diff --git a/digits/model/forms.py b/digits/model/forms.py
index 3f160e007..210513813 100644
--- a/digits/model/forms.py
+++ b/digits/model/forms.py
@@ -120,10 +120,10 @@ def validate_py_ext(form, field):
             tooltip = "If you provide a random seed, then back-to-back runs with the same model and dataset should give identical results."
             )
 
-    batch_size = utils.forms.IntegerField('Batch size',
+    batch_size = utils.forms.MultiIntegerField('Batch size',
             validators = [
-                validators.NumberRange(min=1),
-                validators.Optional(),
+                utils.forms.MultiNumberRange(min=1),
+                utils.forms.MultiOptional(),
                 ],
             tooltip = "How many images to process at once. If blank, values are used from the network definition."
             )
@@ -153,10 +153,10 @@ def validate_solver_type(form, field):
 
     ### Learning rate
 
-    learning_rate = utils.forms.FloatField('Base Learning Rate',
+    learning_rate = utils.forms.MultiFloatField('Base Learning Rate',
             default = 0.01,
             validators = [
-                validators.NumberRange(min=0),
+                utils.forms.MultiNumberRange(min=0),
                 ],
             tooltip = "Affects how quickly the network learns. If you are getting NaN for your loss, you probably need to lower this value."
             )
diff --git a/digits/model/images/classification/test_views.py b/digits/model/images/classification/test_views.py
index 2bb2421f1..b81680496 100644
--- a/digits/model/images/classification/test_views.py
+++ b/digits/model/images/classification/test_views.py
@@ -202,7 +202,11 @@ def create_model(cls, network=None, **kwargs):
             if rv.status_code != 200:
                 print json.loads(rv.data)
                 raise RuntimeError('Model creation failed with %s' % rv.status_code)
-            return json.loads(rv.data)['id']
+            data = json.loads(rv.data)
+            if 'jobs' in data.keys():
+                return [j['id'] for j in data['jobs']]
+            else:
+                return data['id']
 
         # expect a redirect
         if not 300 <= rv.status_code <= 310:
@@ -1123,3 +1127,15 @@ def test_python_layer(self):
         assert rv.status_code == 200, 'json load failed with %s' % rv.status_code
         content = json.loads(rv.data)
         assert len(content['snapshots']), 'should have at least snapshot'
+
+class TestSweepCreation(BaseViewsTestWithDataset):
+    FRAMEWORK = 'caffe'
+    """
+    Model creation tests
+    """
+    def test_sweep(self):
+        job_ids = self.create_model(json=True, learning_rate='[0.01, 0.02]', batch_size='[8, 10]')
+        for job_id in job_ids:
+            assert self.model_wait_completion(job_id) == 'Done', 'create failed'
+            assert self.delete_model(job_id) == 200, 'delete failed'
+            assert not self.model_exists(job_id), 'model exists after delete'
diff --git a/digits/model/images/classification/views.py b/digits/model/images/classification/views.py
index 58561158a..1b12e7fc1 100644
--- a/digits/model/images/classification/views.py
+++ b/digits/model/images/classification/views.py
@@ -118,150 +118,182 @@ def create():
         raise werkzeug.exceptions.BadRequest(
                 'Unknown dataset job_id "%s"' % form.dataset.data)
 
