Example #1
0
    /**
     * Train a Deep Learning model, assumes that all members are populated If checkpoint == null,
     * then start training a new model, otherwise continue from a checkpoint
     */
    public final void buildModel() {
      DeepLearningModel cp = null;
      if (_parms._checkpoint == null) {
        cp =
            new DeepLearningModel(
                dest(),
                _parms,
                new DeepLearningModel.DeepLearningModelOutput(DeepLearning.this),
                _train,
                _valid,
                nclasses());
        cp.model_info().initializeMembers();
      } else {
        final DeepLearningModel previous = DKV.getGet(_parms._checkpoint);
        if (previous == null) throw new IllegalArgumentException("Checkpoint not found.");
        Log.info("Resuming from checkpoint.");
        _job.update(0, "Resuming from checkpoint");

        if (isClassifier() != previous._output.isClassifier())
          throw new H2OIllegalArgumentException(
              "Response type must be the same as for the checkpointed model.");
        if (isSupervised() != previous._output.isSupervised())
          throw new H2OIllegalArgumentException(
              "Model type must be the same as for the checkpointed model.");

        // check the user-given arguments for consistency
        DeepLearningParameters oldP =
            previous._parms; // sanitized parameters for checkpointed model
        DeepLearningParameters newP = _parms; // user-given parameters for restart

        DeepLearningParameters oldP2 = (DeepLearningParameters) oldP.clone();
        DeepLearningParameters newP2 = (DeepLearningParameters) newP.clone();
        DeepLearningParameters.Sanity.modifyParms(
            oldP, oldP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.modifyParms(
            newP, newP2, nclasses()); // sanitize the user-given parameters
        DeepLearningParameters.Sanity.checkpoint(oldP2, newP2);

        DataInfo dinfo;
        try {
          // PUBDEV-2513: Adapt _train and _valid (in-place) to match the frames that were used for
          // the previous model
          // This can add or remove dummy columns (can happen if the dataset is sparse and datasets
          // have different non-const columns)
          for (String st : previous.adaptTestForTrain(_train, true, false)) Log.warn(st);
          for (String st : previous.adaptTestForTrain(_valid, true, false)) Log.warn(st);
          dinfo = makeDataInfo(_train, _valid, _parms, nclasses());
          DKV.put(dinfo);
          cp = new DeepLearningModel(dest(), _parms, previous, false, dinfo);
          cp.write_lock(_job);

          if (!Arrays.equals(cp._output._names, previous._output._names)) {
            throw new H2OIllegalArgumentException(
                "The columns of the training data must be the same as for the checkpointed model. Check ignored columns (or disable ignore_const_cols).");
          }
          if (!Arrays.deepEquals(cp._output._domains, previous._output._domains)) {
            throw new H2OIllegalArgumentException(
                "Categorical factor levels of the training data must be the same as for the checkpointed model.");
          }
          if (dinfo.fullN() != previous.model_info().data_info().fullN()) {
            throw new H2OIllegalArgumentException(
                "Total number of predictors is different than for the checkpointed model.");
          }
          if (_parms._epochs <= previous.epoch_counter) {
            throw new H2OIllegalArgumentException(
                "Total number of epochs must be larger than the number of epochs already trained for the checkpointed model ("
                    + previous.epoch_counter
                    + ").");
          }

          // these are the mutable parameters that are to be used by the model (stored in
          // model_info._parms)
          final DeepLearningParameters actualNewP =
              cp.model_info()
                  .get_params(); // actually used parameters for model building (defaults filled in,
                                 // etc.)
          assert (actualNewP != previous.model_info().get_params());
          assert (actualNewP != newP);
          assert (actualNewP != oldP);
          DeepLearningParameters.Sanity.update(actualNewP, newP, nclasses());

          Log.info(
              "Continuing training after "
                  + String.format("%.3f", previous.epoch_counter)
                  + " epochs from the checkpointed model.");
          cp.update(_job);
        } catch (H2OIllegalArgumentException ex) {
          if (cp != null) {
            cp.unlock(_job);
            cp.delete();
            cp = null;
          }
          throw ex;
        } finally {
          if (cp != null) cp.unlock(_job);
        }
      }
      trainModel(cp);

      // clean up, but don't delete weights and biases if user asked for export
      List<Key> keep = new ArrayList<>();
      try {
        if (_parms._export_weights_and_biases
            && cp._output.weights != null
            && cp._output.biases != null) {
          for (Key k : Arrays.asList(cp._output.weights)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
          for (Key k : Arrays.asList(cp._output.biases)) {
            keep.add(k);
            for (Vec vk : ((Frame) DKV.getGet(k)).vecs()) {
              keep.add(vk._key);
            }
          }
        }
      } finally {
        Scope.exit(keep.toArray(new Key[keep.size()]));
      }
    }
Example #2
0
  @Override
  Val apply(Env env, Env.StackHelp stk, AST asts[]) {

