From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: Received: from mails.dpdk.org (mails.dpdk.org [217.70.189.124]) by inbox.dpdk.org (Postfix) with ESMTP id 7C0B641CA8; Wed, 15 Feb 2023 18:04:26 +0100 (CET) Received: from mails.dpdk.org (localhost [127.0.0.1]) by mails.dpdk.org (Postfix) with ESMTP id 5D67440A7D; Wed, 15 Feb 2023 18:04:26 +0100 (CET) Received: from mail-ua1-f48.google.com (mail-ua1-f48.google.com [209.85.222.48]) by mails.dpdk.org (Postfix) with ESMTP id 998974067E for ; Wed, 15 Feb 2023 18:04:24 +0100 (CET) Received: by mail-ua1-f48.google.com with SMTP id g12so3724430uae.6 for ; Wed, 15 Feb 2023 09:04:24 -0800 (PST) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20210112; h=cc:to:subject:message-id:date:from:in-reply-to:references :mime-version:from:to:cc:subject:date:message-id:reply-to; bh=xlTu7b0wJjymfAZ7oA/uVgSaPYYiw+T2Nov0JQ8/yno=; b=CY1tLGg83I79nps3p8DCxfWplq8SqVFEaZ7i8J+fchS/aXOk+FukP2CLSzYo0m/gVv Br+RKWLiPwf472onehBgJAJidwmWBU0JSiBvFk1sJT3QHnxQTWjf/K6wv3o/+CNjhBYG f7c4YqkpAlAjhgqs6jKFopeVdORKXENhHJ1hrCvflZfZFpJ1G4JvYPc7AlACTiKMimFv fqKmk3JwnY7AUHdANKamN5l70RSaQxdIfRUJB3UKv3aYtFpDH51GuFVrSiCOoL15NOf2 m91dne2YFNVuxcIuFr7jx8PwAE/AHVQugnHEtuT8eSk0RhrobHq1ZdUVP/NQ6s1jAslB ToiA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20210112; h=cc:to:subject:message-id:date:from:in-reply-to:references :mime-version:x-gm-message-state:from:to:cc:subject:date:message-id :reply-to; bh=xlTu7b0wJjymfAZ7oA/uVgSaPYYiw+T2Nov0JQ8/yno=; b=vmUFE+ex3I56dxCm19yZo0yM/DBtU5sbGzF1wLHzzcfvOf+SjOuPrcMWcJOPCktqDo yUjhpTTJU/pd/uBBLjOL0RyfuugHNsdq8wgSAjwPHMh0x2XSryWpGQIrrNR8mH23YhGi gGu+pminVLQj1Gt2RsEKSsr56M81tqdPvnlbQBGNQyhb31IwEtToSb15Ttfuqb5sj0Uf 4LaoyH2/Y5mR+Bz3ts8z9OyBnLdjCYQCi18kwMTpUMW6UR/HN8V+QFzAEgvsyos39I5V iY1J5WZzn4Q5tnG1OGwyGRv57j1eCM7BHSBakyRFrkAV4JxtBEusEqAe5JQynSsSCzwC xQuA== X-Gm-Message-State: AO0yUKVO5XUygotsiTzrzuyuIKH8Pec3sBKxveHWxtJ4Lq81iy7kB+fi MFYZ+U5QcOJnJiuN09Tw9jB98/T2T8/SpCoVp/CsQMZN5UQ= X-Google-Smtp-Source: AK7set9BFO4lgUTPFJlIzqSAZjTzctKiuODIIO1LVD139npKfl1IsX7jJB4QBb6/VTof7b+TgnFodZwnYW46LFkU97c= X-Received: by 2002:ab0:31c6:0:b0:687:afc8:ffb9 with SMTP id e6-20020ab031c6000000b00687afc8ffb9mr408804uan.2.1676480663665; Wed, 15 Feb 2023 09:04:23 -0800 (PST) MIME-Version: 1.0 References: <20230206202453.336280-1-jerinj@marvell.com> <20230207151316.835441-1-jerinj@marvell.com> In-Reply-To: From: Jerin Jacob Date: Wed, 15 Feb 2023 22:33:56 +0530 Message-ID: Subject: Re: [dpdk-dev] [PATCH v3 00/12] mldev: introduce machine learning device library To: Ferruh Yigit Cc: jerinj@marvell.com, dev@dpdk.org, thomas@monjalon.net, stephen@networkplumber.org, dchickles@marvell.com, sshankarnara@marvell.com, "Bahri, Aziz" , "O'Donohoe, Fionn" , Srikanth Yalavarthi Content-Type: text/plain; charset="UTF-8" X-BeenThere: dev@dpdk.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: DPDK patches and discussions List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: dev-bounces@dpdk.org On Wed, Feb 15, 2023 at 6:25 PM Ferruh Yigit wrote: > > On 2/7/2023 3:13 PM, jerinj@marvell.com wrote: > > From: Jerin Jacob > > > > Hi Jerin, > > Please find some comments/questions gathered with the help of some > collegues. Thanks Ferruh for the review. > > > Machine learning inference library > > ================================== > > > > Definition of machine learning inference > > ---------------------------------------- > > Inference in machine learning is the process of making an output prediction > > based on new input data using a pre-trained machine learning model. > > > > The scope of the RFC would include only inferencing with pre-trained machine learning models, > > training and building/compiling the ML models is out of scope for this RFC or > > DPDK mldev API. Use existing machine learning compiler frameworks for model