-    job = None
-    try:
-        job = ImageClassificationModelJob(
-                username    = utils.auth.get_username(),
-                name        = form.model_name.data,
-                dataset_id  = datasetJob.id(),
-                )
-        # get handle to framework object
-        fw = frameworks.get_framework_by_id(form.framework.data)
-
-        pretrained_model = None
-        if form.method.data == 'standard':
-            found = False
-
-            # can we find it in standard networks?
-            network_desc = fw.get_standard_network_desc(form.standard_networks.data)
-            if network_desc:
-                found = True
-                network = fw.get_network_from_desc(network_desc)
-
-            if not found:
-                raise werkzeug.exceptions.BadRequest(
-                        'Unknown standard model "%s"' % form.standard_networks.data)
-        elif form.method.data == 'previous':
-            old_job = scheduler.get_job(form.previous_networks.data)
-            if not old_job:
+    # sweeps will be a list of the the permutations of swept fields
+    # Get swept learning_rate
+    sweeps = [{'learning_rate': v} for v in form.learning_rate.data]
+    add_learning_rate = len(form.learning_rate.data) > 1
+
+    # Add swept batch_size
+    sweeps = [dict(s.items() + [('batch_size', bs)]) for bs in form.batch_size.data for s in sweeps[:]]
+    add_batch_size = len(form.batch_size.data) > 1
+    n_jobs = len(sweeps)
+
+    jobs = []
+    for sweep in sweeps:
+        # Populate the form with swept data to be used in saving and
+        # launching jobs.
+        form.learning_rate.data = sweep['learning_rate']
+        form.batch_size.data = sweep['batch_size']
+
+        # Augment Job Name
+        extra = ''
+        if add_learning_rate:
+            extra += ' learning_rate:%s' % str(form.learning_rate.data[0])
+        if add_batch_size:
+            extra += ' batch_size:%d' % form.batch_size.data[0]
+
+        job = None
+        try:
+            job = ImageClassificationModelJob(
+                    username    = utils.auth.get_username(),
+                    name        = form.model_name.data + extra,
+                    dataset_id  = datasetJob.id(),
+                    )
+            # get handle to framework object
+            fw = frameworks.get_framework_by_id(form.framework.data)
+
+            pretrained_model = None
+            if form.method.data == 'standard':
+                found = False
+
+                # can we find it in standard networks?
+                network_desc = fw.get_standard_network_desc(form.standard_networks.data)
+                if network_desc:
+                    found = True
+                    network = fw.get_network_from_desc(network_desc)
+
+                if not found:
+                    raise werkzeug.exceptions.BadRequest(
+                            'Unknown standard model "%s"' % form.standard_networks.data)
+            elif form.method.data == 'previous':
+                old_job = scheduler.get_job(form.previous_networks.data)
+                if not old_job:
+                    raise werkzeug.exceptions.BadRequest(
+                            'Job not found: %s' % form.previous_networks.data)
+
+                use_same_dataset = (old_job.dataset_id == job.dataset_id)
+                network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
+
+                for choice in form.previous_networks.choices:
+                    if choice[0] == form.previous_networks.data:
+                        epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
+                        if epoch == 0:
+                            pass
+                        elif epoch == -1:
+                            pretrained_model = old_job.train_task().pretrained_model
+                        else:
+                            for filename, e in old_job.train_task().snapshots:
+                                if e == epoch:
+                                    pretrained_model = filename
+                                    break
+
+                            if pretrained_model is None:
+                                raise werkzeug.exceptions.BadRequest(
+                                        "For the job %s, selected pretrained_model for epoch %d is invalid!"
+                                        % (form.previous_networks.data, epoch))
+                            if not (os.path.exists(pretrained_model)):
+                                raise werkzeug.exceptions.BadRequest(
+                                        "Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
+                        break
+
+            elif form.method.data == 'custom':
+                network = fw.get_network_from_desc(form.custom_network.data)
+                pretrained_model = form.custom_network_snapshot.data.strip()
+            else:
                 raise werkzeug.exceptions.BadRequest(
-                        'Job not found: %s' % form.previous_networks.data)
-
-            use_same_dataset = (old_job.dataset_id == job.dataset_id)
-            network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
-
-            for choice in form.previous_networks.choices:
-                if choice[0] == form.previous_networks.data:
-                    epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
-                    if epoch == 0:
-                        pass
-                    elif epoch == -1:
-                        pretrained_model = old_job.train_task().pretrained_model
-                    else:
-                        for filename, e in old_job.train_task().snapshots:
-                            if e == epoch:
-                                pretrained_model = filename
-                                break
-
-                        if pretrained_model is None:
-                            raise werkzeug.exceptions.BadRequest(
-                                    "For the job %s, selected pretrained_model for epoch %d is invalid!"
-                                    % (form.previous_networks.data, epoch))
-                        if not (os.path.exists(pretrained_model)):
-                            raise werkzeug.exceptions.BadRequest(
-                                    "Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
-                    break
-
-        elif form.method.data == 'custom':
-            network = fw.get_network_from_desc(form.custom_network.data)
-            pretrained_model = form.custom_network_snapshot.data.strip()
-        else:
-            raise werkzeug.exceptions.BadRequest(
-                    'Unrecognized method: "%s"' % form.method.data)
-
