csv) and .parquet (via pd.read_parquet); any other suffix reaches the else branch and is rejected. It exists so that dump_bin can ingest either CSV or Parquet source files with per-format kwargs (e.g. low_memory only for CSV).

Source

Thrown at qlib/__init__.py:100

    logger.info("qlib successfully initialized based on %s settings." % default_conf)
    data_path = {_freq: C.dpm.get_data_uri(_freq) for _freq in C.dpm.provider_uri.keys()}
    logger.info(f"data_path={data_path}")


def _mount_nfs_uri(provider_uri, mount_path, auto_mount: bool = False):
    LOG = get_module_logger("mount nfs", level=logging.INFO)
    if mount_path is None:
        raise ValueError(f"Invalid mount path: {mount_path}!")
    if not re.match(r"^[a-zA-Z0-9.:/\-_]+$", provider_uri):
        raise ValueError(f"Invalid provider_uri format: {provider_uri}")
    # FIXME: the C["provider_uri"] is modified in this function
    # If it is not modified, we can pass only  provider_uri or mount_path instead of C
    mount_command = ["sudo", "mount.nfs", provider_uri, mount_path]
    # If the provider uri looks like this 172.23.233.89//data/csdesign'
    # It will be a nfs path. The client provider will be used
    if not auto_mount:  # pylint: disable=R1702
        if not Path(mount_path).exists():
            raise FileNotFoundError(
                f"Invalid mount path: {mount_path}! Please mount manually: {' '.join(mount_command)} or Set init parameter `auto_mount=True`"
            )
    else:
        # Judging system type
        sys_type = platform.system()
        if "windows" in sys_type.lower():
            # system: window
            try:
                subprocess.run(
                    ["mount", "-o", "anon", provider_uri, mount_path],
                    capture_output=True,
                    text=True,
                    check=True,
                )
                LOG.info("Mount finished.")
            except subprocess.CalledProcessError as e:
                error_output = (e.stdout or "") + (e.stderr or "")
                if e.returncode == 85:

View on GitHub (pinned to 79633dd950)

Solutions

1. Inspect the offending file: find <data_dir> -type f ! -name '*.csv' ! -name '*.parquet' — remove temp/partial files or move them out of the data dir.
2. Convert the unsupported file to CSV or Parquet before dumping (df = pd.read_excel(...); df.to_csv(...)).
3. If you control the pipeline, standardize the export step to always emit .csv or .parquet and write atomically (write to temp name, then rename) so partial files never carry a final suffix.

Example fix

# before: data_dir contains prices.xlsx alongside prices.csv
python dump_bin.py dump_all --data_path ./data_dir ... # ValueError: Unsupported file format: .xlsx

# after: convert first, keep only .csv/.parquet in data_dir
import pandas as pd
pd.read_excel('data_dir/prices.xlsx').to_csv('data_dir/prices.csv', index=False)

from pathlib import Path
bad = [str(p) for p in Path(data_dir).iterdir() if p.is_file() and p.suffix.lower() not in (".csv", ".parquet")]
if bad:
    raise SystemExit(f"Unsupported source files in {data_dir}: {bad}")

from pathlib import Path

def is_supported_source(p: Path) -> bool:
    return p.is_file() and p.suffix.lower() in (".csv", ".parquet")

try:
    dumper._get_source_data(file_path)
except ValueError as e:
    if "Unsupported file format" in str(e):
        logger.warning("skipping %s: %s", file_path, e)
        continue
    raise

When it happens

Trigger: Calling dump_bin.py with a --data dir containing files whose extension is neither .csv nor .parquet (e.g. .txt, .json, .xlsx, .feather, .h5, or extensionless temp/partial download files such as '.csv.part'); a partially downloaded or hidden file like .DS_Store or a lock file in the source directory; case-sensitivity issues such as .CSV on case-sensitive filesystems.

Common situations: Mixed-format source directories where most files are CSV but one export is xlsx; leftover partial downloads or editor temp files (~$file.csv, file.csv.tmp) inside the data dir; renaming data files without updating the extension; users assuming dump_bin reads any pandas-compatible format.

Related errors

• Network path not found
• $close is necessray in extra_quote
• stock data from resam_ts_data must be a number, pd.Series or
• Please implement the `droplevel` method
• This type of input is not supported

AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15). Data as JSON: /api/errors/84f20c29eda7ecac. Report an issue: GitHub.