creation. > > > > Motivation for the new library > > ------------------------------ > > Multiple semiconductor vendors are offering accelerator products such as DPU > > (often called Smart-NIC), FPGA, GPU, etc., which have ML inferencing capabilities > > integrated as part of the product. Use of ML inferencing is increasing in the domain > > of packet processing for flow classification, intrusion, malware and anomaly detection. > > > > Agree on this need. > > > Lack of inferencing support through DPDK APIs will involve complexities and > > increased latency from moving data across frameworks (i.e, dataplane to > > non dataplane ML frameworks and vice-versa). Having a standardized DPDK APIs for ML > > inferencing would enable the dataplane solutions to harness the benefit of inline > > inferencing supported by the hardware. > > > > ack > > > Contents > > --------------- > > A) API specification for: > > > > 1) Discovery of ML capabilities (e.g., device specific features) in a vendor > > independent fashion > > 2) Definition of functions to handle ML devices, which includes probing, > > initialization and termination of the devices. > > 3) Definition of functions to handle ML models used to perform inference operations. > > 4) Definition of function to handle quantize and dequantize operations > > > > B) Common code for above specification > > > > rfc..v1: > > - Added programmer guide documentation > > - Added implementation for common code > > > > v2..v1: > > - Moved dynamic log (Stephen) > > - model id to uint16_t from int16t_t (Stephen) > > - added release note updates > > > > v3..v2: > > - Introduced rte_ml_dev_init() similar to rte_gpu_init() (Stephen, Thomas) > > - In struct rte_ml_dev_data, removed reserved[3] and __rte_cache_aligned. > > Also, moved name field to the end(Stephen) > > > > Machine learning library framework > > ---------------------------------- > > > > The ML framework is built on the following model: > > > > > > +-----------------+ rte_ml_[en|de]queue_burst() > > | | | > > | Machine o------+ +--------+ | > > | Learning | | | queue | | +------+ > > | Inference o------+-----o |<===o===>|Core 0| > > | Engine | | | pair 0 | +------+ > > | o----+ | +--------+ > > | | | | > > +-----------------+ | | +--------+ > > ^ | | | queue | +------+ > > | | +-----o |<=======>|Core 1| > > | | | pair 1 | +------+ > > | | +--------+ > > +--------+--------+ | > > | +-------------+ | | +--------+ > > | | Model 0 | | | | queue | +------+ > > | +-------------+ | +-------o |<=======>|Core N| > > | +-------------+ | | pair N | +------+ > > | | Model 1 | | +--------+ > > | +-------------+ | > > | +-------------+ |<------- rte_ml_model_load() > > | | Model .. | |-------> rte_ml_model_info() > > | +-------------+ |<------- rte_ml_model_start() > > | +-------------+ |<------- rte_ml_model_stop() > > | | Model N | |<------- rte_ml_model_params_update() > > | +-------------+ |<------- rte_ml_model_unload() > > +-----------------+ > > > > > Should model load/unload, params_update be part of dpdk, or dpdk can > assume these are already in place. The driver hooks can be NOPs if the model is already loaded or fixed model FPGA solutions. Probably we can add parameter info_get() in case if someone think user needs to be aware of it. Currently its an experimental API, when such device comes we can extent the rte_ml_dev_infostructure if/as needed. > For FPGA both options works, what is > the benefit to have these APIs part of DPDK? Support for runtime load/unload model if ml devices supports it. Also it has some bearing on data-path as inference needs to be stopped if one need to unload the model. > What are usecases for other architectures? When a device supports max N models (rte_ml_dev_info::max_models), a user can replace unused model at runtime when the max number model are