-        policy = {'policy': form.lr_policy.data}
-        if form.lr_policy.data == 'fixed':
-            pass
-        elif form.lr_policy.data == 'step':
-            policy['stepsize'] = form.lr_step_size.data
-            policy['gamma'] = form.lr_step_gamma.data
-        elif form.lr_policy.data == 'multistep':
-            policy['stepvalue'] = form.lr_multistep_values.data
-            policy['gamma'] = form.lr_multistep_gamma.data
-        elif form.lr_policy.data == 'exp':
-            policy['gamma'] = form.lr_exp_gamma.data
-        elif form.lr_policy.data == 'inv':
-            policy['gamma'] = form.lr_inv_gamma.data
-            policy['power'] = form.lr_inv_power.data
-        elif form.lr_policy.data == 'poly':
-            policy['power'] = form.lr_poly_power.data
-        elif form.lr_policy.data == 'sigmoid':
-            policy['stepsize'] = form.lr_sigmoid_step.data
-            policy['gamma'] = form.lr_sigmoid_gamma.data
-        else:
-            raise werkzeug.exceptions.BadRequest(
-                    'Invalid learning rate policy')
-
-        if config_value('caffe_root')['multi_gpu']:
-            if form.select_gpus.data:
-                selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
-                gpu_count = None
-            elif form.select_gpu_count.data:
-                gpu_count = form.select_gpu_count.data
-                selected_gpus = None
+                        'Unrecognized method: "%s"' % form.method.data)
+
+            policy = {'policy': form.lr_policy.data}
+            if form.lr_policy.data == 'fixed':
+                pass
+            elif form.lr_policy.data == 'step':
+                policy['stepsize'] = form.lr_step_size.data
+                policy['gamma'] = form.lr_step_gamma.data
+            elif form.lr_policy.data == 'multistep':
+                policy['stepvalue'] = form.lr_multistep_values.data
+                policy['gamma'] = form.lr_multistep_gamma.data
+            elif form.lr_policy.data == 'exp':
+                policy['gamma'] = form.lr_exp_gamma.data
+            elif form.lr_policy.data == 'inv':
+                policy['gamma'] = form.lr_inv_gamma.data
+                policy['power'] = form.lr_inv_power.data
+            elif form.lr_policy.data == 'poly':
+                policy['power'] = form.lr_poly_power.data
+            elif form.lr_policy.data == 'sigmoid':
+                policy['stepsize'] = form.lr_sigmoid_step.data
+                policy['gamma'] = form.lr_sigmoid_gamma.data
             else:
-                gpu_count = 1
-                selected_gpus = None
-        else:
-            if form.select_gpu.data == 'next':
-                gpu_count = 1
-                selected_gpus = None
+                raise werkzeug.exceptions.BadRequest(
+                        'Invalid learning rate policy')
+
+            if config_value('caffe_root')['multi_gpu']:
+                if form.select_gpus.data:
+                    selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
+                    gpu_count = None
+                elif form.select_gpu_count.data:
+                    gpu_count = form.select_gpu_count.data
+                    selected_gpus = None
+                else:
+                    gpu_count = 1
+                    selected_gpus = None
             else:
-                selected_gpus = [str(form.select_gpu.data)]
-                gpu_count = None
-
-        # Python Layer File may be on the server or copied from the client.
-        fs.copy_python_layer_file(
-            bool(form.python_layer_from_client.data),
-            job.dir(),
-            (flask.request.files[form.python_layer_client_file.name]
-             if form.python_layer_client_file.name in flask.request.files
-             else ''), form.python_layer_server_file.data)
-
-        job.tasks.append(fw.create_train_task(
-                    job_dir         = job.dir(),
-                    dataset         = datasetJob,
-                    train_epochs    = form.train_epochs.data,
-                    snapshot_interval   = form.snapshot_interval.data,
-                    learning_rate   = form.learning_rate.data,
-                    lr_policy       = policy,
-                    gpu_count       = gpu_count,
-                    selected_gpus   = selected_gpus,
-                    batch_size      = form.batch_size.data,
-                    val_interval    = form.val_interval.data,
-                    pretrained_model= pretrained_model,
-                    crop_size       = form.crop_size.data,
-                    use_mean        = form.use_mean.data,
-                    network         = network,
-                    random_seed     = form.random_seed.data,
-                    solver_type     = form.solver_type.data,
-                    shuffle         = form.shuffle.data,
+                if form.select_gpu.data == 'next':
+                    gpu_count = 1
+                    selected_gpus = None
+                else:
+                    selected_gpus = [str(form.select_gpu.data)]
+                    gpu_count = None
+
+            # Python Layer File may be on the server or copied from the client.
+            fs.copy_python_layer_file(
+                bool(form.python_layer_from_client.data),
+                job.dir(),
+                (flask.request.files[form.python_layer_client_file.name]
+                 if form.python_layer_client_file.name in flask.request.files
+                 else ''), form.python_layer_server_file.data)
+
+            job.tasks.append(fw.create_train_task(
+                        job_dir         = job.dir(),
+                        dataset         = datasetJob,
+                        train_epochs    = form.train_epochs.data,
+                        snapshot_interval   = form.snapshot_interval.data,
+                        learning_rate   = form.learning_rate.data[0],
+                        lr_policy       = policy,
+                        gpu_count       = gpu_count,
+                        selected_gpus   = selected_gpus,
+                        batch_size      = form.batch_size.data[0],
+                        val_interval    = form.val_interval.data,
+                        pretrained_model= pretrained_model,
+                        crop_size       = form.crop_size.data,
+                        use_mean        = form.use_mean.data,
+                        network         = network,
+                        random_seed     = form.random_seed.data,
+                        solver_type     = form.solver_type.data,
+                        shuffle         = form.shuffle.data,
+                        )
                     )
-                )
 
