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Diffstat (limited to 'tensorflow/tools/tfprof/g3doc/advise.md')
| -rw-r--r-- | tensorflow/tools/tfprof/g3doc/advise.md | 80 |
1 files changed, 8 insertions, 72 deletions
diff --git a/tensorflow/tools/tfprof/g3doc/advise.md b/tensorflow/tools/tfprof/g3doc/advise.md index e30add6fbf..3bce6270ff 100644 --- a/tensorflow/tools/tfprof/g3doc/advise.md +++ b/tensorflow/tools/tfprof/g3doc/advise.md @@ -3,7 +3,6 @@ tfprof analyzes profiles and generates advises for common issues. ### Run Advise. - ```python # First create a profiler. See profiler tutorials for more details. profiler = model_analyzer.Profiler(sess.graph) @@ -14,63 +13,8 @@ _ = sess.run(r1, run_metadata=run_meta) profiler.add_step(1, run_meta) -# Then Start advise. -profiler.advise(model_analyzer.ALL_ADVICE) - -# For one-shot API -tf.contrib.tfprof.model_analyzer.advise( - sess.graph, run_meta=run_metadata) -``` - -```shell -# Run advisor on CLI -# See CLI tutorial on generating the files. -tfprof --graph_path=graph.pbtxt \ - --run_meta_path=run_metadata \ - --op_log_path=tfprof_log - -tfprof> advise -AcceleratorUtilizationChecker: -device: /job:worker/replica:0/task:0/gpu:0 low utilization: 0.03 -device: /job:worker/replica:0/task:0/gpu:1 low utilization: 0.08 -device: /job:worker/replica:0/task:0/gpu:2 low utilization: 0.04 -device: /job:worker/replica:0/task:0/gpu:3 low utilization: 0.21 - -OperationChecker: -Found operation using NHWC data_format on GPU. Maybe NCHW is faster. - -ExpensiveOperationChecker: -top 1 operation type: SoftmaxCrossEntropyWithLogits, cpu: 1.37sec, accelerator: 0us, total: 1.37sec (26.68%) -top 2 operation type: MatMul, cpu: 427.39ms, accelerator: 280.76ms, total: 708.14ms (13.83%) -top 3 operation type: ConcatV2, cpu: 357.83ms, accelerator: 31.80ms, total: 389.63ms (7.61%) -seq2seq_attention_model.py:360:build_graph:self._add_seq2seq(), cpu: 3.16sec, accelerator: 214.84ms, total: 3.37sec - seq2seq_attention_model.py:293:_add_seq2seq:decoder_outputs, ..., cpu: 2.46sec, accelerator: 3.25ms, total: 2.47sec - seq2seq_lib.py:181:sampled_sequence_...:average_across_ti..., cpu: 2.46sec, accelerator: 3.24ms, total: 2.47sec - seq2seq_lib.py:147:sequence_loss_by_...:crossent = loss_f..., cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec - seq2seq_attention_model.py:289:sampled_loss_func:num_classes=vsize), cpu: 2.46sec, accelerator: 3.06ms, total: 2.46sec - seq2seq_attention_model.py:282:sampled_loss_func:labels = tf.resha..., cpu: 164us, accelerator: 0us, total: 164us - seq2seq_lib.py:148:sequence_loss_by_...:log_perp_list.app..., cpu: 1.33ms, accelerator: 120us, total: 1.45ms - seq2seq_lib.py:151:sequence_loss_by_...:total_size = tf.a..., cpu: 154us, accelerator: 23us, total: 177us - seq2seq_lib.py:184:sampled_sequence_...:return cost / tf...., cpu: 97us, accelerator: 8us, total: 105us - math_ops.py:690:cast:return gen_math_o..., cpu: 62us, accelerator: 3us, total: 65us - math_ops.py:839:binary_op_wrapper:return func(x, y,..., cpu: 35us, accelerator: 5us, total: 40us - seq2seq_attention_model.py:192:_add_seq2seq:sequence_length=a..., cpu: 651.56ms, accelerator: 158.92ms, total: 810.48ms - seq2seq_lib.py:104:bidirectional_rnn:sequence_length, ..., cpu: 306.58ms, accelerator: 73.54ms, total: 380.12ms - core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 306.52ms, accelerator: 73.54ms, total: 380.05ms - rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 303.76ms, accelerator: 73.54ms, total: 377.30ms - rnn.py:216:_rnn_step:time >= max_seque..., cpu: 2.75ms, accelerator: 0us, total: 2.75ms - core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 67us, accelerator: 0us, total: 67us - seq2seq_lib.py:110:bidirectional_rnn:initial_state_bw,..., cpu: 296.21ms, accelerator: 73.54ms, total: 369.75ms - core_rnn.py:195:static_rnn:state_size=cell.s..., cpu: 296.11ms, accelerator: 73.54ms, total: 369.65ms - rnn.py:218:_rnn_step:_maybe_copy_some_..., cpu: 292.04ms, accelerator: 73.54ms, total: 365.58ms - rnn.py:216:_rnn_step:time >= max_seque..., cpu: 4.07ms, accelerator: 0us, total: 4.07ms - core_rnn.py:178:static_rnn:min_sequence_leng..., cpu: 85us, accelerator: 0us, total: 85us - core_rnn.py:179:static_rnn:max_sequence_leng..., cpu: 16us, accelerator: 0us, total: 16us - seq2seq_lib.py:113:bidirectional_rnn:outputs = [tf.con..., cpu: 46.88ms, accelerator: 3.87ms, total: 50.75ms - ...(omitted) -top 1 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_11, cpu: 89.92ms, accelerator: 0us, total: 89.92ms -top 2 graph node: train_step/update_seq2seq/output_projection/w/ApplyAdam, cpu: 84.52ms, accelerator: 0us, total: 84.52ms -top 3 graph node: seq2seq/loss/sampled_sequence_loss/sequence_loss_by_example/SoftmaxCrossEntropyWithLogits_19, cpu: 73.02ms, accelerator: 0us, total: 73.02ms +# Start advise. +profiler.advise() ``` ### Checker @@ -81,24 +25,16 @@ area with the profile and report issues. A `Checker` is like a plugin. For example: -#### JobChecker (Not Available OSS) - -* Checks RecvTensor RPC latency and bandwidth. -* Checks CPU/Memory utilization of the job. +####JobChecker (Not Available OSS) +* Checking RecvTensor RPC latency and bandwidth. +* Checking CPU/Memory utilization of the job. ####AcceleratorUtilization Checker * Checks what percentage of time the accelerator spends on computation. -#### OperationChecker - -* Checks whether the operation runs with optimal options. -* Checks if there is a better implementation to replace the current operation. - -#### ExpensiveOperationChecker - -* Checks the most expensive operation type. -* Checks the most expensive graph nodes. -* Checks the most expensive graph-building Python codes. +####Operation Checker +* Check whether the operation runs with optimal options. +* Checks if there is a better implementation to replace the current operation. ####Contribute Your Checker |