reached. > > > Is multiple active models at same time supported? > For FPGA case multiple models may exist at same time, it would be good > to have a way to select the model to use, like a handle for model that > API accepts. > Similarly a handle for model may help chaining models, possibly with > help of additional APIs to define the chaining. Yes, multiple active models are supported simultaneously. Each model loaded would have unique model_id assigned by the driver which can be used as a handle, while queuing inference requests or doing slow-path operations. > > > ML Device: A hardware or software-based implementation of ML device API for > > running inferences using a pre-trained ML model. > > > > Can this device consume multiple queues in parallel? Yes, spec doesn't impose any restrictions on the number of queues that can be consumed by the device. Actual number of queues permitted is dependent on the device. see rte_ml_dev_info:max_queue_pairs > > > ML Model: An ML model is an algorithm trained over a dataset. A model consists of > > procedure/algorithm and data/pattern required to make predictions on live data. > > Once the model is created and trained outside of the DPDK scope, the model can be loaded > > via rte_ml_model_load() and then start it using rte_ml_model_start() API. > > The rte_ml_model_params_update() can be used to update the model parameters such as weight > > and bias without unloading the model using rte_ml_model_unload(). > > > > ML Inference: ML inference is the process of feeding data to the model via > > rte_ml_enqueue_burst() API and use rte_ml_dequeue_burst() API to get the calculated > > outputs/predictions from the started model. > > > > In all functions of the ML device API, the ML device is designated by an > > integer >= 0 named as device identifier *dev_id*. > > > > The functions exported by the ML device API to setup a device designated by > > its device identifier must be invoked in the following order: > > > > - rte_ml_dev_configure() > > - rte_ml_dev_queue_pair_setup() > > - rte_ml_dev_start() > > > > A model is required to run the inference operations with the user specified inputs. > > Application needs to invoke the ML model API in the following order before queueing > > inference jobs. > > > > - rte_ml_model_load() > > - rte_ml_model_start() > > > > The rte_ml_model_info() API is provided to retrieve the information related to the model. > > The information would include the shape and type of input and output required for the inference. > > > > It seems there is a sandardization effort for model description, called > ONNX (https://onnx.ai/), supported by many vendors. > > Does it make sense that 'rte_ml_model_info()' describes data, and > perhaps model itself too, using onnx format? ONNX format is presents higher number of details, which may not be required from a dataplane point-of-view. We have proposed a rte_ml_model_info struct with the most important fields that required. This structure can be expanded further as required. > > > Data quantization and dequantization is one of the main aspects in ML domain. This involves > > conversion of input data from a higher precision to a lower precision data type and vice-versa > > for the output. APIs are provided to enable quantization through rte_ml_io_quantize() and > > dequantization through rte_ml_io_dequantize(). These APIs have the capability to handle input > > and output buffers holding data for multiple batches. > > Two utility APIs rte_ml_io_input_size_get() and rte_ml_io_output_size_get() can used to get the > > size of quantized and de-quantized multi-batch input and output buffers. > > > > > It seems quantize and dequantize can be part of model and optimized > during training, can you please some information HW architecture that > needs these APIs? Marvell