-        ## Save form data with the job so we can easily clone it later.
-        save_form_to_job(job, form)
+            ## Save form data with the job so we can easily clone it later.
+            save_form_to_job(job, form)
 
-        scheduler.add_job(job)
-        if request_wants_json():
-            return flask.jsonify(job.json_dict())
-        else:
-            return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
+            jobs.append(job)
+            scheduler.add_job(job)
+            if n_jobs == 1:
+                if request_wants_json():
+                    return flask.jsonify(job.json_dict())
+                else:
+                    return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
+
+        except:
+            if job:
+                scheduler.delete_job(job)
+            raise
+
+    if request_wants_json():
+        return flask.jsonify(jobs=[job.json_dict() for job in jobs])
 
-    except:
-        if job:
-            scheduler.delete_job(job)
-        raise
+    # If there are multiple jobs launched, go to the home page.
+    return flask.redirect('/')
 
 def show(job):
     """
diff --git a/digits/model/images/generic/test_views.py b/digits/model/images/generic/test_views.py
index 82068365a..3aa3816e6 100644
--- a/digits/model/images/generic/test_views.py
+++ b/digits/model/images/generic/test_views.py
@@ -194,7 +194,11 @@ def create_model(cls, learning_rate=None, **kwargs):
             if rv.status_code != 200:
                 print json.loads(rv.data)
                 raise RuntimeError('Model creation failed with %s' % rv.status_code)
-            return json.loads(rv.data)['id']
+            data = json.loads(rv.data)
+            if 'jobs' in data.keys():
+                return [j['id'] for j in data['jobs']]
+            else:
+                return data['id']
 
         # expect a redirect
         if not 300 <= rv.status_code <= 310:
@@ -893,3 +897,15 @@ def test_infer_one_json(self):
         output = np.array(data['outputs']['output'][0])
         assert output.shape == (1, self.CROP_SIZE, self.CROP_SIZE), \
                 'shape mismatch: %s' % str(output.shape)
+
+class TestSweepCreation(BaseViewsTestWithDataset):
+    FRAMEWORK = 'caffe'
+    """
+    Model creation tests
+    """
+    def test_sweep(self):
+        job_ids = self.create_model(json=True, learning_rate='[0.01, 0.02]', batch_size='[8, 10]')
+        for job_id in job_ids:
+            assert self.model_wait_completion(job_id) == 'Done', 'create failed'
+            assert self.delete_model(job_id) == 200, 'delete failed'
+            assert not self.model_exists(job_id), 'model exists after delete'
diff --git a/digits/model/images/generic/views.py b/digits/model/images/generic/views.py
index 040d16bb3..4a994208f 100644
--- a/digits/model/images/generic/views.py
+++ b/digits/model/images/generic/views.py
@@ -83,137 +83,169 @@ def create():
         raise werkzeug.exceptions.BadRequest(
                 'Unknown dataset job_id "%s"' % form.dataset.data)
 
-    job = None
-    try:
-        job = GenericImageModelJob(
-                username    = utils.auth.get_username(),
-                name        = form.model_name.data,
-                dataset_id  = datasetJob.id(),
-                )
-
-        # get framework (hard-coded to caffe for now)
-        fw = frameworks.get_framework_by_id(form.framework.data)
+    # sweeps will be a list of the the permutations of swept fields
+    # Get swept learning_rate
+    sweeps = [{'learning_rate': v} for v in form.learning_rate.data]
+    add_learning_rate = len(form.learning_rate.data) > 1
+
+    # Add swept batch_size
+    sweeps = [dict(s.items() + [('batch_size', bs)]) for bs in form.batch_size.data for s in sweeps[:]]
+    add_batch_size = len(form.batch_size.data) > 1
+    n_jobs = len(sweeps)
+
+    jobs = []
+    for sweep in sweeps:
+        # Populate the form with swept data to be used in saving and
+        # launching jobs.
+        form.learning_rate.data = sweep['learning_rate']
+        form.batch_size.data = sweep['batch_size']
+
+        # Augment Job Name
+        extra = ''
+        if add_learning_rate:
+            extra += ' learning_rate:%s' % str(form.learning_rate.data[0])
+        if add_batch_size:
+            extra += ' batch_size:%d' % form.batch_size.data[0]
+
+        job = None
+        try:
+            job = GenericImageModelJob(
+                    username    = utils.auth.get_username(),
+                    name        = form.model_name.data + extra,
+                    dataset_id  = datasetJob.id(),
+                    )
 