    // Execute all args.  Find a canonical frame; all Frames must look like this one.
    // Each argument turns into either a Frame (whose rows are entirely
    // inlined) or a scalar (which is replicated across as a single row).
    Frame fr = null; // Canonical Frame; all frames have the same column count, types and names
    int nchks = 0; // Total chunks
    Val vals[] = new Val[asts.length]; // Computed AST results
    for (int i = 1; i < asts.length; i++) {
      vals[i] = stk.track(asts[i].exec(env));
      if (vals[i].isFrame()) {
        fr = vals[i].getFrame();
        nchks += fr.anyVec().nChunks(); // Total chunks
      } else nchks++; // One chunk per scalar
    }
    // No Frame, just a pile-o-scalars?
    Vec zz = null; // The zero-length vec for the zero-frame frame
    if (fr == null) { // Zero-length, 1-column, default name
      fr = new Frame(new String[] {Frame.defaultColName(0)}, new Vec[] {zz = Vec.makeZero(0)});
      if (asts.length == 1) return new ValFrame(fr);
    }

    // Verify all Frames are the same columns, names, and types.  Domains can vary, and will be the
    // union
    final Frame frs[] = new Frame[asts.length]; // Input frame
    final byte[] types = fr.types(); // Column types
    final int ncols = fr.numCols();
    final long[] espc = new long[nchks + 1]; // Compute a new layout!
    int coffset = 0;

    for (int i = 1; i < asts.length; i++) {
      Val val = vals[i]; // Save values computed for pass 2
      Frame fr0 =
          val.isFrame()
              ? val.getFrame()
              // Scalar: auto-expand into a 1-row frame
              : stk.track(new Frame(fr._names, Vec.makeCons(val.getNum(), 1L, fr.numCols())));

      // Check that all frames are compatible
      if (fr.numCols() != fr0.numCols())
        throw new IllegalArgumentException(
            "rbind frames must have all the same columns, found "
                + fr.numCols()
                + " and "
                + fr0.numCols()
                + " columns.");
      if (!Arrays.deepEquals(fr._names, fr0._names))
        throw new IllegalArgumentException(
            "rbind frames must have all the same column names, found "
                + Arrays.toString(fr._names)
                + " and "
                + Arrays.toString(fr0._names));
      if (!Arrays.equals(types, fr0.types()))
        throw new IllegalArgumentException(
            "rbind frames must have all the same column types, found "
                + Arrays.toString(types)
                + " and "
                + Arrays.toString(fr0.types()));

      frs[i] = fr0; // Save frame

      // Roll up the ESPC row counts
      long roffset = espc[coffset];
      long[] espc2 = fr0.anyVec().espc();
      for (int j = 1; j < espc2.length; j++) // Roll up the row counts
      espc[coffset + j] = (roffset + espc2[j]);
      coffset += espc2.length - 1; // Chunk offset
    }
    if (zz != null) zz.remove();

    // build up the new domains for each vec
    HashMap<String, Integer>[] dmap = new HashMap[types.length];
    String[][] domains = new String[types.length][];
    int[][][] cmaps = new int[types.length][][];
    for (int k = 0; k < types.length; ++k) {
      dmap[k] = new HashMap<>();
      int c = 0;
      byte t = types[k];
      if (t == Vec.T_CAT) {
        int[][] maps = new int[frs.length][];
        for (int i = 1; i < frs.length; i++) {
          maps[i] = new int[frs[i].vec(k).domain().length];
          for (int j = 0; j < maps[i].length; j++) {
            String s = frs[i].vec(k).domain()[j];
            if (!dmap[k].containsKey(s)) dmap[k].put(s, maps[i][j] = c++);
            else maps[i][j] = dmap[k].get(s);
          }
        }
        cmaps[k] = maps;
      } else {
        cmaps[k] = new int[frs.length][];
      }
      domains[k] = c == 0 ? null : new String[c];
      for (Map.Entry<String, Integer> e : dmap[k].entrySet()) domains[k][e.getValue()] = e.getKey();
    }

    // Now make Keys for the new Vecs
    Key<Vec>[] keys = fr.anyVec().group().addVecs(fr.numCols());
    Vec[] vecs = new Vec[fr.numCols()];
    int rowLayout = Vec.ESPC.rowLayout(keys[0], espc);
    for (int i = 0; i < vecs.length; i++)
      vecs[i] = new Vec(keys[i], rowLayout, domains[i], types[i]);

    // Do the row-binds column-by-column.
    // Switch to F/J thread for continuations
    ParallelRbinds t;
    H2O.submitTask(t = new ParallelRbinds(frs, espc, vecs, cmaps)).join();
    return new ValFrame(new Frame(fr.names(), t._vecs));
  }