HW engines don't support quantization/dequantization in HW and the same has to be done by software on ARM64/x64 cores. The same applies for SW based mldevices. So it can NOP for the device which already supports it or we can introduce the capability when such drivers getting added to DPDK. No issue in updating the API when new driver comes. > > Does it make sense to have quantize/dequantize as a capability, like in > case HW has specific support for it this can be used, else host can > provide this functionality. We tried to keep the APIs minimal in the initial version. Yes. quantize/dequantize capability can be part of these device capabilities when such devices added. > > > User can optionally update the model parameters with rte_ml_model_params_update() after > > invoking rte_ml_model_stop() API on a given model ID. > > > > The application can invoke, in any order, the functions exported by the ML API to enqueue > > inference jobs and dequeue inference response. > > > > If the application wants to change the device configuration (i.e., call > > rte_ml_dev_configure() or rte_ml_dev_queue_pair_setup()), then application must stop the > > device using rte_ml_dev_stop() API. Likewise, if model parameters need to be updated then > > the application must call rte_ml_model_stop() followed by rte_ml_model_params_update() API > > for the given model. The application does not need to call rte_ml_dev_stop() API for > > any model re-configuration such as rte_ml_model_params_update(), rte_ml_model_unload() etc. > > > > Once the device is in the start state after invoking rte_ml_dev_start() API and the model is in > > start state after invoking rte_ml_model_start() API, then the application can call > > rte_ml_enqueue() and rte_ml_dequeue() API on the destined device and model ID. > > > > Finally, an application can close an ML device by invoking the rte_ml_dev_close() function. > > > > Typical application utilisation of the ML API will follow the following > > programming flow. > > > > - rte_ml_dev_configure() > > - rte_ml_dev_queue_pair_setup() > > - rte_ml_model_load() > > - rte_ml_model_start() > > - rte_ml_model_info() > > - rte_ml_dev_start() > > - rte_ml_enqueue_burst() > > - rte_ml_dequeue_burst() > > - rte_ml_model_stop() > > - rte_ml_model_unload() > > - rte_ml_dev_stop() > > - rte_ml_dev_close() > > > > is a 'reset()' API needed? We can add in future, if a specific HW needs/supports it. Keeping bare minium for first version. > > > Regarding multi-threading, by default, all the functions of the ML Device API exported by a PMD > > are lock-free functions which assume to not be invoked in parallel on different logical cores > > on the same target object. For instance, the dequeue function of a poll mode driver cannot be > > invoked in parallel on two logical cores to operate on same queue pair. Of course, this function > > can be invoked in parallel by different logical core on different queue pair. > > It is the responsibility of the user application to enforce this rule. > > > > Example application usage for ML inferencing > > -------------------------------------------- > > This example application is to demonstrate the programming model of ML device > > library. This example omits the error checks to simplify the application. This > > example also assumes that the input data received is quantized and output expected > > is also quantized. In order to handle non-quantized inputs and outputs, users can > > invoke rte_ml_io_quantize() or rte_ml_io_dequantize() for data type conversions. > > > > #define ML_MODEL_NAME "model" > > #define IO_MZ "io_mz" > > > > struct app_ctx { > > char model_file[PATH_MAX]; > > char inp_file[PATH_MAX]; > > char out_file[PATH_MAX]; > > > > struct rte_ml_model_params params; > > struct rte_ml_model_info info; > > uint16_t