-        pretrained_model = None
-        #if form.method.data == 'standard':
-        if form.method.data == 'previous':
-            old_job = scheduler.get_job(form.previous_networks.data)
-            if not old_job:
+            # get framework (hard-coded to caffe for now)
+            fw = frameworks.get_framework_by_id(form.framework.data)
+
+            pretrained_model = None
+            #if form.method.data == 'standard':
+            if form.method.data == 'previous':
+                old_job = scheduler.get_job(form.previous_networks.data)
+                if not old_job:
+                    raise werkzeug.exceptions.BadRequest(
+                            'Job not found: %s' % form.previous_networks.data)
+
+                use_same_dataset = (old_job.dataset_id == job.dataset_id)
+                network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
+
+                for choice in form.previous_networks.choices:
+                    if choice[0] == form.previous_networks.data:
+                        epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
+                        if epoch == 0:
+                            pass
+                        elif epoch == -1:
+                            pretrained_model = old_job.train_task().pretrained_model
+                        else:
+                            for filename, e in old_job.train_task().snapshots:
+                                if e == epoch:
+                                    pretrained_model = filename
+                                    break
+
+                            if pretrained_model is None:
+                                raise werkzeug.exceptions.BadRequest(
+                                        "For the job %s, selected pretrained_model for epoch %d is invalid!"
+                                        % (form.previous_networks.data, epoch))
+                            if not (os.path.exists(pretrained_model)):
+                                raise werkzeug.exceptions.BadRequest(
+                                        "Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
+                        break
+
+            elif form.method.data == 'custom':
+                network = fw.get_network_from_desc(form.custom_network.data)
+                pretrained_model = form.custom_network_snapshot.data.strip()
+            else:
                 raise werkzeug.exceptions.BadRequest(
-                        'Job not found: %s' % form.previous_networks.data)
-
-            use_same_dataset = (old_job.dataset_id == job.dataset_id)
-            network = fw.get_network_from_previous(old_job.train_task().network, use_same_dataset)
-
-            for choice in form.previous_networks.choices:
-                if choice[0] == form.previous_networks.data:
-                    epoch = float(flask.request.form['%s-snapshot' % form.previous_networks.data])
-                    if epoch == 0:
-                        pass
-                    elif epoch == -1:
-                        pretrained_model = old_job.train_task().pretrained_model
-                    else:
-                        for filename, e in old_job.train_task().snapshots:
-                            if e == epoch:
-                                pretrained_model = filename
-                                break
-
-                        if pretrained_model is None:
-                            raise werkzeug.exceptions.BadRequest(
-                                    "For the job %s, selected pretrained_model for epoch %d is invalid!"
-                                    % (form.previous_networks.data, epoch))
-                        if not (os.path.exists(pretrained_model)):
-                            raise werkzeug.exceptions.BadRequest(
-                                    "Pretrained_model for the selected epoch doesn't exists. May be deleted by another user/process. Please restart the server to load the correct pretrained_model details")
-                    break
-
-        elif form.method.data == 'custom':
-            network = fw.get_network_from_desc(form.custom_network.data)
-            pretrained_model = form.custom_network_snapshot.data.strip()
-        else:
-            raise werkzeug.exceptions.BadRequest(
-                    'Unrecognized method: "%s"' % form.method.data)
-
-        policy = {'policy': form.lr_policy.data}
-        if form.lr_policy.data == 'fixed':
-            pass
-        elif form.lr_policy.data == 'step':
-            policy['stepsize'] = form.lr_step_size.data
-            policy['gamma'] = form.lr_step_gamma.data
-        elif form.lr_policy.data == 'multistep':
-            policy['stepvalue'] = form.lr_multistep_values.data
-            policy['gamma'] = form.lr_multistep_gamma.data
-        elif form.lr_policy.data == 'exp':
-            policy['gamma'] = form.lr_exp_gamma.data
-        elif form.lr_policy.data == 'inv':
-            policy['gamma'] = form.lr_inv_gamma.data
-            policy['power'] = form.lr_inv_power.data
-        elif form.lr_policy.data == 'poly':
-            policy['power'] = form.lr_poly_power.data
-        elif form.lr_policy.data == 'sigmoid':
-            policy['stepsize'] = form.lr_sigmoid_step.data
-            policy['gamma'] = form.lr_sigmoid_gamma.data
-        else:
-            raise werkzeug.exceptions.BadRequest(
-                    'Invalid learning rate policy')
-
-        if config_value('caffe_root')['multi_gpu']:
-            if form.select_gpu_count.data:
-                gpu_count = form.select_gpu_count.data
-                selected_gpus = None
+                        'Unrecognized method: "%s"' % form.method.data)
+
+            policy = {'policy': form.lr_policy.data}
+            if form.lr_policy.data == 'fixed':
+                pass
+            elif form.lr_policy.data == 'step':
+                policy['stepsize'] = form.lr_step_size.data
+                policy['gamma'] = form.lr_step_gamma.data
+            elif form.lr_policy.data == 'multistep':
+                policy['stepvalue'] = form.lr_multistep_values.data
+                policy['gamma'] = form.lr_multistep_gamma.data
+            elif form.lr_policy.data == 'exp':
+                policy['gamma'] = form.lr_exp_gamma.data
+            elif form.lr_policy.data == 'inv':
+                policy['gamma'] = form.lr_inv_gamma.data
+                policy['power'] = form.lr_inv_power.data
+            elif form.lr_policy.data == 'poly':
+                policy['power'] = form.lr_poly_power.data
+            elif form.lr_policy.data == 'sigmoid':
+                policy['stepsize'] = form.lr_sigmoid_step.data
+                policy['gamma'] = form.lr_sigmoid_gamma.data
             else:
-                selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
-                gpu_count = None
-        else:
-            if form.select_gpu.data == 'next':
-                gpu_count = 1
-                selected_gpus = None
+                raise werkzeug.exceptions.BadRequest(
+                        'Invalid learning rate policy')
+
+            if config_value('caffe_root')['multi_gpu']:
+                if form.select_gpu_count.data:
+                    gpu_count = form.select_gpu_count.data
+                    selected_gpus = None
+                else:
+                    selected_gpus = [str(gpu) for gpu in form.select_gpus.data]
+                    gpu_count = None
             else:
-                selected_gpus = [str(form.select_gpu.data)]
-                gpu_count = None
-
-        # Python Layer File may be on the server or copied from the client.
-        fs.copy_python_layer_file(
-            bool(form.python_layer_from_client.data),
-            job.dir(),
-            (flask.request.files[form.python_layer_client_file.name]
-             if form.python_layer_client_file.name in flask.request.files
-             else ''), form.python_layer_server_file.data)
-
-        job.tasks.append(fw.create_train_task(
-                    job_dir         = job.dir(),
-                    dataset         = datasetJob,
-                    train_epochs    = form.train_epochs.data,
-                    snapshot_interval   = form.snapshot_interval.data,
-                    learning_rate   = form.learning_rate.data,
-                    lr_policy       = policy,
-                    gpu_count       = gpu_count,
-                    selected_gpus   = selected_gpus,
-                    batch_size      = form.batch_size.data,
-                    val_interval    = form.val_interval.data,
-                    pretrained_model= pretrained_model,
-                    crop_size       = form.crop_size.data,
-                    use_mean        = form.use_mean.data,
-                    network         = network,
-                    random_seed     = form.random_seed.data,
-                    solver_type     = form.solver_type.data,
-                    shuffle         = form.shuffle.data,
+                if form.select_gpu.data == 'next':
+                    gpu_count = 1
+                    selected_gpus = None
+                else:
+                    selected_gpus = [str(form.select_gpu.data)]
+                    gpu_count = None
+
+            # Python Layer File may be on the server or copied from the client.
+            fs.copy_python_layer_file(
+                bool(form.python_layer_from_client.data),
+                job.dir(),
+                (flask.request.files[form.python_layer_client_file.name]
+                 if form.python_layer_client_file.name in flask.request.files
+                 else ''), form.python_layer_server_file.data)
+
+            job.tasks.append(fw.create_train_task(
+                        job_dir         = job.dir(),
+                        dataset         = datasetJob,
+                        train_epochs    = form.train_epochs.data,
+                        snapshot_interval   = form.snapshot_interval.data,
+                        learning_rate   = form.learning_rate.data[0],
+                        lr_policy       = policy,
+                        gpu_count       = gpu_count,
+                        selected_gpus   = selected_gpus,
+                        batch_size      = form.batch_size.data[0],
+                        val_interval    = form.val_interval.data,
+                        pretrained_model= pretrained_model,
+                        crop_size       = form.crop_size.data,
+                        use_mean        = form.use_mean.data,
+                        network         = network,
+                        random_seed     = form.random_seed.data,
+                        solver_type     = form.solver_type.data,
+                        shuffle         = form.shuffle.data,
+                        )
                     )
-                )
 
