/TensorFlow Guide

Adding a Custom Filesystem Plugin


The TensorFlow framework is often used in multi-process and multi-machine environments, such as Google data centers, Google Cloud Machine Learning, Amazon Web Services (AWS), and on-site distributed clusters. In order to both share and save certain types of state produced by TensorFlow, the framework assumes the existence of a reliable, shared filesystem. This shared filesystem has numerous uses, for example:

  • Checkpoints of state are often saved to a distributed filesystem for reliability and fault-tolerance.
  • Training processes communicate with TensorBoard by writing event files to a directory, which TensorBoard watches. A shared filesystem allows this communication to work even when TensorBoard runs in a different process or machine.

There are many different implementations of shared or distributed filesystems in the real world, so TensorFlow provides an ability for users to implement a custom FileSystem plugin that can be registered with the TensorFlow runtime. When the TensorFlow runtime attempts to write to a file through the FileSystem interface, it uses a portion of the pathname to dynamically select the implementation that should be used for filesystem operations. Thus, adding support for your custom filesystem requires implementing a FileSystem interface, building a shared object containing that implementation, and loading that object at runtime in whichever process needs to write to that filesystem.

Note that TensorFlow already includes many filesystem implementations, such as:

  • A standard POSIX filesystem

    Note: NFS filesystems often mount as a POSIX interface, and so standard TensorFlow can work on top of NFS-mounted remote filesystems.
  • HDFS - the Hadoop File System

  • GCS - Google Cloud Storage filesystem
  • S3 - Amazon Simple Storage Service filesystem
  • A "memory-mapped-file" filesystem

The rest of this guide describes how to implement a custom filesystem.

Implementing a custom filesystem plugin

To implement a custom filesystem plugin, you must do the following:

  • Implement subclasses of RandomAccessFile, WriteableFile, AppendableFile, and ReadOnlyMemoryRegion.
  • Implement the FileSystem interface as a subclass.
  • Register the FileSystem implementation with an appropriate prefix pattern.
  • Load the filesystem plugin in a process that wants to write to that filesystem.

The FileSystem interface

The FileSystem interface is an abstract C++ interface defined in file_system.h. An implementation of the FileSystem interface should implement all relevant the methods defined by the interface. Implementing the interface requires defining operations such as creating RandomAccessFile, WritableFile, and implementing standard filesystem operations such as FileExists, IsDirectory, GetMatchingPaths, DeleteFile, and so on. An implementation of these interfaces will often involve translating the function's input arguments to delegate to an already-existing library function implementing the equivalent functionality in your custom filesystem.

For example, the PosixFileSystem implementation implements DeleteFile using the POSIX unlink() function; CreateDir simply calls mkdir(); GetFileSize involves calling stat() on the file and then returns the filesize as reported by the return of the stat object. Similarly, for the HDFSFileSystem implementation, these calls simply delegate to the libHDFS implementation of similar functionality, such as hdfsDelete for DeleteFile.

We suggest looking through these code examples to get an idea of how different filesystem implementations call their existing libraries. Examples include:

The File interfaces

Beyond operations that allow you to query and manipulate files and directories in a filesystem, the FileSystem interface requires you to implement factories that return implementations of abstract objects such as the RandomAccessFile, the WritableFile, so that TensorFlow code and read and write to files in that FileSystem implementation.

To implement a RandomAccessFile, you must implement a single interface called Read(), in which the implementation must provide a way to read from an offset within a named file.

For example, below is the implementation of RandomAccessFile for the POSIX filesystem, which uses the pread() random-access POSIX function to implement read. Notice that the particular implementation must know how to retry or propagate errors from the underlying filesystem.

class PosixRandomAccessFile : public RandomAccessFile {
  PosixRandomAccessFile(const string& fname, int fd)
      : filename_(fname), fd_(fd) {}
  ~PosixRandomAccessFile() override { close(fd_); }

  Status Read(uint64 offset, size_t n, StringPiece* result,
              char* scratch) const override {
    Status s;
    char* dst = scratch;
    while (n > 0 && s.ok()) {
      ssize_t r = pread(fd_, dst, n, static_cast<off_t>(offset));
      if (r > 0) {
        dst += r;
        n -= r;
        offset += r;
      } else if (r == 0) {
        s = Status(error::OUT_OF_RANGE, "Read less bytes than requested");
      } else if (errno == EINTR || errno == EAGAIN) {
        // Retry
      } else {
        s = IOError(filename_, errno);
    *result = StringPiece(scratch, dst - scratch);
    return s;

  string filename_;
  int fd_;

To implement the WritableFile sequential-writing abstraction, one must implement a few interfaces, such as Append(), Flush(), Sync(), and Close().

For example, below is the implementation of WritableFile for the POSIX filesystem, which takes a FILE object in its constructor and uses standard posix functions on that object to implement the interface.

class PosixWritableFile : public WritableFile {
  PosixWritableFile(const string& fname, FILE* f)
      : filename_(fname), file_(f) {}

  ~PosixWritableFile() override {
    if (file_ != NULL) {

  Status Append(const StringPiece& data) override {
    size_t r = fwrite(data.data(), 1, data.size(), file_);
    if (r != data.size()) {
      return IOError(filename_, errno);
    return Status::OK();

  Status Close() override {
    Status result;
    if (fclose(file_) != 0) {
      result = IOError(filename_, errno);
    file_ = NULL;
    return result;

  Status Flush() override {
    if (fflush(file_) != 0) {
      return IOError(filename_, errno);
    return Status::OK();

  Status Sync() override {
    Status s;
    if (fflush(file_) != 0) {
      s = IOError(filename_, errno);
    return s;

  string filename_;
  FILE* file_;

For more details, please see the documentations of those interfaces, and look at example implementations for inspiration.

Registering and loading the filesystem

Once you have implemented the FileSystem implementation for your custom filesystem, you need to register it under a "scheme" so that paths prefixed with that scheme are directed to your implementation. To do this, you call REGISTER_FILE_SYSTEM::

REGISTER_FILE_SYSTEM("foobar", FooBarFileSystem);

When TensorFlow tries to operate on a file whose path starts with foobar://, it will use the FooBarFileSystem implementation.

string filename = "foobar://path/to/file.txt";
std::unique_ptr<WritableFile> file;

// Calls FooBarFileSystem::NewWritableFile to return
// a WritableFile class, which happens to be the FooBarFileSystem's
// WritableFile implementation.
TF_RETURN_IF_ERROR(env->NewWritableFile(filename, &file));

Next, you must build a shared object containing this implementation. An example of doing so using bazel's cc_binary rule can be found here, but you may use any build system to do so. See the section on building the op library for similar instructions.

The result of building this target is a .so shared object file.

Lastly, you must dynamically load this implementation in the process. In Python, you can call the tf.load_file_system_library(file_system_library) function, passing the path to the shared object. Calling this in your client program loads the shared object in the process, thus registering your implementation as available for any file operations going through the FileSystem interface. You can see test_file_system.py for an example.

What goes through this interface?

Almost all core C++ file operations within TensorFlow use the FileSystem interface, such as the CheckpointWriter, the EventsWriter, and many other utilities. This means implementing a FileSystem implementation allows most of your TensorFlow programs to write to your shared filesystem.

In Python, the gfile and file_io classes bind underneath to the `FileSystem implementation via SWIG, which means that once you have loaded this filesystem library, you can do:

with gfile.Open("foobar://path/to/file.txt") as w:


When you do this, a file containing "hi" will appear in the "/path/to/file.txt" of your shared filesystem.

© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.