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# MinIO Batch Job
MinIO Batch jobs is an MinIO object management feature that lets you manage objects at scale. Jobs currently supported by MinIO

- Replicate objects between buckets on multiple sites

Upcoming Jobs

- Copy objects from NAS to MinIO
- Copy objects from HDFS to MinIO

## Replication Job
To perform replication via batch jobs, you create a job. The job consists of a job description YAML that describes

        else if (a.cas(v = a.value, fn(v, x))) break;
        else if (n >= NCPU || cells != as) collide = false; // At max size or stale
        else if (!collide) collide = true;
        else if (busy == 0 && casBusy()) {
          try {
            if (cells == as) { // Expand table unless stale
              Cell[] rs = new Cell[n << 1];
              for (int i = 0; i < n; ++i) rs[i] = as[i];
              cells = rs;

				// Following check is for situations
				// when Writes() scale higher than Reads()
				// - practically speaking this never happens
				// and should never happen - however it has
				// been seen recently due to hardware issues
				// causes Reads() to go slower than Writes().
				//
				// Send such results anyways as this shall
				// expose a problem underneath.
				if totalPut > throughputHighestPut {

        else if (a.cas(v = a.value, fn(v, x))) break;
        else if (n >= NCPU || cells != as) collide = false; // At max size or stale
        else if (!collide) collide = true;
        else if (busy == 0 && casBusy()) {
          try {
            if (cells == as) { // Expand table unless stale
              Cell[] rs = new Cell[n << 1];
              for (int i = 0; i < n; ++i) rs[i] = as[i];
              cells = rs;

  - cron: "*/10 * * * *"

permissions:
  contents: read

jobs:
  stale:
    permissions:
      issues: write  # for actions/stale to close stale issues
      pull-requests: write  # for actions/stale to close stale PRs
    runs-on: ubuntu-latest
    env:
      ACTIONS_STEP_DEBUG: true
    steps:
    - name: Close Stale Issues
      uses: actions/stale@v8
      with:
        repo-token: ${{ secrets.GITHUB_TOKEN }}

      const auto& tensor_shape = default_value.shape();
      if (tensor_shape.unknown_rank()) {
        TFE_OpSetAttrShape(op, attr_name, nullptr, -1, status);
      } else {
        const auto num_dims = tensor_shape.dim_size();
        std::unique_ptr<int64_t[]> dims(new int64_t[num_dims]);
        for (int i = 0; i < num_dims; ++i) {
          dims[i] = tensor_shape.dim(i).size();
        }

    ShapeHandle shape_handle = c.output(i);
    TF_ShapeAndType& shape = output_shapes_result->items[i];
    shape.num_dims = c.Rank(shape_handle);
    if (shape.num_dims == InferenceContext::kUnknownRank) {
      shape.dims = nullptr;
      continue;
    }
    shape.dims = new int64_t[shape.num_dims];
    for (size_t j = 0; j < shape.num_dims; ++j) {
      shape.dims[j] = c.Value(c.Dim(shape_handle, j));

                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {

namespace tensorflow {

std::string AbstractTensorHandle::DebugString() const {
  PartialTensorShape shape;
  Status s = Shape(&shape);
  std::string shape_string;
  if (!s.ok()) {
    shape_string = "<error computing shape>";
  } else {
    shape_string = shape.DebugString();
  }
  return absl::StrCat("TensorHandle(shape=", shape_string,
                      ", dtype=", DataType_Name(DataType()),