-        ## Save form data with the job so we can easily clone it later.
-        save_form_to_job(job, form)
+            ## Save form data with the job so we can easily clone it later.
+            save_form_to_job(job, form)
 
-        scheduler.add_job(job)
-        if request_wants_json():
-            return flask.jsonify(job.json_dict())
-        else:
-            return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
+            jobs.append(job)
+            scheduler.add_job(job)
+            if n_jobs == 1:
+                if request_wants_json():
+                    return flask.jsonify(job.json_dict())
+                else:
+                    return flask.redirect(flask.url_for('digits.model.views.show', job_id=job.id()))
+
+        except:
+            if job:
+                scheduler.delete_job(job)
+            raise
+
+    if request_wants_json():
+        return flask.jsonify(jobs=[job.json_dict() for job in jobs])
 
-    except:
-        if job:
-            scheduler.delete_job(job)
-        raise
+    # If there are multiple jobs launched, go to the home page.
+    return flask.redirect('/')
 
 def show(job):
     """
diff --git a/digits/model/views.py b/digits/model/views.py
index c381a0296..f01013708 100644
--- a/digits/model/views.py
+++ b/digits/model/views.py
@@ -160,7 +160,8 @@ def visualize_lr():
     Returns a JSON object of data used to create the learning rate graph
     """
     policy = flask.request.form['lr_policy']
-    lr = float(flask.request.form['learning_rate'])
+	# There may be multiple lrs if the learning_rate is swept
+    lrs = map(float, flask.request.form['learning_rate'].split(','))
     if policy == 'fixed':
         pass
     elif policy == 'step':
@@ -183,26 +184,29 @@ def visualize_lr():
     else:
         raise werkzeug.exceptions.BadRequest('Invalid policy')
 
-    data = ['Learning Rate']
-    for i in xrange(101):
-        if policy == 'fixed':
-            data.append(lr)
-        elif policy == 'step':
-            data.append(lr * math.pow(gamma, math.floor(float(i)/step)))
-        elif policy == 'multistep':
-            if current_step < len(steps) and i >= steps[current_step]:
-                current_step += 1
-            data.append(lr * math.pow(gamma, current_step))
-        elif policy == 'exp':
-            data.append(lr * math.pow(gamma, i))
-        elif policy == 'inv':
-            data.append(lr * math.pow(1.0 + gamma * i, -power))
-        elif policy == 'poly':
-            data.append(lr * math.pow(1.0 - float(i)/100, power))
-        elif policy == 'sigmoid':
-            data.append(lr / (1.0 + math.exp(gamma * (i - step))))
-
-    return json.dumps({'data': {'columns': [data]}})
+    datalist = []
+    for j, lr in enumerate(lrs):
+        data = ['Learning Rate %d' % j]
+        for i in xrange(101):
+            if policy == 'fixed':
+                data.append(lr)
+            elif policy == 'step':
+                data.append(lr * math.pow(gamma, math.floor(float(i)/step)))
+            elif policy == 'multistep':
+                if current_step < len(steps) and i >= steps[current_step]:
+                    current_step += 1
+                data.append(lr * math.pow(gamma, current_step))
+            elif policy == 'exp':
+                data.append(lr * math.pow(gamma, i))
+            elif policy == 'inv':
+                data.append(lr * math.pow(1.0 + gamma * i, -power))
+            elif policy == 'poly':
+                data.append(lr * math.pow(1.0 - float(i)/100, power))
+            elif policy == 'sigmoid':
+                data.append(lr / (1.0 + math.exp(gamma * (i - step))))
+        datalist.append(data)
+
+    return json.dumps({'data': {'columns': datalist}})
 
