What are workspaces?

ND4J offers an additional memory-management model: workspaces. That allows you to reuse memory for cyclic workloads without the JVM Garbage Collector for off-heap memory tracking. In other words, at the end of the workspace loop, all INDArrays’ memory content is invalidated. Workspaces are integrated into DL4J for training and inference.

The basic idea is simple: You can do what you need within a workspace (or spaces), and if you want to get an INDArray out of it (i.e. to move result out of the workspace), you just call INDArray.detach() and you’ll get an independent INDArray copy.

Neural Networks

For DL4J users, workspaces provide better performance out of the box, and are enabled by default from 1.0.0-alpha onwards.Thus for most users, no explicit worspaces configuration is required.

To benefit from worspaces, they need to be enabled. You can configure the workspace mode using:

.trainingWorkspaceMode(WorkspaceMode.SEPARATE) and/or .inferenceWorkspaceMode(WorkspaceMode.SINGLE) in your neural network configuration.

The difference between SEPARATE and SINGLE workspaces is a tradeoff between the performance & memory footprint:

• SEPARATE is slightly slower, but uses less memory.
• SINGLE is slightly faster, but uses more memory.That said, it’s fine to use different modes for training & inference (i.e. use SEPARATE for training, and use SINGLE for inference, since inference only involves a feed-forward loop without backpropagation or updaters involved).

With workspaces enabled, all memory used during training will be reusable and tracked without the JVM GC interference.The only exclusion is the output() method that uses workspaces (if enabled) internally for the feed-forward loop. Subsequently, it detaches the resulting INDArray from the workspaces, thus providing you with independent INDArray which will be handled by the JVM GC.

Please note: After the 1.0.0-alpha release, workspaces in DL4J were refactored - SEPARATE/SINGLE modes have been deprecated, and users should use ENABLED instead.

Garbage Collector

If your training process uses workspaces, we recommend that you disable (or reduce the frequency of) periodic GC calls. That can be done like so:

// this will limit frequency of gc calls to 5000 milliseconds
Nd4j.getMemoryManager().setAutoGcWindow(5000)
// OR you could totally disable it
Nd4j.getMemoryManager().togglePeriodicGc(false);

Put that somewhere before your model.fit(…) call.

ParallelWrapper & ParallelInference

For ParallelWrapper, the workspace-mode configuration option was also added. As such, each of the trainer threads will use a separate workspace attached to the designated device.

ParallelWrapper wrapper = new ParallelWrapper.Builder(model)
      // DataSets prefetching options. Buffer size per worker.
      .prefetchBuffer(8)
      // set number of workers equal to number of GPUs.
      .workers(2)
      // rare averaging improves performance but might reduce model accuracy
      .averagingFrequency(5)
      // if set to TRUE, on every averaging model score will be reported
      .reportScoreAfterAveraging(false)
      // 3 options here: NONE, SINGLE, SEPARATE
      .workspaceMode(WorkspaceMode.SINGLE)
      .build();

Iterators

We provide asynchronous prefetch iterators, AsyncDataSetIterator and AsyncMultiDataSetIterator, which are usually used internally.

These iterators optionally use a special, cyclic workspace mode to obtain a smaller memory footprint. The size of the workspace, in this case, will be determined by the memory requirements of the first DataSet coming out of the underlying iterator, whereas the buffer size is defined by the user. The workspace will be adjusted if memory requirements change over time (e.g. if you’re using variable-length time series).

Caution: If you’re using a custom iterator or the RecordReader, please make sure you’re not initializing something huge within the first next() call. Do that in your constructor to avoid undesired workspace growth.

Caution: With AsyncDataSetIterator being used, DataSets are supposed to be used before calling the next() DataSet. You are not supposed to store them, in any way, without the detach() call. Otherwise, the memory used for INDArrays within DataSet will be overwritten within AsyncDataSetIterator eventually.

If for some reason you don’t want your iterator to be wrapped into an asynchronous prefetch (e.g. for debugging purposes), special wrappers are provided: AsyncShieldDataSetIterator and AsyncShieldMultiDataSetIterator. Basically, those are just thin wrappers that prevent prefetch.

Evaluation

Usually, evaluation assumes use of the model.output() method, which essentially returns an INDArray detached from the workspace. In the case of regular evaluations during training, it might be better to use the built-in methods for evaluation. For example:

Evaluation eval = new Evaluation(outputNum);
ROC roceval = new ROC(outputNum);
model.doEvaluation(iteratorTest, eval, roceval);

This piece of code will run a single cycle over iteratorTest, and it will update both (or less/more if required by your needs) IEvaluation implementations without any additional INDArray allocation.

Workspace Destruction

There are also some situations, say, where you’re short on RAM, and might want do release all workspaces created out of your control; e.g. during evaluation or training.

That could be done like so: Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();

This method will destroy all workspaces that were created within the calling thread. If you’ve created workspaces in some external threads on your own, you can use the same method in that thread, after the workspaces are no longer needed.

Workspace Exceptions

If workspaces are used incorrectly (such as a bug in a custom layer or data pipeline, for example), you may see an error message such as:

org.nd4j.linalg.exception.ND4JIllegalStateException: Op [set] Y argument uses leaked workspace pointer from workspace [LOOP_EXTERNAL]
For more details, see the ND4J User Guide: nd4j.org/userguide#workspaces-panic

DL4J’s LayerWorkspaceMgr

DL4J’s Layer API includes the concept of a “layer workspace manager”.

The idea with this class is that it allows us to easily and precisely control the location of a given array, given different possible configurations for the workspaces.For example, the activations out of a layer may be placed in one workspace during inference, and another during training; this is for performance reasons.However, with the LayerWorkspaceMgr design, implementers of layers don’t need to worry about this.

What does this mean in practice? Usually it’s quite simple…

• When returning activations (activate(boolean training, LayerWorkspaceMgr workspaceMgr) method), make sure the returned array is defined in ArrayType.ACTIVATIONS (i.e., use LayerWorkspaceMgr.create(ArrayType.ACTIVATIONS, …) or similar)
• When returning activation gradients (backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr)), similarly return an array defined in ArrayType.ACTIVATION_GRADYou can also leverage an array defined in any workspace to the appropriate workspace using, for example, LayerWorkspaceMgr.leverageTo(ArrayType.ACTIVATIONS, myArray)

Note that if you are not implementing a custom layer (and instead just want to perform forward pass for a layer outside of a MultiLayerNetwork/ComputationGraph) you can use LayerWorkspaceMgr.noWorkspaces().