id; > > > > uint64_t input_size; > > uint64_t output_size; > > uint8_t *input_buffer; > > uint8_t *output_buffer; > > } __rte_cache_aligned; > > > > struct app_ctx ctx; > > > > static int > > parse_args(int argc, char **argv) > > { > > int opt, option_index; > > static struct option lgopts[] = {{"model", required_argument, NULL, 'm'}, > > {"input", required_argument, NULL, 'i'}, > > {"output", required_argument, NULL, 'o'}, > > {NULL, 0, NULL, 0}}; > > > > while ((opt = getopt_long(argc, argv, "m:i:o:", lgopts, &option_index)) != EOF) > > switch (opt) { > > case 'm': > > strncpy(ctx.model_file, optarg, PATH_MAX - 1); > > break; > > case 'i': > > strncpy(ctx.inp_file, optarg, PATH_MAX - 1); > > break; > > case 'o': > > strncpy(ctx.out_file, optarg, PATH_MAX - 1); > > break; > > default: > > return -1; > > } > > > > return 0; > > } > > > > int > > main(int argc, char **argv) > > { > > struct rte_ml_dev_qp_conf qp_conf; > > struct rte_ml_dev_config config; > > struct rte_ml_dev_info dev_info; > > const struct rte_memzone *mz; > > struct rte_mempool *op_pool; > > struct rte_ml_op *op_enq; > > struct rte_ml_op *op_deq; > > > > FILE *fp; > > int rc; > > > > /* Initialize EAL */ > > rc = rte_eal_init(argc, argv); > > if (rc < 0) > > rte_exit(EXIT_FAILURE, "Invalid EAL arguments\n"); > > argc -= rc; > > argv += rc; > > > > /* Parse application arguments (after the EAL args) */ > > if (parse_args(argc, argv) < 0) > > rte_exit(EXIT_FAILURE, "Invalid application arguments\n"); > > > > /* Step 1: Check for ML devices */ > > if (rte_ml_dev_count() <= 0) > > rte_exit(EXIT_FAILURE, "Failed to find ML devices\n"); > > > > /* Step 2: Get device info */ > > if (rte_ml_dev_info_get(0, &dev_info) != 0) > > rte_exit(EXIT_FAILURE, "Failed to get device info\n"); > > > > /* Step 3: Configure ML device, use device 0 */ > > config.socket_id = rte_ml_dev_socket_id(0); > > config.max_nb_models = dev_info.max_models; > > config.nb_queue_pairs = dev_info.max_queue_pairs; > > if (rte_ml_dev_configure(0, &config) != 0) > > rte_exit(EXIT_FAILURE, "Device configuration failed\n"); > > > > /* Step 4: Setup queue pairs, used qp_id = 0 */ > > qp_conf.nb_desc = 1; > > if (rte_ml_dev_queue_pair_setup(0, 0, &qp_conf, config.socket_id) != 0) > > rte_exit(EXIT_FAILURE, "Queue-pair setup failed\n"); > > > > /* Step 5: Start device */ > > if (rte_ml_dev_start(0) != 0) > > rte_exit(EXIT_FAILURE, "Device start failed\n"); > > > > /* Step 6: Read model data and update load params structure */ > > fp = fopen(ctx.model_file, "r+"); > > if (fp == NULL) > > rte_exit(EXIT_FAILURE, "Failed to open model file\n"); > > > > fseek(fp, 0, SEEK_END); > > ctx.params.size = ftell(fp); > > fseek(fp, 0, SEEK_SET); > > > > ctx.params.addr = malloc(ctx.params.size); > > if (fread(ctx.params.addr, 1, ctx.params.size, fp) != ctx.params.size){ > > fclose(fp); > > rte_exit(EXIT_FAILURE, "Failed to read model\n"); > > } > > fclose(fp); > > strcpy(ctx.params.name, ML_MODEL_NAME); > > > > /* Step 7: Load the model */ > > if (rte_ml_model_load(0, &ctx.params, &ctx.id) != 0) > > rte_exit(EXIT_FAILURE, "Failed to load model\n"); > > free(ctx.params.addr); > > > > /* Step 8: Start the model */ > > if (rte_ml_model_start(0, ctx.id) != 0) > > rte_exit(EXIT_FAILURE, "Failed to start model\n"); > > > > /* Step 9: Allocate buffers for quantized input and output */ > > > > /* Get model information */ > > if (rte_ml_model_info_get(0, ctx.id, &ctx.info) != 0) > > rte_exit(EXIT_FAILURE, "Failed to get model info\n"); > > > > /* Get the buffer size for input and output */ > > rte_ml_io_input_size_get(0, ctx.id, ctx.info.batch_size, &ctx.input_size, NULL); > > rte_ml_io_output_size_get(0, ctx.id, ctx.info.batch_size, &ctx.output_size, NULL); > > > > mz = rte_memzone_reserve(IO_MZ, ctx.input_size + ctx.output_size, config.socket_id, 