 @blueprint.route('/<job_id>/download',
         methods=['GET', 'POST'],
diff --git a/digits/templates/models/images/classification/new.html b/digits/templates/models/images/classification/new.html
index a28faab60..8ef574f1d 100644
--- a/digits/templates/models/images/classification/new.html
+++ b/digits/templates/models/images/classification/new.html
@@ -152,6 +152,9 @@ <h4>Solver Options</h4>
                 <div class="form-group{{mark_errors([form.batch_size])}}">
                     {{form.batch_size.label}}
                     {{form.batch_size.tooltip}}
+                    <small class="pull-right">
+                        {{form.batch_size.small_text}}
+                    </small>
                     {{form.batch_size(class='form-control', placeholder='[network defaults]')}}
                 </div>
                 <div class="form-group{{mark_errors([form.solver_type])}}">
@@ -162,6 +165,9 @@ <h4>Solver Options</h4>
                 <div class="form-group{{mark_errors([form.learning_rate])}}">
                     {{form.learning_rate.label}}
                     {{form.learning_rate.tooltip}}
+                    <small class="pull-right">
+                        {{form.learning_rate.small_text}}
+                    </small>
                     {{form.learning_rate(class='form-control learning-rate-option')}}
                 </div>
 
diff --git a/digits/templates/models/images/generic/new.html b/digits/templates/models/images/generic/new.html
index 795135950..f88e99676 100644
--- a/digits/templates/models/images/generic/new.html
+++ b/digits/templates/models/images/generic/new.html
@@ -152,6 +152,9 @@ <h4>Solver Options</h4>
                 <div class="form-group{{mark_errors([form.batch_size])}}">
                     {{form.batch_size.label}}
                     {{form.batch_size.tooltip}}
+                    <small class="pull-right">
+                        {{form.batch_size.small_text}}
+                    </small>
                     {{form.batch_size(class='form-control', placeholder='[network defaults]')}}
                 </div>
                 <div class="form-group{{mark_errors([form.solver_type])}}">
@@ -162,6 +165,9 @@ <h4>Solver Options</h4>
                 <div class="form-group{{mark_errors([form.learning_rate])}}">
                     {{form.learning_rate.label}}
                     {{form.learning_rate.tooltip}}
+                    <small class="pull-right">
+                        {{form.learning_rate.small_text}}
+                    </small>
                     {{form.learning_rate(class='form-control learning-rate-option')}}
                 </div>
 
diff --git a/digits/utils/forms.py b/digits/utils/forms.py
index 2c8786457..a6b5d09a0 100644
--- a/digits/utils/forms.py
+++ b/digits/utils/forms.py
@@ -5,6 +5,7 @@
 import wtforms
 from wtforms import SubmitField
 from wtforms import validators
+from wtforms.compat import string_types
 
 from digits.utils.routing import get_request_arg
 
@@ -225,6 +226,166 @@ def __init__(self, label='', validators=None, tooltip='', explanation_file = '',
         self.tooltip = Tooltip(self.id, self.short_name, tooltip)
         self.explanation = Explanation(self.id, self.short_name, explanation_file)
 
+class MultiIntegerField(wtforms.Field):
+    """
+    A text field, except all input is coerced to one of more integers.
+    Erroneous input is ignored and will not be accepted as a value.
+    """
+    widget = wtforms.widgets.TextInput()
+
+    def is_int(self, v):
+        try:
+            v = int(v)
+            return True
+        except:
+            return False
+
+    def __init__(self, label='', validators=None, tooltip='', explanation_file = '', **kwargs):
+        super(MultiIntegerField, self).__init__(label, validators, **kwargs)
+        self.tooltip = Tooltip(self.id, self.short_name, tooltip + ' (accepts comma separated list)')
+        self.explanation = Explanation(self.id, self.short_name, explanation_file)
+        self.small_text = 'multiples allowed'
+
+    def __setattr__(self, name, value):
+        if name == 'data':
+            if not isinstance(value, (list, tuple)):
+                value = [value]
+            value = [int(x) for x in value if self.is_int(x)]
+            if len(value) == 0:
+                value = [None]
+        self.__dict__[name] = value
+
+    def _value(self):
+        if self.raw_data:
+            return self.raw_data[0]
+        return ','.join([str(x) for x in self.data if self.is_int(x)])
+
+    def process_formdata(self, valuelist):
+        if valuelist:
+            try:
+                valuelist[0] = valuelist[0].replace('[', '')
+                valuelist[0] = valuelist[0].replace(']', '')
+                valuelist[0] = valuelist[0].split(',')
+                self.data = [int(float(datum)) for datum in valuelist[0]]
+            except ValueError:
+                self.data = [None]
+                raise ValueError(self.gettext('Not a valid integer value'))
+
+class MultiFloatField(wtforms.Field):
+    """
+    A text field, except all input is coerced to one of more floats.
+    Erroneous input is ignored and will not be accepted as a value.
+    """
+    widget = wtforms.widgets.TextInput()
+
+    def is_float(self, v):
+        try:
+            v = float(v)
+            return True
+        except:
+            return False
+
+    def __init__(self, label='', validators=None, tooltip='', explanation_file = '', **kwargs):
+        super(MultiFloatField, self).__init__(label, validators, **kwargs)
+        self.tooltip = Tooltip(self.id, self.short_name, tooltip + ' (accepts comma separated list)')
+        self.explanation = Explanation(self.id, self.short_name, explanation_file)
+        self.small_text = 'multiples allowed'
+
+    def __setattr__(self, name, value):
+        if name == 'data':
+            if not isinstance(value, (list, tuple)):
+                value = [value]
+            value = [float(x) for x in value if self.is_float(x)]
+            if len(value) == 0:
+                value = [None]
+        self.__dict__[name] = value
+
+    def _value(self):
+        if self.raw_data:
+            return self.raw_data[0]
+        return ','.join([str(x) for x in self.data if self.is_float(x)])
+
+    def process_formdata(self, valuelist):
+        if valuelist:
+            try:
+                valuelist[0] = valuelist[0].replace('[', '')
+                valuelist[0] = valuelist[0].replace(']', '')
+                valuelist[0] = valuelist[0].split(',')
+                self.data = [float(datum) for datum in valuelist[0]]
+            except ValueError:
+                self.data = [None]
+                raise ValueError(self.gettext('Not a valid float value'))
+
+    def data_array(self):
+        if isinstance(self.data, (list, tuple)):
+            return self.data
+        else:
+            return [self.data]
+
+class MultiNumberRange(object):
+    """
+    Validates that a number is of a minimum and/or maximum value, inclusive.
+    This will work with any comparable number type, such as floats and
+    decimals, not just integers.
+
+    :param min:
+        The minimum required value of the number. If not provided, minimum
+        value will not be checked.
+    :param max:
+        The maximum value of the number. If not provided, maximum value
+        will not be checked.
+    :param message:
+        Error message to raise in case of a validation error. Can be
+        interpolated using `%(min)s` and `%(max)s` if desired. Useful defaults
+        are provided depending on the existence of min and max.
+    """
+    def __init__(self, min=None, max=None, message=None):
+        self.min = min
+        self.max = max
+        self.message = message
+
+    def __call__(self, form, field):
+        fdata = field.data if isinstance(field.data, (list, tuple)) else [field.data]
+        for data in fdata:
+            if data is None or (self.min is not None and data < self.min) or \
+                    (self.max is not None and data > self.max):
+                message = self.message
+                if message is None:
+                    # we use %(min)s interpolation to support floats, None, and
+                    # Decimals without throwing a formatting exception.
+                    if self.max is None:
+                        message = field.gettext('Number %(data)s must be at least %(min)s.')
+                    elif self.min is None:
+                        message = field.gettext('Number %(data)s must be at most %(max)s.')
+                    else:
+                        message = field.gettext('Number %(data)s must be between %(min)s and %(max)s.')
+
+                raise validators.ValidationError(message % dict(data=data, min=self.min, max=self.max))
+
+class MultiOptional(object):
+    """
+    Allows empty input and stops the validation chain from continuing.
+
+    If input is empty, also removes prior errors (such as processing errors)
+    from the field.
+
+    :param strip_whitespace:
+        If True (the default) also stop the validation chain on input which
+        consists of only whitespace.
+    """
+    field_flags = ('optional', )
+
+    def __init__(self, strip_whitespace=True):
+        if strip_whitespace:
+            self.string_check = lambda s: s.strip()
+        else:
+            self.string_check = lambda s: s
+
+    def __call__(self, form, field):
+        if not field.raw_data or isinstance(field.raw_data[0], string_types) and not self.string_check(field.raw_data[0]):
+            field.errors[:] = []
+            raise validators.StopValidation()
+
 ## Used to save data to populate forms when cloning
 def add_warning(form, warning):
     if not hasattr(form, 'warnings'):
@@ -245,7 +406,8 @@ def iterate_over_form(job, form, function, prefix = ['form'], indent = ''):
     whitelist_fields = [
         'BooleanField', 'FloatField', 'HiddenField', 'IntegerField',
         'RadioField', 'SelectField', 'SelectMultipleField',
-        'StringField', 'TextAreaField', 'TextField']
+        'StringField', 'TextAreaField', 'TextField',
+        'MultiIntegerField', 'MultiFloatField']
 
     blacklist_fields = ['FileField', 'SubmitField']
 
@@ -278,7 +440,6 @@ def set_data(job, form, key, value):
 
     if isinstance(value, basestring):
         value = '\'' + value + '\''
-    # print '\'' + key + '\': ' + str(value) +','
     return False
 
 ## function to pass to iterate_over_form to get data from job