0); > > if (mz == NULL) > > rte_exit(EXIT_FAILURE, "Failed to create IO memzone\n"); > > > > ctx.input_buffer = mz->addr; > > ctx.output_buffer = ctx.input_buffer + ctx.input_size; > > > > /* Step 10: Fill the input data */ > > fp = fopen(ctx.inp_file, "r+"); > > if (fp == NULL) > > rte_exit(EXIT_FAILURE, "Failed to open input file\n"); > > > > if (fread(ctx.input_buffer, 1, ctx.input_size, fp) != ctx.input_size) { > > fclose(fp); > > rte_exit(EXIT_FAILURE, "Failed to read input file\n"); > > } > > fclose(fp); > > > > /* Step 11: Create ML op mempool */ > > op_pool = rte_ml_op_pool_create("ml_op_pool", 1, 0, 0, config.socket_id); > > if (op_pool == NULL) > > rte_exit(EXIT_FAILURE, "Failed to create op pool\n"); > > > > /* Step 12: Form an ML op */ > > rte_mempool_get_bulk(op_pool, (void *)op_enq, 1); > > op_enq->model_id = ctx.id; > > op_enq->nb_batches = ctx.info.batch_size; > > op_enq->mempool = op_pool; > > op_enq->input.addr = ctx.input_buffer; > > op_enq->input.length = ctx.input_size; > > op_enq->input.next = NULL; > > op_enq->output.addr = ctx.output_buffer; > > op_enq->output.length = ctx.output_size; > > op_enq->output.next = NULL; > > > > /* Step 13: Enqueue jobs */ > > rte_ml_enqueue_burst(0, 0, &op_enq, 1); > > > > /* Step 14: Dequeue jobs and release op pool */ > > while (rte_ml_dequeue_burst(0, 0, &op_deq, 1) != 1) > > ; > > > > /* Step 15: Write output */ > > fp = fopen(ctx.out_file, "w+"); > > if (fp == NULL) > > rte_exit(EXIT_FAILURE, "Failed to open output file\n"); > > fwrite(ctx.output_buffer, 1, ctx.output_size, fp); > > fclose(fp); > > > > /* Step 16: Clean up */ > > /* Stop ML model */ > > rte_ml_model_stop(0, ctx.id); > > /* Unload ML model */ > > rte_ml_model_unload(0, ctx.id); > > /* Free input/output memory */ > > rte_memzone_free(rte_memzone_lookup(IO_MZ)); > > /* Free the ml op back to pool */ > > rte_mempool_put_bulk(op_pool, (void *)op_deq, 1); > > /* Free ml op pool */ > > rte_mempool_free(op_pool); > > /* Stop the device */ > > rte_ml_dev_stop(0); > > rte_ml_dev_close(0); > > rte_eal_cleanup(); > > > > return 0; > > } > > > > > > Jerin Jacob (1): > > mldev: introduce machine learning device library > > > > Srikanth Yalavarthi (11): > > mldev: support PMD functions for ML device > > mldev: support ML device handling functions > > mldev: support ML device queue-pair setup > > mldev: support handling ML models > > mldev: support input and output data handling > > mldev: support ML op pool and ops > > mldev: support inference enqueue and dequeue > > mldev: support device statistics > > mldev: support device extended statistics > > mldev: support to retrieve error information > > mldev: support to get debug info and test device > > > > MAINTAINERS | 5 + > > doc/api/doxy-api-index.md | 1 + > > doc/api/doxy-api.conf.in | 1 + > > doc/guides/prog_guide/img/mldev_flow.svg | 714 ++++++++++++++ > > doc/guides/prog_guide/index.rst | 1 + > > doc/guides/prog_guide/mldev.rst | 186 ++++ > > doc/guides/rel_notes/release_23_03.rst | 5 + > > lib/meson.build | 1 + > > lib/mldev/meson.build | 27 + > > lib/mldev/rte_mldev.c | 947 ++++++++++++++++++ > > lib/mldev/rte_mldev.h | 1119 ++++++++++++++++++++++ > > lib/mldev/rte_mldev_core.h | 717 ++++++++++++++ > > lib/mldev/rte_mldev_pmd.c | 62 ++ > > lib/mldev/rte_mldev_pmd.h | 151 +++ > > lib/mldev/version.map | 51 + > > 15 files changed, 3988 insertions(+) > > create mode 100644 doc/guides/prog_guide/img/mldev_flow.svg > > create mode 100644 doc/guides/prog_guide/mldev.rst > > create mode 100644 lib/mldev/meson.build > > create mode 100644 lib/mldev/rte_mldev.c > > create mode 100644 lib/mldev/rte_mldev.h > > create mode 100644 lib/mldev/rte_mldev_core.h > > create mode 100644 lib/mldev/rte_mldev_pmd.c > > create mode 100644 lib/mldev/rte_mldev_pmd.h > > create mode 100644 lib/mldev/version.map > > >